An observational study in Pulmonary Nodules, sponsored by Guangdong Provincial People's Hospital. Not yet recruiting at 5 sites in China. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-07-27.
Sponsored by Guangdong Provincial People's Hospital · Observational
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.
Lung cancer is the leading cause of cancer-related morbidity and mortality worldwide. Low-dose CT (LDCT) screening improves early detection but generates a high prevalence of indeterminate pulmonary nodules and substantial false positives, leading to unnecessary invasive procedures and anxiety while still risking missed early cancers. Traditional radiologic assessment of pulmonary nodules relies on visual features and clinical risk factors, with limited performance and substantial reader variability, especially for small or subsolid nodules.
Recent advances in deep learning enable AI models to extract high-dimensional imaging features from CT scans and to predict nodule malignancy and risk stratification. Several commercial AI systems for pulmonary nodule assessment have been approved and deployed in clinical practice, and academic groups have proposed novel algorithms such as the multi-view coupled self-attention (MVCS) model. However, most prior studies have been single-center, retrospective, and used reference standards such as imaging follow-up or expert reading rather than biopsy pathology. Head-to-head comparisons of different AI models in prospective, multicenter real-world populations with pathological gold standard are lacking.
This study is a multicenter, prospective, diagnostic accuracy comparison of three purely imaging-based AI models-MVCS, LungDoc (Shukun Technology), and a United Imaging AI model-for predicting the malignancy of pulmonary nodules. Eligible patients are adults (≥18 years) with at least one pulmonary nodule ≤3 cm on CT, who undergo surgical or biopsy pathology with a definitive benign or malignant diagnosis, and with a CT-pathology interval ≤6 months. CT images in DICOM format will be collected using standardized acquisition parameters across centers and processed by the three AI models, which output continuous malignancy probabilities or suspicion scores. Investigators will be blinded to AI outputs.
The primary endpoint is the AUC for malignancy prediction for each model, and pairwise AUC comparisons using DeLong's test. Secondary endpoints include binary performance metrics (sensitivity, specificity, PPV, NPV, accuracy, F1 score) at model-native thresholds and optimal Youden index thresholds, as well as calibration (calibration curves, intercept, slope). Prespecified subgroup analyses will examine performance by age, sex, smoking status, nodule size, morphology, and study center, and random-effects methods will be used to assess center effects.
An exploratory aim will validate the MVCS model for predicting pathological invasion degree by classifying nodules into pre-invasive lesions (atypical adenomatous hyperplasia [AAH] / adenocarcinoma in situ [AIS]), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (ADC), using metrics such as multi-class accuracy, weighted F1 score, confusion matrix, Matthews correlation coefficient, and AUC for predefined binary sub-tasks. Another exploratory analysis will evaluate the MVCSN model, which incorporates CT images plus clinical and radiologic features, in a subset with complete data.
The study plans to enroll 3,000 pathologically confirmed pulmonary nodules across five centers in China over approximately 30 months. No experimental treatment is administered; all clinical management, imaging, and pathology follow standard of care. Risks are limited to those associated with clinically indicated pathology procedures (e.g., surgery or biopsy).
99 studies on the registry are indexed under Multiple Pulmonary Nodules; 42 are open to participants now.
This study's planned enrollment of 3,000 is above the median of 255 across 51 observational studies indexed under Multiple Pulmonary Nodules.
Browse Multiple Pulmonary Nodules studies →Guangdong Provincial People's Hospital is the lead sponsor of 237 studies on the registry; 102 are open to participants now.
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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
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
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 .
Exclusion Criteria:
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:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
A biopsy takes a small piece of tissue to examine. “Archival tissue” is a sample already taken, for example at diagnosis, and kept by the hospital.
Whether a stored sample will do depends on when it was taken, how much there is and what the study needs. Ask whether the study accepts your stored sample or needs a new biopsy.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Malignancy means cancer. Criteria about another or earlier malignancy ask whether you have had a different cancer, usually within a set number of years.
Some are commonly allowed, such as treated skin cancers other than melanoma and early cancers removed completely — check the exceptions this study lists.
Biopsy and archival tissue
A biopsy takes a small piece of tissue to examine. “Archival tissue” is a sample already taken, for example at diagnosis, and kept by the hospital.
Whether a stored sample will do depends on when it was taken, how much there is and what the study needs. Ask whether the study accepts your stored sample or needs a new biopsy.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Cancer (malignancy)
Malignancy means cancer. Criteria about another or earlier malignancy ask whether you have had a different cancer, usually within a set number of years.
Some are commonly allowed, such as treated skin cancers other than melanoma and early cancers removed completely — check the exceptions this study lists.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
This study has only one group.
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.
Time frame: At the time of availability of pathology results, up to 6 months after index chest CT
Sensitivity and Specificity for Malignancy Prediction
Sensitivity and specificity for classifying nodules as malignant vs benign for each AI model, using both (a) model-predefined thresholds and (b) optimal cut-off points determined by maximizing the Youden index. 95% confidence intervals will be reported; paired comparisons will use McNemar's test.
Time frame: 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)
PPV and NPV for malignancy prediction for each AI model at the same thresholds as above, with 95% confidence intervals.
Time frame: At the time of availability of pathology results, up to 6 months after index chest CT
Overall Diagnostic Accuracy and F1 Score
Proportion of correctly classified nodules (accuracy) and F1 score for each AI model in the binary task of malignant versus benign nodules, with 95% confidence intervals.
Time frame: At the time of availability of pathology results, up to 6 months after index chest CT
Calibration Metrics
Calibration performance of each AI model will be evaluated by calibration plots, calibration intercept, and calibration slope for predicted malignancy probability versus observed malignant proportion. Hosmer-Lemeshow goodness-of-fit test will be reported.
Time frame: At the time of availability of pathology results, up to 6 months after index chest CT
Multi-Class Accuracy of MVCS for Pathological Invasion Degree
For nodules with specific pathological subtypes (AAH, AIS, MIA, ADC), performance of the MVCS model in three-class classification: pre-invasive lesions (AAH-AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (ADC). Metrics include overall three-class accuracy and weighted F1 score.
Time frame: 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
Time frame: At the time of availability of pathology results, up to 6 months after index chest CT
Exploratory Performance of MVCSN Model for Malignancy Prediction
In a subset with complete clinical and imaging feature data, the MVCSN model (CT + clinical + imaging features) will be evaluated for malignancy prediction using similar metrics (AUC, sensitivity, specificity, PPV, NPV, accuracy, F1, calibration). Results will be used exploratorily and not for primary hypothesis testing.
Time frame: At the time of availability of pathology results, up to 6 months after index chest CT
Plan to share: Undecided
No publications or documents are linked to this record.
Start is estimated for Jul 2026; primary completion for Mar 2028, 20 months later.
Of 24 observational studies under Multiple Pulmonary Nodules, half reached primary completion within 35 months of starting (the median), counting those still running; a quarter within 8 and three quarters within 62. This study's planned 20 months is below that.
How these figures are worked out, and which studies are left out of them.
This study is not yet recruiting, as verified in Jul 2026. You cannot join it, but the record below documents what was studied.
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