Advanced Classification of Colon Tumors From CT Scans Using Deep Learning for Optimized Treatment Decision-making.

Overview

This study aims to improve the classification of colon tumors using deep learning models trained on CT scans, specifically to distinguish between T1-T2 vs. T3-T4 stages and N- vs. N+ lymph node involvement. This classification is critical to guide preoperative treatment such as chemotherapy or immunotherapy. Given the limited accuracy of radiologists in current staging practice, automated image-based AI tools could enhance diagnostic precision and reproducibility, leading to more personalized and effective treatment planning. The investigator will develop and validate convolutional and transformer-based deep learning models using a large annotated dataset from multiple centers. Secondary objectives include fine-grained staging (T1 to T4), subgroup-specific models (MSS vs MSI), and predictive models for surgical

This is a retrospective, non-interventional, observational study evaluating the use of deep learning methods to improve preoperative CT-based TNM staging in patients with colon cancer. The study is conducted across multiple sites within the AP-HP hospital network (Paris, France) and uses data extracted from the institutional Health Data Warehouse. Radiologic accuracy in assessing tumor stage (T) and lymph node status (N) remains limited, despite being critical for selecting neoadjuvant treatments. Artificial intelligence models trained on annotated imaging data may provide more consistent, reproducible, and accurate classification. The study cohort includes adult patients who underwent colon resection between January 2017 and November 2024, with a preoperative CT scan and corresponding pathology report. Eligible cases are identified using standardized diagnostic (ICD-10) and procedural (CCAM) codes. Imaging and clinical data are de-identified prior to analysis. Several AI model architectures will be tested, including 3D convolutional neural networks and transformer-based approaches. CT scans will be pre-processed using standard pipelines; pathology labels will be extracted using natural language processing (NLP) techniques or manual review when needed. Model performance will be assessed through cross-validation and evaluated using AUC, F1-score, sensitivity, and specificity. Exploratory analyses will include fine-grained tumor staging and the potential prognostic value of image-based features for clinical outcomes such as survival. No study-related procedures are performed. All analyses are conducted on existing data, in compliance with French data protection and ethical regulations.