Shuai Leng1, Naoki Takahashi2, Daniel Gomez Cardona2,3, Kazuhiro Kitajima2,4, Brian McCollough2, Zhoubo Li2,5, Akira Kawashima2, Bradley C Leibovich6, Cynthia H McCollough2. 1. Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA. leng.shuai@mayo.edu. 2. Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA. 3. Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI, 53705-2275, USA. 4. Department of Radiology, Faculty of Medicine, Kobe University, Kobe, Hyogo, Japan. 5. GE Healthcare, 3000 N. Grandview Blvd, Waukesha, WI, 53188, USA. 6. Department of Urology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
Abstract
PURPOSE: The aim of this study was to assess the effect of denoising on objective heterogeneity scores and its diagnostic capability for the diagnosis of angiomyolipoma (AML) and renal cell carcinoma (RCC). MATERIALS AND METHODS: A total of 158 resected renal masses ≤4 cm [98 clear cell (cc) RCCs, 36 papillary (pap)-RCCs, and 24 AMLs] from 139 patients were evaluated. A representative contrast-enhanced computed tomography (CT) image for each mass was selected by a genitourinary radiologist. A largest possible region of interest was drawn on each mass by the radiologist, from which three objective heterogeneity indices were calculated: standard deviation (SD), entropy (Ent), and uniformity (Uni). Objective heterogeneity indices were also calculated after images were processed with a denoising algorithm (non-local means) at three strengths: weak, medium, and strong. Two genitourinary radiologists also subjectively scored each mass independently using a three-point scale (1-3; with 1 the least and 3 the most heterogeneous), which were added to represent the final subjective heterogeneity score of each mass. Heterogeneity scores were compared among mass types, and area under the ROC curve (AUC) was calculated. RESULTS: For all heterogeneity indices, cc-RCC was significantly more heterogeneous than pap-RCC and AML (p < 0.001), but no significant difference was found between pap-RCC and AML (p > 0.01). For cc-RCC and pap-RCC differentiation, AUCs were 0.91, 0.81, 0.78, and 0.78 for the subjective score, SD, Ent, and Uni, respectively, using original images. The corresponding AUC values were 0.84, 0.74, 0.79, and 0.80 for differentiation of AML and cc-RCC. Noise reduction at weak setting improves AUC values by 0.03, 0.05, and 0.05 for SD, entropy, and uniformity for differentiation of cc-RCC from pap-RCC. Further increase of filtering strength did not improve AUC values. For differentiation of AML vs. cc-RCC, the AUC values stayed relatively flat using the noise reduction technique at different strengths for all three indices. CONCLUSIONS: Both subjective and objective heterogeneity indices can differentiate cc-RCC from pap-RCC and AML. Noise reduction improved differentiation of cc-RCC from pap-RCC, but not differentiation of AML from cc-RCC.
PURPOSE: The aim of this study was to assess the effect of denoising on objective heterogeneity scores and its diagnostic capability for the diagnosis of angiomyolipoma (AML) and renal cell carcinoma (RCC). MATERIALS AND METHODS: A total of 158 resected renal masses ≤4 cm [98 clear cell (cc) RCCs, 36 papillary (pap)-RCCs, and 24 AMLs] from 139 patients were evaluated. A representative contrast-enhanced computed tomography (CT) image for each mass was selected by a genitourinary radiologist. A largest possible region of interest was drawn on each mass by the radiologist, from which three objective heterogeneity indices were calculated: standard deviation (SD), entropy (Ent), and uniformity (Uni). Objective heterogeneity indices were also calculated after images were processed with a denoising algorithm (non-local means) at three strengths: weak, medium, and strong. Two genitourinary radiologists also subjectively scored each mass independently using a three-point scale (1-3; with 1 the least and 3 the most heterogeneous), which were added to represent the final subjective heterogeneity score of each mass. Heterogeneity scores were compared among mass types, and area under the ROC curve (AUC) was calculated. RESULTS: For all heterogeneity indices, cc-RCC was significantly more heterogeneous than pap-RCC and AML (p < 0.001), but no significant difference was found between pap-RCC and AML (p > 0.01). For cc-RCC and pap-RCC differentiation, AUCs were 0.91, 0.81, 0.78, and 0.78 for the subjective score, SD, Ent, and Uni, respectively, using original images. The corresponding AUC values were 0.84, 0.74, 0.79, and 0.80 for differentiation of AML and cc-RCC. Noise reduction at weak setting improves AUC values by 0.03, 0.05, and 0.05 for SD, entropy, and uniformity for differentiation of cc-RCC from pap-RCC. Further increase of filtering strength did not improve AUC values. For differentiation of AML vs. cc-RCC, the AUC values stayed relatively flat using the noise reduction technique at different strengths for all three indices. CONCLUSIONS: Both subjective and objective heterogeneity indices can differentiate cc-RCC from pap-RCC and AML. Noise reduction improved differentiation of cc-RCC from pap-RCC, but not differentiation of AML from cc-RCC.
Authors: Bino A Varghese; Frank Chen; Darryl H Hwang; Steven Y Cen; Inderbir S Gill; Vinay A Duddalwar Journal: Br J Radiol Date: 2018-06-21 Impact factor: 3.039