An Ensemble Deep Learning-Based Approach for Microsatellite Instability Prediction in Gastrointestinal Cancer | ||
| International Journal of Telecommunications | ||
| Volume 05, Issue 02, July 2025, Pages 1-16 PDF (1.98 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijt.2025.412498.1131 | ||
| Authors | ||
| youmna Ahmed radwan* 1; Hossam El- din Moustafa2; Adel F. Mohamed Moustafa3; Ahmed Saleh1 | ||
| 1Computers Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt | ||
| 2Department of Electronics and Communications Engineering at the Faculty of Engineering, Mansoura Uni-versity | ||
| 3Oncology Center, Mansoura University, Mansoura, Egypt. | ||
| Abstract | ||
| Microsatellite instability (MSI) is considered a significant biomarker for gastrointestinal (GI) cancer prognosis and treatment planning. Traditionally, molecular assays such as polymerase chain reaction (PCR) testing and immunohistochemistry (IHC) have been used to determine MSI status. Despite their effectiveness, these methods are labor-intensive and time-consuming .MSI tumors, in particular, are known to respond better to immunotherapy due to their high mutational burden and increased immunogenicity, making accurate MSI assessment vital for selecting appropriate treatments. The ability to classify MSI status directly from histopathological images offers a faster and less invasive alternative that could greatly enhance clinical decision-making and help guide personalized immunotherapy strategies. In this study, an average ensemble classifier combining two pretrained convolutional neural network (CNN) architectures—InceptionResNet-v2 and Xception—has been developed to improve the robustness and accuracy of MSI prediction from histological images. The suggested ensemble model outperformed earlier methods with an accuracy of 96.97% and an area under the curve (AUC) of 99.57%. These results demonstrate that ensemble-based deep learning models can accurately and reliably classify MSI from histological slides, facilitating more personalized treatment decisions and ultimately improving outcomes for patients receiving immunotherapy. | ||
| Keywords | ||
| Microsatellite instability; Gastrointestinal cancer; Transfer learning; ensemble model | ||
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