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

Shanghai Pulmonary Hospital, Shanghai, China1,500 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

1,500

Conditions

Non-small Cell Lung Cancer Spread Through Air Space Visceral Pleural Invasion Lymphovascular Invasion

Interventions

PET/CT-based Deep Learning SignatureDIAGNOSTIC_TEST

Deep Learning Signature Based on PET-CT for Predicting the Aggressive Histological Pattern in Resected Non-small Cell Lung Cancer

Eligibility

Sex: ALLMin age: 20 YearsMax age: 75 YearsHealthy volunteers:

Medical Language ↔ Plain English

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.

Locations (3)

Affiliated Hospital of Zunyi Medical University

Zunyi, Guizhou, China

RECRUITING

The First Affiliated Hospital of Nanchang University

Nanchang, Jiangxi, China

RECRUITING

Ningbo HwaMei Hospital

Ningbo, Zhejiang, China

RECRUITING

Outcomes

Primary Outcomes

Area under the receiver operating characteristic curve

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.

Time frame: 2023.5.1-2023.10.31

Secondary Outcomes

Sensitivity

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.

Time frame: 2023.5.1-2023.10.31

Data from ClinicalTrials.gov

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