ArticlePloS one2022
Deep learning can predict survival directly from histology in clear cell renal cell carcinoma.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 4 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review.Frontiers in oncology · 2025Pooled it
- Diagnostic accuracy of deep learning in detection and prognostication of renal cell carcinoma: a systematic review and meta-analysis.Frontiers in medicine · 2024Pooled it
- Application of Deep Learning in Cancer Prognosis Prediction Model.Technology in cancer research & treatmentPooled it
- Developing a Histology-Based Artificial Intelligence Biomarker to Predict Adjuvant Chemotherapy Benefit in Pancreatic Cancer.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026Article
- Deep-learning-based artificial intelligence approaches for grading and progression prediction of clear cell renal cell carcinoma.iScience · 2026Article
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- Artificial intelligence-driven digital pathology in urological cancers: current trends and future directions.Prostate international · 2025Review
- Multimodal deep learning framework integrating multiphase CT and histopathological whole slide imaging for predicting recurrence in ccRCC.Scientific reports · 2025Article
- MobileDANet integrating transfer learning and dynamic attention for classifying multi target histopathology images with explainable AI.Scientific reports · 2025Article
- AI-assisted diagnosis of renal cell carcinoma: educational needs and cognitive assessment based on the WHO classification 2022.BMC medical education · 2025Article
- Automated Detection of Kaposi Sarcoma-Associated Herpesvirus-Infected Cells in Immunohistochemical Images of Skin Biopsies.JCO global oncology · 2025Article
- Deep Learning Model for Predicting Immunotherapy Response in Advanced Non-Small Cell Lung Cancer.JAMA oncology · 2025Article
- A multimodal ensemble approach for clear cell renal cell carcinoma treatment outcome prediction.ArXiv · 2024Article
- Image Analysis in Histopathology and Cytopathology: From Early Days to Current Perspectives.Journal of imaging · 2024Review
- Application of artificial intelligence model in pathological staging and prognosis of clear cell renal cell carcinoma.Discover oncology · 2024Article
- Applications of artificial intelligence in urologic oncology.Investigative and clinical urology · 2024Review
- Deep learning in cancer genomics and histopathology.Genome medicine · 2024Review
- Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study.PloS one · 2024Article
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17 authors.
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Abstract
For clear cell renal cell carcinoma (ccRCC) risk-dependent diagnostic and therapeutic algorithms are routinely implemented in clinical practice. Artificial intelligence-based image analysis has the potential to improve outcome prediction and thereby risk stratification. Thus, we investigated whether a convolutional neural network (CNN) can extract relevant image features from a representative hematoxylin and eosin-stained slide to predict 5-year overall survival (5y-OS) in ccRCC. The CNN was trained to predict 5y-OS in a binary manner using slides from TCGA and validated using an independent in-house cohort. Multivariable logistic regression was used to combine of the CNNs prediction and clinicopathological parameters. A mean balanced accuracy of 72.0% (standard deviation [SD] = 7.9%), sensitivity of 72.4% (SD = 10.6%), specificity of 71.7% (SD = 11.9%) and area under receiver operating characteristics curve (AUROC) of 0.75 (SD = 0.07) was achieved on the TCGA training set (n = 254 patients / WSIs) using 10-fold cross-validation. On the external validation cohort (n = 99 patients / WSIs), mean accuracy, sensitivity, specificity and AUROC were 65.5% (95%-confidence interval [CI]: 62.9-68.1%), 86.2% (95%-CI: 81.8-90.5%), 44.9% (95%-CI: 40.2-49.6%), and 0.70 (95%-CI: 0.69-0.71). A multivariable model including age, tumor stage and metastasis yielded an AUROC of 0.75 on the TCGA cohort. The inclusion of the CNN-based classification (Odds ratio = 4.86, 95%-CI: 2.70-8.75, p < 0.01) raised the AUROC to 0.81. On the validation cohort, both models showed an AUROC of 0.88. In univariable Cox regression, the CNN showed a hazard ratio of 3.69 (95%-CI: 2.60-5.23, p < 0.01) on TCGA and 2.13 (95%-CI: 0.92-4.94, p = 0.08) on external validation. The results demonstrate that the CNN's image-based prediction of survival is promising and thus this widely applicable technique should be further investigated with the aim of improving existing risk stratification in ccRCC.
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