ArticleJournal of translational medicine2024
Deep learning-based pathological prediction of lymph node metastasis for patient with renal cell carcinoma from primary whole slide images.
Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Deep learning-based prediction of lymph node metastasis and occult tumor cells in gastric cancer using histopathological images: a retrospective study.British journal of cancer · 2026Article
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- Article
- Construction and validation of deep learning-based pathomics signature model for predicting postoperative recurrence of patients with clear cell renal cell carcinoma.American journal of cancer research · 2026Article
- Customized transformer for lymph node metastasis prediction from lung adenocarcinoma histology in a multicentric study.NPJ precision oncology · 2025Article
- Artificial intelligence-driven digital pathology in urological cancers: current trends and future directions.Prostate international · 2025Review
- Computer vision assisted deep transfer learning model for accurate grading of renal cell carcinoma from kidney histopathology images.Scientific reports · 2025Article
- OncoMet: a deep learning framework for the prediction of oncogenic signaling pathways and metastasis in esophageal cancer patients using histopathology images from primary tumors.Journal of translational medicine · 2025Article
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13 authors.
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Abstract
backgroundMetastasis renal cell carcinoma (RCC) patients have extremely high mortality rate. A predictive model for RCC micrometastasis based on pathomics could be beneficial for clinicians to make treatment decisions.
methodsA total of 895 formalin-fixed and paraffin-embedded whole slide images (WSIs) derived from three cohorts, including Shanghai General Hospital (SGH), Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Cancer Genome Atlas (TCGA) cohorts, and another 588 frozen section WSIs from TCGA dataset were involved in the study. The deep learning-based strategy for predicting lymphatic metastasis was developed based on WSIs through clustering-constrained-attention multiple-instance learning method and verified among the three cohorts. The performance of the model was further verified in frozen-pathological sections. In addition, the model was also tested the prognosis prediction of patients with RCC in multi-source patient cohorts.
resultsThe AUC of the lymphatic metastasis prediction performance was 0.836, 0.865 and 0.812 in TCGA, SGH and CPTAC cohorts, respectively. The performance on frozen section WSIs was with the AUC of 0.801. Patients with high deep learning-based prediction of lymph node metastasis values showed worse prognosis.
conclusionsIn this study, we developed and verified a deep learning-based strategy for predicting lymphatic metastasis from primary RCC WSIs, which could be applied in frozen-pathological sections and act as a prognostic factor for RCC to distinguished patients with worse survival outcomes.
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