Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 13 appraisals
Journal of chemical information and modeling
Protein Language Model-Based Fitness Estimates Facilitate Resistance Mutation Identification
Drug resistance is a major challenge in cancer therapy. Cancer cells with pre-existing or acquired mutations that confer resistance to a given drug treatment outgrow the susceptible cell population and cause cancer recurrence after an initial successful treatment response. Knowledge about resistance mutations before they occur in the clinic could prevent unnecessary patient treatment with ineffective drugs, in clinical trials as well as clinical practice, or potentially speed up the development of follow-up compounds. Here, we focused on on-target amino acid mutations that confer resistance to an inhibitor compound with a known binding mode. We evaluated whether a combination of physics-based free energy perturbation (FEP) affinity estimates and protein language model-based protein fitness estimates could improve the in silico identification of resistance mutations. Validation was done with data from deep mutational scanning (DMS) experiments that tested for resistance to single amino acid mutations. A public data set testing ERK2 resistance against the inhibitor SCH772984 and an internal data set testing resistance of an EGFR_exon20 mutant against a Bayer small molecule inhibitor were used. Our results show that protein fitness estimates can facilitate the identification of resistance mutations by filtering mutations with a low estimated fitness. Even though FEP has flagged such mutations as affinity-decreasing and thus potentially resistant, they were not resistant according to the DMS experiment and therefore correctly filtered out. This indicates that protein language model-based protein fitness estimates could be a computationally efficient method to filter mutations without having to model the negative impact of mutations on native function or protein stability, which is error-prone and computationally expensive.
28 July 2026
Read appraisal →Science advances
Interpretable dynamic quantitative vascular morphometry features using SHAP for anti-angiogenic therapy response prediction
Anti-angiogenic therapy benefits vary, with response rates of 40 to 70%, highlighting the need for early biomarkers to identify responders. We developed an automated machine learning framework that uses delta quantitative vascular morphometry features from standard contrast-enhanced CT to evaluate treatment response. This workflow combines automated tumor and vessel segmentation with feature extraction from routine scans for clinical use. Shapley additive explanations (SHAP)-based attributions identify key vascular and clinical features, providing meaningful, imaging-visible evidence aligned with therapy targets beyond traditional radiomics. Using baseline and follow-up CTs from 163 patients with lung cancer, we built three models using fivefold cross-validation, with the delta-merge model achieving high accuracy (area under the receiver operating characteristic curve = 0.842 internally, 0.806 externally). SHAP analysis uncovered an "arterial-dominant, venous-adaptive" pattern, where arterial involvement and venous recovery distinguish responders. This automated workflow and visualization support early, imaging-based response assessment and personalized treatment.
27 July 2026
Read appraisal →Physics in medicine and biology
Four-dimensional on-beam computed tomography reconstruction using projection-difference images
Purpose.Accurate verification of delivered dose requires anatomical information during the treatment beam delivery. However, conventional cone-beam computed tomography (CBCT) is typically acquired prior to beam delivery and fails to account for intrafractional tumor shift/drift. This study aims to develop a 4D on-beam computed tomography (4D OBCT) framework to capture anatomical states during irradiation.Materials and methods.A thoracic phantom with rigid tumor motion was used to develop a conformal arc treatment plan. During delivery, the target was displaced by 1 cm superior to its planned position to simulate a baseline shift, and megavoltage transmission data were acquired using an electronic portal imaging device. Transmission data corresponding to the planned tumor position were obtained from the planning CT (pCT) via Monte Carlo simulation. Volumetric difference images were reconstructed from the differences between the measured and simulated transmission data for each phase. Subsequently, the images were superimposed onto the corresponding phases of the 4D pCT to obtain 4D OBCT. For evaluation, we compared 4D OBCT with 4D pCT (ground-truth) and 4D CBCT which were acquired after the target displacement. A dosimetric performance was evaluated by recalculating 4D dose distributions on the three images and comparing in dose-volume histograms and gamma indices.Results.4D OBCT achieved lower root mean square error than 4D CBCT in both the lung (58.46 HU vs 74.37 HU) and chest wall regions (23.78 HU vs 66.84 HU). This quantitative accuracy was also reflected in the dosimetric comparison. Under 2%/2 mm and 1%/1 mm criteria, 4D OBCT achieved gamma pass rates of 99.76% and 99.08%, respectively, whereas 4D CBCT yielded 72.50% and 57.77%, respectively.Conclusions.The proposed 4D OBCT framework successfully reconstructed accurate phase-resolved images of the target during irradiation. By providing high-resolution volumetric images that reflect intrafractional motion, 4D OBCT could provide a robust solution for precise post-delivery dosimetric verification and adaptation.
