ArticleInsights into imaging2024
Convolutional neural networks for the differentiation between benign and malignant renal tumors with a multicenter international computed tomography dataset.
Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 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
- Diagnostic performance of artificial intelligence in detection of renal cell carcinoma: a systematic review and meta-analysis.BMC cancer · 2025Pooled it
- Multimodal Prediction of Renal Tumor Malignancy From Radiology Reports and Structured Electronic Health Records: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- CT-Based Radiomics in the Characterization of Solid Renal Tumors: A Systematic Review.Cancers · 2026Review
- Development and validation of a deep learning radiomics model for predicting capsular invasion in small renal masses: a multicenter retrospective study.Quantitative imaging in medicine and surgery · 2026Article
- Artificial intelligence in urological malignancy diagnosis and prognosis: current status and future prospects.The Canadian journal of urology · 2026Review
- A deep learning ensemble framework for multi-subtype renal tumor classification using contrast-enhanced CT.Scientific reports · 2026Article
- NephroNet: a calibration-aware, patient-disjoint benchmark for multiclass kidney CT classification with a compact depthwise-separable CNN.Frontiers in medicine · 2026Article
- An interpretable machine learning model for preoperative prediction of renal mass malignancy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Article
- MediScan: A Framework of U-Health and Prognostic AI Assessment on Medical Imaging.Journal of imaging · 2024Article
- A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System.Journal of imaging informatics in medicine · 2024Article
- Deep Learning Identification of Clear Cell Renal Cell Carcinoma on MR Imaging.Journal of computer assisted tomographyArticle
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6 authors.
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Abstract
objectivesTo use convolutional neural networks (CNNs) for the differentiation between benign and malignant renal tumors using contrast-enhanced CT images of a multi-institutional, multi-vendor, and multicenter CT dataset.
methodsA total of 264 histologically confirmed renal tumors were included, from US and Swedish centers. Images were augmented and divided randomly 70%:30% for algorithm training and testing. Three CNNs (InceptionV3, Inception-ResNetV2, VGG-16) were pretrained with transfer learning and fine-tuned with our dataset to distinguish between malignant and benign tumors. The ensemble consensus decision of the three networks was also recorded. Performance of each network was assessed with receiver operating characteristics (ROC) curves and their area under the curve (AUC-ROC). Saliency maps were created to demonstrate the attention of the highest performing CNN.
resultsInception-ResNetV2 achieved the highest AUC of 0.918 (95% CI 0.873-0.963), whereas VGG-16 achieved an AUC of 0.813 (95% CI 0.752-0.874). InceptionV3 and ensemble achieved the same performance with an AUC of 0.894 (95% CI 0.844-0.943). Saliency maps indicated that Inception-ResNetV2 decisions are based on the characteristics of the tumor while in most tumors considering the characteristics of the interface between the tumor and the surrounding renal parenchyma.
conclusionDeep learning based on a diverse multicenter international dataset can enable accurate differentiation between benign and malignant renal tumors. CRITICAL RELEVANCE STATEMENT: Convolutional neural networks trained on a diverse CT dataset can accurately differentiate between benign and malignant renal tumors. KEY POINTS: • Differentiation between benign and malignant tumors based on CT is extremely challenging. • Inception-ResNetV2 trained on a diverse dataset achieved excellent differentiation between tumor types. • Deep learning can be used to distinguish between benign and malignant renal tumors.
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