ArticleClinical imaging2021
A deep-learning based artificial intelligence (AI) approach for differentiation of clear cell renal cell carcinoma from oncocytoma on multi-phasic MRI.
Article in Clinical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 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
- Deep Learning for Classification of Solid Renal Parenchymal Tumors Using Contrast-Enhanced Ultrasound.Journal of imaging informatics in medicine · 2026Article
- Current controversies with active surveillance management of small renal masses.Mini-invasive surgery · 2026Article
- Review
- Fusion-Based Deep Learning Approach for Renal Cell Carcinoma Subtype Detection Using Multi-Phasic MRI Data.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Medicine and Imaging Applications.Current pharmaceutical design · 2025Review
- MediScan: A Framework of U-Health and Prognostic AI Assessment on Medical Imaging.Journal of imaging · 2024Article
- MRI-based radiomics machine learning model to differentiate non-clear cell renal cell carcinoma from benign renal tumors.European journal of radiology open · 2024Article
- RCC-Supporter: supporting renal cell carcinoma treatment decision-making using machine learning.BMC medical informatics and decision making · 2024Article
- Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results.Abdominal radiology (New York) · 2024Article
- Deep learning-assisted survival prognosis in renal cancer: A CT scan-based personalized approach.Heliyon · 2024Article
- Convolutional neural networks for the differentiation between benign and malignant renal tumors with a multicenter international computed tomography dataset.Insights into imaging · 2024Article
- A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- Review
- MR texture analysis in the differentiation of renal oncocytoma with localized renal cell carcinoma subtypes.The British journal of radiology · 2023Article
- The Present and Future of Artificial Intelligence in Urological Cancer.Journal of clinical medicine · 2023Review
- Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review.Cancers · 2023Review
- A Deep Learning Model for Classification of Parotid Neoplasms Based on Multimodal Magnetic Resonance Image Sequences.The Laryngoscope · 2023Article
- Recognizing pathology of renal tumor from macroscopic cross-section image by deep learning.Biomedical engineering online · 2023Article
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13 authors.
Funding
Abstract
purposeTo investigate the diagnostic performance of a deep convolutional neural network for differentiation of clear cell renal cell carcinoma (ccRCC) from renal oncocytoma.
methodsIn this retrospective study, 74 patients (49 male, mean age 59.3) with 243 renal masses (203 ccRCC and 40 oncocytoma) that had undergone MR imaging 6 months prior to pathologic confirmation of the lesions were included. Segmentation using seed placement and bounding box selection was used to extract the lesion patches from T2-WI, and T1-WI pre-contrast, post-contrast arterial and venous phases. Then, a deep convolutional neural network (AlexNet) was fine-tuned to distinguish the ccRCC from oncocytoma. Five-fold cross validation was used to evaluate the AI algorithm performance. A subset of 80 lesions (40 ccRCC, 40 oncocytoma) were randomly selected to be classified by two radiologists and their performance was compared to the AI algorithm. Intra-class correlation coefficient was calculated using the Shrout-Fleiss method.
resultsOverall accuracy of the AI system was 91% for differentiation of ccRCC from oncocytoma with an area under the curve of 0.9. For the observer study on 80 randomly selected lesions, there was moderate agreement between the two radiologists and AI algorithm. In the comparison sub-dataset, classification accuracies were 81%, 78%, and 70% for AI, radiologist 1, and radiologist 2, respectively.
conclusionThe developed AI system in this study showed high diagnostic performance in differentiation of ccRCC versus oncocytoma on multi-phasic MRIs.
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