12 July 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Prediction of PD-L1 expression in nonsmall cell lung cancer using artificial intelligence models based on radiomics: a systematic review and meta-analysis
BACKGROUND: Since 1992, the incidence of lung cancer has shown an overall downward trend, with survival rates significantly improving in recent years due to the application of immune checkpoint inhibitors (ICIs). Assessing programmed death ligand 1 (PD-L1) expression status in lung cancer patients is crucial for predicting the efficacy of ICIs and formulating treatment plans. Therefore, there is an urgent need to assess PD-L1 expression status in lung cancer patients, particularly those with nonsmall cell lung cancer (NSCLC). While numerous studies have demonstrated promising results using artificial intelligence (AI) models based on radiomics, current understanding in this area remains incomplete. METHOD: We conducted a search of medical databases and screened studies that meet the criteria. We performed feature extraction and data extraction from these studies, analysed them using R software, and generated forest plots. We conducted subgroup analyses based on imaging modality and algorithm model. RESULTS: A total of 35 studies were included in the analysis. For the Tumor Proportion Score (TPS)1 group, the pooled area under the curve (AUC) for the validation set was 0.782. For the TPS50 group, the pooled AUC for the validation set was 0.798. Subgroup analysis revealed that machine learning performed better when predicting TPS50, while deep learning was superior for predicting TPS1. When predicting TPS, prediction models constructed using 2-fluoro-2-deoxy-d-glucose (18F) positron emission tomography/computed tomography (CT) and contrast-enhanced CT outperformed those based on noncontrast CT. Conversely, when predicting a TPS, the noncontrast-CT-based prediction model significantly outperformed the other two groups. CONCLUSIONS: Imaging modality and algorithm model are key factors influencing AI prediction models for PD-L1 expression in NSCLC patients.
10 July 2026
Read appraisal →European journal of radiology
Deep learning-based segmentation and quantification of pulmonary masses on apparent diffusion coefficient maps: a multicentre reproducibility study
OBJECTIVES: To develop and validate a deep learning model for automatic segmentation of pulmonary masses on apparent diffusion coefficient (ADC) maps and to assess repeatability of automated ADC quantification. METHODS: We proposed ADCSegNet, a deep learning model tailored to pulmonary ADC maps, trained and tested on ADC maps from centre 1 (303 MRI examinations) and externally evaluated on datasets from centre 2 (70 examinations) and centre 3 (12 examinations) for generalisability. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC). Agreement between model-derived and manual ADC measurements (senior and junior radiologists) was examined using Bland-Altman analysis, the intraclass correlation coefficient [ICC(2,1)], and the reproducibility coefficient (RDC) following QIBA profiles. Repeat-scan stability was assessed on an independent prospective test-retest dataset (22 × 2 examinations) using Lin's concordance correlation coefficient (CCC). RESULTS: ADCSegNet achieved DSCs of 0.843 internally and 0.701 externally, outperforming nnU-Net and other contemporary backbones. Agreement was highest between the model and the senior radiologist; for mean ADC, the mean difference was 0.00 (95% limits of agreement, -0.13 to 0.13) × 10⁻3 mm2/s, exceeding model-junior and inter-radiologist agreement. ICC(2,1) exceeded 0.75 for most ADC metrics, and RDC confirmed high reproducibility for key metrics (mean ADC RDC%: 12.98% for model-senior vs. 22.87% for senior-junior radiologist). Test-retest analyses showed higher repeatability of ADCSegNet than radiologists for core metrics such as mean ADC (CCC 0.931 vs. 0.908). CONCLUSIONS: ADCSegNet enables accurate segmentation of pulmonary masses on ADC maps and provides reproducible automated ADC quantification, outperforming state-of-the-art backbone models while showing strong agreement with expert readers and stable performance on repeat scans.
4 July 2026
Read appraisal →The Journal of international medical research
Diagnostic performance of machine learning-based radiomics models for predicting epidermal growth factor receptor mutation status in lung adenocarcinoma in Chinese patients: A systematic review and meta-analysis
ObjectiveThis systematic review and meta-analysis evaluates the diagnostic performance of machine learning-based radiomics models for predicting epidermal growth factor receptor mutation status in Chinese patients with lung adenocarcinoma.MethodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines and prospectively registered in the International Prospective Register of Systematic Reviews (CRD420251273027), a systematic search of PubMed, Embase, Web of Science, the Cochrane Library, Scopus, China National Knowledge Infrastructure, Wanfang, VIP, and Chinese Biomedical Literature Database was conducted from inception to 31 October 2025. Two reviewers independently screened studies, extracted data, and assessed bias using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate random-effects model was used to synthesize the data. Subgroup analyses were conducted for three factors: (a) imaging modality (computed tomography vs. positron emission tomography-computed tomography); (b) algorithm type (deep learning vs. conventional machine learning); and (3) validation strategy (external vs. internal).ResultsThirteen studies encompassing 6628 patients were included. The pooled sensitivity was 71% (95% confidence interval: 68-74), the pooled specificity was 81% (95% confidence interval: 78-84), and the summary area under the curve was 0.85 (95% confidence interval: 0.82-0.88). Deep learning models significantly outperformed conventional machine learning models (area under the curve: 0.871 vs. 0.798; P = 0.012). Computed tomography-based models yielded higher accuracy than positron emission tomography-computed tomography-based models (area under the curve: 0.879 vs. 0.828; P = 0.038). Models validated on independent external cohorts demonstrated superior performance compared with those relying solely on internal validation (area under the curve: 0.922 vs. 0.841; P = 0.006). Imaging modality was a significant source of heterogeneity (P < 0.05). No threshold effect or publication bias was detected.ConclusionMachine learning-based radiomics models exhibit promising diagnostic accuracy for the noninvasive prediction of epidermal growth factor receptor mutations in Chinese patients with lung adenocarcinoma. Computed tomography-based deep learning models subjected to independent external validation represent the current optimal approach. However, the retrospective nature and substantial heterogeneity of the included studies necessitate large-scale, prospective, multicenter trials with standardized workflows before clinical translation.
4 July 2026
Read appraisal →Physics in medicine and biology
Deep learning based time series analysis for breathing phase prediction in phase-gated proton therapy
Objective. Phase gating is a critical technique to mitigate tumor motion during radiotherapy, particularly in spot-scanned particle therapy where internal motion can interfere with dynamic spot scanning patterns and, simultaneously, introducing substantial range uncertainties. However, the current commercial state of the art in real-time phase prediction is challenged by patient-specific breathing variability as well as detection and delivery system latencies. This leads to suboptimal efficiency, mis-timed radiation delivery and requires frequent manual intervention. This study aims to improve phase prediction accuracy using deep learning (DL)-based time series forecasting to enable more accurate dose delivery.Approach. Retrospective breathing waveforms from 69 proton therapy patients, sampled at 30 Hz, were labeled with inspiratory peaks and assigned subjective regularity scores (four levels). DL models with various architectures were trained using waveform amplitude to predict current or future breathing phases. Model performance was evaluated using mean squared error, phase binning accuracy, and timing deviation for radiation on/off events. Bayesian optimization was used for hyperparameter tuning. Results were compared between models and against a commercial algorithm currently in clinical use.Main Results. The curated waveform dataset included 165 242 s for training, 24 057 s for validation, and 30 322 s for testing, with an additional 40 604 s from separate patients for extended validation. The long short-term memory and temporal fusion transformer models significantly outperformed the commercial algorithm, improving phase prediction accuracy by nearly 20% and reducing timing deviations across all regularity levels.Significance. DL-based time series forecasting may substantially improve breathing phase prediction accuracy over current clinically available methods, offering a more precise and reliable approach to phase-gated radiation delivery.
2 July 2026
Read appraisal →International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group
The role of capacitive hyperthermia as an adjunct treatment in oncology: a systematic review of randomized phase III trials.
INTRODUCTION: Capacitive hyperthermia (cHT) has been established for superficial tumors but the efficacy of cHT for intermediate or deep-seated tumors remains a subject of debate. This review is an evaluation of the current clinical evidence on the effectiveness of cHT in the treatment of tumors that are located deeper within the body. METHODS: A systematic review was conducted in accordance with the PRISMA guidelines of Phase III randomized controlled trials that included cancer patients of all ages and sexes with confirmed malignant tumors undergoing radiotherapy (RT) and/or chemotherapy (CT), with or without additional cHT. The literature search was conducted in PubMed and the Cochrane Register of Controlled Trials (CENTRAL). The primary endpoints included overall survival (OS), tumor response (TR; encompassing complete response (CR)), local control (LC), and cHT treatment-related toxicity (TRT). RESULTS: A total of nine randomized phase III trials were included in the study. The combination of cHT with RT and/or CT was found to improve both LC and OS in three out of five trials conducted in patients with cervical cancer. The analysis of both head and neck cancer trials demonstrated a marked increase in TR rates in cases where cHT was employed. Conversely, no such benefit was observed in non-small cell lung cancers and in a large multicenter cervical cancer trial. The treatment was well tolerated, with mild to moderate adverse events being the most common. CONCLUSION: It has been demonstrated that the combination of cHT with either RT or CT may improve TR and LC and, in certain cases, OS when compared with RT with or without CT alone. The occurrence of significant side effects is rare.
