Differentiation of benign from malignant solid renal lesions with MRI-based radiomics and machine learning.

Autor: Massa'a RN; Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Avenue, Madison, WI, 53792, USA., Stoeckl EM; Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Avenue, Madison, WI, 53792, USA., Lubner MG; Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Avenue, Madison, WI, 53792, USA., Smith D; Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Avenue, Madison, WI, 53792, USA., Mao L; Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA., Shapiro DD; Department of Urology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA., Abel EJ; Department of Urology, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA., Wentland AL; Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Avenue, Madison, WI, 53792, USA. alwentland@wisc.edu.; Department of Medical Physics, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA. alwentland@wisc.edu.; Department of Biomedical Engineering, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA. alwentland@wisc.edu.
Jazyk: angličtina
Zdroj: Abdominal radiology (New York) [Abdom Radiol (NY)] 2022 Aug; Vol. 47 (8), pp. 2896-2904. Date of Electronic Publication: 2022 Jun 20.
DOI: 10.1007/s00261-022-03577-3
Abstrakt: Background: Solid renal masses are often indeterminate for benignity versus malignancy on magnetic resonance imaging. Such masses are typically evaluated with either percutaneous biopsy or surgical resection. Percutaneous biopsy can be non-diagnostic and some surgically resected lesions are inadvertently benign.
Purpose: To assess the performance of ten machine learning (ML) algorithms trained with MRI-based radiomics features in distinguishing benign from malignant solid renal masses.
Methods: Patients with solid renal masses identified on pre-intervention MRI were curated from our institutional database. Masses with a definitive diagnosis via imaging (for angiomyolipomas) or via biopsy or surgical resection (for oncocytomas or renal cell carcinomas) were selected. Each mass was segmented for both T2- and post-contrast T1-weighted images. Radiomics features were derived from the segmented masses for each imaging sequence. Ten ML algorithms were trained with the radiomics features gleaned from each MR sequence, as well as the combination of MR sequences.
Results: In total, 182 renal masses in 160 patients were included in the study. The support vector machine algorithm trained on radiomics features from T2-weighted images performed superiorly, with an accuracy of 0.80 and an area under the curve (AUC) of 0.79. Linear discriminant analysis (accuracy = 0.84 and AUC = 0.77) and logistic regression (accuracy = 0.78 and AUC = 0.78) algorithms trained on T2-based radiomics features performed similarly. ML algorithms trained on radiomics features from post-contrast T1-weighted images or the combination of radiomics features from T2- and post-contrast T1-weighted images yielded lower performance.
Conclusion: Machine learning models trained with radiomics features derived from T2-weighted images can provide high accuracy for distinguishing benign from malignant solid renal masses.
Clinical Impact: Machine learning models derived from MRI-based radiomics features may improve the clinical management of solid renal masses and have the potential to reduce the frequency with which benign solid renal masses are biopsied or surgically resected.
(© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze: MEDLINE
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