TY - JOUR
T1 - Hyperechoic Renal Masses
T2 - Differentiation of Angiomyolipomas from Renal Cell Carcinomas using Tumor Size and Ultrasound Radiomics
AU - Habibollahi, Peiman
AU - Sultan, Laith R.
AU - Bialo, Darren
AU - Nazif, Abdulrahman
AU - Faizi, Nauroze A.
AU - Sehgal, Chandra M.
AU - Chauhan, Anil
N1 - Publisher Copyright:
© 2022 World Federation for Ultrasound in Medicine & Biology
PY - 2022/5
Y1 - 2022/5
N2 - A retrospective single-center study was performed to assess the performance of ultrasound image-based texture analysis in differentiating angiomyolipoma (AML) from renal cell carcinoma (RCC) on incidental hyperechoic renal lesions. Ultrasound reports of patients from 2012 to 2017 were queried, and those with a hyperechoic renal mass <5 cm in diameter with further imaging characterization and/or pathological correlation were included. Quantitative texture analysis was performed using a model including 18 texture features. Univariate logistic regression was used to identify texture variables differing significantly between AML and RCC, and the performance of the model was measured using the area under the receiver operating characteristic (ROC) curve. One hundred thirty hyperechoic renal masses in 127 patients characterized as RCCs (25 [19%]) and AMLs (105 [81%]) were included. Size (odds ratio [OR] = 0.12, 95% confidence interval [CI]: 0.04–0.43, p < 0.001) and 4 of 18 texture features, including entropy (OR = 0.09, 95% CI: 0.01–0.81, p = 0.03), gray-level non-uniformity (OR = 0.12, 95% CI: 0.02–0.72, p = 0.02), long-run emphasis (OR = 0.49, 95% CI: 0.27–0.91, p = 0.02) and run-length non-uniformity (OR = 2.18, 95% CI: 1.14–4.16, p = 0.02) were able to differentiate AMLs from RCCs. The area under the ROC curve for the performance of the model, including texture features and size, was 0.945 (p < 0.001). Ultrasound image-based textural analysis enables differentiation of hyperechoic RCCs from AMLs with high accuracy, which improves further when combined with tumor size.
AB - A retrospective single-center study was performed to assess the performance of ultrasound image-based texture analysis in differentiating angiomyolipoma (AML) from renal cell carcinoma (RCC) on incidental hyperechoic renal lesions. Ultrasound reports of patients from 2012 to 2017 were queried, and those with a hyperechoic renal mass <5 cm in diameter with further imaging characterization and/or pathological correlation were included. Quantitative texture analysis was performed using a model including 18 texture features. Univariate logistic regression was used to identify texture variables differing significantly between AML and RCC, and the performance of the model was measured using the area under the receiver operating characteristic (ROC) curve. One hundred thirty hyperechoic renal masses in 127 patients characterized as RCCs (25 [19%]) and AMLs (105 [81%]) were included. Size (odds ratio [OR] = 0.12, 95% confidence interval [CI]: 0.04–0.43, p < 0.001) and 4 of 18 texture features, including entropy (OR = 0.09, 95% CI: 0.01–0.81, p = 0.03), gray-level non-uniformity (OR = 0.12, 95% CI: 0.02–0.72, p = 0.02), long-run emphasis (OR = 0.49, 95% CI: 0.27–0.91, p = 0.02) and run-length non-uniformity (OR = 2.18, 95% CI: 1.14–4.16, p = 0.02) were able to differentiate AMLs from RCCs. The area under the ROC curve for the performance of the model, including texture features and size, was 0.945 (p < 0.001). Ultrasound image-based textural analysis enables differentiation of hyperechoic RCCs from AMLs with high accuracy, which improves further when combined with tumor size.
KW - Angiomyolipoma
KW - Renal cell carcinoma
KW - Texture analysis
KW - Ultrasound
UR - http://www.scopus.com/inward/record.url?scp=85125116942&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125116942&partnerID=8YFLogxK
U2 - 10.1016/j.ultrasmedbio.2022.01.011
DO - 10.1016/j.ultrasmedbio.2022.01.011
M3 - Article
C2 - 35219511
AN - SCOPUS:85125116942
SN - 0301-5629
VL - 48
SP - 887
EP - 894
JO - Ultrasound in Medicine and Biology
JF - Ultrasound in Medicine and Biology
IS - 5
ER -