ArticleNature communications2025
Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in the radiologic diagnosis of major urological cancers: a meta-analysis.World journal of urology · 2026Pooled it
- Robot-assisted versus open partial nephrectomy in renal tumor patients with chronic kidney disease: a meta-analysis of renal functional and perioperative outcomes.Journal of robotic surgery · 2025Pooled it
- Micro/Nanostructured Materials and Microfluidic Diagnostic Platforms Enable Multimodal Detection of Circulating Tumor Cells.Exploration (Beijing, China) · 2026Review
- DWI-derived intratumoral, peritumoral, and habitat features for preoperative prediction of lymph node metastasis in early-stage cervical cancer using machine learning method.Abdominal radiology (New York) · 2026Article
- Noninvasive Preoperative Stratification of Renal Tumors for Aggressiveness and Prognosis.Bioengineering (Basel, Switzerland) · 2026Article
- Biomarkers in Clear Cell Renal Cell Carcinoma: From Biological Association to Clinical Decision-Making.Genes · 2026Review
- Staging and imaging-based prognostication in renal cell carcinoma: current guidelines and practical considerations.Abdominal radiology (New York) · 2026Review
- Review
- Differentiating Fat-Poor Angiomyolipoma from Renal Cell Carcinoma Using Contrast-Enhanced CT.Bioengineering (Basel, Switzerland) · 2026Article
- Identification of pulmonary hypertension with pulmonary vascular surface volume ratio by non-contrast CT.BMC cardiovascular disorders · 2026Article
- Review
- A disease-centric vision-language foundation model for precision oncology in kidney cancer.Nature communications · 2026Article
- The seventh kidney cancer research summit: progress in accelerating cures.The oncologist · 2026Article
- A comprehensive multi-task deep learning model for kidney cancer: histological subtyping, clinical staging, and anatomical complexity grading.European radiology · 2026Article
- Multimodal Prediction of Renal Tumor Malignancy From Radiology Reports and Structured Electronic Health Records: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Multiphase CT-based deep learning radiomics nomogram models for preoperative WHO/ISUP grading of clear cell renal cell carcinoma: a two-center validation study.BMC medical imaging · 2026Article
- Artificial intelligence in urological malignancy diagnosis and prognosis: current status and future prospects.The Canadian journal of urology · 2026Review
- Targeting urological cancers with CAR-T cell therapy: current landscape and future directions.Journal of translational medicine · 2026Review
- Ferroptosis in renal cell carcinoma: integrative multi-omics insights and therapeutic perspectives.International journal of surgery (London, England) · 2026Article
- Deciphering the mechanistic landscape of immune checkpoint blockade in ccRCC: from molecular drivers to therapeutic responses.Frontiers in immunology · 2026Review
Corrections and comments
- Commented on byBeyond the2025
Authors and funding
19 authors.
Funding
Abstract
Treatment decisions for an incidental renal mass are mostly made with pathologic uncertainty. Improving the diagnosis of benign renal masses and distinguishing aggressive cancers from indolent ones is key to better treatment selection. We analyze 13261 pre-operative computed computed tomography (CT) volumes of 4557 patients. Two multi-phase convolutional neural networks are developed to predict the malignancy and aggressiveness of renal masses. The first diagnostic model designed to predict the malignancy of renal masses achieves area under the curve (AUC) of 0.871 in the prospective test set. This model surpasses the average performance of seven seasoned radiologists. The second diagnostic model differentiating aggressive from indolent tumors has AUC of 0.783 in the prospective test set. Both models outperform corresponding radiomics models and the nephrometry score nomogram. Here we show that the deep learning models can non-invasively predict the likelihood of malignant and aggressive pathology of a renal mass based on preoperative multi-phase CT images.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.