A combinatorial MRI sequence-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer

Abstract

This study aimed to develop an optimal radiomics model for preoperatively predicting microsatellite instability (MSI) in patients with rectal cancer (RC) based on multiparametric magnetic resonance imaging. The retrospective study included 308 RC patients who did not receive preoperative antitumor therapy, among whom 51 had MSI. Radiomics features were extracted and dimensionally reduced from T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), diffusion-weighted imaging (DWI), and T1-weighted contrast enhanced (T1CE) images for each patient, and the features of each sequence were combined. Multifactor logistic regression was used to screen the optimal feature set for each combination. Different machine learning methods were applied to construct predictive MSI status models. Relative standard deviation values were determined to evaluate model performance and select the optimal model. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses were performed to evaluate model performance. The model constructed using the k-nearest neighbor (KNN) method combined with T2WI and T1CE images performed best. The area under the curve values for prediction of MSI with this model were 0.849 (0.804-0.887), with a sensitivity of 0.784 and specificity of 0.805. The Delong test showed no significant difference in diagnostic efficacy between the KNN-derived model and the traditional logistic regression model constructed using T1WI + DWI + T1CE and T2WI + T1WI + DWI + T1CE data (P > 0.05) and the diagnostic efficiency of the KNN-derived model was slightly better than that of the traditional model. From ROC curve analysis, the KNN-derived model significantly distinguished patients at low- and high-risk of MSI with the optimal threshold of 0.2, supporting the clinical applicability of the model. The model constructed using the KNN method can be applied to noninvasively predict MSI status in RC patients before surgery based on radiomics features from T2WI and T1CE images. Thus, this method may provide a convenient and practical tool for formulating treatment strategies and optimizing individual clinical decision-making for patients with RC.

Keywords: Machine learning; Magnetic resonance imaging; Microsatellite instability; Radiomics; Rectal cancer.

Figure 1

Flowchart of patient inclusion.

Figure 2

Cross-validation procedure of machine learning algorithm in the training cohort.

Figure 3

Research flow chart.

Figure 4

Average, standard deviation, and RSD values for the diagnostic effectiveness of radiomics models obtained using five machine learning algorithms. RSD is the ratio of the standard deviation to the mean of AUC values obtained from 50 machine learning model building cycles.

Figure 5

AUC density maps for radiomics models constructed using different machine learning methods for different combinations of sequence features.

Figure 6

ROC curve analysis of different models for the preoperative prediction of MSI in RC patients. (a) Diagnostic performance of traditional models constructed by combining two sequence types. (b) Diagnostic performance of traditional models combining more than two sequence types and the developed KNN-based model. (c) Correction curve for the KNN-based model. The dotted line represents ideal prediction performance, and the solid line represents the actual prediction performance. (d) Static benefit analysis of the developed model.