Ensemble learning for predicting microsatellite instability in colorectal cancer using pretreatment colonoscopy images and clinical data

Jia You¹Shenghan Zhang²Jianjie Zhang¹Yaru Chen¹Mengmeng Zhang¹Chungen Zhou¹Bin Jiang^1*

• ¹Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing, China
• ²Harvard Medical School Department of Biomedical Informatics, Boston, United States

Background: Microsatellite instability (MSI) is an important molecular biomarker in colorectal cancer (CRC), associated with favorable prognosis and response to immune checkpoint inhibitors. Conventional MSI testing, including immunohistochemistry (IHC) and polymerase chain reaction (PCR), is invasive, time-consuming, and resource-dependent, underscoring the need for non-invasive and automated alternatives. This study aimed to develop and evaluate an ensemble learning framework integrating pretreatment colonoscopy images and routine clinical data for non-invasive MSI prediction in CRC. Methods: In this retrospective study, patients with pathologically confirmed CRC and IHC-determined MSI status were included. Pretreatment colonoscopy images and routine clinical variables were collected. Five deep learning architectures (ResNet-50, EfficientNet, DenseNet, VGG-16, and Vision Transformer) were trained on image data, while four machine learning algorithms (Logistic Regression, Random Forest, Support Vector Machine, and Gradient Boosting) were trained on clinical data. The best-performing models from each modality were combined using a majority-voting ensemble. Model performance was assessed using accuracy, precision, recall, and area under the receiver operating characteristic curve (AUROC). Interpretability was evaluated using Gradient-weighted Class Activation Mapping (Grad-CAM) for image models and SHapley Additive exPlanations (SHAP) for clinical models. Results: Among 1,844 patients, VGG-16 achieved the best image-based performance (AUROC = 0.896, accuracy = 0.832, recall = 0.708). Logistic Regression outperformed other clinical models (AUROC = 0.898, accuracy = 0.825, recall = 0.828). The ensemble model integrating both modalities achieved AUROC = 0.886, precision = 0.920, and recall = 0.845, outperforming single-modality approaches. Conclusion: The proposed ensemble learning framework provides a non-invasive, interpretable, and accurate method for MSI prediction, offering potential to improve preoperative precision diagnostics and clinical decision-making in colorectal cancer.