Clinical trial · Observational
Combination of CT and Ultrasound Radiomics Combined With Liquid Biopsy to Predict Neoadjuvant Chemotherapy Response in Patients With Locally Advanced Gastric Cancer
Combination of CT and Ultrasound Radiomics Combined With Liquid Biopsy to Predict Neoadjuvant Chemotherapy Response in Patients With Locally Advanced Gastric Cancer: A Prospective Study
NCT07697079CI-TRIAL-00117812not yet recruitingClinicalTrials.gov clinicaltrialsProvenance
- 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)
This prospective cohort study aims to construct an artificial intelligence (AI)-derived predictive model for neoadjuvant chemotherapy response prediction in patients with locally advanced gastric cancer based on preoperative ultrasound (US), computed tomography (CT) images and liquid biopsy. Additionally, we explore the potential biological mechanisms behind this model.
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 |
|---|---|---|---|
| Gastric Cancer | Malignant Gastric Neoplasm | CURATED_BROADER | 0.80 |
Interventions
Interventions (0)
Data not yet available
No intervention recorded.
Design
Arms and outcomes
Arms (2)
- label
- Good pathological response
- description
- Patients with locally advanced gastric cancer achieved TRG grade 0-1 after the neoadjuvant chemotherapy
- label
- Poor pathological response
- description
- Patients with locally advanced gastric cancer achieved TRG grade 2-3 after the neoadjuvant chemotherapy
Primary outcomes (1)
- measure
- Accuracy of pathological response to neoadjuvant chemotherapyin patients with locally advanced gastric cancer models
- timeFrame
- The pathological response prediction model will be assessed immediately after its development.
- description
- This prospective study will collect contrast-enhanced abdominal CT and ultrasound images, as well as peripheral blood samples, from 300 patients with locally advanced gastric cancer (LAGC) prior to neoadjuvant chemotherapy. Using deep learning and machine learning algorithms, we will construct a tumor regression grade (TRG)-oriented model to predict pathological response to treatment. TRG classification is defined in accordance with the NCCN Guidelines (Version 4, 2021): TRG 0-1 indicates favorable response; TRG 2-3 poor response. The diagnostic accuracy and stability of the model will be evaluated, with performance quantified via the AUC and precision-recall curve.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 80 Years
Show eligibility criteria text
Inclusion Criteria: 1. Capable of understanding the study and voluntarily signing the written informed consent form (ICF) prior to any study-specified research procedures. 2. Aged ≥18 and ≤80 years old at the time of ICF signing. 3. Pathologically confirmed locally advanced gastric cancer (LAGC, cT2NxM0-cT4NxM0) with clinical indications for neoadjuvant chemotherapy. 4. Completion of gastrointestinal contrast-enhanced ultrasound and contrast-enhanced abdominal CT before neoadjuvant chemotherapy. 5. Provision of peripheral blood samples before chemotherapy (for genetic and protein detection). 6. Availability of postoperative pathological specimens for TRG grading after standardized neoadjuvant chemotherapy. 7. Willing and able to comply with all study protocol requirements. Exclusion Criteria: 1. Diagnosis of non-primary gastric cancer. 2. Incomplete imaging data, failure to collect peripheral blood samples, or substandard sample quality. 3. Discontinued chemotherapy, modified treatment regimen, or lack of complete postoperative pathological assessment. 4. Unavailable follow-up data precluding evaluation of chemotherapy response. 5. Concurrent participation in another clinical trial; or any other conditions judged by investigators to warrant subject withdrawal, including severe comorbidities requiring simultaneous treatment (psychiatric disorders included), alcohol dependence, substance abuse, or familial/social factors that may compromise subject safety or treatment compliance.
References
Publications (0)
Data not yet available
No reference posted for this study.