MRI‐based radiomics signature for tumor grading of rectal carcinoma using random forest model

• Published in: Journal of cellular physiology Vol. 234; no. 11; pp. 20501 - 20509
• Main Authors: He, Bo, Ji, Tao, Zhang, Hong, Zhu, Yun, Shu, Ruo, Zhao, Wei, Wang, Kunhua
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.11.2019
• Subjects: Cancer; Classifiers; Colorectal cancer; Evaluation; Feature extraction; Learning algorithms; Lesions; Machine learning; Magnetic resonance imaging; MRI; NMR; Nuclear magnetic resonance; Oversampling; Prediction models; Radiomics; radiomics feature; random forest; rectal carcinoma; Rectum; Training; tumor grading; Tumors; radiomics feature; rectal carcinoma; random forest; MRI; tumor grading

The present study aimed to construct prospective models for tumor grading of rectal carcinoma by using magnetic resonance (MR)‐based radiomics features. A set of 118 patients with rectal carcinoma was analyzed. After imbalance‐adjustments of the data using Synthetic Minority Oversampling Technique (...