Preoperative prediction of the stage, size, grade, and necrosis score in clear cell renal cell carcinoma using MRI-based radiomics.
Purpose: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma. Currently, there is a lack of noninvasive methods to stratify ccRCC prognosis prior to any invasive therapies. The purpose of this study was to preoperatively predict the tumor stage, size, grade, an...
| Publicado en: | Abdominal Radiology Vol. 46; no. 6; pp. 2656 - 2665 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2021
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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=150935447&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150935447 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Jun2021 vid: 46 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 150935447 147868425 10.1007/s00261-020-02876-x 150935447 ppf: 2656 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Preoperative prediction of the stage, size, grade, and necrosis score in clear cell renal cell carcinoma using MRI-based radiomics. aug: au: Choi, Ji Whae Hu, Rong Zhao, Yijun Purkayastha, Subhanik Wu, Jing McGirr, Aidan J. Stavropoulos, S. William Silva, Alvin C. Soulen, Michael C. Palmer, Matthew B. Zhang, Paul J. L. Zhu, Chengzhang Ahn, Sun Ho Bai, Harrison X. affil: Warren Alpert Medical School, Brown University, 02903, Providence, RI, USA sug: ab: Purpose: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma. Currently, there is a lack of noninvasive methods to stratify ccRCC prognosis prior to any invasive therapies. The purpose of this study was to preoperatively predict the tumor stage, size, grade, and necrosis (SSIGN) score of ccRCC using MRI-based radiomics. Methods: A multicenter cohort of 364 histopathologically confirmed ccRCC patients (272 low [< 4] and 92 high [≥ 4] SSIGN score) with preoperative T2-weighted and T1-contrast-enhanced MRI were retrospectively identified and divided into training (254 patients) and testing sets (110 patients). The performance of a manually optimized radiomics model was assessed by measuring accuracy, sensitivity, specificity, area under receiver operating characteristic curve (AUROC), and area under precision-recall curve (AUPRC) on an independent test set, which was not included in model training. Lastly, its performance was compared to that of a machine learning pipeline, Tree-Based Pipeline Optimization Tool (TPOT). Results: The manually optimized radiomics model using Random Forest classification and Analysis of Variance feature selection methods achieved an AUROC of 0.89, AUPRC of 0.81, accuracy of 0.89 (95% CI 0.816–0.937), specificity of 0.95 (95% CI 0.875–0.984), and sensitivity of 0.72 (95% CI 0.537–0.852) on the test set. The TPOT using Extra Trees Classifier achieved an AUROC of 0.94, AUPRC of 0.83, accuracy of 0.89 (95% CI 0.816–0.937), specificity of 0.95 (95% CI 0.875–0.984), and sensitivity of 0.72 (95% CI 0.537–0.852) on the test set. Conclusion: Preoperative MR radiomics can accurately predict SSIGN score of ccRCC, suggesting its promise as a prognostic tool that can be used in conjunction with diagnostic markers. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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