Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 14 appraisals
Cancer medicine
Applications of Artificial Intelligence in Cancer Diagnosis and Treatment
Driven by changes in lifestyle and environmental factors, the global incidence of cancer is steadily increasing, which has established it as a leading cause of mortality worldwide. The current paradigm for cancer diagnosis and treatment relies on conventional methods, such as imaging, endoscopy, and tissue biopsy, which present significant limitations regarding sensitivity in early screening, diagnostic specificity, and personalized treatment. Consequently, the development of more efficient and accurate technologies remains a major objective in modern oncology research, and artificial intelligence (AI) has emerged as a particularly promising solution. Through machine learning and deep learning algorithms, AI is reshaping cancer care by enabling automated detection of minute lesions during screening and quantitative analysis of pathological features for diagnosis. It may also advance tumor theranostics through multimodal data integration for treatment stratification, response prediction, and image-guided or targeted therapeutic decision-making, whereas providing data-driven recommendations for personalized treatment. Despite these prospects, medical AI development faces several key issues, including data bias, model explainability, clinical reliability and generalizability, emerging limitations of foundation models and generative AI, and regulatory and ethical issues that need to be addressed. By reviewing recent advances in AI across screening, diagnosis, theranostics, and treatment, we aim to clarify where these methods are already useful, where evidence remains limited, and why closer collaboration among clinicians, engineers, and data scientists is needed for clinical translation. We hope this review serves as a practical reference for researchers and clinicians evaluating how AI may be integrated into oncology in a more standardized, clinically responsible way.
1 Aug 2026
Read appraisal →Head and neck pathology
A Deep Learning AI Model for Histopathological Diagnosis and Grading of Mucoepidermoid Carcinoma of Salivary Glands
PURPOSE: This study aims to evaluate the effectiveness of deep learning AI model in the diagnosis and grading of mucoepidermoid carcinoma. METHODS: We developed a multiple instance learning based-deep learning model which consisted of two modules. One to perform slide-level classification of whole slide images into mucoepidermoid carcinoma and non-mucoepidermoid carcinoma classes and the second one to perform grading into low, intermediate, and high grades. A total of 194 whole slide images were included in the study. Of these, 92 corresponded to mucoepidermoid carcinoma, while the remaining 102 represented non-mucoepidermoid carcinoma cases. RESULTS: Our results showed that the classification model achieved 90.62% accuracy, 90.62% macro F1 score, 90.60% weighted F1 score, 91.67% macro precision, 91.18% macro recall. The grading model achieved an accuracy of 73.3%, weighted F1-score of 73.9%, weighted precision of 76.1% and weighted recall of 73.3%. CONCLUSION: The proposed deep learning classification model achieved promising performance in differentiating mucoepidermoid carcinoma cases. However, the grading model achieved an overall accuracy of 73.3%. Thus, depending on AI solely is still questionable, however it can be used to augment the work of the pathologist.
22 July 2026
Read appraisal →Journal of gastrointestinal cancer
Tumour-Associated Neutrophils in Colorectal Cancer: a Systematic Review, Meta-analysis, and Comprehensive Literature Review
PURPOSE: Neutrophils are at the forefront of innate immune response. The prognostic impact of tumour-associated neutrophils (TANs) remains unclear. This study aimed to evaluate the prognostic role of TANs in colorectal cancer (CRC). METHODS: A systematic review and meta-analysis were conducted using PubMed, Embase, and Scopus to identify studies correlating TANs with time-to-event survival analysis in patients with stages I-IV CRC from the date of inception until April 2024. Articles were included if they involved the identification of TANs through hematoxylin-eosin (HE) or immunohistochemistry (IHC) and reported survival analysis. Data was collected from the tumour core (TC) and invasive margin (IM). The hazard ratios (HRs) were extracted from the study results. Heterogeneity was evaluated using Cochran's Q and I². Primary and secondary endpoints were overall survival (OS) and disease-free survival (DFS). RESULTS: Of 4,292 citations found, 18 studies fulfilled eligibility criteria, encompassing 7,406 patients. IHC was used to identify TANs in 12 trials, while six performed HE. Five studies included patients with disease stages I-III, and 13 had stages I-IV. High TANs at the IM were associated with improved OS (HR = 0.64, 95% Confidence Interval [CI] 0.50-0.81, I2 = 48%) and DFS (HR = 0.47, 95% CI 0.25-0.88, I2 = 32%). At the TC, high TANs showed a trend towards better OS (HR = 0.82, 95% CI 0.61-1.10, I2 = 83%) and DFS (HR = 0.48, 95% CI 0.21-1.07, I2 = 93%), although not statistically significant and with high heterogeneity. CONCLUSION: In this systematic review and meta-analysis, high TANs at the invasive margin (IM), the tumour-host interface, was associated with improved prognosis in CRC. In contrast, no statistically significant association was observed for high TANs at the tumour core (TC).
