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

Status

Not yet recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07639567

Enrollment

100000

Registered

2026-06-10

Start date

2026-07-01

Completion date

2028-07-01

Last updated

2026-07-31

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Tumor

Brief summary

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Healthy volunteers

Yes

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.

Design outcomes

Primary

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

Secondary

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

Countries

China

Contacts

CONTACTLian Yang

yanglian@hust.edu.cn18986273791

Outcome results