ArticleBMC cancer2025
Fully automated segmentation and classification of renal tumors on CT scans via machine learning.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in the radiologic diagnosis of major urological cancers: a meta-analysis.World journal of urology · 2026Pooled it
- Deep learning model based on CT images to predict Ki-67 expression in renal carcinoma.Oncology letters · 2026Article
- Automated deep learning for detection and measurement of adrenal masses in contrast-enhanced abdominal CT.European radiology · 2026Article
- Automated vs. manual segmentation for small renal mass imaging.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2026Article
- A novel CT-based normalization strategy using lesion-to-spleen density ratios for renal mass characterization.Abdominal radiology (New York) · 2026Article
- Classification of Renal Lesions by Leveraging Hybrid Features from CT Images Using Machine Learning Techniques.Journal of imaging informatics in medicine · 2026Article
- A deep learning ensemble framework for multi-subtype renal tumor classification using contrast-enhanced CT.Scientific reports · 2026Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Optimized machine learning based comparative analysis of predictive models for classification of kidney tumors.Scientific reports · 2025Article
- Three-Dimensional Virtual Models in Robot Assisted Partial Nephrectomy: A Needed Tool in the Era of Precision Surgery.Current urology reports · 2025Review
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Authors and funding
12 authors.
Funding
Abstract
backgroundTo develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification. MATERIALS AND
methodsThe model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas. Renal tumor masks manually drawn on contrast-enhanced CT images were used to develop a 3D U-Net-based deep learning model for fully automated tumor segmentation. After segmentation, the entire classification pipeline, including feature extraction and subtype classification, was conducted without any manual intervention. Both conventional radiological features (Hounsfield units, HUs) and radiomic features extracted from areas predicted by the deep learning models were used to develop an algorithm for classifying renal tumor subtypes via a random forest classifier. The performance of the segmentation model was evaluated using the Dice similarity coefficient, while the classification model was assessed based on accuracy, sensitivity, and specificity.
resultsFor tumors larger than 4 cm, the Dice similarity coefficient (DSC) for automated segmentation was 0.83, while for tumors smaller than 4 cm, the DSC was 0.65. The classification accuracy (ACC) for distinguishing RCC subtypes was 0.77 for tumors larger than 4 cm and 0.68 for tumors smaller than 4 cm. Additionally, the accuracy for benign versus malignant classification was 0.85.
conclusionsOur automatic segmentation and classifier model showed promising results for renal tumor segmentation and classification.
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