9 July 2026
Read appraisal →Nature communications
A deep learning framework for efficient pathology image analysis
Artificial intelligence has transformed digital pathology by enabling biomarker prediction from high-resolution whole-slide images. However, current methods are computationally inefficient, processing thousands of redundant tiles per slide and requiring complex aggregation models. We introduce EAGLE (Efficient Approach for Guided Local Examination), a deep learning framework that emulates pathologists by selectively analyzing informative regions. EAGLE combines task-agnostic tile selection with detailed feature extraction and is benchmarked against leading slide- and tile-level foundation models across 43 tasks from nine cancer types spanning morphology, biomarker prediction, treatment response and prognosis. EAGLE outperforms patch aggregation methods by up to 23% and achieves the highest overall classification performance. It processes one slide in 2.27 s, reducing computational time by more than 99% compared with existing models. This efficiency supports rapid and auditable workflows by enabling review of the exact tiles used for each prediction and reducing dependence on high-performance computing. By reliably identifying informative regions and minimizing artifacts, EAGLE provides robust and auditable outputs, supported by systematic negative controls and attention concentration analyses. Its unified embedding enables rapid slide search, integration into multi-omics pipelines and emerging clinical foundation models.
2 July 2026
Read appraisal →The American journal of pathology
Deep Learning-Enhanced Biomarker Interpretation on Cytology Cell Blocks: Foundations and Emerging Opportunities in Spatial Pathobiology
Cytology cell block specimens are essential for diagnosing patients with advanced-stage malignancy and often represents the only available strategy for therapeutic biomarker evaluation. The use of cell blocks preserves tumor cells, captures high-grade or metastatic populations, and retains meaningful microenvironmental context, making them well suited for IHC analysis. With the rapid expansion of computational pathology, deep learning-assisted biomarker interpretation in cell blocks is emerging as a promising frontier for improving reproducibility, reducing interobserver variability, and enabling quantitative assessment of spatial tumor-immune interactions. Because many treatment-defining biomarkers are routinely assessed on cytology cell blocks, this mini-review highlights artificial intelligence-based applications for PD-L1, HER2, ER/PR, Ki-67, ALK/ROS1, BRAF V600E, and p16 markers that directly inform decisions about immunotherapy, targeted therapy, and hormone therapy. Also reviewed are emerging predictive models that convey biomarker status directly from morphology, extending the utility of artificial intelligence beyond conventional IHC interpretation. Finally, spatial pathobiology-related opportunities afforded by cell block preparations are discussed, and future directions for integrating artificial intelligence-enabled analysis into cytology workflows are outlined. Together, these advances position cytology cell blocks as an important platform for computational biomarker interpretation and morphology-driven precision oncology.
2 July 2026
Read appraisal →Medical image analysis
Autodidactic dense anatomical models
Humans effortlessly interpret images by parsing them into part-whole hierarchies. Yet, deep learning models, despite excelling at capturing multi-level features, often fail to explicitly encode these part-whole hierarchies-an essential aspect of medical imaging, which boasts anatomical hierarchies in nature. To address this limitation, we introduce Adam-v2, a self-supervised learning framework that explicitly learns to encode inherent part-whole hierarchies within medical images through three key branches: (1) "localizability", which acquires discriminative representations to distinguish different anatomical structures; (2) "composability", which learns each anatomical structure in a parts-to-whole manner; and (3) "decomposability", which comprehends each anatomical structure in a whole-to-parts manner. Our extensive experiments showcase Adam-v2's advanced capability in anatomy understanding, unveiled through its embeddings (Eve-v2). Particularly, Eve-v2 demonstrates a zero-shot understanding of anatomy as revealed through its learned properties: ➀ preserving localizability of anatomical structures and ➁ encoding part-whole relations of anatomical structures as well as its emergent properties: ➂ understanding anatomical layouts via interpolation and extrapolation, ➃ associating each image pixel with (dense) semantic embeddings, ➄ recognizing anatomical symmetries, ➅ generating embeddings consistent across scales, and ➆ matching anatomical structures across images of the same patient with different diseases, images of different patients, and augmented views of the same image. Adam-v2 also offers robust and generalizable representations and stands out in ➇ few-shot learning, ➈ full-transfer learning, and ➉ novelty and anomaly detection. These capabilities and performance directly stem from our crafted anatomy learning strategy, which explicitly constructs hierarchies for distinct anatomical structures from unlabeled medical images. Project page: GitHub.com/JLiangLab/Eden.
