Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 30 appraisals
Breast cancer research and treatment
Extended applications of chest wall perforator flaps in breast surgery: case series and systematic literature review
PURPOSE: Chest wall perforator flaps (CWPFs) are integral to modern oncoplastic breast surgery, offering volume replacement without muscle sacrifice. While their role in partial breast reconstruction is established, data on extended and non-conventional applications remain limited. This study aims to synthesize current clinical experience and delineate the extended applications of chest wall perforator flaps beyond conventional oncoplastic contexts. METHODS: This study combined a prospective institutional case series from two tertiary centers (Singapore and UK) with a systematic literature review of PubMed, Embase, Web of Science, and Cochrane databases. Eligible studies described CWPF use for extended oncologic, reconstructive, or aesthetic indications. Data were synthesized descriptively according to PRISMA guidelines. RESULTS: Six women underwent LICAP-based reconstruction for complex indications including skin-replacing excisions, implant salvage, chronic sinus closure, and total autologous reconstruction with adjunct lipofilling. All flaps were fully viable with good contour restoration and symmetry, and adjuvant therapy proceeded without delay. The systematic review identified 38 studies (≈ 324 patients, 349 flaps). CWPFs, most commonly LICAP, LTAP, and TDAP, were used for defects following oncologic resection, radiotherapy injury, implant failure, or post-bariatric contour correction. Overall flap success exceeded 95%, with major complications under 5%. Donor-site morbidity was minimal, with high aesthetic satisfaction (> 90%). CONCLUSIONS: CWPFs represent muscle-sparing reconstructive options whose utility may extend beyond conventional oncoplastic indications; this descriptive series supports their feasibility and short-term outcomes across a range of selected clinical settings, suggesting that CWPFs warrant further consideration in the management of complex breast and chest wall defects.
17 July 2026
Read appraisal →BMJ supportive & palliative care
Efficacy and safety of acupuncture for breast cancer-related insomnia: a systematic review and meta-analysis
Breast cancer-related insomnia (BCRI) is a common and burdensome sequela. Acupuncture is frequently used, but robust evidence is limited, and previous systematic reviews included few trials yielding imprecise estimates. This updated systematic review and meta-analysis evaluated the efficacy and safety of acupuncture for BCRI. We systematically searched eight databases for randomized controlled trials (RCTs) up to July 1, 2025. RCTs comparing acupuncture with any control for BCRI were eligible. Methodological quality was assessed using the Cochrane Risk of Bias tool (RoB 2.0) and the modified Jadad scale. Meta-analyses employed a random-effects model. Subgroup analyses explored heterogeneity sources, and the GRADE framework rated evidence certainty. Twenty-seven RCTs (n=2,025) were included. Acupuncture significantly reduced Pittsburgh Sleep Quality Index (PSQI) scores (MD= -2.74, 95% CI [-3.92, -1.55], P<0.00001) and increased the overall efficacy rate (RR=1.51, 95% CI [1.27, 1.81], P<0.00001) compared to control, but did not significantly improve Insomnia Severity Index (ISI) scores. Subgroup analyses identified control intervention type and treatment frequency as significant sources of heterogeneity. Adverse events were infrequent and typically mild. GRADE assessments rated the certainty of evidence as low or very low, primarily due to risk of bias and substantial heterogeneity. Current evidence suggests acupuncture may improve sleep in patients with BCRI, with effects moderated by comparator type and treatment frequency. However, conclusions are limited by methodological weaknesses and heterogeneity. Future rigorous RCTs using validated sham controls, standardized protocols, and objective outcomes are needed to confirm efficacy and guide practice. PROSPERO Registration number (ID: CRD42024559828).
1 July 2026
Read appraisal →World journal of surgical oncology
Robotic-assisted versus conventional nipple-sparing mastectomy with immediate implant-based breast reconstruction: a GRADE-assessed systematic review and meta-analysis of prospective studies
BACKGROUND: Robotic nipple-sparing mastectomy with immediate implant-based reconstruction is increasingly adopted to improve cosmetic outcomes and patient experience; however, its comparative perioperative value against conventional or open nipple-sparing mastectomy remains uncertain when restricted to prospective evidence. METHODS: We performed a PRISMA-aligned systematic review and random-effects meta-analysis of prospective comparative studies and randomized trials comparing robotic versus conventional or open nipple-sparing mastectomy with immediate implant-based reconstruction. The primary outcome was the total operative time, and the secondary outcomes were major complications, nipple-areolar complex ischemia or necrosis, and length of hospital stay. The risk of bias was assessed using RoB 2 for randomized data and ROBINS-I for nonrandomized data. Leave-one-out sensitivity analyses were conducted for outcomes with substantial heterogeneity, and the certainty of the evidence was graded using GRADE. RESULTS: Three prospective studies were included. Robotic surgery was associated with a significantly longer total operative time (mean difference 64.01 min, 95% confidence interval 8.54-119.47). Major complications did not differ between the approaches (risk ratio 0.63, 95% confidence interval 0.27-1.47), nor did nipple areolar complex ischemia or necrosis (risk ratio 0.56, 95% confidence interval 0.28-1.11) or length of hospital stay (mean difference 0.22 days, 95% confidence interval - 0.26 to 0.70). Heterogeneity was high for operative time and moderate to substantial for length of stay. The certainty of the evidence was low overall, driven by imprecision, heterogeneity, and limitations of the non-randomized design. CONCLUSIONS: Robotic nipple-sparing mastectomy is associated with a longer operative time than conventional or open approaches. No statistically significant differences were observed in major complications or nipple-areolar complex viability; however, the current prospective evidence base is limited, heterogeneous, and imprecise, and clinically important differences cannot be excluded. In experienced centers, robotic nipple-sparing mastectomy may be considered for carefully selected patients who prioritize scar concealment and minimally invasive access; however, its broader role in routine practice remains uncertain pending stronger prospective data on safety, patient-reported outcomes, and resource use.
26 June 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Neoadjuvant Taxane Plus Trastuzumab and Pertuzumab With or Without Carboplatin in Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer: The Randomized Noninferiority Phase III neoCARHP Trial
PURPOSE: The neoCARHP aimed to investigate the efficacy and safety of investigator-selected taxane (docetaxel, paclitaxel, or nab-paclitaxel) plus trastuzumab and pertuzumab, with carboplatin (TCbHP) or without carboplatin (THP), in stage II and III human epidermal growth factor receptor 2 (HER2)-positive breast cancer. METHODS: The neoCARHP was a multicenter, randomized, phase III, noninferiority study. Eligible patients were women age 18 years or older with previously untreated, stage II and III, HER2-positive invasive breast cancer. Patients were randomly assigned (1:1) to receive six 3-week cycles of TCbHP or THP. The primary end point was pathologic complete response (pCR) rate in the breast and axilla (ypT0/is ypN0) in the modified intention-to-treat (mITT) population (all randomly assigned patients receiving at least one dose of study treatment). Safety was evaluated in all patients who received any study treatment. RESULTS: Between April 30, 2021, and August 27, 2024, 774 patients were randomly assigned and 766 were included in the mITT population (382 in THP and 384 in TCbHP). pCR was achieved in 245 (64.1% [95% CI, 59.1 to 69.0]) patients in the THP group and 253 (65.9% [60.9-70.6]) in the TCbHP group (absolute difference, -1.8% [95% CI, -8.5 to 5.0]; odds ratio, 0.93 [95% CI, 0.69 to 1.25]; Pnoninferiority = .0089). The THP group had fewer grade 3 and 4 adverse events (20.7% v 34.6%) and serious adverse events (1.3% v 4.7%) than the TCbHP group. The most common grade 3 and 4 adverse events with THP were neutropenia (6.8% v 16.4% with TCbHP), leukopenia (5.5% v 14.8%), and diarrhea (2.6% v 4.2%). No treatment-associated deaths occurred. CONCLUSION: THP provided noninferior pCR rates and improved tolerability compared with TCbHP. Omitting carboplatin may be applicable in HER2-positive breast cancer.
