Clinical trial · Observational
Post-Neoadjuvant Treatment MRI Based AI System to Predict pCR for Rectal Cancer
A Post-Neoadjuvant Treatment MRI Based AI System to Predict Pathologic Complete Response for Patients With Rectal Cancer: A Multicenter, Prospective Clinical Study
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
In this study, investigators seek for a better way to identify the potential pathologic complete response (pCR) patients form non-pCR patients with locally advanced rectal cancer (LARC), based on their post-neoadjuvant treatment Magnetic Resonance Imaging (MRI) data. Previously, a post neoadjuvant treatment MRI based radiomics AI model had been constructed and trained. Here, the predictive power of this artificial intelligence system and expert radiologist to identify pCR patients from non-pCR LARC patients will be compared in this prospective, multicenter, back-to-back clinical study
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Rectal Cancer | Malignant Rectal Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (2)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| artificial intelligence prediction system | Procedure | — | UNRESOLVED |
| the radiologists | Procedure | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- patients will be evaluated by artificial intelligence system and expert radiologist
- description
- the patients with locally advanced rectal cancer (LARC) finished the neoadjuvant treatment, and not yet receive total mesorectum excision (TME) surgery will be enrolled. The post-neoadjuvant treatment MRI images features of each enrolled patients will be captured by the artificial intelligence system, and evaluated by experienced radiologists as well. Blind to the pathologic report of TME specimen, both approaches further respectively yield a predicted pathologic response to neoadjuvant treatment for each enrolled patient, shown as pCR or non-pCR.
- interventionNames
- Procedure: artificial intelligence prediction system
- Procedure: the radiologists
Primary outcomes (1)
- measure
- The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system and expert radiologists in prediction tumor response
- timeFrame
- baseline
- description
- The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system and expert radiologists in identifying the pCR candidates from non-pCR individuals among neoadjuvant chemotherapy or chemoradiotherapy treated LARC patients will be calculated respectively.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 75 Years
Show eligibility criteria text
Inclusion Criteria: * pathologically diagnosed as rectal adenocarcinoma * defined as clinical II-III staging (≥T3, and/or positive nodal status) without distant metastasis * receive neoadjuvant chemoradiotherapy or chemotherapy * pre- and post-neoadjuvant treatment MRI data obtained * receive total mesorectum excision (TME) surgery after neoadjuvant therapy and get the pathologic assessment of tumor response Exclusion Criteria: * with history of other cancer * insufficient imaging quality of MRI to delineate tumor volume or obtain measurements (e.g., lack of sequence, motion artifacts) * not completing neoadjuvant chemotherapy or chemoradiotherapy * tumor recurrence or distant metastasis during neoadjuvant treatment * not undergoing surgery resulting in lack of pathologic assessment of tumor response
References
Publications (0)
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