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

NCT07406958 | Not Yet Recruiting

• 1 other identifier: APHP251408
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief Summary

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

Conditions

Colonic Neoplasm

Keywords

Colon cancer; deep learning; CT scan; TNM classification; radiology; AI; machine learning; tumor staging; colorectal neoplasms

Outcome Measures

Primary Outcomes (1)

• Diagnostic performance of CT-based deep learning models for T (T1-2 vs T3-4) and N (N- vs N+) staging.

  Area under the ROC curve (AUC) and F1-score of deep learning models in (a) classifying early vs advanced T stage (T1-2 vs T3-4) and (b) nodal status (N- vs N+) compared with pathology gold standard. Sensitivity, specificity, PPV, NPV reported as supportive metrics; model performance compared to historical radiologist benchmarks when available.

  Index preoperative CT through postoperative pathology report (within 90 days of surgery).

Secondary Outcomes (3)

• Detection performance for T4 tumors on preoperative CT.

  Index CT to pathology confirmation (≤90 days post-surgery).

• Multiclass T-stage classification accuracy (T1, T2, T3, T4).

  Index CT to pathology confirmation (≤90 days).

• Prognostic value of CT-derived model features for clinical outcomes and survival.

  From index CT to last follow-up (up to 5 years, or maximum available follow-up in EHR).

Study Arms (2)

Tran-validation group

Adults with pathologically confirmed colon cancer who underwent preoperative thoraco-abdominopelvic CT and subsequent colectomy at participating centers; cases meeting imaging and pathology quality criteria used for model training and internal cross-validation. AI / Deep Learning CT Analysis: Retrospective analysis of existing preoperative CT scans and linked pathology/clinical data to develop and evaluate automated staging models. No experimental drug, device, or procedure is administered.

Test group

Distinct subset (aleatory taken at the beginning of model training) of eligible colon cancer cases withheld from model development to provide independent performance validation for T and N classification models and exploratory secondary analyses.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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.

You may qualify if:

• 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).

You may not qualify if:

• 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.

Sponsors & Collaborators

• Assistance Publique - Hôpitaux de Paris: lead
• Institut National de Recherche en Informatique et en Automatique: collaborator

Study Sites (1)

Departement of radiology, saint Antoin Hospital

Paris, 75012, France

Location

MeSH Terms

Conditions

Colonic Neoplasms; Colorectal Neoplasms

Condition Hierarchy (Ancestors)

Intestinal Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Colonic Diseases; Intestinal Diseases; Rectal Diseases

Study Officials

• Quentin Vanderbecq, MD

  Assistance Publique - Hôpitaux de Paris

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Quentin Vanderbecq, MD

CONTACT

00 33 1 49 28 20 00; quentin.vanderbecq@aphp.fr

Mathilde WAGNER, MD,PhD

CONTACT

00 33 1 49 28 20 00; mathilde.wagner@aphp.fr

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 6, 2026
• First Posted: February 12, 2026
• Study Start: February 1, 2026
• Primary Completion (Estimated): February 1, 2028
• Study Completion (Estimated): February 1, 2028
• Last Updated: February 12, 2026

Record last verified: 2025-02

Locations

FR (1)