Splicing-based Predictive Learning for Individual Chemotherapy Evaluation in Colorectal Cancer
This is not medical advice. AI-assisted translation — inaccuracies may occur. Always verify the original and consult your oncologist before taking any steps.
About the trial
Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Although adjuvant chemotherapy improves survival after curative resection, its efficacy varies widely among patients. The absence of reliable predictive biomarkers often leads to overtreatment or undertreatment. This study aims to develop a machine learning-based predictive model for adjuvant chemotherapy response using tumor-derived alternative splicing signatures. By integrating RNA-seq data, splicing isoform and clinical outcomes, this study seeks to identify molecular predictors of treatment response and recurrence risk after surgery.
Original English text from ClinicalTrials.gov
Who can (and can't) join
✓ Qualifies
- •Rak jelita grubego potwierdzony badaniem tkanki, w stopniu zaawansowania II lub III.
- •Pacjent otrzymał standardową chemioterapię po operacji guza.
- •Dostępny jest materiał z guza (z bloczka formaliny lub zamrożony) pobrany przed chemioterapią.
- •Posiadamy wystarczające dane o przebiegu choroby pacjenta po leczeniu (np. nawroty, przeżycie).
- •Pacjent ma od 18 do 80 lat.
✗ Disqualifies
- •Choroby zapalne jelit.
- •Materiały z guza są niskiej jakości lub pacjent nie wyraził zgody na wykorzystanie danych.
Simplified criteria — AI translation
Trial details
- Minimum age
- 18 Years
- Maximum age
- 80 Years
- Last updated (source)
- July 7, 2026
- Sex
- No restrictions
Therapies / drugs in trial
Locations (1)
City of Hope Medical Center
Duarte, United States
Trial contact
Ajay Goel, PhD
Contact information from ClinicalTrials.gov. Contact in English.
Share this trial
Data from ClinicalTrials.gov. AI-assisted translation, last sync: 7/8/2026.
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