NCT07727122Not yet recruiting
Multicenter Prospective Validation of AI Models for Malignancy Risk Prediction in Pulmonary Nodules
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
Source: ClinicalTrials.gov · Last updated 27 Jul 2026
Brief summary (registry, in English)
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 (registry)
- Not yet recruiting
- Study type
- Observational
- Enrollment
- 3000 (estimated)
- First submitted
- 18 Jul 2026
- Start date
- Jul 2026
- Primary completion date
- Mar 2028
- Lead sponsor
- Guangdong Provincial People's Hospital
- NCT number
- NCT07727122
What you commit to
- Study type: Observational
- Planned participants: 3000
- From Jul 2026 to the primary completion date, Mar 2028
- 5 sites in 1 country
- Visits and tests: ask the trial team, the registry does not list them
Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.
Who cannot take part
Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.
Prior treatment
- The patient receives treatments between CT and pathology that may significantly alter nodule appearance (e.g., chemotherapy, radiotherapy, targeted therapy).
Other illness
- 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:
- Required AI model assessments are not completed (e.g., technical failure to generate outputs).
Other
- Pathological results are unclear, inconclusive, or disputed; nodule nature or grade cannot be reliably determined.
- CT imaging data are incomplete (missing essential series) or have severe motion, metal, or other artifacts preventing accurate AI analysis.
- They are later found not to meet inclusion criteria or to meet exclusion criteria.
- No usable data are available after enrollment.
- Critical data are missing, preventing contribution to primary analysis.
- The interval between CT and pathology exceeds 6 months.
Who the study is looking for
- 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.
Age in the registry: 18 years to - · Sex: All · Does not accept healthy volunteers
Sites (5)
Sites in registry order. Pick your city on the disease page to sort them by distance.
| Site | City | Country | Site status |
|---|---|---|---|
| Second Affiliated Hospital of Army Medical University (Xinqiao Hospital) Coordinator of this site? Confirm its status | Chongqing | China | Unknown status |
| Zhujiang Hospital, Southern Medical University Coordinator of this site? Confirm its status | Guangzhou | China | Unknown status |
| Guangdong Provincial People's Hospital Coordinator of this site? Confirm its status | Guangzhou | China | Unknown status |
| Zhejiang University Coordinator of this site? Confirm its status | Hangzhou | China | Unknown status |
| Affiliated Hospital of Xuzhou Medical University Coordinator of this site? Confirm its status | Xuzhou | China | Unknown status |
This page is information, not medical advice. Discuss any trial with your doctor.