Characterization of solid renal neoplasms using MRI-based quantitative radiomics features

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dc.coverageDOI: 10.1007/s00261-020-02540-4
dc.creatorSaid, Daniela
dc.creatorHectors, Stefanie J.
dc.creatorWilck, Eric
dc.creatorRosen, Ally
dc.creatorStocker, Daniel
dc.creatorBane, Octavia
dc.creatorBeksaç, Alp Tuna
dc.creatorLewis, Sara
dc.creatorBadani, Ketan
dc.creatorTaouli, Bachir
dc.date2020
dc.date.accessioned2025-11-18T19:52:01Z
dc.date.available2025-11-18T19:52:01Z
dc.description<p>Purpose: To assess the diagnostic value of magnetic resonance imaging (MRI)-based radiomics features using machine learning (ML) models in characterizing solid renal neoplasms, in comparison/combination with qualitative radiologic evaluation. Methods: Retrospective analysis of 125 patients (mean age 59 years, 67% males) with solid renal neoplasms that underwent MRI before surgery. Qualitative (signal and enhancement characteristics) and quantitative radiomics analyses (histogram and texture features) were performed on T2-weighted imaging (WI), T1-WI pre- and post-contrast, and DWI. Mann–Whitney U test and receiver-operating characteristic analysis were used in a training set (n = 88) to evaluate diagnostic performance of qualitative and radiomics features for differentiation of renal cell carcinomas (RCCs) from benign lesions, and characterization of RCC subtypes (clear cell RCC [ccRCC] and papillary RCC [pRCC]). Random forest ML models were developed for discrimination between tumor types on the training set, and validated on an independent set (n = 37). Results: We assessed 104 RCCs (51 ccRCC, 29 pRCC, and 24 other subtypes) and 21 benign lesions in 125 patients. Significant qualitative and quantitative radiomics features (area under the curve [AUC] between 0.62 and 0.90) were included for ML analysis. Models with best diagnostic performance on validation sets showed AUC of 0.73 (confidence interval [CI] 0.5–0.96) for differentiating RCC from benign lesions (using combination of qualitative and radiomics features); AUC of 0.77 (CI 0.62–0.92) for diagnosing ccRCC (using radiomics features), and AUC of 0.74 (CI 0.53–0.95) for diagnosing pRCC (using qualitative features). Conclusion: ML models incorporating MRI-based radiomics features and qualitative radiologic assessment can help characterize renal masses.</p>eng
dc.identifierhttps://investigadores.uandes.cl/en/publications/3fa14ea5-7c1f-4d0a-9ac3-b03f815b733c
dc.identifier.urihttps://repositorio.uandes.cl/handle/uandes/57496
dc.languageeng
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourcevol.45 (2020) date: 2020-09-01 nr.9 p.2840-2850
dc.subjectHistogram
dc.subjectMagnetic resonance imaging
dc.subjectRadiomics
dc.subjectRenal cell carcinoma
dc.subjectRenal mass
dc.subjectTexture
dc.titleCharacterization of solid renal neoplasms using MRI-based quantitative radiomics featureseng
dc.typeArticleeng
dc.typeArtículospa
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