ArticleEuropean radiology2020
Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed, 70 citations in OpenAlex.
- Clinical Study on the Efficacy of Microwave Ablation (MA) in the Treatment of Stage I Renal Clear Cell Carcinoma by CT and MRI Imaging.Journal of healthcare engineering · 2022Trial
- Noninvasive renal mass diagnostic models based on radiomic features and clinical data: a multi-center study.Abdominal radiology (New York) · 2026Article
- Multimodal deep learning model for multiclass classification of renal tumors.NPJ digital medicine · 2026Article
- Application of contrast-enhanced CT-based radiomics with machine learning to predict WHO/ISUP grading in clear cell renal cell carcinoma.BMC cancer · 2026Article
- Predicting aggressiveness of clear cell renal cell carcinoma via mri using artificial intelligence: implications for surgical planning in a retrospective multicenter study.Scientific reports · 2026Article
- MRI-based habitat radiomics for predicting WHO/ISUP nuclear grade in clear cell renal cell carcinoma.Frontiers in oncology · 2026Article
- Machine learning-based radiomics from multiparametric MRI for predicting aggressive pathology in clear cell renal cell carcinoma.BMC medical imaging · 2025Article
- From Radiomics to Radiogenomics: Decoding Renal Cell Carcinoma Biology for Precision Medicine-a narrative review.Insights into imaging · 2025Review
- Intra- and inter-tumoural heterogeneity in von Hippel-Lindau disease-related renal cancer: a multimodal data study protocol.European radiology experimental · 2025Article
- Quantitative MRI radiomics approach for evaluating muscular alteration in Crohn disease: development of a machine learning-nomogram composite diagnostic tool.Abdominal radiology (New York) · 2025Article
- State of the art review of AI in renal imaging.Abdominal radiology (New York) · 2025Review
- Preoperative prediction of WHO/ISUP grade of ccRCC using intratumoral and peritumoral habitat imaging: multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Prediction of Distant Metastasis of Renal Cell Carcinoma Based on Interpretable Machine Learning: A Multicenter Retrospective Study.Journal of multidisciplinary healthcare · 2025Article
- MRI-based radiomics machine learning model to differentiate non-clear cell renal cell carcinoma from benign renal tumors.European journal of radiology open · 2024Article
- Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients.Abdominal radiology (New York) · 2024Article
- Potential of quantitative T1 mapping to serve as a novel prognostic predictor of clear cell renal cell carcinoma after nephrectomy.Quantitative imaging in medicine and surgery · 2024Article
- Deep learning-based pathological prediction of lymph node metastasis for patient with renal cell carcinoma from primary whole slide images.Journal of translational medicine · 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
- The radiomics nomogram predicts the prognosis of pancreatic cancer patients with hepatic metastasis after chemoimmunotherapy.Cancer immunology, immunotherapy : CII · 2024Article
- Management of Renal Cell Carcinoma: Promising Biomarkers and the Challenges to Reach the Clinic.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024Review
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo explore whether clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), and chromophobe renal cell carcinoma (cRCC) can be distinguished using radiomics features extracted from magnetic resonance (MR) images.
methodsSeventy-seven patients (ccRCC = 32, pRCC = 23, cRCC = 22) underwent MRI before surgery between May 2013 and August 2018 in this retrospective study. Thirty-nine radiomics features were extracted from tumor volumes on three sequences (T2WI, EN-T1WI CMP, and EN-T1WI NP). The Kruskal-Wallis test with Bonferonni correction and variance threshold were used for feature selection among the three RCC subtypes. ROC curves for the three subtypes were generated based on radiomics features. AUC, accuracy, sensitivity, and specificity for subtype differentiation are reported. Linear discriminant analysis (LDA) was used to assess the discriminative ability of these radiomics features.
resultsSignificant radiomics features among the three subtypes were identified, and ROC curves achieved excellent AUCs for T2WI, EN-T1WI CMP, EN-T1WI NP, and combined three MR sequences (0.631, 0.790, 0.959, and 0.959 between ccRCC and cRCC; 0.688, 0.854, 0.909, and 0.955 between pRCC and cRCC; 0.747, 0.810, 0.814, and 0.890 between ccRCC and pRCC). In addition, LDA demonstrated the three RCC subtypes were correctly classified by radiomics analysis (66.2% for EN-T1WI CMP, 71.4% for EN-T1WI NP, 55.8% for T2WI, and 71.4% for the combined three MR sequences).
conclusionsRadiomics analysis can be used to differentiate among ccRCC, pRCC, and cRCC based on radiomics features extracted from multiple-sequence MRI and may help diagnose and treat RCC patients in the future, while further study is still needed. KEY POINTS: • Radiomics features on multiple-sequence MRI can help differentiate the three subtypes of renal cell carcinoma (clear cell, papillary renal cell, and chromophobe renal cell carcinoma). • Radiomics features based on MRI indicate greater textural heterogeneity on ccRCCs than pRCCs and cRCCs (the highest AUCs on EN-T1WI NP are 0.814 for ccRCCs vs pRCCs and 0.959 for ccRCCs vs cRCCs, respectively). • There is a significant difference in the textural heterogeneity of radiomics features between pRCCs and cRCCs (the AUC is 0.909, 0.854, and 0.688 on EN-T1WI NP, EN-T1WI CMP, and T2WI, respectively).
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