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.

Inclusion Criteria:

Adults who underwent colon resection surgery at an AP-HP hospital between 01/01/2017 and 01/11/2024, with:

A preoperative abdominopelvic CT scan available within 60 days prior to surgery.

A corresponding pathology report (anatomopathological results) available within 90 days post-surgery.

Colon resection identified by CCAM procedure codes:

HHFA002, HHFA004, HHFA005, HHFA006, HHFA008, HHFA009, HHFA010, HHFA014, HHFA017, HHFA018, HHFA021, HHFA022, HHFA023, HHFA024, HHFA026, HHFA028, HHFA029, HHFA030, HHFA031, HHFC040, HHFC296.

Confirmed diagnosis of colon tumor by ICD-10 code:

C18* (colonic neoplasms).

Exclusion Criteria:

Patients who received neoadjuvant chemotherapy prior to surgery, identified by ICD-10 codes Z511 or Z512 recorded before the surgical act.

These exclusions will be refined and confirmed through manual medical record review to ensure accuracy.

Absence of usable CT imaging or anatomical pathology data linked to the surgical event.

Adults (≥18 years) with pathologically confirmed colon cancer who underwent surgical resection at participating hospitals and had an available preoperative thoraco-abdominopelvic CT performed within the institution and linked to a pathology report containing TNM staging within 90 days of surgery. Imaging and reports extracted from institutional data warehouse; ambiguous or missing labels resolved by manual medical review.