Clinical Application Value of Deep Learning-Based "Opportunistic Screening" for Malignant Tumors on Routine Non-Contrast Chest-Abdomen-Pelvis CT

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Tumor

• Study Type: Observational
• Enrollment (Estimated): 100000

Contacts and Locations

• Study Contact: Name: Lian Yang; Phone Number: 18986273791; Email: yanglian@hust.edu.cn

Study Locations

• China
  • Hubei
    • Wuhan, Hubei, China, 430000
      • Union Hospital，Tongji Medical College，Huazhong University of Science and Technology
      • Contact: Lian Yang; Phone Number: 18986273791; Email: yanglian@hust.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Patients with a confirmed diagnosis of the target malignancy who received treatment.

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort
Positive Group / Malignant Cohort
Negative Control Group I / Benign Cohort
Negative Control Group II / Healthy Cohort

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy	Proportion of correct classifications	1.5 years
Sensitivity	Proportion of true positive cases	1.5years
Specificity	Proportion of true negative cases	1.5years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Delta Sensitivity (AI-assisted vs. unassisted)	Absolute difference in sensitivity between AI-assisted and unassisted human readings	1.5years
Delta Specificity (AI-assisted vs. unassisted)	Absolute difference in specificity between AI-assisted and unassisted human readings	1.5years
Delta Accuracy (AI-assisted vs. unassisted)	Absolute difference in accuracy between AI-assisted and unassisted human readings	1.5years

Collaborators and Investigators

• Sponsor: Lian Yang

Study record dates

Study Major Dates

• Study Start (Estimated): July 1, 2026
• Primary Completion (Estimated): July 1, 2028
• Study Completion (Estimated): July 1, 2028

Study Registration Dates

• First Submitted: June 5, 2026
• First Submitted That Met QC Criteria: June 5, 2026
• First Posted (Actual): June 10, 2026

Study Record Updates

• Last Update Posted (Actual): July 31, 2026
• Last Update Submitted That Met QC Criteria: July 30, 2026
• Last Verified: June 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms
• Other Study ID Numbers: 2026-0527

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No