24 June 2026
Read appraisal →Clinical & experimental metastasis
Local control and dose selection for lung cancer brain metastases treated with radiosurgery: an artificial intelligence model
Clinical and radiological outcomes after Stereotactic radiosurgery (SRS) for lung cancer brain metastases are heterogeneous, and prescription dose selection is often guided by experience rather than individual patient and tumor specific features. A data-driven approach that links dose to quantitative local control and adverse effect predictions could improve planning and follow-up strategies. We performed a retrospective single-center cohort study of lung cancer brain metastases treated with GKRS at the University of Pittsburgh Medical Center (2014-2024). Using routinely available clinical, tumor, and dosimetric variables, we trained a tumor-level Random Survival Forest to predict Local Control and Dose Selection for Lung Cancer Brain Metastases after GKRS treatment. Internal validation used leakage-resistant patient-level grouped cross-validation. Performance was assessed using Harrell's concordance index (C-index) and integrated Brier score (IBS). A connected dose-sweep decision layer evaluated predicted local control across a clinically feasible margin dose grid and selected the dose associated with the most favorable predicted profile at prespecified horizons. The survival model demonstrated good internal performance (C-index 0.83; IBS 0.15). A baseline dose imitation model predicted historical margin dose with low error (OOF MAE 1.59 Gy). The integrated decision layer generated individualized local control trajectories and returned horizon-specific local control probabilities and model-based prescription dose recommendations. An Artificial Intelligence framework integrating time-to-event prediction with outcome-linked dose sweeping can provide individualized GKRS decision support for lung cancer brain metastases by delivering quantitative local control forecasts and model-based dose recommendations.
28 May 2026
Read appraisal →Physics in medicine and biology
A delta-based multi-omics model to predict adaptive radiotherapy benefit in patients with non-small cell lung cancer during carbon-ion radiotherapy
Objective.Patients with non-small cell lung cancer (NSCLC) treated with carbon-ion radiotherapy (CIRT) are potential candidates for adaptive radiotherapy (ART). However, ART is time- and resource-intensive. This study aimed to develop a decision-support tool to estimate ART demand before treatment.Approach.This retrospective study included 69 patients with NSCLC treated with CIRT. Baseline clinical characteristics, dose-volume histogram (DVH) parameters, and radiomic and dosiomic features from the plan-computed tomography (CT) (1-2 weeks before treatment) and confirm-CT (1-2 d before treatment) were collected, and relative change rates in features (ΔDVH, Δradiomics, and Δdosiomics) were calculated. Feature selection involved univariable testing, correlation analysis, least absolute shrinkage and selection operator regression, and stepwise regression with 1000 bootstrap resamples. A multivariable logistic regression model was constructed and validated using 200 iterations of five-fold cross-validation.Main results.ART was triggered in 32 (46.4%) patients. A five-feature delta-omic model (one ΔDVH, two Δradiomic, and two Δdosiomic features) achieved a validated median area under the curve of 0.897 (interquartile range, 0.833-0.950) with good calibration. At a high-sensitivity threshold (0.11), the model identified all ART candidates while potentially reducing the frequency of weekly-CT evaluations in 21.4% of patients; at the maximum-Youden threshold (0.41), the median accuracy reached 78.6%, with up to 50.0% classified as low ART demand.Significance.A delta-omic model predicting ART demand in patients with NSCLC receiving CIRT was developed. The model may help optimize resource allocation and streamline ART workflows, although prospective validation in larger cohorts is warranted before widespread clinical adoption.
13 May 2026
Read appraisal →The oncologist
Characterization and clinical management of adverse events following treatment with repotrectinib: a TRIDENT-1 analysis
BACKGROUND: Repotrectinib, a next-generation ROS1/TRK tyrosine kinase inhibitor, is approved for ROS1 fusion-positive non-small cell lung cancer and NTRK fusion-positive solid tumors. Its side effects and safety management strategies require further characterization. PATIENTS AND METHODS: The safety profile of repotrectinib (treatment-emergent/related adverse events [TEAEs/TRAEs]) was established in patients who initiated treatment at the recommended dose (160 mg daily [QD] for 14 days, then 160 mg twice daily [BID]) across all cohorts of the global, multicenter phase 1/2 TRIDENT-1 study. AE management strategies were outlined. RESULTS: In 472 patients, the most common TRAEs (dizziness [58%] and dysgeusia [50%]) were likely TRK inhibition-related. Median relative dose intensity was 90%; 14% (n = 66/472) of patients did not increase their initial QD dose to BID (mostly due to CNS AEs). Rates of dizziness (median onset, 7 days) were similar in patients with/without baseline brain metastases. Dose modifications downgraded severity or resolved dizziness in 78% of patients; 58% of patients had pharmacologic intervention without dose modification. Dizziness was downgraded/resolved in 62% (n = 120/195) of patients who did not receive dose modification or pharmacologic intervention. Treatment-related cognitive impairment and weight gain occurred in 19% and 12% of patients, respectively. Treatment-emergent withdrawal pain occurred in 14% of patients (median resolution time, 2.1 weeks). Dose interruption and reduction from TRAEs occurred in 39% and 38% of patients, respectively; 10% reported later re-escalation back to 160 mg BID. CONCLUSION: Many repotrectinib AEs, including neurological AEs secondary to TRK inhibition, were mitigated with appropriate management, including dose modification and/or pharmacologic intervention.
12 May 2026
Read appraisal →Cancer cell
HS-20093, a B7-H3-targeted antibody-drug conjugate in lung cancer: Results from the ARTEMIS-001 phase 1a/b trial
This phase 1a/b study (NCT05276609) evaluated the safety, pharmacokinetics, and efficacy of B7-H3-targeted antibody-drug conjugate HS-20093 (GSK5764227) in 306 patients with previously treated advanced solid tumors. In phase 1a, 12.0 mg/kg was established as the maximum tolerated dose. Among 236 lung cancer patients who received 8.0 or 10.0 mg/kg HS-20093, the most frequent grade ≥3 treatment-related adverse events (AEs) included decreased neutrophil (25.5% vs. 50.5%) and white blood cell counts (19.7% vs. 42.4%), and anemia (16.8% vs. 34.3%), respectively. Treatment-related interstitial lung disease and AEs leading to death occurred in 3.4% and 3.8% of patients, respectively. Among response-evaluable patients, the confirmed objective response rate was 52.3% (95% CI: 39.5, 64.9) for extensive-stage SCLC (ES-SCLC, N = 65) and 22.4% (95% CI: 16.0, 29.8) for non-small cell lung cancer (NSCLC, N = 152) patients, with comparable rates between 8.0 mg/kg and 10.0 mg/kg dose cohorts across both groups. These results support further development of HS-20093, with 8.0 mg/kg selected for phase 3 trials.
16 Apr 2026
Read appraisal →Annals of oncology : official journal of the European Society for Medical Oncology
Deep learning discriminates thymic epithelial tumors' histological subtypes using digital pathology.
BACKGROUND: Thymic epithelial tumors (TETs) are rare malignancies that pose significant diagnostic challenges due to their heterogeneous histological patterns and substantial interobserver variability in classification. Despite standardized World Health Organization (WHO) classification criteria, diagnostic concordance remains suboptimal, particularly in nonexpert settings, where second-opinion reviews lead to diagnostic reclassification in up to 57% of cases. Deep learning may offer a tool to reduce diagnostic variability and improve the consistency of histological classification. MATERIALS AND METHODS: We trained a deep learning-based model using hematoxylin-eosin (H&E) whole-slide images from The Cancer Genome Atlas as a training dataset. The model incorporated a novel hierarchical loss function designed to reflect clinically relevant tumor groupings based on treatment strategies and patient outcomes. We validated the model on 112 consecutive cases from the University of Chicago, with diagnoses confirmed by an expert thoracic pathologist. Model performances were evaluated using both a three-group hierarchical scheme and the six-class WHO classification. RESULTS: In the clinically relevant hierarchical three-group classification (As: A + AB; Bs: B1 + B2 + B3; and thymic carcinoma), the model achieved an accuracy of 91.1% with Cohen's κ = 0.859, indicating almost perfect agreement. In the six-class classification (A, AB, B1, B2, B3, and thymic carcinoma), the accuracy was 77.7% with κ = 0.716. The model demonstrated 100% sensitivity and 94.6% accuracy for thymic carcinoma detection. Notably, 60% of misclassifications occurred within the same clinical management group, thereby limiting their impact on therapeutic decision making. CONCLUSION: This deep learning model demonstrates strong potential as a diagnostic tool for TETs classification, particularly in settings with limited thoracic pathology expertise. The high sensitivity for thymic carcinoma detection and robust performance across different tissue processing conditions suggest its clinical applicability for improving diagnostic consistency and supporting pathological decision making in both specialized and nonspecialized settings.
4 Apr 2026
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