Development and Validation of a Machine Learning Model Based on Clinical and MRI Features for Predicting Pathological Complete Response in Rectal Cancer Following Neoadjuvant Chemoradiotherapy
Sponsor: Peking University People's Hospital
Trial Details
| Condition | Rectal Cancers, Pathological Complete Response, Neoadjuvant Chemoradiotherapy, Machine Learning |
| Intervention | No interventions |
| Status | Active |
| Participants | 320 |
| Est. Completion | 25 Apr 2026 |
| Last Updated | 3 Apr 2026 |
Primary Outcome
Pathological Complete Response (pCR) defined by Tumor Regression Grade (TRG)
Eligibility Criteria
Inclusion Criteria
Inclusion Criteria: 1. Patients with histopathologically confirmed rectal adenocarcinoma; 2. Clinical stage cT3-4, or cN+, or M1 advanced rectal cancer; 3. Received standardized neoadjuvant chemoradiotherapy or neoadjuvant chemotherapy; 4. Underwent total mesorectal excision (TME) after the completion of neoadjuvant therapy, with complete postoperative pathological data available. Exclusion Criteria: 1. Previous history of other malignant tumors; 2. Incomplete clinical data; 3. Underwent emergency surgery during nCRT; 4. Complicated with systemic infection or hematological diseases.
Trial Sites
Countries
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