1 July 2026
Read appraisal →Journal of clinical pathology
Clinicopathological and molecular characterisation of superficial CD34-positive fibroblastic tumour: A systematic review
AIM: This systematic review aims to comprehensively evaluate the clinicopathological and molecular features of superficial CD34-positive fibroblastic tumour (SCD34FT), a recently described intermediate-grade soft-tissue neoplasm recognised in the 5th edition of the WHO classification of soft tissue tumours. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we systematically searched for English-language studies in PubMed, Scopus, Google Scholar and Web of Science. Retrospective or original case series with ≥3 histologically confirmed cases were selected for review, whereas review articles, single case reports and conference abstracts were excluded. Risk of bias was assessed using the Joanna Briggs Institute checklist. RESULTS: A total of 190 patients across 14 selected studies met the inclusion criteria. SCD34FT mainly affected middle-aged adults, with the most common site being the lower extremity. Diffuse CD34 expression was universal across all studies. A high number of cases displayed SynCAM/CADM3 positivity, while WT1 and Pan-CK (focal) were frequent. PRDM10 fusion was detected in 73% of cases, and the most common fusions were PRDM10::MED12 (66%) and PRDM10::CITED2 (28%). Only 9/169 (5.3%) had local recurrences, whereas 4/190 (2.1%) cases had lymph node metastasis, with no distant metastasis or disease-related death. CONCLUSION: SCD34FT is a novel entity with consistent CD34 expression and recurrent PRDM10 rearrangements and shows indolent behaviour. A high index of suspicion for SCD34FT should be warranted in a superficially located tumour with striking nuclear pleomorphism but an unexpectedly low mitotic index. Molecular studies demonstrating PRDM10 rearrangement may be helpful in atypical cases.
19 May 2026
Read appraisal →Histochemistry and cell biology
Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.
Small tissue biopsies, including renal core biopsies, bone marrow trephines, gastrointestinal endoscopic samples, prostate needle cores, liver biopsies, and skin punch or shave specimens, are fundamental to contemporary diagnostic pathology. Their limited volume, focal sampling, and susceptibility to technical artifacts impose distinct interpretive challenges, often requiring semi-quantitative assessment within a restricted architectural context. Although artificial intelligence (AI) has rapidly expanded in digital pathology, most models have been developed using large surgical resection specimens, with comparatively limited attention to small biopsy material. This minireview examines current and emerging applications of AI in small biopsy diagnostics across renal, hepatic, gastrointestinal, hematopathological, dermatopathological, and urological pathology. Reported applications include glomerular segmentation and fibrosis quantification in renal biopsies; automated cellularity, blast detection, and fibrosis grading in bone marrow trephines; dysplasia and microorganism detection in gastrointestinal biopsies; quantitative steatosis and fibrosis assessment in liver samples; tumor detection and grading in prostate cores; and neoplastic pattern recognition in skin specimens. Despite encouraging performance in research settings, substantial barriers to routine clinical implementation remain, including limited dataset size, class imbalance, pre-analytical variability, inter-institutional heterogeneity, and insufficient external validation. We discuss methodological considerations relevant to diagnostic practice, including multi-institutional validation, stain normalization, multimodal integration with histochemistry and ancillary testing, explainability, and regulatory oversight. In the context of small biopsies, AI should be regarded as a quantitative adjunct to morphological interpretation rather than an autonomous diagnostic system. Careful integration within established histopathological workflows is essential to ensure reproducibility, safety, and clinical accountability.
