Clinical Application Value of Deep Learning-Based "Opportunistic Screening" for Malignant Tumors on Routine Non-Contrast Chest-Abdomen-Pelvis CT
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Tumor. Basic parameters: No limits · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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.
Primary outcome measures
- Accuracy [Time frame: 1.5 years]
- Sensitivity [Time frame: 1.5years]
- Specificity [Time frame: 1.5years]
Secondary outcome measures (3)
- Delta Sensitivity (AI-assisted vs. unassisted) [Time frame: 1.5years]
- Delta Specificity (AI-assisted vs. unassisted) [Time frame: 1.5years]
- Delta Accuracy (AI-assisted vs. unassisted) [Time frame: 1.5years]
Eligibility criteria
Inclusion criteria
- Patients with a confirmed diagnosis of the target malignancy who received treatment at our institution;
- Diagnostic-quality CT images without substantial metal or motion artifacts and with complete anatomical coverage of the target organ (breast, liver, kidney, or bladder);
- Availability of complete pre-treatment non-contrast CT imaging data.
Exclusion criteria
- Non-diagnostic image quality;
- Absence of a definitive reference-standard diagnosis;
- Incomplete clinical or imaging data.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- Union Hospital，Tongji Medical College，Huazhong University of Science and Technology — Wuhan
Identifiers
NCT: NCT07639567 · 2026-0527