Clinical trial · Observational
Clinical Application Value of Deep Learning-Based "Opportunistic Screening" for Malignant Tumors on Routine Non-Contrast Chest-Abdomen-Pelvis CT
- 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 study aims to develop and validate a deep learning-based opportunistic multi-cancer screening system using routine non-contrast chest-abdomen-pelvis CT examinations, including CHANCE-Breast, CHANCE-Liver, CHANCE-Kidney, and CHANCE-Bladder, for the early detection of breast, liver, kidney, and bladder cancers. In addition, the study will assess a human-AI collaborative framework to determine its potential for improving cancer detection and reducing missed diagnoses in clinical practice.
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 |
|---|---|---|---|
| Tumor | Neoplasm | ALIAS | 0.90 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (3)
- label
- Positive Group / Malignant Cohort
- label
- Negative Control Group I / Benign Cohort
- label
- Negative Control Group II / Healthy Cohort
Primary outcomes (3)
- measure
- Accuracy
- timeFrame
- 1.5 years
- description
- Proportion of correct classifications
- measure
- Sensitivity
- timeFrame
- 1.5years
- description
- Proportion of true positive cases
- measure
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria: 1. Patients with a confirmed diagnosis of the target malignancy who received treatment at our institution; 2. Diagnostic-quality CT images without substantial metal or motion artifacts and with complete anatomical coverage of the target organ (breast, liver, kidney, or bladder); 3. Availability of complete pre-treatment non-contrast CT imaging data. Exclusion Criteria: 1. Non-diagnostic image quality; 2. Absence of a definitive reference-standard diagnosis; 3. Incomplete clinical or imaging data.
References
Publications (0)
Data not yet available