12 June 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Durvalumab in Combination With Neoadjuvant Chemotherapy in Early Triple-Negative Breast Cancer: Long-Term Analysis From the GeparNuevo Trial
The phase II GeparNuevo trial investigated whether adding durvalumab to neoadjuvant chemotherapy (NACT) only in patients with early triple-negative breast cancer cT1b-cT4a-d would improve pathologic complete response (pCR) rate and patient survival. Hundred and seventy-four patients were randomly assigned to receive durvalumab or placebo concurrently with nab-paclitaxel once per week and followed by dose-dense epirubicin and cyclophosphamide. With 86.4 months of median follow-up compared with the previously reported 43.7 months, durvalumab showed sustained significant improvements in long-term outcomes as defined by STEEP compared with placebo regarding not only invasive disease-free survival (iDFS; hazard ratio [HR], 0.56 [95% CI, 0.32 to 0.99]; stratified log-rank P = .0431), but also distant disease-free survival (DDFS; HR, 0.41 [95% CI, 0.21 to 0.80]; P = .0069) and overall survival (OS; HR, 0.33 [95% CI, 0.14 to 0.79]; P = .0085). All analyses were stratified by stromal tumor-infiltrating lymphocytes (sTILs) at baseline (low [≤10%], intermediate [11%-59%], high [≥60%]). In exploratory subgroup analysis, patients with nodal involvement at baseline demonstrated a greater iDFS benefit (HR, 0.33 [95% CI, 0.144 to 0.771]; P = .01; Pinteraction = 0.045). sTILs in residual disease (RD) could be assessed in 39/71 patients without pCR. Post hoc analyses by sTILs high (>10%) versus low (≤10%) in RD showed estimated 7-year iDFS rates of 92.3% (95% CI, 56.6 to 98.9) and 51.4% (95% CI, 29.2 to 69.7), respectively. Hence, adding durvalumab to dose-dense NACT without adjuvant continuation of checkpoint inhibition improved long-term survival outcomes, irrespective of the extent of pathologic response. This underscores the necessity to re-evaluate the adjuvant continuation of checkpoint inhibition.
11 June 2026
Read appraisal →Science advances
Generalizability of an AI-based mammogram risk score (MRS) for breast cancer among diverse populations of women
Conventional prediction models incorporating genetic and clinical factors including breast density underperform in non-European populations. We investigated an artificial intelligence-derived mammogram risk score (MRS), a summary of texture features that captures intrinsic breast-tissue characteristics-the substrate for cancer development. This study leveraged data from two North American screening cohorts totaling >226,000 women, including non-Hispanic white, non-Hispanic Black, East Asian, South Asian, and Indigenous women. MRS distributions showed nonsignificant shifts (and similar SDs) between cohorts and across race and ethnic subgroups. MRS increased with age and was significantly associated with breast cancer risk, with hazard ratios per SD ranging from 2.24 [95% confidence interval (CI), 2.03 to 2.46] to 2.32 (95% CI, 2.25 to 2.39) after age adjustment. Associations remained significant within all subgroups. Calibration was excellent across the racial and ethnic groups and across full-field digital mammograms and tomosynthesis. These findings establish MRS as a strong predictor that is independent of race or ethnicity, demonstrating its potential for broader clinical utility.
24 May 2026
Read appraisal →European journal of cancer (Oxford, England : 1990)
Invasive lobular and ductal breast cancers: a systematic review and metanalysis in association with BRCA1/2 mutation status
BACKGROUND: Invasive lobular breast carcinoma (ILC) differs from invasive ductal breast carcinoma (IDC) with respect to genetic alterations and risk factors. We aimed to quantify differences in the prevalence of germline BRCA1/2 mutations between these two patients' populations. MATERIALS AND METHODS: We conducted a systematic literature search to extract data on the association between BRCA1/2 mutation status and breast cancer (BC) histological subtype (ILC and IDC). Summary odds ratios (SOR) and 95% CI were calculated using random effects univariate and bivariate models and between-study heterogeneity investigated through meta-regression and subgroup analyses. RESULTS: Twelve studies, published between 1998 and 2025, were considered, including 8004 BC women. BRCA1 mutations were significantly less frequent in patients with ILC than IDC (SOR=0.32, 95%CI (0.16-0.65)) whereas no association was found overall for BRCA2 (SOR=1.28, 95%CI (0.95-1.72)). The difference between the association with BRCA1 and BRCA2 was statistically significant (p-value<0.001). There was no indication of publication bias in BRCA1 (Egger's and Begg's test p-value=0.23, 0.12). The between study heterogeneity was 29% in BRCA1 and 0% in BRCA2. Excluding papers with high risk of bias, BRCA2 mutations were more frequent in patients with ILC than in IDC (SOR=2.56, 95%CI (1.15-5.7)). This association was primarily driven by the bivariate random-effects model, which accounts for the correlation between BRCA1 and BRCA2 estimates. CONCLUSIONS: In studies with lower risk of bias, germline BRCA2 mutations were more frequent in patients with ILC than IDC. Conversely, BRCA1 mutations are more frequently observed in IDC, particularly among younger patients and those with a positive family history of BC.
17 May 2026
Read appraisal →International immunopharmacology
NY-ESO-1 in Triple-Negative Breast Cancer: Systematic Review, Meta-Analysis, and Immunotherapeutic Implications
Breast cancer remains one of the most common types of cancer, including the subtype triple-negative breast cancer (TNBC) which is challenging to treat due to its aggressiveness. Cancer-testis antigens (CTAs), especially New York Esophageal Squamous Cell Carcinoma 1 (NY-ESO-1), have become promising immunotherapeutic targets attributable to their restricted expression profile in normal tissues but overexpressed in TNBC, leading to immunogenicity. This review provides comprehensive discussion of NY-ESO-1 including its structural basis of NY-ESO-1 recognition by T cell receptors (TCRs) and antibodies, epigenetic regulation via DNA methylation or histone modifications, and expression patterns or clinical relevance in TNBC. A systematic meta-analysis of 12 studies comprising 1,545 TNBC patients showed a pooled NY-ESO-1 expression prevalence of 16.1% (95% CI: 11.4-22.2%), with heterogeneity (I2=83.7%) attributable to antibody clone used for NY-ESO-1 detection (E978: 15.1% vs. D8.38: 32.6%, p<0.0001) and scoring methods (composite scoring: 15.9% vs. dual scoring: 16.9% vs. H-score: 10.1% vs. simple threshold: 32.6%, p<0.001). Structural analyses reveal NY-ESO-1157-165 recognition by TCRs, antibodies, and engineered binding scaffolds. We examine preclinical and clinical evidence for NY-ESO-1-targeted therapies, including adoptive cell therapy and peptide vaccines, which show manageable safety profiles and induce immunological responses. Epigenetic regulation is a therapeutic avenue, whereby hypomethylating agents and histone deacetylase inhibitors can upregulate NY-ESO-1 expressions that promote tumor immunogenicity. Nonetheless, challenges persist such as NY-ESO-1 expression heterogeneity, lack of TNBC-specific clinical trials, and inadequate immunogenicity. Future research should standardize NY-ESO-1 detection protocols to identify TNBC patients for NY-ESO-1-targeted immunotherapy, prioritize TNBC-specific trials and combinations with epigenetic priming agents.
