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

NCT07406958 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 1000

Last updated 2026-02-12

No results posted yet for this study

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

Sponsors & Collaborators

  • Institut National de Recherche en Informatique et en Automatique

    collaborator OTHER

  • Assistance Publique - Hôpitaux de Paris

    lead OTHER

Principal Investigators

  • Quentin Vanderbecq, MD · Assistance Publique - Hôpitaux de Paris

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-02-28
Primary Completion
2028-02-29
Completion
2028-02-29

Countries

  • France

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07406958 on ClinicalTrials.gov