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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

5000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-01-01

Study Completion Date

2023-12-31

Brief Summary

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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.

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Detailed Description

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Conditions

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Non-small Cell Lung Cancer

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Interventions

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PET/CT-based Deep Learning Signature

Deep Learning Signature Based on PET-CT for Predicting Occult Nodal Metastasis of Clinical N0 Non-small Cell Lung Cancer

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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 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.

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.

Minimum Eligible Age

20 Years

Maximum Eligible Age

75 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No