ArticleNPJ precision oncology2022
Usability of deep learning and H&E images predict disease outcome-emerging tool to optimize clinical trials.
Article in NPJ precision oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Integration of Histologic Pattern Classifiers and Graph Convolutional Networks for Prognostic Prediction in Lung Adenocarcinoma.JTO clinical and research reports · 2026Article
- Targeting IFN-I driven tertiary lymphoid structures halts B cell-mediated bone loss in periodontitis.International journal of oral science · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- A deep learning framework to iDentify prOgnostically releVant cancEr Regions (DOVER) within whole slide histopathology images.Cancer letters · 2026Article
- Explainable Computational Imaging for Precision Oncology: An Interpretable Deep Learning Framework for Bladder Cancer Histopathology Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- Pathology image-based predictive model for individual survival time of early-stage lung adenocarcinoma patients.Scientific reports · 2025Article
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- Computational Morphological Assessment of Bladder Cancer Tissue Is Prognostic of Recurrence and Overall Survival Following Transurethral Resection.JCO clinical cancer informatics · 2025Article
- AI in Histopathology Explorer for comprehensive analysis of the evolving AI landscape in histopathology.NPJ digital medicine · 2025Article
- PortNet: Achieving lightweight architecture and high accuracy in lung cancer cell classification.Heliyon · 2025Article
- Whole slide image based deep learning refines prognosis and therapeutic response evaluation in lung adenocarcinoma.NPJ digital medicine · 2025Article
- Cervical whole-slide images dataset for multiclass classification.GigaScience · 2025Article
- Making space for spatial biology in the clinic.Nature biotechnology · 2025Article
- Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology.NPJ precision oncology · 2024Article
- Next-generation lung cancer pathology: Development and validation of diagnostic and prognostic algorithms.Cell reports. Medicine · 2024Article
- Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology.Research square · 2024Article
- Evaluating Cellularity Estimation Methods: Comparing AI Counting with Pathologists' Visual Estimates.Diagnostics (Basel, Switzerland) · 2024Article
- From pixels to patient care: deep learning-enabled pathomics signature offers precise outcome predictions for immunotherapy in esophageal squamous cell cancer.Journal of translational medicine · 2024Article
- Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer.NPJ digital medicine · 2024Article
- ML-driven segmentation of microvascular features during histological examination of tissue-engineered vascular grafts.Frontiers in bioengineering and biotechnology · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding factors that impact prognosis for cancer patients have high clinical relevance for treatment decisions and monitoring of the disease outcome. Advances in artificial intelligence (AI) and digital pathology offer an exciting opportunity to capitalize on the use of whole slide images (WSIs) of hematoxylin and eosin (H&E) stained tumor tissue for objective prognosis and prediction of response to targeted therapies. AI models often require hand-delineated annotations for effective training which may not be readily available for larger data sets. In this study, we investigated whether AI models can be trained without region-level annotations and solely on patient-level survival data. We present a weakly supervised survival convolutional neural network (WSS-CNN) approach equipped with a visual attention mechanism for predicting overall survival. The inclusion of visual attention provides insights into regions of the tumor microenvironment with the pathological interpretation which may improve our understanding of the disease pathomechanism. We performed this analysis on two independent, multi-center patient data sets of lung (which is publicly available data) and bladder urothelial carcinoma. We perform univariable and multivariable analysis and show that WSS-CNN features are prognostic of overall survival in both tumor indications. The presented results highlight the significance of computational pathology algorithms for predicting prognosis using H&E stained images alone and underpin the use of computational methods to improve the efficiency of clinical trial studies.
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Registered trials
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