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

NCT05425134 | Unknown

• 1 other identifier: DLNMS
• Study Type: observational
• Target: 5,000
• Locations: 1 country
• Sites: 4

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.

Conditions

Non-small Cell Lung Cancer

Keywords

Lung cancer; Lymph node metastasis; Deep learning; PET-CT

Outcome Measures

Primary Outcomes (1)

• Area under the receiver operating characteristic curve

  Area under the receiver operating characteristic curve

  2022.1-2023.12

Secondary Outcomes (5)

• Sensitivity Sensitivity

  2022.1-2023.12

• Specificity

  2022.1-2023.12

• Positive predictive value

  2022.1-2023.12

• Negative predictive value

  2022.1-2023.12

• Accuracy

  2022.1-2023.12

Interventions

PET/CT-based Deep Learning Signature DIAGNOSTIC_TEST

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

Eligibility Criteria

• Age: 20 Years - 75 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Clinica N0 Non-small Cell Lung Cancer

You may qualify if:

• (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.

You may not qualify if:

• (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.

Sponsors & Collaborators

• Shanghai Pulmonary Hospital, Shanghai, China: lead
• Ningbo No.2 Hospital: collaborator
• Zunyi Medical College: collaborator
• The First Affiliated Hospital of Nanchang University: collaborator

Study Sites (4)

Affiliated Hospital of Zunyi Medical University

Zunyi, Guizhou, China

RECRUITING

The First Affiliated Hospital of Nanchang University

Nanchang, Jiangxi, China

RECRUITING

Shanghai Pulmonary Hospital

Yangpu, Shanghai Municipality, China

RECRUITING

Ningbo HwaMei Hospital

Ningbo, Zhejiang, China

RECRUITING

MeSH Terms

Conditions

Carcinoma, Non-Small-Cell Lung; Lung Neoplasms; Lymphatic Metastasis

Condition Hierarchy (Ancestors)

Carcinoma, Bronchogenic; Bronchial Neoplasms; Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases; Neoplasm Metastasis; Neoplastic Processes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Chang Chen, MD, PhD

CONTACT

+86-021-65115006; chenthoracic@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: June 15, 2022
• First Posted: June 21, 2022
• Study Start: January 1, 2022
• Primary Completion: December 31, 2023
• Study Completion: December 31, 2023
• Last Updated: February 9, 2023

Record last verified: 2023-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (4)