Deep Learning Signature for Predicting Occult Nodal Metastasis of Clinical N0 Lung Cancer
- Conditions
- Non-small Cell Lung Cancer
- Registration Number
- NCT05425134
- Lead Sponsor
- Shanghai Pulmonary Hospital, Shanghai, China
- Brief Summary
The purpose of this study is to evaluate the performance of a PET/CT-based deep learning signature for predicting occult nodal metastasis of clinical stage N0 non-small cell lung cancer in a multicenter prospective cohort.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 5000
(1) Participants scheduled for surgery for radiological finding of pulmonary lesions from the preoperative thin-section CT scans; (2) The maximum short-axis diameter of N1 and N2 lymph nodes less than 1 cm on CT scan; (3) The SUVmax of N1 and N2 lymph nodes less than 2.5; (4) Pathological confirmation of primary NSCLC; (5) Age ranging from 20-75 years; (6) Obtained written informed consent.
(1) Multiple lung lesions; (2) Poor quality of PET-CT images; (3) Participants with incomplete clinical information; (4) Participants not receiving systematic lymph node dissection; (5) Participants who have received neoadjuvant therapy.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Area under the receiver operating characteristic curve 2022.1-2023.12 Area under the receiver operating characteristic curve
- Secondary Outcome Measures
Name Time Method Accuracy 2022.1-2023.12 Accuracy
Positive predictive value 2022.1-2023.12 Positive predictive value
Negative predictive value 2022.1-2023.12 Negative predictive value
Sensitivity Sensitivity 2022.1-2023.12 Sensitivity
Specificity 2022.1-2023.12 Specificity
Trial Locations
- Locations (4)
Affiliated Hospital of Zunyi Medical University
π¨π³Zunyi, Guizhou, China
Shanghai Pulmonary Hospital
π¨π³Yangpu, Shanghai, China
The First Affiliated Hospital of Nanchang University
π¨π³Nanchang, Jiangxi, China
Ningbo HwaMei Hospital
π¨π³Ningbo, Zhejiang, China