Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer

Shanghai Pulmonary Hospital, Shanghai, China100 enrolled

Overview

The purpose of this study is to evaluate the performance of a CT/PET/ WSI-based deep learning signature for predicting complete pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer

Study Type

OBSERVATIONAL

Enrollment

100

Conditions

Non-small Cell Lung Cancer Neoadjuvant Chemoimmunotherapy Complete Pathological Response

Interventions

CT/PET/WSI-based Deep Learning SignatureDIAGNOSTIC_TEST

CT/PET/WSI-based Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer

Eligibility

Sex: ALLMin age: 20 YearsMax age: 75 Years

Medical Language ↔ Plain English

Inclusion Criteria: 1. Age ranging from 20-75 years; 2. Patients who underwent curative surgery after neoadjuvant chemoimmunotherapy for NSCLC; 3. Obtained written informed consent. Exclusion Criteria: 1. Missing image data; 2. Pathological N3 disease.

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 complete pathological response (CPR). CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy.

Time frame: 2023.5.1-2023.10.31

Secondary Outcomes

Sensitivity

The sensitivity of the deep learning model in predicting complete pathological response. CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy.

Time frame: 2023.5.1-2023.10.31

Data from ClinicalTrials.gov

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