Deep Learning Signature for Predicting Aggressive Histological Pattern in Resected Non-small Cell Lung Cancer

Recruiting

• Conditions: Spread Through Air Space; Non-small Cell Lung Cancer; Visceral Pleural Invasion; Lymphovascular Invasion

• Registration Number: NCT05925738
• 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 aggressive histological pattern in resected non-small cell lung cancer based on a multicenter prospective cohort.

Detailed Description

Not available

Study Timeline

• Study Start: 2023-05-01
• Study First Submitted: 2023-05-12
• Status Verified: 2023-06
• Study First Submitted QC: 2023-06-27
• Last Update Submitted: 2023-06-27
• Study First Posted: 2023-06-29
• Last Update Posted: 2023-06-29
• Primary Completion: 2023-10-31
• Study Completion: 2023-10-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1500

Inclusion Criteria

(1) Participants scheduled for surgery for radiological finding of pulmonary lesions from the preoperative thin-section CT scans; (2) Pathological confirmation of primary NSCLC; (3) Age ranging from 20-75 years; (4) 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 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	2023.5.1-2023.10.31	The area under the receiver operating characteristic curve (ROC) of the deep learning model in predicting the presence or absence of the aggressive histological pattern. The aggressive histological pattern includes spread through air space (STAS), visceral pleural invasion (VPI), and lymphovascular invasion (LVI). And the model will output all predictive values (presence or absence) of the three kinds of aggressive histological patterns.

Secondary Outcome Measures

Name	Time	Method
Sensitivity	2023.5.1-2023.10.31	The sensitivity of the deep learning model in predicting the presence or absence of the aggressive histological pattern. The aggressive histological pattern includes spread through air space (STAS), visceral pleural invasion (VPI), and lymphovascular invasion (LVI). And the model will output all predictive values (presence or absence) of the three kinds of aggressive histological patterns.

Trial Locations

Locations (3)

Affiliated Hospital of Zunyi Medical University; 🇨🇳 Zunyi, Guizhou, China

The First Affiliated Hospital of Nanchang University; 🇨🇳 Nanchang, Jiangxi, China

Ningbo HwaMei Hospital; 🇨🇳 Ningbo, Zhejiang, China