16 May 2026
Read appraisal →Computer methods and programs in biomedicine
Explainable machine learning framework for the molecular classification of triple negative breast cancer
BACKGROUND AND OBJECTIVE: The difference in molecular characteristics of Triple negative breast cancer (TNBC) aids in distinguishing between its four prominent subtypes- basal-like 1, basal-like 2, mesenchymal, and luminal androgen receptor. This study presents the first integrative framework that combines explainable AI with machine learning approaches to classify TNBC subtypes. Unlike conventional models, our approach offers interpretability while enabling biomarker prioritization by identifying key hub genes that drive subtype-specific predictions. METHODS: In the experiment 783 cases (BL1 (160), BL2 (75), M (151), LAR (106), non-TNBC (291) reported in Gene Expression Omnibus (GEO) and Genomic Data Commons (GDC) data portal were used for the analysis. The proposed framework comprises modules for the identification of gene signatures for the four-subtype followed by the classification model based on eight different machine learning algorithms. Random Forest classifier was found to be best model with 96 % testing accuracy, which was elected for Explainable framework using Shapley Additive Explanations. RESULTS: Explainable biomarker module could provide a set of 47 biomarkers which is relevant in distinguishing the four types on triple negative breast cancer. The biomarkers could have the potential to be considered for TNBC prognosis in clinical setting. CONCLUSION: Key findings highlight the hub genes CDC20, CDCA2, PIMREG, KIF2C, and CENPW, implicating pathways such as ubiquitin-proteasome signaling and microtubule dynamics. These insights pave the way for biomarker-driven therapies and precision medicine in triple negative breast cancer.
16 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Night-time compression with a Mobiderm® auto-adjustable arm-sleeve in addition to daytime compression was superior to daytime compression alone for maintenance therapy of upper limb lymphedema in breast cancer patients in a randomized controlled trial: LYMphoNIGHT study.
PURPOSE: To assess the benefit of night-time compression in addition to daytime compression on arm excess volume after 3 months of maintenance treatment in patients with secondary upper limb breast cancer-related lymphedema (BCRL). METHODS: This multicenter, controlled, randomized study was carried out in six centers in France and Turkey. Women with upper limb BCRL who had undergone the intensive phase of decongestive lymphedema therapy (DLT) were randomized into two groups: the Control group wore a daytime compression sleeve without any night compression for 3 months and the MOBIDERM Autofit (MobA) group used a night-time compression sleeve in addition to the daytime compression. The primary outcome was the change in arm excess volume between day 0 (D0) and D90. The main secondary endpoints were: quality of life (QoL), sleep quality, skin thickness and suppleness, satisfaction, compliance, and safety. RESULTS: Fifty-six patients were recruited (mean (± SD) age: 61.4 ± 12.3 years). Between D0 and D90, the mean excess volume decreased by 29.2% in the MobA group and increased by 10.7% in the Control group (p = 0.001). Only 3.6% of patients in the MobA group presented with treatment failure vs. 23.1% in the Control group. At D90, patients in the MobA group had a significantly greater improvement in QoL (p < 0.05), sleep quality (p = 0.018), skin suppleness (p = 0.01) reduced skin thickness (p = 0.025) compared with the Control group. CONCLUSION: MOBIDERM Autofit used in addition to daytime compression for 3 months during the maintenance phase after DLT was superior to daytime compression alone for the reduction of arm excess volume. Registered on ClinicalTrials.gov (NCT04203069; 17/12/2019).
11 May 2026
Read appraisal →Lasers in medical science
Machine learning enhanced optical spectroscopy for breast cancer diagnosis: A review
This literature review examines the transformative role of machine learning (ML) and deep learning (DL) in enhancing optical spectroscopy for breast cancer diagnosis. By synthesizing advancements from peer-reviewed studies (2015-2025), we evaluate how ML/DL integration improves the detection of malignancy-associated biochemical changes, enabling noninvasive, rapid, and accurate differentiation between healthy and cancerous tissues. This review highlights key spectroscopic modalities, such as Raman, fluorescence, diffusive optical spectroscopy (DOS), and photoacoustic spectroscopy (PAS), and their integration with AI-driven models, such as convolutional neural networks (CNNs), support vector machines (SVMs), and logistic regression. These techniques achieve diagnostic accuracies of up to 94% in subtype classification (e.g., luminal A, HER2-positive) by analyzing spectral biomarkers such as hemoglobin, lipids, and collagen. Challenges such as data variability, model interpretability, and clinical integration barriers are critically assessed. These findings underscore the potential of ML/DL-enhanced spectroscopy to standardize diagnostics, reduce unnecessary biopsies, and personalize treatment monitoring. Future directions emphasize the need for explainable AI (XAI), multimodal data fusion, and large-scale, diverse datasets to bridge translational gaps. By addressing technical, ethical, and regulatory hurdles, this integration promises to advance early detection, improve clinical outcomes, and reshape precision oncology.
11 May 2026
Read appraisal →Journal for immunotherapy of cancer
Safety and immunogenicity of a tri-antigen vaccine targeting IGFBP-2, HER2, and IGF-IR in participants with non-metastatic breast cancer
BACKGROUND: Ductal carcinoma in situ (DCIS) is a preinvasive form of breast cancer. Current treatment consists of surgery, radiation, and often systemic therapy exposing patients to unnecessary health risks. Vaccines targeting DCIS may be a way to intercept preinvasive lesions and prevent the development of invasive breast cancer. METHODS: We developed a Th1 selective multiantigen, polyepitope plasmid-DNA vaccine encoding segments of IGFBP-2, HER2, and IGF-IR, all antigens expressed in hormone receptor positive and negative DCIS. We then performed a Phase I study in participants with non-metastatic breast cancer with no evidence of disease. The primary objective was to assess the safety of 3 monthly intradermal doses (150, 300, or 600 µg) of the tri-antigen vaccine with granulocyte macrophage colony-stimulating factor as an adjuvant. 32 participants were enrolled, 10 per dose level. Toxicity evaluations occurred monthly with vaccination and at 1 and 6 months after the last vaccine. Blood was collected at baseline and at 1 and 6 months after the last immunization to assess cellular immune responses. Participants were followed annually for 5 years for long-term toxicity. RESULTS: There was no significant difference in adverse events (AEs) across dose levels and all related AEs were grades 1 or 2. All doses were immunogenic; responders included 70% of participants at the 150 µg dose level, 67% at the 300 µg dose, and 40% at the 600 µg dose level. All participants at the 300 µg dose retained significant Th1-antigen-specific immune response at 6 months after end of immunizations. T-cells derived from vaccine immunologic responders exhibited gene expression profiles that indicated an increased metabolic fitness as compared with immunologic non-responders. CONCLUSIONS: The tri-antigen vaccine appears safe and immunogenic. The intermediate dose (300 µg) was chosen as the Phase II dose due to the long-term persistence of immunity after vaccination. The vaccine will be studied in Phase II trials for the treatment of DCIS. TRIAL REGISTRATION NUMBER: NCT02780401.
