TY - JOUR
T1 - Diagnostic performance and interreader agreement of a standardized MR imaging approach in the prediction of small renal mass histology
AU - Kay, Fernando Uliana
AU - Canvasser, Noah E.
AU - Xi, Yin
AU - Pinho, Daniella F
AU - Costa, Daniel N
AU - Diaz De Leon III, Alberto
AU - Khatri, Gaurav
AU - Leyendecker, John
AU - Yokoo, Takeshi
AU - Lay, Aaron H.
AU - Kavoussi, Nicholas
AU - Koseoglu, Ersin
AU - Cadeddu, Jeffrey A
AU - Pedrosa, Ivan
N1 - Funding Information:
Supported by the National Institutes of Health (5R01CA154475, P50CA196516).
Funding Information:
Acknowledgment: The authors thank Pam Curry, MA, for the graphic work in Figure 2. The authors would like to acknowledge UT South-western Academic Information Systems for providing data management resources used in this study (Research Electronic Data Capture–REDCap), which were supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award UL1TR001 105 (Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture [REDCap]: A metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform 2009;42[2]:377–381).
Publisher Copyright:
© RSNA, 2018.
PY - 2018/5
Y1 - 2018/5
N2 - Purpose: To assess the diagnostic performance and interreader agreement of a standardized diagnostic algorithm in determining the histologic type of small (≤4 cm) renal masses (SRMs) with multiparametric magnetic resonance (MR) imaging. Materials and Methods: This single-center retrospective HIPAA-compliant institutional review board.approved study included 103 patients with 109 SRMs resected between December 2011 and July 2015. The requirement for informed consent was waived. Presurgical renal MR images were reviewed by seven radiologists with diverse experience. Eleven MR imaging features were assessed, and a standardized diagnostic algorithm was used to determine the most likely histologic diagnosis, which was compared with histopathology results after surgery. Interreader variability was tested with the Cohen κ statistic. Regression models using MR imaging features were used to predict the histopathologic diagnosis with 5% significance level. Results: Clear cell renal cell carcinoma (RCC) and papillary RCC were diagnosed, with sensitivities of 85% (47 of 55) and 80% (20 of 25), respectively, and specificities of 76% (41 of 54) and 94% (79 of 84), respectively. Interreader agreement was moderate to substantial (clear cell RCC, κ = 0.58; papillary RCC, κ = 0.73). Signal intensity (SI) of the lesion on T2-weighted MR images and degree of contrast enhancement (CE) during the corticomedullary phase were independent predictors of clear cell RCC (SI odds ratio [OR]: 3.19; 95% confidence interval [CI]: 1.4, 7.1; P =.003; CE OR, 4.45; 95% CI: 1.8, 10.8; P <.001) and papillary RCC (CE OR, 0.053; 95% CI: 0.02, 0.2; P <.001), and both had substantial interreader agreement (SI, κ = 0.69; CE, κ = 0.71). Poorer performance was observed for chromophobe histology, oncocytomas, and minimal fat angiomyolipomas, (sensitivity range, 14%.67%; specificity range, 97%.99%), with fair to moderate interreader agreement (k range = 0.23.0.43). Segmental enhancement inversion was an independent predictor of oncocytomas (OR, 16.21; 95% CI: 1.0, 275.4; P =.049), with moderate interreader agreement (k = 0.49). Conclusion: The proposed standardized MR imaging.based diagnostic algorithm had diagnostic accuracy of 81% (88 of 109) and 91% (99 of 109) in the diagnosis of clear cell RCC and papillary RCC, respectively, while achieving moderate to substantial interreader agreement among seven radiologists.
AB - Purpose: To assess the diagnostic performance and interreader agreement of a standardized diagnostic algorithm in determining the histologic type of small (≤4 cm) renal masses (SRMs) with multiparametric magnetic resonance (MR) imaging. Materials and Methods: This single-center retrospective HIPAA-compliant institutional review board.approved study included 103 patients with 109 SRMs resected between December 2011 and July 2015. The requirement for informed consent was waived. Presurgical renal MR images were reviewed by seven radiologists with diverse experience. Eleven MR imaging features were assessed, and a standardized diagnostic algorithm was used to determine the most likely histologic diagnosis, which was compared with histopathology results after surgery. Interreader variability was tested with the Cohen κ statistic. Regression models using MR imaging features were used to predict the histopathologic diagnosis with 5% significance level. Results: Clear cell renal cell carcinoma (RCC) and papillary RCC were diagnosed, with sensitivities of 85% (47 of 55) and 80% (20 of 25), respectively, and specificities of 76% (41 of 54) and 94% (79 of 84), respectively. Interreader agreement was moderate to substantial (clear cell RCC, κ = 0.58; papillary RCC, κ = 0.73). Signal intensity (SI) of the lesion on T2-weighted MR images and degree of contrast enhancement (CE) during the corticomedullary phase were independent predictors of clear cell RCC (SI odds ratio [OR]: 3.19; 95% confidence interval [CI]: 1.4, 7.1; P =.003; CE OR, 4.45; 95% CI: 1.8, 10.8; P <.001) and papillary RCC (CE OR, 0.053; 95% CI: 0.02, 0.2; P <.001), and both had substantial interreader agreement (SI, κ = 0.69; CE, κ = 0.71). Poorer performance was observed for chromophobe histology, oncocytomas, and minimal fat angiomyolipomas, (sensitivity range, 14%.67%; specificity range, 97%.99%), with fair to moderate interreader agreement (k range = 0.23.0.43). Segmental enhancement inversion was an independent predictor of oncocytomas (OR, 16.21; 95% CI: 1.0, 275.4; P =.049), with moderate interreader agreement (k = 0.49). Conclusion: The proposed standardized MR imaging.based diagnostic algorithm had diagnostic accuracy of 81% (88 of 109) and 91% (99 of 109) in the diagnosis of clear cell RCC and papillary RCC, respectively, while achieving moderate to substantial interreader agreement among seven radiologists.
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U2 - 10.1148/radiol.2018171557
DO - 10.1148/radiol.2018171557
M3 - Article
C2 - 29390196
AN - SCOPUS:85046041213
SN - 0033-8419
VL - 287
SP - 543
EP - 553
JO - RADIOLOGY
JF - RADIOLOGY
IS - 2
ER -