NCT07406958Not yet recruiting
Advanced Classification of Colon Tumors From CT Scans Using Deep Learning for Optimized Treatment Decision-making.
Advanced Classification of Colon Tumors From CT Scans Using Deep Learning for Optimized Treatment Decision-making : a Multicenter Study
Source: ClinicalTrials.gov · Last updated 12 Feb 2026
Brief summary (registry, in English)
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
- Status (registry)
- Not yet recruiting
- Study type
- Observational
- Enrollment
- 1000 (estimated)
- First submitted
- 6 Feb 2026
- Start date
- 1 Feb 2026
- Primary completion date
- 29 Feb 2028
- Lead sponsor
- Assistance Publique - Hôpitaux de Paris
- Diseases
- Colorectal cancer
- NCT number
- NCT07406958
What you commit to
- Study type: Observational
- Planned participants: 1000
- From 1 Feb 2026 to the primary completion date, 29 Feb 2028
- 1 site in 1 country
- Visits and tests: ask the trial team, the registry does not list them
Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.
Who cannot take part
Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.
Prior treatment
- Patients who received neoadjuvant chemotherapy prior to surgery, identified by ICD-10 codes Z511 or Z512 recorded before the surgical act.
- Absence of usable CT imaging or anatomical pathology data linked to the surgical event.
Other
- These exclusions will be refined and confirmed through manual medical record review to ensure accuracy.
Who the study is looking for
- 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).
Age in the registry: 18 years to - · Sex: All · Does not accept healthy volunteers
Sites (1)
Sites in registry order. Pick your city on the disease page to sort them by distance.
| Site | City | Country | Site status |
|---|---|---|---|
| Departement of radiology, saint Antoin Hospital Coordinator of this site? Confirm its status | Paris | France | Unknown status |
This page is information, not medical advice. Discuss any trial with your doctor.