Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis

• Published in: European radiology 2020-10, Vol.30 (10), p.5738-5747
• Main Authors: Wang, Wei, Cao, KaiMing, Jin, ShengMing, Zhu, XiaoLi, Ding, JianHui, Peng, WeiJun
• Format: Article
• Language: English
• Subjects: Adult; Aged; Aged, 80 and over; Carcinoma, Renal Cell - classification; Carcinoma, Renal Cell - diagnostic imaging; Cell Differentiation; Clear cell-type renal cell carcinoma; Diagnosis, Differential; Diagnostic Radiology; Differentiation; Discriminant Analysis; Feature extraction; Female; Heterogeneity; Humans; Imaging; Internal Medicine; Interventional Radiology; Kidney - pathology; Kidney cancer; Kidney Neoplasms - classification; Kidney Neoplasms - diagnostic imaging; Kidney stones; Magnetic Resonance; Magnetic Resonance Imaging; Male; Medicine; Medicine & Public Health; Middle Aged; Neuroradiology; Papillary renal cell carcinoma; Radiology; Radiomics; Retrospective Studies; ROC Curve; Surgery; Ultrasound; Young Adult
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-020-06896-5

Objectives To 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. Methods Seventy-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. Results Significant 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). Conclusions Radiomics 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) .