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

Condition(s) studied

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

Eligibility

Primary outcome measure(s)

• Diagnostic performance of CT-based deep learning models for T (T1-2 vs T3-4) and N (N- vs N+) staging. — Index preoperative CT through postoperative pathology report (within 90 days of surgery).
  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.

Trial sites (1)

More Assistance Publique - Hôpitaux de Paris trials in France