11 May 2026
Read appraisal →European journal of cancer (Oxford, England : 1990)
MuTriM: A multiscale deep learning model integrating longitudinal radiomics and pathomic features for predicting recurrence and adjuvant radiation benefit in breast cancer.
PURPOSE: We present MuTriM, a multimodal deep learning model integrating DCE-MRI and whole-slide pathology to predict survival and radiation benefit in breast cancer. PATIENTS AND METHODS: MuTriM employs an attention-based cross-modal and cross-temporal fusion framework that concurrently integrates DCE-MRI radiomic features with pathomic features of cellular morphology on WSI. MuTriM was trained on the Fudan University Shanghai Cancer Center (FUSCC) cohort (N = 335) and externally tested on the TCGA cohort (N = 126) using matched DCE-MRI and WSI. RESULTS: MuTriM was prognostic of recurrence-free survival (RFS) in a subtype-agnostic setting (HR = 5.26, 95% CI, 1.69-16.4, p = 0.004; C-index = 0.75), outperforming unimodal models based on DCE-MRI alone (C-index = 0.65) or WSI alone (C-index = 0.70). MuTriM was also prognostic among HER2 + (HR = 6.82, 95% CI, 1.08-43.2, p = 0.04) and ER+ populations (HR = 4.61, 95% CI, 1.25-17.1, p = 0.02). A high MuTriM score was associated with improved RFS (HR = 0.15, 95% CI 0.03-0.87; P = 0.03), whereas a low MuTriM score showed no significant benefit (HR = 4.06, 95% CI 0.05-31; P = 0.53) (interaction P = 0.04). Transcriptomic analyses revealed that high-risk tumors showed suppressed cytotoxic T-cell activity and enrichment of extracellular matrix remodeling, neutrophil degranulation, and neuronal signaling, consistent with immune evasion. Upregulation of NRXN1, GABRA3, KCNK3, and KLK12 was linked to worse survival, whereas SYNGR3 and CD207 were associated with favorable outcomes. CONCLUSION: By unifying radiology, pathology, and transcriptomics, MuTriM provides an interpretable multimodal framework for breast cancer prognosis and treatment guidance.
10 May 2026
Read appraisal →Radiology. Artificial intelligence
Radiopathomic Graph Deep Learning for Multiscale Spatial-Contextual Modeling of Intratumoral Heterogeneity to Predict Breast Cancer Response to Neoadjuvant Therapy
Purpose To develop an explainable radiopathomic graph deep learning (RPGDL) system for multiscale spatial-contextual modeling of intratumoral heterogeneity and evaluate its performance for the prediction of pathologic complete response (pCR) to neoadjuvant therapy in breast cancer. Materials and Methods The RPGDL system was developed from dual-center retrospective analysis of patients with biopsy-proven invasive breast cancer (May 2018-August 2024). For each tumor, individual radiomic and pathomic graphs were generated from pretherapeutic MRI and hematoxylin-eosin-stained biopsy slide images, respectively. These graphs were then processed by three distinct graph neural networks (GNNs): radiomic, pathomic, and radiopathomic. GNN performance was assessed with the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). A multifaceted approach was used to explain the GNNs' predictions. Results The training set included 582 (mean age, 52 years ± 9 [SD]) patients and the external test set 468 (50 years ± 10) patients from centers 1 and 2, respectively. The radiomic GNN achieved AUCs of 0.89 (95% CI: 0.85, 0.93) in the training set and 0.84 (95% CI: 0.80, 0.89) in the external test set; the pathomic GNN achieved AUCs of 0.87 (95% CI: 0.83, 0.91) in the training set and 0.83 (95% CI: 0.78, 0.88) in the external test set, with no significant difference between them (P > .05). The radiopathomic GNN outperformed both single-modality GNNs (training set: AUC, 0.95 [95% CI: 0.92, 0.98]; external test set: AUC, 0.91 [95% CI: 0.87, 0.94]; P < .05; NRI and IDI confirmed). Pathomic graphs dominated probability increases for pCR predictions, while radiomic graphs drove probability decreases for non-pCR predictions. Multifaceted analyses verified GNNs' explainability. Conclusion The developed RPGDL system enabled multiscale spatial-contextual intratumoral heterogeneity modeling for high-performance, explainable prediction of pCR to neoadjuvant therapy in breast cancer. Keywords: Dynamic Contrast-enhanced MRI, Breast, Tumor Response, Radiology-Pathology Integration, Prognosis, Principal Component Analysis, Perception, Supervised Learning, Reconstruction Algorithm Supplemental material is available for this article. © RSNA, 2026.
4 May 2026
Read appraisal →Biomedizinische Technik. Biomedical engineering
Advanced deep learning framework for breast cancer detection using digital breast tomosynthesis images
OBJECTIVES: Early and accurate detection of breast cancer is critical for improving patient survival outcomes. This study proposes a robust deep learning-based framework for breast cancer detection using digital breast tomosynthesis (DBT) images, leveraging both single-slice and multi-slice inputs. METHODS: The proposed work includes image normalization, resizing and Laplacian Pyramid Enhancement (LPE). Various features were extracted and fused in different combinations. To retain the most discriminative features Exhaustive Feature Selection (EFS) is used. A hybrid model integrated using ResNet V2, MobileNet V3 and Inception V3+ for classification. Finally ensemble learning with XGBoost was applied and Hyperparameters were optimized using a grid search strategy. RESULTS: The hybrid model with multi-slice DBT inputs achieved better improvements with accuracy, sensitivity, specificity and area under the curve (AUC). While applying LPE, feature fusion and EFS substantially enhanced the hybrid model's diagnostic performance. CONCLUSIONS: The findings demonstrate the strong potential of advanced methods in enhancing performance. Future research work will focus on integrating this pipeline with clinical decision support systems, multi-center datasets and extending to other breast imaging modalities.
26 Apr 2026
Read appraisal →Breast cancer research and treatment
Oral selective estrogen receptor degraders in hormone receptor-positive, HER2-negative advanced breast cancer: a systematic review and meta-analysis
PURPOSE: The development of endocrine resistance is frequent in hormone receptor-positive, HER2-negative advanced breast cancer (HR + /HER2-ABC), particularly after CDK4/6 inhibitor exposure. Next-generation oral selective estrogen receptor degraders (SERDs) have been developed to improve estrogen receptor blockade; however, randomized trials have yielded heterogeneous results with uncertain clinical benefit. METHODS: A PRISMA 2020 compliant systematic review and meta-analysis with PROSPERO registration was conducted. PubMed, Embase, and Cochrane CENTRAL were searched through October 2025 for phase II-III randomized trials comparing oral SERDs with standard endocrine therapy (ET) in HR + /HER2-ABC after prior ET. The primary endpoint was progression-free survival (PFS); secondary endpoints included overall survival (OS), objective response rate (ORR), and treatment-related adverse events (TRAEs). Treatment effects were pooled using random effects models with prespecified subgroup analyses by ESR1 mutation status and key clinical characteristics. RESULTS: Six randomized trials, including 2808 patients, were analyzed. Oral SERDs improved PFS versus standard ET in the overall population (hazard ratio [HR] 0.79; 95% confidence interval [CI] 0.70 to 0.89). ORR was higher with oral SERDs (odds ratio [OR] 1.67; 95% CI 1.23 to 2.28), corresponding to absolute response rates of approximately 21% versus 14%. An OS improvement was observed (HR 0.72; 95% CI 0.57 to 0.90), although follow-up was limited. Benefit was concentrated in ESR1-mutated tumors (PFS, HR 0.57; 95% CI 0.48 to 0.67) with no significant PFS advantage in ESR1 wild-type disease. Gastrointestinal adverse events were more frequent with oral SERDs compared with the control ET. CONCLUSIONS: Pooled randomized evidence supports a clinically meaningful benefit of oral SERDs over standard ET after endocrine progression in HR + /HER2-ABC, with the strongest and most consistent efficacy observed in ESR1-mutated disease.
