Computed Tomography-Based Radiomic Features Could Potentially Predict Microsatellite Instability Status in Stage II Colorectal Cancer: A Preliminary Study

Abstract

To investigate whether quantitative radiomics features extracted from computed tomography (CT) can predict microsatellite instability (MSI) status in an Asian cohort of patients with stage Ⅱ colorectal cancer (CRC). This retrospective study was approved by our institutional review board, and the informed consent requirement was waived. From March 2016 to March 2018, 119 Chinese patients with pathologically confirmed stage Ⅱ CRC, available MSI status, and preoperative contrast-enhanced CT images were included in this study. Clinical and pathological information was obtained from the institutional database. The radiomics features were extracted from portal venous-phase CT images of segmented volumes of each entire primary tumor by using Matrix Laboratory (MATLAB), and radiomics signatures were generated using the least absolute shrinkage and selection operator logistic regression model. The minority group was balanced via synthetic minority over-sampling technique method. The association between the clinicopathologic characteristics and MSI status was assessed using Student's t test, Chi-square, or Fisher's exact test. The predictive efficacy of MSI status using radiomics features, clinical factors (including age, gender, CT-reported tumor location, differentiation degree of tumor, smoking history, hypertension history, family history of cancer, diabetes history, level of the Ki-67 expression, and laboratory analysis) and the combined models were evaluated, respectively. Predictive performance was evaluated by the area under receiver operating characteristic curve, accuracy, sensitivity, and specificity. MSI status was significantly associated with tumor location (p = 0.043); differentiation degree of tumor (p < 0.0001), hypertension history (p = 0.012), and the level of the Ki-67 expression (p = 0.015). Six radiomics features and 11 clinical characteristics were selected for predicting MSI status. The model that used the combination of clinical factors and radiomics features achieved the overall best performance than using either of the two features alone, yielding the area under the curve, sensitivity, and specificity of 0.752, 0.663, 0.841 for the combined model, 0.598, 0.371, 0.825 for clinical factors alone, and 0.688, 0.517, 0.858 for radiomics features alone, respectively. CT-based radiomic features of stage Ⅱ CRC are associated with MSI status. Combining analysis of clinical features and CT features could improve predictive efficacy and could potentially select the patients for individualized therapy noninvasively.