- author = "Ji Whae Choi and Rong Hu and Yijun Zhao and Subhanik Purkayastha and Jing Wu and Aidan J. McGirr and S. William Stavropoulos and Alvin C. Silva and Michael C. Soulen and Matthew B. Palmer and Paul J. L. Zhang and Chengzhang Zhu and Sun Ho Ahn and Harrison X. Bai",
- title = "Preoperative prediction of the stage, size, grade, and necrosis score in clear cell renal cell carcinoma using {MRI}-based radiomics",
- journal = "Abdominal Radiology",
- year = "2021",
- volume = "46",
- number = "6",
- pages = "2656--2664",
- month = jun,
- note = "Special Section on Ovarian Cancer",
- keywords = "genetic algorithms, genetic programming, TPOT, Renal cancer, Neoplasm progression, Imaging analysis, Medical imaging, Kidneys, Ureters, Bladder, Retroperitoneum",
- ISSN = "2366-004X",
-
DOI = "
doi:10.1007/s00261-020-02876-x",
-
abstract = "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 [ge 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 (95percent CI 0.816-0.937), specificity of 0.95 (95percent CI 0.875-0.984), and sensitivity of 0.72 (95percent 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 (95percent CI 0.816-0.937), specificity of 0.95 (95percent CI 0.875-0.984), and sensitivity of 0.72 (95percent 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.
",
-
notes = "Warren Alpert Medical School, Brown University,
Providence, RI, 02903, USA
PMID: 33386910",
