Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer
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To develop a T2-weighted (T2W) image-based radiomics signature for the individual prediction of KRAS mutation status in patients with rectal cancer.
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).
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.
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.
• 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.
KeywordsMagnetic resonance imaging Rectal neoplasms Radiomics Mutation
Analysis of variance
Amplification-refractory mutation system
Area under the ROC curve
Decision curve analysis
Diffusion kurtosis imaging
Diffusion weighted imaging
Epidermal growth factor receptor
Gray-level co-occurrence matrix
Gray-level dependence matrix
Gray-level run length matrix
gray-Level size zone matrix
Intravoxel incoherent motion
Kirsten rat sarcoma
Laplacian of Gaussian
Magnetic resonance imaging
National Comprehensive Cancer Network
Picture archiving and communication system
Pathological complete response
Polymerase chain reaction
Radial basis function
Receiver operating characteristic
Regions of interests
Support vector machine
Volume of interest
This study was supported by the National Key Research and Development Program of China (No. 2017YFC0109003), the Special Research Program of Shanghai Municipal Commission of Heath and Family Planning on medical intelligence (No. 2018ZHYL0108), Shanghai Sailing Program (19YF1433100), the Science and Technology Project of Shanxi Province (No. 20150313007-5), and Applied Basic Research Programs of Shanxi Province (Grant No. 201801D121307 and 201801D221390). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Compliance with ethical standards
The scientific guarantor of this publication is Dengbin Wang.
Conflict of interest
One of the authors (JR) is an employee of GE Healthcare. The remaining authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
• Diagnostic or prognostic study
• Multicenter study
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