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

NCT05925751 | Unknown

• 1 other identifier: DLCPR
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 3

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

Conditions

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

Outcome Measures

Primary Outcomes (1)

• 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.

  2023.5.1-2023.10.31

Secondary Outcomes (1)

• Sensitivity

  2023.5.1-2023.10.31

Other Outcomes (4)

• Specificity

  2023.5.1-2023.10.31

• Positive predictive value

  2023.5.1-2023.10.31

• Negative predictive value

  2023.5.1-2023.10.31

Interventions

CT/PET/WSI-based Deep Learning Signature DIAGNOSTIC_TEST

CT/PET/WSI-based Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in 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

Resected Stage I-III NSCLC following neoadjuvant chemoimmunotherapy

You may qualify if:

• Age ranging from 20-75 years;
• Patients who underwent curative surgery after neoadjuvant chemoimmunotherapy for NSCLC;
• Obtained written informed consent.

You may not qualify if:

• Missing image data;
• Pathological N3 disease.

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 (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

MeSH Terms

Conditions

Carcinoma, Non-Small-Cell Lung; Pathologic Complete Response

Condition Hierarchy (Ancestors)

Carcinoma, Bronchogenic; Bronchial Neoplasms; Lung Neoplasms; Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases; Disease Progression; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

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: May 12, 2023
• First Posted: June 29, 2023
• Study Start: May 1, 2023
• Primary Completion: October 31, 2023
• Study Completion: October 31, 2023
• Last Updated: June 29, 2023

Record last verified: 2023-06

Data Sharing

• IPD Sharing: Will not share

Locations

CN (3)