Clinical trial · Observational
RadioPathomics Artificial Intelligence Model to Predict Tumor Regression Grading in Locally Advanced Rectal Cancer
A RadioPathomics Integrated Artificial Intelligence System to Predict Tumor Regression Grading of Neoadjuvant Treatment in Locally Advanced Rectal Cancer: A Multicenter, Prospective and Observational 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 apply a radiopathomics artificial intelligence (AI) supportive model to predict neoadjuvant chemoradiotherapy (nCRT) response before the nCRT is delivered for the patients with locally advanced rectal cancer (LARC). The radiopathomics AI system predicts individual tumor regression grading (TRG) category based on each patient's radiopathomics features extracted from the Magnetic Resonance Imaging (MRI) and biopsy images. The predictive power to classify each patient into particular TRG category will be validated in this multicenter, prospective 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 (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- The prediction accuracy of the radiopathomics artificial intelligence model
- timeFrame
- baseline
- description
- The prediction accuracy of the radiopathomics artificial intelligence model for classifying each individual into particular AJCC/CAP TRG category will be calculated.
Secondary outcomes (3)
- measure
- The specificity of the radiopathomics artificial intelligence model
- timeFrame
- baseline
- description
- The specificity of the radiopathomics artificial intelligence model for classifying each individual into particular AJCC/CAP TRG category will be calculated.
- measure
- The sensitivity of the radiopathomics artificial intelligence model
- timeFrame
- baseline
- description
- The sensitivity of the radiopathomics artificial intelligence model for classifying each individual into particular AJCC/CAP TRG category will be calculated.
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 by enhanced Magnetic Resonance Imaging (MRI) * intending to receive or undergoing neoadjuvant concurrent chemoradiotherapy (5-fluorouracil based chemotherapy, given orally or intravenously; Intensity-Modulated Radiotherapy or Volume-Modulated Radiotherapy delivered at 50 gray (Gy) in gross tumor volume (GTV) and 45 Gy in clinical target volume (CTV) by 25 fractions) * intending to receive total mesorectum excision (TME) surgery after neoadjuvant therapy (not completed at the enrollment), and adjuvant chemotherapy * MRI (high-solution T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging are required) examination is completed before the neoadjuvant chemoradiotherapy * biopsy H\&E stained slides are available and scanned with high resolution before the neoadjuvant chemoradiotherapy 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) * insufficient imaging quality of biopsy slides imaging to delineate tumor volume or obtain measurements (e.g., tissue dissection, color anomaly) * incomplete neoadjuvant chemoradiotherapy * no surgery after neoadjuvant chemoradiotherapy resulting in lack of pathologic assessment of tumor response * tumor recurrence or distant metastasis during neoadjuvant chemoradiotherapy
References
Publications (0)
Data not yet available