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

Recruiting

• Conditions: Neoadjuvant Chemoimmunotherapy; Complete Pathological Response; Non-small Cell Lung Cancer

• Registration Number: NCT05925751
• Lead Sponsor: Shanghai Pulmonary Hospital, Shanghai, China

Brief Summary

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

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: 100

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.

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 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.

Secondary Outcome Measures

Name	Time	Method
Sensitivity	2023.5.1-2023.10.31	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.

Trial Locations

Locations (3)

Ningbo HwaMei Hospital; 🇨🇳 Ningbo, Zhejiang, China

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

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