CT texture analysis for the prediction of KRAS mutation status in colorectal cancer via a machine learning approach. Issue 118 (September 2019)

• Record Type: Journal Article
• Authors: Taguchi, Narumi; Oda, Seitaro; Yokota, Yasuhiro; Yamamura, Sadahiro; Imuta, Masanori; Tsuchigame, Tadatoshi; Nagayama, Yasunori; Kidoh, Masafumi; Nakaura, Takeshi; Shiraishi, Shinya; Funama, Yoshinori; Shinriki, Satoru; Miyamoto, Yuji; Baba, Hideo; Yamashita, Yasuyuki
• Is Part Of: European journal of radiology. Issue 118(2019)
• Page Start: 38
• Page End: 43
• Publication Date: 2019-09
• Subjects: Colorectal cancer -- CT texture analysis -- Machine learning -- KRAS mutation -- Radiogenomics; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2019.06.028
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: CT texture analysis might help to predict the KRAS mutation status in CRC. Prediction performance of comprehensive CT texture analysis was superior to that of SUVmax . A machine learning approach may be applicable to CT texture analysis. Abstract: Purpose: This study aimed to investigate whether a machine learning-based computed tomography (CT) texture analysis could predict the mutation status of V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) in colorectal cancer. Method: This retrospective study comprised 40 patients with pathologically confirmed colorectal cancer who underwent KRAS mutation testing, contrast-enhancement CT, and 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) before treatment. Of the 40 patients, 20 had mutated KRAS genes, whereas 20 had wild-type KRAS genes. Fourteen CT texture parameters were extracted from portal venous phase CT images of primary tumors, and the maximum standard uptake values (SUVmax ) on 18 F-FDG PET images were recorded. Univariate logistic regression was used to develop predictive models for each CT texture parameter and SUVmax, and a machine learning method (multivariate support vector machine) was used to develop a comprehensive set of CT texture parameters. The area under the receiver operating characteristic (ROC) curve (AUC) of each model was calculated using five-fold cross validation. In addition, the performance of the machine learning method with the CT texture parameters was compared with that of SUVmax . Results: In the univariate analyses, the AUC of each CT texture parameter ranged from 0.4 to 0.7, while the AUC of the SUVmax was 0.58. Comparatively, the multivariate support vector machine with comprehensive CT texture parameters yielded an AUC of 0.82, indicating a superior prediction performance when compared to the SUVmax . Conclusions: A machine learning-based CT texture analysis was superior to the SUVmax for predicting the KRAS mutation status of a colorectal cancer.