Clinical trial · Observational
Radiomics-based Artificial Intelligence System to Predict Neoadjuvant Treatment Response in Rectal Cancer
Predicting Neoadjuvant Chemoradiotherapy Response by Radiomics-based Artificial Intelligence System 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 utilize a radiomics prediction model to predict the tumor response to neoadjuvant chemoradiotherapy (nCRT) before the nCRT is administered for patients with locally advanced rectal cancer (LARC). Previously, the radiomics prediction model has been constructed based on the radiomics features extracted from pretreatment Magnetic Resonance Imaging (MRI) in the training set, and optimized in the external validation set. The predictive power of this radiomics prediction model to discriminate the pathologic complete response (pCR) patients from non-pCR individuals, will be further verified in this prospective, multicenter 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 radiomics prediction model
- timeFrame
- baseline
- description
- The prediction accuracy of the MRI radiomics-based artificial intelligence prediction system for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.
Secondary outcomes (3)
- measure
- The specificity of the radiomics prediction model
- timeFrame
- baseline
- description
- The specificity of the MRI radiomics-based artificial intelligence prediction system for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.
- measure
- The sensitivity of the radiomics prediction model
- timeFrame
- baseline
- description
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 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) * 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
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