Daniela Said1,2, Stefanie J Hectors1,3,4, Eric Wilck3, Ally Rosen3,5, Daniel Stocker1,6, Octavia Bane1, Alp Tuna Beksaç7, Sara Lewis1,3, Ketan Badani7, Bachir Taouli8,9. 1. BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 2. Department of Radiology, Universidad de los Andes, Santiago, Chile. 3. Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 4. Department of Radiology, Weill Cornell Medicine, New York, NY, USA. 5. Department of Radiology, Long Island School of Medicine, NYU-Winthrop Hospital, Mineola, NY, USA. 6. Institute of Interventional and Diagnostic Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland. 7. Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 8. BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. bachir.taouli@mountsinai.org. 9. Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. bachir.taouli@mountsinai.org.
Abstract
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.
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.
Authors: Ruben Ngnitewe Massa'a; Elizabeth M Stoeckl; Meghan G Lubner; David Smith; Lu Mao; Daniel D Shapiro; E Jason Abel; Andrew L Wentland Journal: Abdom Radiol (NY) Date: 2022-06-20
Authors: Chunxiang Li; Ge Qiao; Jinghan Li; Lisha Qi; Xueqing Wei; Tan Zhang; Xing Li; Shu Deng; Xi Wei; Wenjuan Ma Journal: Front Oncol Date: 2022-03-04 Impact factor: 6.244