OBJECTIVE: The purpose of this article is to evaluate differences in texture measures on apparent diffusion coefficient (ADC) maps between low- and high-stage clear cell renal cell carcinomas (RCCs). MATERIALS AND METHODS: In this retrospective study, 61 patients with clear cell RCC at pathologic examination and who underwent preoperative MRI with diffusion-weighted imaging were included. Clear cell RCCs were clinically staged on review of preoperative MRI by a board-certified radiologist blinded to the pathologic findings. Whole lesions were segmented on ADC maps by two readers independently, from which first-order texture features (i.e., mean and skewness) and second-order texture features (i.e., cooccurrence matrix measures) were calculated. Texture metrics were compared between low- and high-stage clear cell RCC. RESULTS: In 61 patients, there were 62 clear cell RCCs (33 low stage [stages I and II] and 29 high stage [stages III and IV]) at pathologic examination. Staging accuracy of qualitative interpretation was 100% for low-stage lesions and 37.9% (11/29) for high-stage lesions. There was no statistically significant difference in mean ADC between high- and low-stage clear cell RCCs (1.77×10(-3) vs 1.80×10(-3) mm2/s; p=0.7). However, high-stage clear cell RCCs were larger (6.96±2.93 vs 3.49±1.57 cm; p<0.0001) and had statistically significantly (p≤0.0001) higher ADC skewness (0.02±0.33 vs -0.52±0.65) and cooccurrence matrix correlation (0.64±0.11 vs 0.49±0.13). Multivariate logistic regression identified size, skewness, and cooccurrence matrix correlation as significant independent predictors of high stage (AUC=0.92). Interreader correlation in texture metrics ranged from 0.82 to 0.89. CONCLUSION: First- and second-order ADC texture metrics differ between low- and high-stage clear cell RCCs. A model that includes size and ADC texture measures may help to stage clear cell RCCs noninvasively.
OBJECTIVE: The purpose of this article is to evaluate differences in texture measures on apparent diffusion coefficient (ADC) maps between low- and high-stage clear cell renal cell carcinomas (RCCs). MATERIALS AND METHODS: In this retrospective study, 61 patients with clear cell RCC at pathologic examination and who underwent preoperative MRI with diffusion-weighted imaging were included. Clear cell RCCs were clinically staged on review of preoperative MRI by a board-certified radiologist blinded to the pathologic findings. Whole lesions were segmented on ADC maps by two readers independently, from which first-order texture features (i.e., mean and skewness) and second-order texture features (i.e., cooccurrence matrix measures) were calculated. Texture metrics were compared between low- and high-stage clear cell RCC. RESULTS: In 61 patients, there were 62 clear cell RCCs (33 low stage [stages I and II] and 29 high stage [stages III and IV]) at pathologic examination. Staging accuracy of qualitative interpretation was 100% for low-stage lesions and 37.9% (11/29) for high-stage lesions. There was no statistically significant difference in mean ADC between high- and low-stage clear cell RCCs (1.77×10(-3) vs 1.80×10(-3) mm2/s; p=0.7). However, high-stage clear cell RCCs were larger (6.96±2.93 vs 3.49±1.57 cm; p<0.0001) and had statistically significantly (p≤0.0001) higher ADC skewness (0.02±0.33 vs -0.52±0.65) and cooccurrence matrix correlation (0.64±0.11 vs 0.49±0.13). Multivariate logistic regression identified size, skewness, and cooccurrence matrix correlation as significant independent predictors of high stage (AUC=0.92). Interreader correlation in texture metrics ranged from 0.82 to 0.89. CONCLUSION: First- and second-order ADC texture metrics differ between low- and high-stage clear cell RCCs. A model that includes size and ADC texture measures may help to stage clear cell RCCs noninvasively.
Authors: Massimo Galia; Domenico Albano; Alberto Bruno; Antonino Agrusa; Giorgio Romano; Giuseppe Di Buono; Francesco Agnello; Giuseppe Salvaggio; Ludovico La Grutta; Massimo Midiri; Roberto Lagalla Journal: Br J Radiol Date: 2017-07-13 Impact factor: 3.039
Authors: Natalie L Demirjian; Bino A Varghese; Steven Y Cen; Darryl H Hwang; Manju Aron; Imran Siddiqui; Brandon K K Fields; Xiaomeng Lei; Felix Y Yap; Marielena Rivas; Sharath S Reddy; Haris Zahoor; Derek H Liu; Mihir Desai; Suhn K Rhie; Inderbir S Gill; Vinay Duddalwar Journal: Eur Radiol Date: 2021-11-10 Impact factor: 5.315
Authors: Kathleen Nguyen; Nicola Schieda; Nick James; Matthew D F McInnes; Mark Wu; Rebecca E Thornhill Journal: Eur Radiol Date: 2020-09-10 Impact factor: 5.315