Yanfen Cui1, Huanhuan Liu2, Jialiang Ren3, Xiaosong Du1, Lei Xin1, Dandan Li1, Xiaotang Yang4, Dengbin Wang5. 1. Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China. 2. Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China. 3. GE Healthcare China, Beijing, China. 4. Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China. yangxt210@126.com. 5. Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China. wangdengbin@xinhuamed.com.cn.
Abstract
OBJECTIVE: To develop a T2-weighted (T2W) image-based radiomics signature for the individual prediction of KRAS mutation status in patients with rectal cancer. METHODS: Three hundred four consecutive patients from center I with pathologically diagnosed rectal adenocarcinoma (training dataset, n = 213; internal validation dataset, n = 91) were enrolled in our retrospective study. The patients from center II (n = 86) were selected as an external validation dataset. A total of 960 imaging features were extracted from high-resolution T2W images for each patient. Five steps, mainly univariate statistical tests, were applied for feature selection. Subsequently, three classification methods, i.e., logistic regression (LR), decision tree (DT), and support vector machine (SVM) algorithm, were applied to develop the radiomics signature for KRAS prediction in the training dataset. The predictive performance was evaluated by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). RESULTS: Seven radiomics features were screened as a KRAS-associated radiomics signature of rectal cancer. Our best prediction model was obtained with SVM classifiers with AUC of 0.722 (95%CI, 0.654-0.790) in the training dataset. This was validated in the internal and external validation datasets with good calibration, and the corresponding AUCs were 0.682 (95% CI, 0.569-0.794) and 0.714 (95% CI, 0.602-0.827), respectively. DCA confirmed its clinical usefulness. CONCLUSIONS: The proposed T2WI-based radiomics signature has a moderate performance to predict KRAS status, and may be useful for supplementing genomic analysis to determine KRAS expression in rectal cancer patients. KEY POINTS: • T2WI-based radiomics showed a moderate diagnostic significance for KRAS status. • The best prediction model was obtained with SVM classifier. • The baseline clinical and histopathological characteristics were not associated with KRAS mutation.
OBJECTIVE: To develop a T2-weighted (T2W) image-based radiomics signature for the individual prediction of KRAS mutation status in patients with rectal cancer. METHODS: Three hundred four consecutive patients from center I with pathologically diagnosed rectal adenocarcinoma (training dataset, n = 213; internal validation dataset, n = 91) were enrolled in our retrospective study. The patients from center II (n = 86) were selected as an external validation dataset. A total of 960 imaging features were extracted from high-resolution T2W images for each patient. Five steps, mainly univariate statistical tests, were applied for feature selection. Subsequently, three classification methods, i.e., logistic regression (LR), decision tree (DT), and support vector machine (SVM) algorithm, were applied to develop the radiomics signature for KRAS prediction in the training dataset. The predictive performance was evaluated by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). RESULTS: Seven radiomics features were screened as a KRAS-associated radiomics signature of rectal cancer. Our best prediction model was obtained with SVM classifiers with AUC of 0.722 (95%CI, 0.654-0.790) in the training dataset. This was validated in the internal and external validation datasets with good calibration, and the corresponding AUCs were 0.682 (95% CI, 0.569-0.794) and 0.714 (95% CI, 0.602-0.827), respectively. DCA confirmed its clinical usefulness. CONCLUSIONS: The proposed T2WI-based radiomics signature has a moderate performance to predict KRAS status, and may be useful for supplementing genomic analysis to determine KRAS expression in rectal cancerpatients. KEY POINTS: • T2WI-based radiomics showed a moderate diagnostic significance for KRAS status. • The best prediction model was obtained with SVM classifier. • The baseline clinical and histopathological characteristics were not associated with KRAS mutation.
Entities:
Keywords:
Magnetic resonance imaging; Mutation; Radiomics; Rectal neoplasms
Authors: Kyrre E Emblem; Marco C Pinho; Frank G Zöllner; Paulina Due-Tonnessen; John K Hald; Lothar R Schad; Torstein R Meling; Otto Rapalino; Atle Bjornerud Journal: Radiology Date: 2014-12-08 Impact factor: 11.105
Authors: Kenneth A Miles; Balaji Ganeshan; Manuel Rodriguez-Justo; Vicky J Goh; Zia Ziauddin; Alec Engledow; Marie Meagher; Raymondo Endozo; Stuart A Taylor; Stephen Halligan; Peter J Ell; Ashley M Groves Journal: J Nucl Med Date: 2014-02-10 Impact factor: 10.057