18 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Beyond breast cancer: mapping unmet needs and advancing an evidence-based framework of survivorship care
PURPOSE: The growing population of breast cancer survivors (BCS) underscores the need to address ongoing challenges that persist beyond active treatment. This scoping review aimed to systematically map the unmet needs of BCS and inform the development of an evidence-based survivorship care framework. METHODS: A systematic search of databases identified 13,401 studies, of which 170 met the inclusion criteria. Data were analyzed using qualitative content analysis and descriptive statistics to categorize unmet needs across studies. RESULTS: Six major categories of unmet needs were identified: physical symptom burden, psychosocial/emotional needs, informational needs, healthcare support needs, relational/sexual needs, and financial/occupational needs. Physical symptoms (33.5% of studies) and psychosocial/emotional concerns (32.4%) were most frequently reported. Based on these domains, a three-phase conceptual framework was developed: (1) universal needs screening, (2) targeted, multidisciplinary interventions tailored to individual survivors, and (3) ongoing evaluation through patient-reported outcomes to optimize care. CONCLUSIONS: Unmet needs among BCS are widespread and multifaceted, particularly regarding chronic physical symptoms and persistent psychosocial distress. Existing care frameworks are fragmented and insufficient. The proposed three-phase framework provides a structured approach to delivering proactive, patient-centred survivorship care. IMPLICATIONS FOR CANCER SURVIVORS: Implementing this framework can improve identification and management of unmet needs, enhance quality of life, support long-term physical and emotional recovery, and foster resilience throughout survivorship.
17 Apr 2026
Read appraisal →International immunopharmacology
CXCL9 as a key feature for deep learning-based immune subtyping and prediction of immune checkpoint blockade response in triple-negative breast cancer
BACKGROUND: Immune checkpoint blockade (ICB) therapy has provided a promising treatment option for patients with triple-negative breast cancer (TNBC). However, existing efficacy prediction strategies based on programmed death-ligand 1 (PD-L1) expression and Tumor Mutational Burden (TMB) have significant limitations, leading to inaccurate patient selection. METHODS: This study integrated multi-omics data obtained from GEO, TCGA, and GTEx datasets. Based on ComBat correction results, two deep learning-driven unsupervised clustering methods were constructed to identify potential immune subtypes. These identified TNBC subtypes were subjected to comprehensive bioinformatics analyses to investigate their differences in overall survival, immune cell infiltration, and functional enrichment analysis, which clarified the biological rationale of the proposed classification. Subsequently, differential expression analysis (DEA) and multiple machine learning algorithms were applied to identify subtype-specific features, with the expression and differentiation status of the key feature in immune cells further validated using single-cell sequencing (scRNA-seq). Finally, co-expression networks at multiple cellular levels were constructed to predict the co-expressed regulatory targets of the key feature, and the hypothesized regulatory mechanism was preliminarily validated in in vitro experiments (qRT-PCR, western blotting, immunofluorescence, and ELISA). RESULTS: After 5-fold cross-validation, the AE-K-means clustering method classified TNBC into three distinct latent subtypes. This algorithm exhibited superior classification efficacy compared with the other models (including NMF, ConsensusClusterPlus and VAE-GMM), as demonstrated by markedly elevated Silhouette Coefficients (training: 0.585 ± 0.030; test: 0.698 ± 0.103; validation: 0.611) and substantially reduced Davies-Bouldin Index values (training: 0.623 ± 0.032; test: 0.385 ± 0.218; validation: 0.558) at K = 3. These three identified TNBC subtypes displayed significant differences in overall survival and exhibited pronounced immune heterogeneity. Specifically, the K2 subtype was characterized by high PD-L1 expression and abundant M1 macrophage infiltration, while the K3 subtype displayed an opposing, immunosuppressive microenvironment. Feature selection results from multiple machine learning models were intersected to identify key features including CXCL9, and an RF model constructed based on these intersected genes showed high efficacy, particularly in distinguishing the K2 and K3 subtypes (AUC = 0.941). Further scRNA-seq analysis revealed that CXCL9 was specifically highly expressed in myeloid cells, and the proportion of CXCL9+ macrophages differed significantly between responders and non-responders. Given the strong positive correlation between CXCL9 and IDO1 expression, we subsequently treated macrophages with IDO1 inhibitors and observed a marked upregulation in both the expression and secretion of CXCL9, suggesting that IDO1 overexpression in macrophages may represent a potential mechanism underlying CXCL9 exhaustion in the tumor microenvironment. CONCLUSIONS: Our study developed a novel immune classification system for TNBC, identifying CXCL9 as a key biomarker positively correlated with ICB response and regulated by IDO1. These findings provide a basis for precision immunotherapy in TNBC and support the exploration of IDO1 inhibitors in combination with ICB.
15 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Mepitel Film versus StrataXRT for the prevention of radiation dermatitis in post-mastectomy radiotherapy: a systematic review and meta-analysis of randomized controlled trials
BACKGROUND: Radiation dermatitis (RD) affects up to 90% of patients receiving post-mastectomy radiotherapy, with moist desquamation causing significant morbidity. Barrier films and dressings such as Mepitel Film and StrataXRT have been shown to be effective compared to moisturizers, but their relative effectiveness remains debated. This systematic review and meta-analysis compares these interventions for preventing RD. METHODS: Following PRISMA guidelines, we systematically searched MEDLINE, Embase, and Cochrane Library through March 14, 2026. An additional grey literature search was performed in clinicaltrials.gov and Google Scholar. Eligible studies were RCTs directly comparing StrataXRT and Mepitel Film in breast cancer patients receiving adjuvant radiation therapy. Two reviewers independently screened studies, extracted data, and assessed risk of bias using RoB 2.0 and certainty of evidence with the GRADE appraoch. Meta-analysis used random-effects models to pool risk ratios (RR) for moist desquamation, and incidence of grade 2 and 3 RD, with I2 statistics quantifying heterogeneity. RESULTS: From 33 screened records, two intra-patient, non-inferiority RCTs (n = 120) met inclusion criteria. Both studies randomized medial/lateral chest wall halves to each intervention in post-mastectomy patients. Pooled analysis revealed no significant difference in moist desquamation incidence between StrataXRT (21.7%) and Mepitel Film (20.0%) (RR 1.03; 95% CI 0.52-2.04; P = 0.93), with low heterogeneity (I2 = 35%). Secondary outcomes showed Mepitel Film significantly reduced grade 2 or 3 RD (RR 1.34, 95% CI 1.10-1.64, P = 0.004). However, Mepitel Film trended toward higher incidence of itchiness (10.8% vs. 5.0%, P = 0.11). Patient preference data were conflicting, with one trial favoring StrataXRT for ease of use and the other showing no clear preference. CONCLUSION: While StrataXRT and Mepitel Film demonstrated similar efficacy for preventing moist desquamation, Mepitel Film offered superior protection against grade 2 to 3 RD. However, this conclusion should be interpreted with caution given the underlying methodological limitations of the included studies, including small and potentially underpowered sample sizes, and variations in radiation fractionation schedules and RT techniques.
