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

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Conditions studied: Pulmonary Nodules

In brief

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.

Key facts

Study ID

NCT07727122

Run by

Guangdong Provincial People's Hospital

People needed

3000

Starts

2026-07-01

Expected to finish

2028-12-01

Last updated by the study team

2026-07-27

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• 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.

You may not qualify if…

• 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.

Where it is running

• Second Affiliated Hospital of Army Medical University (Xinqiao Hospital) — Chongqing, Chongqing Municipality, China
• Guangdong Provincial People's Hospital — Guangzhou, Guangdong, China
• Zhujiang Hospital, Southern Medical University — Guangzhou, Guangdong, China
• Affiliated Hospital of Xuzhou Medical University — Xuzhou, Jiangsu, China
• Zhejiang University — Hangzhou, Zhejiang, China

Full record on ClinicalTrials.gov

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