Deep Learning Signature for Predicting Occult Nodal Metastasis of Clinical N0 Lung Cancer

  • End date
    Dec 31, 2023
  • participants needed
  • sponsor
    Shanghai Pulmonary Hospital, Shanghai, China
Updated on 7 October 2022
ct scan
lung carcinoma


The purpose of this study is to compare the predictive performance of a PET/CT-based deep learning signature with the tumor maximum standardized uptake value (SUVmax) on PET for occult nodal metastasis prediction of clinical stage N0 non-small cell lung cancer in a multicenter prospective cohort.

Condition Non-small Cell Lung Cancer
Treatment PET/CT-based Deep Learning Signature
Clinical Study IdentifierNCT05425134
SponsorShanghai Pulmonary Hospital, Shanghai, China
Last Modified on7 October 2022


Yes No Not Sure

Inclusion Criteria

(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 lymph nodes
less than 1 cm on CT scan; (3) The SUVmax of hilar and mediastinal lymph nodes less than
5; (4) Pathological confirmation of primary NSCLC; (5) Age ranging from 20-75 years; (6)
Obtained written informed consent

Exclusion Criteria

(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 before initial PET-CT
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