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

N/ANot Yet RecruitingNCT07639567

Lian Yang100,000 enrolled

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.

Study Type

OBSERVATIONAL

Enrollment

100,000

Conditions

Tumor

Eligibility

Sex: ALLHealthy volunteers:

Medical Language ↔ Plain English

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.

Locations (1)

Union Hospital，Tongji Medical College，Huazhong University of Science and Technology

Wuhan, Hubei, China

Outcomes

Primary Outcomes

Accuracy

Proportion of correct classifications

Time frame: 1.5 years

Sensitivity

Proportion of true positive cases

Time frame: 1.5years

Specificity

Proportion of true negative cases

Time frame: 1.5years

Secondary Outcomes

Delta Sensitivity (AI-assisted vs. unassisted)

Absolute difference in sensitivity between AI-assisted and unassisted human readings

Time frame: 1.5years

Delta Specificity (AI-assisted vs. unassisted)

Absolute difference in specificity between AI-assisted and unassisted human readings

Time frame: 1.5years

Delta Accuracy (AI-assisted vs. unassisted)

Absolute difference in accuracy between AI-assisted and unassisted human readings

Time frame: 1.5years

Central Contacts

Lian Yang

CONTACT

18986273791 yanglian@hust.edu.cn

Data from ClinicalTrials.gov

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