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.
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.
Non-diagnostic image quality;
Absence of a definitive reference-standard diagnosis;
Incomplete clinical or imaging data.
Patients with a confirmed diagnosis of the target malignancy who received treatment.
Cohort
Retrospective
Not listed
Accuracy
Proportion of correct classifications
Sensitivity
Proportion of true positive cases
Specificity
Proportion of true negative cases
Delta Sensitivity (AI-assisted vs. unassisted)
Absolute difference in sensitivity between AI-assisted and unassisted human readings
Delta Specificity (AI-assisted vs. unassisted)
Absolute difference in specificity between AI-assisted and unassisted human readings
Delta Accuracy (AI-assisted vs. unassisted)
Absolute difference in accuracy between AI-assisted and unassisted human readings
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Lian Yang
Lead sponsor
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Sponsor institution
This trial is not recruiting at the moment. You can still explore other options:
