arXiv:2511.15067v1 Announce Type: new 
Abstract: Precise prognostic stratification of colorectal cancer (CRC) remains a major clinical challenge due to its high heterogeneity. The conventional TNM staging system is inadequate for personalized medicine. We aimed to develop and validate a novel multiple instance learning model TDAM-CRC using histopathological whole-slide images for accurate prognostic prediction and to uncover its underlying molecular mechanisms. We trained the model on the TCGA discovery cohort (n=581), validated it in an independent external cohort (n=1031), and further we integrated multi-omics data to improve model interpretability and identify novel prognostic biomarkers. The results demonstrated that the TDAM-CRC achieved robust risk stratification in both cohorts. Its predictive performance significantly outperformed the conventional clinical staging system and multiple state-of-the-art models. The TDAM-CRC risk score was confirmed as an independent prognostic factor in multivariable analysis. Multi-omics analysis revealed that the high-risk subtype is closely associated with metabolic reprogramming and an immunosuppressive tumor microenvironment. Through interaction network analysis, we identified and validated Mitochondrial Ribosomal Protein L37 (MRPL37) as a key hub gene linking deep pathomic features to clinical prognosis. We found that high expression of MRPL37, driven by promoter hypomethylation, serves as an independent biomarker of favorable prognosis. Finally, we constructed a nomogram incorporating the TDAM-CRC risk score and clinical factors to provide a precise and interpretable clinical decision-making tool for CRC patients. Our AI-driven pathological model TDAM-CRC provides a robust tool for improved CRC risk stratification, reveals new molecular targets, and facilitates personalized clinical decision-making.

تم تطوير نموذج جديد للتعلم عبر الحالات المتعددة، TDAM-CRC، لتحسين التنبؤات التشخيصية في سرطان القولون والمستقيم (CRC) باستخدام صور الأنسجة الكاملة. تم تدريب هذا النموذج على مجموعة من 581 مريضًا وتم التحقق من صحته في مجموعة مستقلة تضم 1,031 مريضًا، مما أظهر أداءً تنبؤيًا متفوقًا مقارنةً بأنظمة التصنيف السريرية التقليدية. كما دمجت الدراسة بيانات متعددة الأوميات لتحسين قابلية تفسير النموذج وتحديد مؤشرات حيوية تنبؤية جديدة.

Se ha desarrollado un nuevo modelo de aprendizaje por instancias múltiples, TDAM-CRC, para mejorar la predicción pronóstica en el cáncer colorrectal (CRC) utilizando imágenes histopatológicas de diapositivas completas. Este modelo fue entrenado en una cohorte de 581 pacientes y validado en una cohorte externa independiente de 1,031 pacientes, demostrando un rendimiento predictivo superior en comparación con los sistemas de estadificación clínica convencionales. El estudio también integró datos multi-ómicos para mejorar la interpretabilidad del modelo e identificar nuevos biomarcadores pronósti…

Un nouveau modèle d'apprentissage par instances multiples, TDAM-CRC, a été développé pour améliorer la prédiction pronostique dans le cancer colorectal (CRC) en utilisant des images histopathologiques de diapositives entières. Ce modèle a été formé sur une cohorte de 581 patients et validé avec une cohorte indépendante de 1 031 patients, démontrant une performance prédictive supérieure par rapport aux systèmes de stadification clinique conventionnels. L'étude a également intégré des données multi-omiques pour améliorer l'interprétabilité du modèle et identifier de nouveaux biomarqueurs pronost…

A novel multiple instance learning model, TDAM-CRC, has been developed to enhance prognostic prediction in colorectal cancer (CRC) by utilizing histopathological whole-slide images. This model was trained on a cohort of 581 patients and validated with an independent cohort of 1,031 patients, demonstrating superior predictive performance compared to conventional clinical staging systems. The study also integrated multi-omics data to improve model interpretability and identify new prognostic biomarkers.

Deep Pathomic Learning Defines Prognostic Subtypes and Molecular Drivers in Colorectal Cancer

arXiv:2601.08336v1 Announce Type: new 
Abstract: Automatic integration of whole slide images (WSIs) and gene expression profiles has demonstrated substantial potential in precision clinical diagnosis and cancer progression studies. However, most existing studies focus on individual gene sequences and slide level classification tasks, with limited attention to spatial transcriptomics and patch level applications. To address this limitation, we propose a multimodal network, BioMorphNet, which automatically integrates tissue morphological features and spatial gene expression to support tissue classification and differential gene analysis. For considering morphological features, BioMorphNet constructs a graph to model the relationships between target patches and their neighbors, and adjusts the response strength based on morphological and molecular level similarity, to better characterize the tumor microenvironment. In terms of multimodal interactions, BioMorphNet derives clinical pathway features from spatial transcriptomic data based on a predefined pathway database, serving as a bridge between tissue morphology and gene expression. In addition, a novel learnable pathway module is designed to automatically simulate the biological pathway formation process, providing a complementary representation to existing clinical pathways. Compared with the latest morphology gene multimodal methods, BioMorphNet's average classification metrics improve by 2.67%, 5.48%, and 6.29% for prostate cancer, colorectal cancer, and breast cancer datasets, respectively. BioMorphNet not only classifies tissue categories within WSIs accurately to support tumor localization, but also analyzes differential gene expression between tissue categories based on prediction confidence, contributing to the discovery of potential tumor biomarkers.

تم اقتراح شبكة متعددة الوسائط جديدة تُدعى BioMorphNet لتحسين تصنيف الأنسجة وتحليل صور الشرائح الكاملة من خلال دمج الميزات الشكلية والتعبير الجيني المكاني، مما يعالج القيود في منهجيات البحث الحالية حول السرطان.

Se ha propuesto una nueva red multimodal llamada BioMorphNet para mejorar la clasificación de tejidos y el análisis de imágenes de diapositivas completas mediante la integración de características morfológicas y expresión génica espacial, abordando las limitaciones de las metodologías actuales en la investigación del cáncer.

Un nouveau réseau multimodal nommé BioMorphNet a été proposé pour améliorer la classification des tissus et l'analyse des images à grande échelle en intégrant des caractéristiques morphologiques et l'expression génique spatiale, répondant aux limitations des méthodologies de recherche sur le cancer actuelles.

A new multimodal network named BioMorphNet has been proposed to enhance tissue classification and whole-slide image analysis by integrating morphological features and spatial gene expression, addressing limitations in current cancer research methodologies.

Deep Pathomic Learning Defines Prognostic Subtypes and Molecular Drivers in Colorectal Cancer

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