Predictive model using CT scans, blood tests, and pathology to forecast pancreatic cancer outcomes
A Study on a Predictive Model for Efficacy and Prognosis of Pancreatic Carcinoma Based on Multimodal Data
This project will try to see if combining CT scans, blood tests, and pathology reports can better predict outcomes for adults who had surgery for pancreatic cancer.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 1030 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | Shanghai Zhongshan Hospital Academic / other |
| Locations | 1 site (Shanghai, Shanghai Municipality) |
| Trial ID | NCT07561814 on ClinicalTrials.gov |
What this trial studies
This observational project will use preoperative and postoperative contrast-enhanced CT images, routine blood tests, and pathology reports from patients who underwent surgery for pancreatic cancer to build a multimodal predictive model using deep learning methods. Researchers will extract clinical and imaging features from existing medical records without adding tests or treatments and will conduct telephone follow-up about every three months for up to three years to capture outcomes. The main goals are to improve estimates of overall survival and risk of recurrence by integrating multiple data types. Data quality checks and exclusion of cases with excessive missing data or extreme outliers will be applied before modeling.
Who should consider this trial
Good fit: Adults (age ≥18) with histologically or cytologically confirmed pancreatic cancer who had surgical treatment at a participating center and have available pre- and postoperative CT images, clinical data, pathology reports, and blood test results are ideal candidates.
Not a fit: Patients without the required imaging or pathology data, those with other severe comorbidities, minors, pregnant or breastfeeding women, or people unable to consent or complete follow-up are unlikely to benefit.
Why it matters
Potential benefit: If successful, the model could help clinicians give more accurate survival and recurrence estimates and better personalize follow-up and treatment planning.
How similar studies have performed: Previous radiomics and multimodal machine-learning studies in pancreatic and other cancers have shown promising but mixed results, so the approach is supported by preliminary evidence but not yet standard practice.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Age ≥ 18 years. * Histologically or cytologically confirmed diagnosis of pancreatic cancer. * Availability of preoperative and postoperative contrast-enhanced CT images at the participating center; patient records include corresponding clinical data, postoperative pathological reports, and preoperative and postoperative blood test results. * Underwent surgical treatment for pancreatic cancer at a participating center of this study. Exclusion Criteria: * Age \< 18 years (minors). * Presence of other severe diseases (e.g., severe liver or kidney failure, cardiovascular or cerebrovascular diseases, malignancies other than pancreatic cancer); pregnant or breastfeeding women; individuals with mental illness; * patients unable to comply with follow-up or provide informed consent. * Presence of significant outliers (e.g., laboratory values exceeding 10 times the normal range without clinically reasonable explanation) or samples with excessive missing data.
Where this trial is running
Shanghai, Shanghai Municipality
- Zhongshan Hospital Affiliated to Fudan University — Shanghai, Shanghai Municipality, China (Recruiting)
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.