15 May 2026
Read appraisal →Scientific reports
The impact of tissue detection on diagnostic artificial intelligence algorithms in prostate digital pathology
Tissue detection is a crucial first step in most digital pathology applications. By applying image segmentation algorithms, all tissue is delineated and background discarded from further analyses, improving both computational efficiency and analytical results. Details of the segmentation algorithm are rarely reported, and there is a lack of studies investigating the downstream effects of a poor segmentation algorithm. Disregarding tissue detection quality could jeopardize patient safety if diagnostically relevant parts of the specimen are excluded from analysis in clinical applications. This study aims to determine whether performance of downstream tasks is sensitive to the tissue detection method, and to compare the performance of a classical and an AI-based tissue detection approach. To this end, we trained an AI model for Gleason grading of prostate cancer in whole slide images (WSIs) using two different tissue detection algorithms: thresholding (classical) and UNet++ (AI). A total of 33,823 WSIs scanned on seven digital pathology scanners were used to train the segmentation AI model. The downstream Gleason grading algorithm was trained and tested using 70,524 WSIs from 13 clinical sites scanned on 13 different scanners. On the slides where tissue could be detected by both algorithms, no significant difference in overall Gleason grading performance was observed. There was a decrease from 118 (0.43%) to 24 (0.09%) fully undetected tissue samples when switching from thresholding-based tissue detection to AI-based, suggesting this AI model may be more reliable than the classical model for avoiding total failures on slides with unusual appearance. Moreover, tissue detection dependent clinically significant variations in AI grading were observed in 3.5% of malignant slides, highlighting the role of tissue detection for optimal clinical performance of diagnostic AI.
15 May 2026
Read appraisal →Endocrine pathology
Lineage Classification of Pituitary Neuroendocrine Tumors From Whole-Slide Images Using Attention-Guided Graph Representation Learning
Pituitary neuroendocrine tumors (PitNETs) are common sellar neoplasms and represent a major component of routine pituitary pathology. In the 2022 World Health Organization (WHO) Classification, transcription factor-defined lineage assignment is central to diagnosis. However, lineage-related morphologic information on routine hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) has not been systematically characterized. We developed an attention-guided graph neural network to predict PitNET lineage directly from H&E-stained WSIs and to identify regions prioritized for classification. Consecutive patients who underwent surgical resection for PitNETs at Beijing Tiantan Hospital between 2021 and 2025 were included. In the internal hold-out set, the five fold-specific models achieved a mean F1-score of 92.78% and a mean balanced accuracy of 94.84%. In a temporally independent validation cohort, the final model achieved an F1-score of 87.64% and a balanced accuracy of 89.48%. Performance varied across lineages and histomorphologic subtypes, and the most common error pattern was misassignment of PIT-1 and T-PIT tumors to SF-1. Attention maps predominantly highlighted tumor-rich regions. Quantitative cell-level morphometry supported lineage-associated patterns, including larger cell and nuclear sizes in PIT-1 tumors, more elongated nuclei in SF-1 tumors, and higher cellular density with reduced intercellular spacing in T-PIT tumors. In six cases with multiple synchronous PitNETs of distinct lineages, patch-level prediction maps corresponded closely to transcription factor immunohistochemistry. In a small exploratory subset with available DNA methylation data, methylation classifier results were concordant with model predictions in 11 of 15 cases and with routine histopathologic diagnoses in 8 of 15 cases. These findings indicate that routine H&E-stained WSIs contain learnable morphologic information related to PitNET lineage. Attention-guided spatial modeling provides an interpretable framework for characterizing lineage-associated patterns in PitNETs.
