arXiv:2502.04946v2 Announce Type: replace 
Abstract: Cancer remains one of the leading causes of morbidity and mortality worldwide. Comprehensive datasets that combine histopathological images with genetic and survival data across various tumour sites are essential for advancing computational pathology and personalised medicine. We present SurGen, a dataset comprising 1,020 H&E-stained whole-slide images (WSIs) from 843 colorectal cancer cases. The dataset includes detailed annotations for key genetic mutations (KRAS, NRAS, BRAF) and mismatch repair status, as well as survival data for 426 cases. We illustrate SurGen's utility with a proof-of-concept model that predicts mismatch repair status directly from WSIs, achieving a test area under the receiver operating characteristic curve of 0.8273. These preliminary results underscore the dataset's potential to facilitate research in biomarker discovery, prognostic modelling, and advanced machine learning applications in colorectal cancer and beyond. SurGen offers a valuable resource for the scientific community, enabling studies that require high-quality WSIs linked with comprehensive clinical and genetic information on colorectal cancer. Our initial findings affirm the dataset's capacity to advance diagnostic precision and foster the development of personalised treatment strategies in colorectal oncology. Data available online: https://doi.org/10.6019/S-BIAD1285.

SurGen هو مجموعة بيانات رائدة تحتوي على 1,020 صورة شريحة كاملة ملونة بصبغة H&E من 843 حالة سرطان قولوني مستعرض، والتي تجمع بين الصور النسيجية الحيوية والبيانات الجينية وبيانات البقاء. تعتبر هذه المورد ضرورية لتقدم علم الأمراض الحاسوبي والطب الشخصي، حيث تتيح للباحثين فهم تقدم السرطان ونتائج العلاج بشكل أفضل. من خلال توفير تعليقات تفصيلية، يعزز SurGen القدرة على تطوير علاجات مستهدفة وتحسين رعاية المرضى، مما يجعله مساهمة مهمة في أبحاث السرطان.

SurGen es un conjunto de datos innovador que presenta 1,020 imágenes de diapositivas completas teñidas con H&E de 843 casos de cáncer colorrectal, combinando imágenes histopatológicas cruciales con datos genéticos y de supervivencia. Este recurso es vital para avanzar en la patología computacional y la medicina personalizada, ya que permite a los investigadores comprender mejor la progresión del cáncer y los resultados del tratamiento. Al proporcionar anotaciones detalladas, SurGen mejora la capacidad de desarrollar terapias dirigidas y mejorar la atención al paciente, convirtiéndose en una contribución significativa a la investigación del cáncer.

SurGen est un ensemble de données révolutionnaire comprenant 1 020 images de lames entières colorées à l'H&E provenant de 843 cas de cancer colorectal, qui combine des images histopathologiques cruciales avec des données génétiques et de survie. Cette ressource est essentielle pour faire progresser la pathologie computationnelle et la médecine personnalisée, car elle permet aux chercheurs de mieux comprendre la progression du cancer et les résultats des traitements. En fournissant des annotations détaillées, SurGen améliore la capacité à développer des thérapies ciblées et à améliorer les soins aux patients, ce qui en fait une contribution significative à la recherche sur le cancer.

SurGen is a groundbreaking dataset featuring 1,020 H&E-stained whole-slide images from 843 colorectal cancer cases, which combines crucial histopathological images with genetic and survival data. This resource is vital for advancing computational pathology and personalized medicine, as it enables researchers to better understand cancer progression and treatment outcomes. By providing detailed annotations, SurGen enhances the ability to develop targeted therapies and improve patient care, making it a significant contribution to cancer research.

SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markers

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.

SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markers

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