Multicenter Prospective Validation of AI Models for Malignancy Risk Prediction in Pulmonary Nodules | NCT07727122

NCT07727122

Overview

NCT ID: NCT07727122
Lead Sponsor: Guangdong Provincial People's HospitalOTHER
Official Title: A Multicenter Prospective Diagnostic Accuracy Study of Three CT-Based Artificial Intelligence Models for Predicting Malignancy Risk in Pulmonary Nodules Using Pathology as the Gold Standard
Brief Summary: This multicenter prospective diagnostic accuracy study will compare the performance of three artificial intelligence (AI) models (MVCS, LungDoc, and a United Imaging AI model) for predicting the malignancy risk of pulmonary nodules on chest CT. All enrolled patients will have pulmonary nodules ≤3 cm on CT and a definitive postoperative or biopsy pathological diagnosis. The AI models will generate continuous malignancy probability scores based only on CT images. Pathology will serve as the gold standard.

The primary objective is to compare the area under the receiver operating characteristic curve (AUC) for malignancy prediction among the three AI models. Secondary objectives include comparison of sensitivity, specificity, positive and negative predictive values, accuracy, F1 score, and calibration. Exploratory analyses will evaluate the MVCS model for predicting pathological invasion degree (pre-invasive, minimally invasive, and invasive adenocarcinoma) and an extended MVCSN model that incorporates clinical and imaging features in a data-complete subset.

Status and Timeline

Status: Not yet recruiting
Last Update Posted: Jul 27, 2026Actual
Start Date: Jul 2026Estimated
Primary Completion Date: Mar 2028Estimated
Completion Date

Participation Criteria

Ages Eligible for Study: Adult (18-64)Older Adult (65+)
Sexes Eligible for Study: All
Accepts Healthy Volunteers?: No
Eligibility Criteria: Inclusion Criteria:

Age ≥ 18 years, any sex.

At least one pulmonary nodule detected on chest CT, with initial nodule diameter ≤ 3 cm.

The nodule undergoes surgical resection or biopsy with a definitive benign or malignant pathological diagnosis.

Time interval between CT examination and pathological examination ≤ 6 months.

Availability of complete CT imaging data in DICOM format with adequate image quality (no severe artifacts), meeting input requirements of all three AI models.

Availability of complete clinicopathologic information including histologic type and grade, with clear pathological diagnosis suitable as gold standard labels for AI validation.

-The patient (or legally authorized representative) is willing and able to sign written informed consent.

Exclusion Criteria:

Pathological results are unclear, inconclusive, or disputed; nodule nature or grade cannot be reliably determined.

The patient receives treatments between CT and pathology that may significantly alter nodule appearance (e.g., chemotherapy, radiotherapy, targeted therapy).

CT imaging data are incomplete (missing essential series) or have severe motion, metal, or other artifacts preventing accurate AI analysis.

Required metadata for any AI model are missing and cannot be imputed. History of other malignant tumors (malignancies other than the index non-small cell lung cancer).

Severe psychiatric illness, cognitive impairment, or other conditions that prevent cooperation with study-related procedures and follow-up.

Participation in another clinical study that may interfere with the results of this research.

-The patient or legal representative refuses participation.

Exclusion (Post-Enrollment / Removal from Analysis)

Participants already enrolled may be excluded from the analysis set if:

They are later found not to meet inclusion criteria or to meet exclusion criteria.

No usable data are available after enrollment.

Required AI model assessments are not completed (e.g., technical failure to generate outputs).

Critical data are missing, preventing contribution to primary analysis.

The interval between CT and pathology exceeds 6 months.

Study Plan

Study Type: Observational
Phase: No data available
Enrollment: 3,000Estimated
Primary Purpose: No data available
Allocation: No data available
Interventional Model: No data available
Masking: No data available

Contacts and Locations

Central Contacts: Yijing Feng, PhDContact
yfeng@g.harvard.edu8613650882360

Overview

Status and Timeline

Participation Criteria

Study Plan

Contacts and Locations

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