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

NCT07727122 | Not Yet Recruiting

• 2 other identifiers: KY2026-585-022024ZD0529400
• Study Type: observational
• Target: 3,000
• Locations: 1 country
• Sites: 5

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.

Conditions

Pulmonary Nodules

Keywords

Pulmonary nodule; Artificial intelligence; Deep learning; Diagnostic accuracy

Outcome Measures

Primary Outcomes (1)

• Area Under the ROC Curve (AUC) for Malignancy Prediction

  For each pure imaging AI model (MVCS, LungDoc, United Imaging model), the AUC of the receiver operating characteristic curve for predicting malignant versus benign pulmonary nodules, based on continuous malignancy probabilities or suspicion scores. AUCs will be reported with 95% confidence intervals, and pairwise comparisons will be conducted using DeLong's test.

  At the time of availability of pathology results, up to 6 months after index chest CT

Secondary Outcomes (4)

• Sensitivity and Specificity for Malignancy Prediction

  At the time of availability of pathology results, up to 6 months after index chest CT

• Positive Predictive Value (PPV) and Negative Predictive Value (NPV)

  At the time of availability of pathology results, up to 6 months after index chest CT

• Overall Diagnostic Accuracy and F1 Score

  At the time of availability of pathology results, up to 6 months after index chest CT

• Calibration Metrics

  At the time of availability of pathology results, up to 6 months after index chest CT

Other Outcomes (3)

• Multi-Class Accuracy of MVCS for Pathological Invasion Degree

  At the time of availability of pathology results, up to 6 months after index chest CT

• Matthews Correlation Coefficient (MCC) and Confusion Matrix for Invasion Classification

  At the time of availability of pathology results, up to 6 months after index chest CT

• Exploratory Performance of MVCSN Model for Malignancy Prediction

  At the time of availability of pathology results, up to 6 months after index chest CT

Study Arms (1)

Single group

This study has only one group.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adults (≥18 years) undergoing routine clinical care at five tertiary hospitals in China who have at least one pulmonary nodule (≤3 cm) detected on chest CT and receive surgical or biopsy pathology with a definitive benign or malignant diagnosis. All participants have adequate CT image quality and complete clinicopathologic information for AI model validation

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

Sponsors & Collaborators

• Guangdong Provincial People's Hospital: lead

Study Sites (5)

Second Affiliated Hospital of Army Medical University (Xinqiao Hospital)

Chongqing, Chongqing Municipality, 400037, China

Location

Guangdong Provincial People's Hospital

Guangzhou, Guangdong, 510000, China

Location

Zhujiang Hospital, Southern Medical University

Guangzhou, Guangdong, 510000, China

Location

Affiliated Hospital of Xuzhou Medical University

Xuzhou, Jiangsu, 221006, China

Location

Zhejiang University

Hangzhou, Zhejiang, 310003, China

Location

MeSH Terms

Conditions

Multiple Pulmonary Nodules

Condition Hierarchy (Ancestors)

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

Central Study Contacts

Yijing Feng, PhD

CONTACT

8613650882360; yfeng@g.harvard.edu

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Vice President, Guangdong Provincial People's Hospital

Study Record Dates

• First Submitted: July 18, 2026
• First Posted: July 27, 2026
• Study Start: July 1, 2026
• Primary Completion (Estimated): March 1, 2028
• Study Completion (Estimated): December 1, 2028
• Last Updated: July 27, 2026

Record last verified: 2026-07

Locations

CN (5)