Four-class classification of tumor-induced colorectal obstruction histopathology: A ResNet–mamba-mased study on cellular interaction pattern recognition

Abstract

This study aimed to develop a deep learning model to recognize cell interaction patterns in pathological slides of malignant bowel obstruction. The model classifies lesions into four categories—normal mucosa, serrated lesions, adenomas, and adenocarcinomas—and evaluates its diagnostic utility in tumor-associated obstruction. Pathological slides from patients with tumor-induced intestinal obstruction (TICO) were retrospectively collected from First Affiliated Hospital of Bengbu Medical University and annotated into four histological categories: normal, serrated lesions, adenomas, and adenocarcinomas. The proposed deep learning framework combines a residual convolutional network with a bidirectional state-space module (SSM), enabling multiscale feature extraction through convolution and down-sampling, while modeling the spatiotemporal dynamics of cellular interactions. The model was designed to learn spatial and structural characteristics of cell interactions—such as glandular organization, intercellular spacing, and nuclear density—across different lesion types. Grad-CAM was used to visualize attention regions and assess consistency between model focus and pathological features. However, Grad-CAM was used solely for interpretability and not clinical validation; no expert verification of the visualizations has been performed. On an independent Chaoyang test set, the model achieved a validation accuracy of 85% and a macro-F1 score of 0.843 (95% CI: 0.829–0.857), showing only a 3% decline from training accuracy (88%), thus demonstrating strong generalizability. In addition, we calculated 95% confidence intervals using 1,000 bootstrap resamples and applied both the DeLong test and McNemar test to compare the performance of our model with baseline methods. The results demonstrated statistically significant improvements (P < 0.05) in Accuracy, Macro-F1, and ROC-AUC, thereby further strengthening the reliability of our conclusions. The recall for adenocarcinoma (Class 3) reached 88%, while Classes 0–2 (normal, serrated lesions, and adenomas) ranged from 78% to 83%. These results highlight the impact of sample imbalance and morphological similarity, which will be addressed in future work through Focal Loss reweighting and detailed error analysis. Grad-CAM visualizations identified regions of glandular disruption and abnormal nuclear density, aligning with WHO-2022 diagnostic criteria and enhancing model interpretability. Overall performance is comparable to state-of-the-art gastrointestinal pathology AI systems from recent years, offering rapid and quantitative diagnostic support in emergency pathology settings. The proposed deep learning model effectively distinguishes four categories of tumor-associated colorectal lesions, demonstrating strong diagnostic potential. Limitations include: (i) all data were retrospectively collected from a single center, without external multicenter validation. Differences in population composition, scanning platforms, and staining batches may affect the model’s external generalizability; future studies will prioritize the inclusion of multicenter datasets to systematically evaluate the robustness and applicability of the model under diverse clinical conditions; (ii) the model has so far been assessed only in an offline environment, lacking prospective clinical validation within real-world workflows. Nonetheless, this model provides an important foundation for the early diagnosis of TICO, the formulation of personalized treatment strategies, and the advancement of pathological image analysis technologies.