Utility of radiomics features of diffusion-weighted magnetic resonance imaging for differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma: model development and external validation

• Published in: Abdominal imaging 2022-06, Vol.47 (6), p.2178-2186
• Main Authors: Matsumoto, Shunya, Arita, Yuki, Yoshida, Soichiro, Fukushima, Hiroshi, Kimura, Koichiro, Yamada, Ichiro, Tanaka, Hajime, Yagi, Fumiko, Yokoyama, Minato, Matsuoka, Yoh, Oya, Mototsugu, Tateishi, Ukihide, Jinzaki, Masahiro, Fujii, Yasuhisa
• Format: Article
• Language: English
• Subjects: Angiomyolipoma; Angiomyolipoma - diagnostic imaging; Angiomyolipoma - pathology; Bladder; Carcinoma; Carcinoma, Renal Cell - diagnostic imaging; Carcinoma, Renal Cell - pathology; Clear cell-type renal cell carcinoma; Cluster analysis; Confidence intervals; Diagnosis, Differential; Diffusion; Diffusion coefficient; Diffusion Magnetic Resonance Imaging; Feature extraction; Female; Gastroenterology; Hamartoma; Hepatology; Humans; Imaging; Kidney cancer; Kidney Neoplasms - diagnostic imaging; Kidney Neoplasms - pathology; Kidneys; Magnetic Resonance Imaging; Male; Medical imaging; Medicine; Medicine & Public Health; Radiology; Radiomics; Resonance; Retroperitoneum; Retrospective Studies; Tumors; Ureters
• ISSN: 2366-0058; 2366-004X; 2366-0058
• DOI: 10.1007/s00261-022-03486-5

Purpose To investigate the utility of radiomics features of diffusion-weighted magnetic resonance imaging (DW-MRI) to differentiate fat-poor angiomyolipoma (fpAML) from clear cell renal cell carcinoma (ccRCC). Materials and methods This multi-institutional study included two cohorts with pathologically confirmed renal tumors: 65 patients with ccRCC and 18 with fpAML in the model development cohort, and 17 with ccRCC and 13 with fpAML in the external validation cohort. All patients underwent magnetic resonance imaging (MRI) including DW-MRI. Radiomics analysis was used to extract 39 imaging features from the apparent diffusion coefficient (ADC) map. The radiomics features were analyzed with unsupervised hierarchical cluster analysis. A random forest (RF) model was used to identify radiomics features important for differentiating fpAML from ccRCC in the development cohort. The diagnostic performance of the RF model was evaluated in the development and validation cohorts. Results The cases in the developmental cohort were classified into three groups with different frequencies of fpAML by cluster analysis of radiomics features. RF analysis of the development cohort showed that the mean ADC value was important for differentiating fpAML from ccRCC, as well as higher-texture features including gray-level run length matrix (GLRLM)_long-run low gray-level enhancement (LRLGE), and GLRLM_low gray-level run emphasis (LGRE). The area under the curve values of the development [0.90, 95% confidence interval (CI) 0.80–1.00] and validation cohorts (0.87, 95% CI 0.74–1.00) were similar ( P  = 0.91). Conclusion The radiomics features of ADC maps are useful for differentiating fpAML from ccRCC. Graphical abstract