12 May 2026
Read appraisal →Computers in biology and medicine
MIPHEI-ViT: Multiplex immunofluorescence prediction from H&E images using ViT foundation models
Histopathological analysis is a cornerstone of cancer diagnosis, with Hematoxylin and Eosin (H&E) staining routinely acquired for every patient to visualize cell morphology and tissue architecture. On the other hand, multiplex immunofluorescence (mIF) enables more precise cell type identification via proteomic markers, but has yet to achieve widespread clinical adoption due to cost and logistical constraints. To bridge this gap, we introduce MIPHEI (Multiplex Immunofluorescence Prediction from H&E Images), a U-Net-inspired architecture that leverages a ViT pathology foundation model as an encoder to predict mIF signals from H&E images using rich pretrained representations. MIPHEI targets a comprehensive panel of markers spanning nuclear content, immune lineages (T cells, B cells, myeloid), epithelium, stroma, vasculature, and proliferation. We train our model using the publicly available OrionCRC dataset of restained H&E and mIF images from colorectal cancer tissue, and validate it on five independent datasets: HEMIT, PathoCell, IMMUcan, Lizard and PanNuke. On OrionCRC test set, MIPHEI achieves accurate cell-type classification from H&E alone, with F1 scores of 0.93 for Pan-CK, 0.83 for α-SMA, 0.68 for CD3e, 0.36 for CD20, and 0.28 for CD68, substantially outperforming both a state-of-the-art baseline and a random classifier for most markers. Our results indicate that, for some molecular markers, our model captures the complex relationships between nuclear morphologies in their tissue context, as visible in H&E images and molecular markers defining specific cell types. MIPHEI offers a promising step toward enabling cell-type-aware analysis of large-scale H&E datasets, in view of uncovering relationships between spatial cellular organization and patient outcomes.
15 Apr 2026
Read appraisal →Journal for immunotherapy of cancer
Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application
The tumor microenvironment (TME), composed of tumor cells together with stromal cells, immune cells, vascular networks, and other components, constitutes a complex ecosystem that plays a decisive role in tumor initiation, progression, metastasis and therapeutic response. Traditional pathological diagnosis mainly relies on pathologists manually examining H&E-stained tissue sections under the microscope, a method that not only suffers from substantial interobserver variability but also has relatively low analytical efficiency. With the rapid development of computational pathology, the integration of whole-slide imaging technology and deep learning algorithms has provided powerful tools for characterizing tumor microenvironment. These techniques enable automated characterization of cellular, spatial and molecular heterogeneity within the tumor microenvironment, providing integrated insights that advance precision diagnostics and improve prediction of therapeutic response and patient outcomes. Based on a comprehensive review of existing research, this paper highlights recent advances in deep learning-driven recognition of panoramic TME features from H&E slides and their clinical applications and further discusses both the translational potential and current limitations of this technology in oncology research and clinical applications.
11 Apr 2026
Read appraisal →Cancer research
Multicenter Histology Image Integration and Multiscale Deep Learning Support Machine Learning-Enabled Pediatric Sarcoma Classification
UNLABELLED: Pediatric sarcomas present diagnostic challenges due to their rarity and diverse subtypes, often requiring specialized pathology expertise and costly genetic tests. To overcome these barriers, we developed a computational pipeline leveraging deep learning methods to accurately classify pediatric sarcoma subtypes from digitized histology slides. To ensure classifier generalizability and minimize center-specific artifacts, a dataset comprising 867 whole-slide images (WSI) from three medical centers and the Children's Oncology Group was collected and harmonized. Multiple convolutional neural network and vision transformer (ViT) architectures were systematically evaluated as feature extractors for SAMPLER-based WSI representations, and input parameters, such as tile size combinations and resolutions, were tested and optimized. The analysis showed that advanced ViT foundation models (UNI and CONCH) significantly outperformed earlier approaches, and incorporating multiscale features enhanced classification accuracy. The optimized models achieved high performance, distinguishing rhabdomyosarcoma (RMS) from non-RMS soft-tissue sarcomas (NRSTS) with an AUC of 0.969 and differentiating RMS subtypes (alveolar vs. embryonal) with an AUC of 0.961. Additionally, a two-stage pipeline effectively identified scarce Ewing sarcoma images from other NRSTS (AUC = 0.929). Compared with conventional transformer-encoder architectures used for WSI representations, these SAMPLER-based classifiers were three orders of magnitude faster to train, despite operating entirely without a graphical processing unit. This study highlights that digital histopathology paired with rigorous image harmonization provides a powerful solution for pediatric sarcoma classification. SIGNIFICANCE: An approach pairing a multi-institutional dataset with a published pipeline extends imaging-based diagnostic capabilities and creates the potential for accurate, rapid cancer diagnoses across resource-limited and remote settings. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .
5 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
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