ArticleNature communications2020
Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review.Frontiers in oncology · 2025Pooled it
- From fusion partner to clinical practice: A treatment-oriented framework for transcription factor E3 (Cancer pathogenesis and therapy · 2026Review
- TFE3-DualNet: An Interpretable Foundation Model-Based Deep Learning Ensemble for Diagnosing TFE3-Rearranged Renal Cell Carcinoma From Whole-Slide Images in a Two-Center Cohort.Cancer medicine · 2026Article
- Unlocking the prognostic power of pathomics in bladder cancer: a machine learning odyssey across multiple centers.BMC medical imaging · 2026Article
- Biological feature-based machine learning in histopathological images: a systematic review.Journal of pathology informatics · 2026Review
- Joint similarity nonnegative matrix factorization model for identification of recurrence-related association patterns in tumor.Briefings in bioinformatics · 2025Article
- MED15-TFE3 rearranged renal cell carcinoma: a subtype of TFE3-rearranged renal cell carcinoma with unique clinicopathologic features and better prognosis.World journal of surgical oncology · 2025Article
- Automated multi-regional IHC scoring enhances prognostication in colorectal cancer.The journal of pathology. Clinical research · 2025Article
- Diagnosis and management of TFE3-rearranged renal cell carcinoma: case report and literature review.BMC urology · 2025Review
- [Clinicopathological features and survival analysis ofBeijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2025Article
- Automatic segmentation of clear cell renal cell carcinoma based on deep learning and a preliminary exploration of the tumor microenvironment.Translational andrology and urology · 2025Article
- Investigating lung cancer microenvironment from cell segmentation of pathological image and its application in prognostic stratification.Scientific reports · 2025Article
- From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology.Nature protocols · 2025Article
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- A Multi-Omics Prognostic Model Capturing Tumor Stemness and the Immune Microenvironment in Clear Cell Renal Cell Carcinoma.Biomedicines · 2024Article
- Comprehensive molecular characterization of TFE3-rearranged renal cell carcinoma.Experimental & molecular medicine · 2024Article
- Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis.Biomedicines · 2023Review
- Article
- Review
- Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance.Diagnostics (Basel, Switzerland) · 2023Review
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10 authors.
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Abstract
TFE3 Xp11.2 translocation renal cell carcinoma (TFE3-RCC) generally progresses more aggressively compared with other RCC subtypes, but it is challenging to diagnose TFE3-RCC by traditional visual inspection of pathological images. In this study, we collect hematoxylin and eosin- stained histopathology whole-slide images of 74 TFE3-RCC cases (the largest cohort to date) and 74 clear cell RCC cases (ccRCC, the most common RCC subtype) with matched gender and tumor grade. An automatic computational pipeline is implemented to extract image features. Comparative study identifies 52 image features with significant differences between TFE3-RCC and ccRCC. Machine learning models are built to distinguish TFE3-RCC from ccRCC. Tests of the classification models on an external validation set reveal high accuracy with areas under ROC curve ranging from 0.842 to 0.894. Our results suggest that automatically derived image features can capture subtle morphological differences between TFE3-RCC and ccRCC and contribute to a potential guideline for TFE3-RCC diagnosis.
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