Characterization of solid renal neoplasms using MRI-based quantitative radiomics features.
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...
| Publicado en: | Abdominal Radiology Vol. 45; no. 9; pp. 2840 - 2851 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145053697&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145053697 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Sep2020 vid: 45 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 145053697 144132102 10.1007/s00261-020-02540-4 145053697 ppf: 2840 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Characterization of solid renal neoplasms using MRI-based quantitative radiomics features. aug: au: Said, Daniela Hectors, Stefanie J. Wilck, Eric Rosen, Ally Stocker, Daniel Bane, Octavia Beksaç, Alp Tuna Lewis, Sara Badani, Ketan Taouli, Bachir affil: BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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