Journal Article

Non-invasive prediction of microsatellite instability in colorectal cancer by a genetic algorithm–enhanced artificial neural network–based CT radiomics signature

Liu, Zaiyi, Chen, Xin, Liu, Liu, Zhang, Yuan, Chen, Xiaobo, He, Lan, Li, Suyun, Li, Qingshu, Huang, Yanqi, Mao, Yun

2023

Overview

Objective The stratification of microsatellite instability (MSI) status assists clinicians in making treatment decisions for colorectal cancer (CRC) patients. This study aimed to establish a CT-based radiomics signature to predict MSI status in patients with CRC. Methods A total of 837 CRC patients who underwent preoperative enhanced CT and had available MSI status data were recruited from two hospitals. Radiomics features were extracted from segmented tumours, and a series of data balancing and feature selection strategies were used to select MSI-related features. Finally, an MSI-related radiomics signature was constructed using a genetic algorithm–enhanced artificial neural network model. Combined and clinical models were constructed using multivariate logistic regression analyses by integrating the clinical factors with or without the signature. A Kaplan–Meier survival analysis was conducted to explore the prognostic information of the signature in patients with CRC. Results Ten features were selected to construct a signature which showed robust performance in both the internal and external validation cohorts, with areas under the curves (AUC) of 0.788 and 0.775, respectively. The performance of the signature was comparable to that of the combined model (AUCs of 0.777 and 0.767, respectively) and it outperformed the clinical model constituting age and tumour location (AUCs of 0.768 and 0.623, respectively). Survival analysis demonstrated that the signature could stratify patients with stage II CRC according to prognosis (HR: 0.402, p = 0.029). Conclusions This study built a robust radiomics signature for identifying the MSI status of CRC patients, which may assist individualised treatment decisions. Key Points • Our well-designed modelling strategies helped overcome the problem of data imbalance caused by the low incidence of MSI. • Genetic algorithm–enhanced artificial neural network–based CT radiomics signature can effectively distinguish the MSI status of CRC patients. • Kaplan–Meier survival analysis demonstrated that our signature could significantly stratify stage II CRC patients into high- and low-risk groups.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Algorithms / Artificial neural networks / Cancer / Colorectal cancer / Colorectal Neoplasms - diagnostic imaging / Colorectal Neoplasms - genetics / Decisions / Diagnostic Radiology / Feature extraction / Genetic algorithms / Health services / Humans / Imaging / Imaging Informatics and Artificial Intelligence / Internal Medicine / Interventional Radiology / Medical prognosis / Medicine / Medicine & Public Health / Microsatellite Instability / Neural networks / Neural Networks, Computer / Neuroradiology / Patients / Radiology / Radiomics / Regression analysis / Retrospective Studies / Risk groups / Robustness / Survival / Survival analysis / Tomography, X-Ray Computed / Tumors / Ultrasound