AI-Assisted Chest CT Interpretation Across the Lung Cancer Care Continuum

NCT07829640 | Not Yet Recruiting

• 1 other identifier: Radio-20260816
• Study Type: interventional
• Target: 15
• Locations: 1 country
• Sites: 1

Brief Summary

This investigator-initiated, single-center study consists of a retrospective artificial intelligence model-development stage and a prospective physician reader-study stage. Deidentified chest CT examinations acquired during routine clinical care between January 1, 2021, and December 31, 2024, will be used to develop, validate, and lock artificial intelligence models for lung cancer-related imaging tasks. In the prospective stage, approximately 12 to 15 physicians with experience in chest CT interpretation will complete two reading sessions in randomized order: unaided interpretation and AI-assisted interpretation. The sessions will be separated by a washout period of at least 4 weeks. The primary objective is to compare diagnostic performance between AI-assisted and unaided interpretation. Secondary objectives include reading time, diagnostic confidence, inter-reader agreement, and errors related to incorrect AI suggestions. All readings will be performed in an offline research environment. AI outputs will not be used for patient care, and the study will not add imaging examinations, treatment, or follow-up for patients.

Conditions

Lung Cancer

Keywords

Artificial Intelligence; Computed Tomography; Clinical Decision Support; Diagnostic Performance; Reader Study

Outcome Measures

Primary Outcomes (1)

• Difference in Diagnostic Accuracy Between AI-Assisted and Unaided Chest CT Interpretation

  Diagnostic accuracy will be calculated as the proportion of case-level interpretations that agree with the prespecified reference standard based on pathology or clinical follow-up. The paired difference in diagnostic accuracy between AI-assisted and unaided interpretation will be estimated across participating physicians and cases using a multi-reader multi-case analysis and reported with a 95% confidence interval. Higher accuracy indicates better diagnostic performance.

  At completion of the second reading session, after a washout period of at least 4 weeks

Study Arms (2)

Sequence AB: Unaided Then AI-Assisted Interpretation

EXPERIMENTAL

Participants first complete an unaided chest CT interpretation session. After a washout period of at least 4 weeks, they complete an AI-assisted interpretation session.

Device: Chest CT Artificial Intelligence Decision-Support System

Sequence BA: AI-Assisted Then Unaided Interpretation

EXPERIMENTAL

Participants first complete an AI-assisted chest CT interpretation session. After a washout period of at least 4 weeks, they complete an unaided interpretation session.

Device: Chest CT Artificial Intelligence Decision-Support System

Interventions

Chest CT Artificial Intelligence Decision-Support System DEVICE

A locked research-use-only artificial intelligence system analyzes deidentified chest CT images and provides decision-support outputs to participating physicians. The system is evaluated only in an offline reader-study environment and is not used for actual patient care.

Sequence AB: Unaided Then AI-Assisted Interpretation; Sequence BA: AI-Assisted Then Unaided Interpretation

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Physicians with education or training relevant to medical imaging
• Experience in interpreting chest CT examinations
• Ability to complete the required study training and both reading sessions
• Willingness to provide written informed consent and comply with study procedures

You may not qualify if:

• Failure to complete the required study training
• Inability or unwillingness to complete both reading sessions as required
• Any protocol deviation likely to compromise the validity of the reader-study data

Sponsors & Collaborators

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

Study Sites (1)

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

Wuhan, Hubei, 430022, China

Location

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Central Study Contacts

Lian Yang, PhD

CONTACT

+86 18986273791; yanglian@hust.edu.cn

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 14, 2026
• First Posted: September 21, 2026
• Study Start: September 1, 2026
• Primary Completion (Estimated): August 31, 2028
• Study Completion (Estimated): August 31, 2028
• Last Updated: September 21, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)