ArticleNPJ precision oncology2021
Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography.
Article in NPJ precision oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of artificial intelligence in detection of renal cell carcinoma: a systematic review and meta-analysis.BMC cancer · 2025Pooled it
- A comprehensive multi-task deep learning model for kidney cancer: histological subtyping, clinical staging, and anatomical complexity grading.European radiology · 2026Article
- Multimodal deep learning model for AI-based functional prognostic risk stratification in patients undergoing radical nephrectomy.Nature communications · 2026Article
- Deep learning model using contrast-enhanced CT for predicting overall survival in oropharyngeal squamous cell carcinoma: a prospective multicenter study.European radiology · 2026Article
- UCSF RMaC: University of California San Francisco 3D Multi-Phase Renal Mass CT Dataset with Tumor Segmentations.medRxiv : the preprint server for health sciences · 2026Article
- Artificial Intelligence in Nephrology-State of the Art on Theoretical Background, Molecular Applications, and Clinical Interpretation.International journal of molecular sciences · 2026Review
- A phase-aware Cross-Scale U-MAMba with uncertainty-aware segmentation and Switch Atrous Bifovea EfficientNetB7 classification of kidney lesion subtype.Lasers in medical science · 2025Article
- Deep Learning for Detecting and Subtyping Renal Cell Carcinoma on Contrast-Enhanced CT Scans Using 2D Neural Network with Feature Consistency Techniques.The Indian journal of radiology & imaging · 2025Article
- Fusion-Based Deep Learning Approach for Renal Cell Carcinoma Subtype Detection Using Multi-Phasic MRI Data.Diagnostics (Basel, Switzerland) · 2025Article
- A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma Based on CT Images.Journal of imaging informatics in medicine · 2025Article
- Smart Healthcare: The Role of Digital Health in Modern Medicine.Health care science · 2025Article
- Computed Tomography-Based Radiomics Diagnostic Model for Fat-Poor Small Renal Tumor Subtypes.Diagnostics (Basel, Switzerland) · 2025Article
- Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer.PloS one · 2025Article
- Aggressiveness classification of clear cell renal cell carcinoma using registration-independent radiology-pathology correlation learning.Medical physics · 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
- Automating cancer diagnosis using advanced deep learning techniques for multi-cancer image classification.Scientific reports · 2024Article
- Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Article
- Classification of superficial suspected lymph nodes: non-invasive radiomic model based on multiphase contrast-enhanced ultrasound for therapeutic options of lymphadenopathy.Quantitative imaging in medicine and surgery · 2024Article
- Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer.Heliyon · 2024Article
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12 authors.
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Abstract
In 2020, it is estimated that 73,750 kidney cancer cases were diagnosed, and 14,830 people died from cancer in the United States. Preoperative multi-phase abdominal computed tomography (CT) is often used for detecting lesions and classifying histologic subtypes of renal tumor to avoid unnecessary biopsy or surgery. However, there exists inter-observer variability due to subtle differences in the imaging features of tumor subtypes, which makes decisions on treatment challenging. While deep learning has been recently applied to the automated diagnosis of renal tumor, classification of a wide range of subtype classes has not been sufficiently studied yet. In this paper, we propose an end-to-end deep learning model for the differential diagnosis of five major histologic subtypes of renal tumors including both benign and malignant tumors on multi-phase CT. Our model is a unified framework to simultaneously identify lesions and classify subtypes for the diagnosis without manual intervention. We trained and tested the model using CT data from 308 patients who underwent nephrectomy for renal tumors. The model achieved an area under the curve (AUC) of 0.889, and outperformed radiologists for most subtypes. We further validated the model on an independent dataset of 184 patients from The Cancer Imaging Archive (TCIA). The AUC for this dataset was 0.855, and the model performed comparably to the radiologists. These results indicate that our model can achieve similar or better diagnostic performance than radiologists in differentiating a wide range of renal tumors on multi-phase CT.
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