15 Apr 2026
Read appraisal →Annals of plastic surgery
Immediate Lymphatic Reconstruction to Prevent Postmastectomy Lymphedema: A National Analysis of Outcomes and Regional Trends
BACKGROUND: Lymphedema is a chronic disease characterized by swelling, inflammation, adipose deposition, and fibrosis. In the United States, lymphedema occurs most frequently as a sequela of oncologic lymphadenectomy. Axillary lymph node dissection (ALND) for breast cancer has been associated with a 5%-40% incidence of upper limb lymphedema. In immediate lymphatic reconstruction (ILR), surgeons perform lymphovenous anastomosis (LVA) with ALND to prevent the development of future lymphedema. Although early evidence supports a protective effect of ILR, there have been few large-scale studies on the topic. We leveraged a large claims database to better characterize the outcomes of ILR and national trends related to its adoption. METHODS: Adult female patients with breast cancer who underwent axillary lymph node dissection (ALND) between 2007 and 2022 were identified within the Merative MarketScan Research Databases and stratified according to whether they underwent ILR. Adjusted odds of undergoing ILR, both overall and regionally, and of experiencing postmastectomy lymphedema syndrome (PMLS) were calculated. RESULTS: Of all patients undergoing mastectomy and ALND, 1.3% received ILR. ILR was associated with decreased odds of developing PMLS (OR, 0.81; P = 0.02). The frequency of ILR procedures increased in all regions between 2017 and 2022, most dramatically in the Midwest and East US. More recent surgery year, younger age, complete mastectomy, and delayed ALND elevated the odds of undergoing ILR ( P ≤ 0.01). CONCLUSIONS: The popularity of ILR has grown rapidly since 2017, with significant regional variability. Patients with breast cancer who undergo ILR at the time of ALND are significantly less likely to develop lymphedema.
13 Apr 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Resistance to neoadjuvant talazoparib in triple-negative breast cancer by BRN2-induced ATR/STAT3 pathways or SHLD2 subclone expansion
Intrinsic and acquired resistance to poly(ADP-ribose) polymerase (PARP) inhibitors (PARPi) remains a major barrier in treating homologous recombination (HR) repair-deficient tumors, including those with germline or somatic BRCA1/2 mutations. Although PARPi are FDA approved for adjuvant treatment of locally advanced or metastatic breast cancer in patients with germline BRCA1/2 mutations, emerging data support their use as monotherapy in the neoadjuvant setting. Promising safety profiles of newer-generation PARPi further support this potential. However, resistance mechanisms specific to the neoadjuvant setting are poorly understood. To address this gap, we leveraged resources from a phase II neoadjuvant clinical trial (NCT03499353), analyzing tumors from patients with germline BRCA1/2 mutant breast tumors before and after six months of talazoparib monotherapy. Whole-transcriptome analyses were performed on these samples. Additionally, we established orthotopic patient-derived xenograft models from a subset of the patient tumors and conducted whole-exome and whole-transcriptome analysis. This integrative approach revealed both known and previously unknown PARPi resistance mechanisms. In one case, overexpression of BRN2, encoding a transcription factor that plays a critical role in neurogenesis, led to activation of ATR/RAD51 and STAT3 pathways, restoring HR repair. BRN2-driven resistance could be reversed with ATR and STAT3 inhibitors, resensitizing cells to talazoparib. In another, an HR repair proficient tumor subclone lacking Shieldin 2 expression expanded during treatment and accounted for intrinsic resistance. Our findings highlight the need to determine intrinsic and anticipate acquired resistance pathways in treatment-naïve tumors and support combining PARPi with targeted agents to improve outcomes in the neoadjuvant setting.
12 Apr 2026
Read appraisal →Biomedical physics & engineering express
Efficient machine learning models leveraging DCE-MRI morphological and dynamic features allow accurate breast lesion classification.
We propose an ensemble learning approach to classify malignant versus benign breast lesions by leveraging morphological and dynamic features derived from Magnetic Resonance Images (MRI). The analysis was performed on 164 breast lesions of the publicly available 'Advanced MRI Breast Lesions' dataset from The Cancer Imaging Archive, containing T2-weighted and Dynamic Contrast-Enhanced (DCE)-MRI sequences, along with the segmentation masks of suspicious lesions. After extracting radiomic features using the Pyradiomics Python package, we computed dynamic features from DCE-MRI kinetic curves, which describe the contrast agent wash-in and wash-out. These features have been defined as the derivatives of image intensity measures, such as mean and standard deviation, computed within the masks on the five DCE-MRI time steps. We trained and evaluated an eXtreme Gradient Boosting (XGBoost) classifier, experimenting with different combinations of features and segmentation masks in a stratified 5-fold cross-validation. The best model trained on T2-weighted MRI morphological features achieved an Area Under the Curve (AUC) score of 0.83 ± 0.04 (95% C.I. 0.826-0.835) on the independent test set consisting of 20 lesions, while the model using only dynamic features performed an AUC of 0.91 ± 0.03 (95% C.I. 0.907-0.915). Despite being obtained on a small test sample, these results show the potential of features derived from DCE images for breast lesions classification.
10 Apr 2026
Read appraisal →JMIR cancer
Acceptance, Perceived Usefulness, and Data Sharing in Mobile Health Apps Among Patients With Breast Cancer: Cross-Sectional Survey Study
BACKGROUND: Mobile health (mHealth) apps promise to enhance patient empowerment, enable real-time health monitoring, and support self-management. Patients with breast cancer stand to benefit particularly from these capabilities, given the demanding pre- and posttreatment procedures they face. However, the effective adoption of these tools is challenged by issues including accessibility, usability limitations, and privacy concerns. OBJECTIVE: This study aimed to investigate the current usage behavior and attitudes toward mHealth apps among patients with breast cancer in Germany, focusing on acceptance, usefulness, empowerment, and data-sharing. Specifically, we aimed to identify the incentives and barriers influencing use and the intention to use mHealth apps at the time of this writing to guide their further implementation and use. METHODS: A cross-sectional online survey was conducted between November 2023 and June 2024 using a structured questionnaire developed in German. Participants with a self-reported breast cancer diagnosis were recruited via university hospital channels, patient associations, and social media. Topics included technology and health app usage, perceived usefulness, personal empowerment, and attitudes toward data sharing. The survey comprised both closed- and open-ended questions to capture quantitative patterns and qualitative insights into participants' experiences and perspectives. RESULTS: We collected responses from 90 participants with breast cancer. Participants demonstrated high engagement with digital technology; 97% (87/90) reported daily smartphone use, and 70% (63/90) owned a smartwatch or fitness tracker. Approximately 67% (60/90) of respondents had used health apps in the past year, with many relying on general fitness and lifestyle apps to support their health management. Most participants (62/90, 69%) indicated that health apps contribute to a better understanding of their personal health. Health apps were generally perceived as useful, with users highlighting their ability to provide continuous support and timely, reliable health information (n=31). This, in turn, was seen as instrumental in promoting self-management and empowerment. Respondents also showed a conditional willingness to share personal health data for research, particularly when clear benefits for treatment advancements were evident (n=82). Nonetheless, concerns were raised regarding data privacy, app usability, the need for multiple apps to cover all necessary features, and the potential increase in fear of illness (cyberchondria). CONCLUSIONS: The findings underscore the potential of mHealth apps to empower patients with breast cancer by improving health literacy and supporting self-management. To maximize their impact, future digital health tools must align closely with patient needs by incorporating adaptive, interactive features and integrating seamlessly with clinical care. Addressing key barriers-particularly data privacy and usability issues-is essential for broader adoption. Longitudinal studies are warranted to evaluate the long-term effects of health app usage on patient empowerment, quality of life, and clinical outcomes.
