Globally, colorectal cancer (CRC) is the third most commonly diagnosed cancer in males and the second in females. Rectal cancer (RC) accounts for about 28% of all newly diagnosed CRC cases. The treatment of choice for locally advanced RC is a combination of surgical resection and chemotherapy and/or radiotherapy. These patients can potentially be cured, but the clinical outcome depends on the tumor biology. Microsatellite instability (MSI) is an important biomarker in CRC, with crucial diagnostic, prognostic, and predictive implications. It is important to develop a noninvasive, repeatable, and reproducible method to reflect the microsatellite status. Magnetic resonance imaging (MRI) has been recommended as the preferred imaging examination for RC in clinical practice by both the National Comprehensive Cancer Network and the European Society for Medical Oncology guidelines. T2WI is the core sequence of MRI scanning protocol for RC. Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research.
We proposed a hypothesis: A simple radiomics model based on only T2WI images can accurately evaluate the MSI status of RC preoperatively.
To develop a radiomics model based on T2WI images for accurate preoperative diagnosis the MSI status of RC.
All patients with RC were retrospectively enrolled. The dataset was randomly split into training cohort (70% of all patients) and testing cohort (30% of all patients). The radiomics features will be extracted from T2WI–MR images of the entire primary tumor region. Least absolute shrinkage and selection operator was used to select the most predictive radiomics features. Logistic regression models were constructed in the training/validation cohort to discriminate the MSI status using clinical factors, radiomics features, or their integration. The diagnostic performance of these 3 models was evaluated in the testing cohort based on their area under the curve, sensitivity, specificity, and accuracy.