9 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Digital and AI-enhanced psychosocial interventions in breast cancer patients: a systematic review and meta-analysis
PURPOSE: Digital and AI-supported psychosocial interventions are increasingly implemented to address psychological distress among women with breast cancer. However, their effectiveness and sustainability remain uncertain. This review aimed to systematically evaluate randomised controlled trials (RCTs) assessing the impact of digital and AI-enhanced psychosocial interventions on stress, anxiety, depression, and health-related quality of life (HRQoL). METHODS: Following PROSPERO registration and PRISMA 2020 guidelines, comprehensive searches of multiple databases were conducted up to June 2025. Eligible studies included RCTs involving breast cancer populations and reporting validated psychosocial outcomes. Risk of bias was assessed using the Cochrane RoB 2.0 tool, and certainty of evidence was evaluated with GRADE. Random-effects meta-analyses using standardised mean differences (Hedges' g) were conducted to pool outcomes measured with different instruments. RESULTS: Thirty-five RCTs (n ≈ 7551 participants) met inclusion criteria. Pooled analyses indicated modest but consistent reductions in stress, anxiety, and depressive symptoms, alongside improvements in HRQoL. Interventions grounded in cognitive behavioural therapy, mindfulness, or psychoeducation produced the most reliable outcomes. AI-assisted and blended formats tended to show stronger effects compared with static or minimally interactive platforms. However, heterogeneity in intervention design, short follow-up periods, reliance on self-reported outcomes, and small single-centre samples limited certainty. Cultural adaptation and representation from low- and middle-income countries were minimal. CONCLUSION: Digital and AI-supported psychosocial interventions show promise in reducing distress and improving HRQoL among women with breast cancer. Evidence certainty remains moderate, constrained by methodological limitations. Larger, multicentre trials with longer follow-up and diverse populations are needed to establish sustainability, generalisability, and clinical integration. RELEVANCE: These findings suggest that digital and AI-enhanced psychosocial interventions may serve as adjuncts to conventional care, with potential to improve access to supportive oncology services. Further high-quality, long-term studies are needed before informing guidelines, policy, or widespread implementation. TRIAL REGISTRATION: PROSPERO (CRD420251074813; registered 16 June 2025).
9 Apr 2026
Read appraisal →Cancer causes & control : CCC
Comparing endocrine therapy adherence across EHR and claims data by race and ethnicity, language, and insurance.
PURPOSE: We compared adherence measurements across electronic health records (EHRs) and claims data for 79 HR + breast cancer survivors, considering their race, ethnicity, language, and insurance status. METHODS: Adherence was evaluated using the medication possession ratio (MPR) and proportion of days covered (PDC). The correlation between EHR and claims data was assessed with Spearman correlation coefficients, while Kaplan-Meier curves were employed to visualize and analyze the time to the first instance of non-adherence and the time to discontinuation. RESULTS: A total of 75 breast cancer patients receiving care at Massachusetts General Hospital (MGH) were identified. While there was a positive correlation, EHR data tended to overestimate adherence based on medication prescriptions, while claims data captured adherence through prescription fills. Kaplan-Meier analyses demonstrated differences in the time to first non-adherence between EHR and claims data sources, with observed variations based on language and insurance status. CONCLUSION: Our findings suggest that EHR and claims data offer complementary insights into adherence measurement, and their integration could improve adherence assessment across diverse populations.
5 Apr 2026
Read appraisal →Annals of plastic surgery
Predicting Surgical Outcomes in Breast Reconstruction With Machine Learning: A Systematic Review
INTRODUCTION: The applications of artificial intelligence (AI) in plastic surgery have grown considerably in recent years. As large patient datasets become more accessible, surgeons are increasingly leveraging machine learning (ML), a subset of AI, to predict patient outcomes and guide surgical decision-making. This review evaluates the relative performance of ML prediction models in breast reconstruction. METHODS: A systematic review was conducted utilizing PubMed, Scopus, and EMBASE according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies using ML to predict patient outcomes in breast reconstruction were included. The type of ML model and the specific outcome measures were reported. Performance of the models was reported as area under the receiver operating characteristic curve and compared using descriptive statistics, multivariate linear regression, and random-effects meta-regression in RStudio. RESULTS: Our search yielded 1025 citations, of which 24 were assessed for eligibility. Fourteen studies met the inclusion criteria and were sought for data extraction. There were 19 ML models and 11,013 patients assessed across 92 testing conditions. Models were trained on varying patient demographics, comorbidities, and operative characteristics, whereas outcomes assessed included various surgical complications or patient satisfaction using BREAST-Q. The median area under the receiver operating characteristic curve of all models was 0.71 (interquartile range = 0.16). When adjusting for the number of patients, number of predictors, ML model category, and outcome of interest, models predicting BREAST-Q performed higher with skin necrosis used as the reference outcome (β = 0.13, P < 0.01). After adjusting for number of patients and predictors, models that employed strategies to mitigate class imbalance were associated with higher model discrimination (β = 0.038; 95% CI, 0.002-0.075; P = 0.041). CONCLUSION: Machine learning applications for risk prediction and surgical planning are growing rapidly. The models evaluated in this review demonstrated the ability to predict a variety of outcomes, with models predicting BREAST-Q, various surgical outcomes, and those reporting class imbalance methods leading to higher model discrimination. Notably, covariate adjustment and study heterogeneity may have impacted these associations. As ML models are increasingly integrated into plastic surgery practice, standardized reporting practices are essential to promote reproducibility and cross-study comparison.
4 Apr 2026
Read appraisal →Critical reviews in oncology/hematology
Artificial intelligence and multi-omics convergence in breast cancer: Revolutionizing diagnosis, prognostication, and precision oncology
Breast cancer (BC) is a highly heterogeneous malignancy and remains a major cause of cancer-related mortality among women worldwide. Advances in multi-omics profiling spanning genomics, transcriptomics, epigenomics, proteomics, and metabolomics have enabled finer subtype stratification and more comprehensive characterisation of tumour biology, thereby accelerating the discovery of diagnostic and prognostic biomarkers and actionable therapeutic targets. Nonetheless, translating multi-layer molecular signals into clinically robust decision support remains challenging because of the high dimensionality and heterogeneity of omics data, cross-cohort and cross-platform variability, and the fragmentation inherent to single-modality analyses. This review summarises how multi-omics studies have refined BC subtype definitions and advanced biomarker and target identification, and then synthesises recent progress in artificial intelligence, particularly deep learning, for integrating multi-omics with imaging, pathology, and clinical variables to improve diagnosis, risk stratification, prognosis prediction, and treatment response assessment. We critically examine representative multimodal integration frameworks and emerging deep learning architectures that learn both shared and modality-specific representations, which in many settings enable more accurate patient-level prediction than unimodal baselines. We further delineate key barriers to clinical translation, including cross-centre heterogeneity and inconsistent endpoint definitions, structural missingness of modalities in real-world workflows, inadequate cross-platform normalisation, limited interpretability and auditability, and a lack of prospective validation. Finally, we propose realistic next steps, including standardised and auditable preprocessing pipelines, missingness-aware fusion strategies, explainable and uncertainty-aware modelling, privacy-preserving multi-centre learning, and prospective, workflow-based evaluation. Collectively, these perspectives provide a roadmap for advancing multimodal AI-multi-omics integration toward reliable clinical deployment in BC management.
4 Apr 2026
Read appraisal →International journal of medical informatics
Automated extraction of fluoropyrimidine treatment and treatment-related toxicities from clinical notes using natural language processing.
OBJECTIVE: Fluoropyrimidines are widely prescribed for colorectal and breast cancers, but are associated with toxicities such as hand-foot syndrome and cardiotoxicity. Since toxicity documentation is often embedded in clinical notes, we aimed to develop and evaluate natural language processing (NLP) methods to extract treatment and toxicity information. MATERIALS AND METHODS: We constructed a gold-standard dataset of 236 clinical notes from 204,165 adult oncology patients. Domain experts annotated categories related to treatment regimens and toxicities. We developed rule-based, machine learning-based (Random Forest [RF], Support Vector Machine [SVM], Logistic Regression [LR]), deep learning-based (BERT, ClinicalBERT), and large language models (LLM)-based NLP approaches (zero-shot and error analysis prompting). A 5-fold cross validation were conducted to validate each model. RESULTS: Error analysis prompting achieved optimal precision, recall, and F1 scores for treatment (F1 = 1.000) and toxicities extraction (F1 = 0.965), whereas zero-shot perform moderately (treatment F1 = 0.889, toxicities extraction F1 = 0.854) Rule-based reached F1 = 1.000 for treatment and F1 = 0.904 for toxicities extraction. LR and SVM ranked second and fourth for toxicities extraction (LR F1 = 0.914, SVM F1 = 0.903). Deep learning and RF underperformed, with performance of BERT reached F1 = 0.792 for treatment and F1 = 0.837 for toxicities extraction.,ClinicalBERT reached F1 = 0.797 for treatment and F1 = 0.884 for toxicities extraction). RF reached F1 = 0.745 for treatment and F1 = 0.853 for toxicities extraction. DISCUSSION: LMM-based error analysis outperformed all others, followed by machine learning methods. Machine learning and deep learning methods were limited by small training data and showed limited generalizability, particularly for rare categories. CONCLUSION: LLM-based error analysis most effectively extracted fluoropyrimidine treatment and toxicity information from clinical notes, and has strong potential to support oncology research and pharmacovigilance.
4 Apr 2026
Read appraisal →The Lancet. Oncology
Deep learning on histopathological images to predict breast cancer recurrence risk and chemotherapy benefit: a multicentre, model development and validation study
BACKGROUND: Genomic assays such as Oncotype DX have transformed adjuvant treatment selection for hormone receptor-positive, HER2-negative, early breast cancer but remain inaccessible to many patients because of high cost and logistical barriers. We aimed to develop and validate an artificial intelligence (AI) model that estimates Oncotype DX 21-gene recurrence scores directly from routine histopathology slides and clinicopathological variables. METHODS: In this multicentre, model development and validation study, a multimodal deep-learning model was trained on digital whole-slide images and clinical features using a foundation model pre-trained on 171 189 histopathology slides for predicting Oncotype DX recurrence score. We included slides from patients with hormone receptor-positive, HER2-negative, invasive breast cancers and without scanning artifacts and with at least 100 tissue tiles (1·6 mm2). The model was fine-tuned and validated on the TAILORx randomised trial (8284 patients after quality control). Prognostic and predictive performance was assessed in the TAILORx-test set and externally validated in six independent cohorts (Carmel, Haemek, and Sheba medical centres [Israel], the University of Chicago Medical Center [USA], the Australian Breast Cancer Tissue Bank [Australia], and the Cancer Genome Atlas Breast Invasive Carcinoma project [USA]). FINDINGS: In the TAILORx-test set (n=2407), the AI model classified 1097 (45·6%) patients as low risk, 1021 (42·4%) as intermediate risk, and 289 (12·0%) as high risk. For identifying high genomic-risk disease (recurrence score ≥26), the area under the curve (AUC) was 0·898 (95% CI 0·879-0·913). AI-based risk stratification was prognostic for recurrence-free interval (hazard ratio 2·61 [95% CI 1·68-4·04]), distant recurrence-free interval (2·88 [1·73-4·79]), and disease-free survival (1·32 [0·92-1·89]). Chemotherapy benefit was evident in premenopausal patients classified by AI as being at high risk (0·63 [0·46-0·86]) but absent in postmenopausal patients classified by AI as being at low risk (0·94 [0·78-1·12]). 151 (31·3%) clinically high-risk postmenopausal women (by MINDACT criteria) were reclassified as low AI risk with no chemotherapy benefit. Analysis on external cohorts (5497 patients) showed that the model is transferable to new data with high generalisability (recurrence score ≥26 AUC ranging from 0·858 to 0·903). INTERPRETATION: These findings show that AI applied to routine histopathology can serve as a practical and scalable tool for guiding chemotherapy decisions in hormone receptor-positive, HER2-negative, early breast cancer. This approach has the potential to reduce unnecessary chemotherapy and broaden access to precision oncology, particularly in resource-limited settings where genomic testing remains unavailable or unaffordable. FUNDING: Israel Innovation Authority (Kamin), Zimin Institute for Artificial Intelligence Solutions in Healthcare, Israel Precision Medicine Partnership program, and Israel Cancer Research Fund.
4 Apr 2026
Read appraisal →Cancer Cell
Precision treatment with artificial intelligence assisted subtyping enhances therapeutic efficacy in HR+/HER2- breast cancer: The LINUXtrial.
We report the results of LINUX (NCT05594095), a multicenter, randomized, controlled phase II platform trial aiming to identify effective precision treatments for hormone receptor-positive/human epidermal growth factor receptor 2-negative metastatic breast cancer after resistance to cyclin-dependent kinase 4/6 inhibitor. A total of 105 patients were categorized into four similarity network fusion (SNF) subtypes by artificial intelligence-assisted classification and randomly assigned to receive subtyping-based precision therapy (N = 70) or treatment of physician's choice (N = 35). Results demonstrate superior primary endpoint of objective response rates in the subtyping-based groups compared to controls: 10% versus 0% for SNF1, 65% versus 30% for SNF2, 40% versus 30% for SNF3, and 70% versus 20% for SNF4. Grade 3-4 treatment-related adverse events occurred in 37% of both groups. These findings highlight the clinical benefits of subtyping-based precision therapies, particularly for SNF2 and SNF4 subtypes, warranting further validation in phase III trials.
1 Feb 2026
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