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

Non-small Cell Lung Cancer Clinical Trial

Official title:

Positron Emission Tomography/ Computed Tomography (PET/CT) Based Deep Learning Signature for Predicting Aggressive Histological Pattern in Resected Non-small Cell Lung Cancer

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05925738
• Other study ID #: DLAHP
• Status: Recruiting
• Start date: May 1, 2023
• Est. completion date: October 31, 2023

Study information

• Verified date: June 2023
• Source: Shanghai Pulmonary Hospital, Shanghai, China
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1500
• Est. completion date: October 31, 2023
• Est. primary completion date: October 31, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 20 Years to 75 Years

Eligibility

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.

Related Conditions & MeSH terms

• Carcinoma, Non-Small-Cell Lung
• Lung Neoplasms
• Non-small Cell Lung Cancer

Intervention

Diagnostic Test:
• PET/CT-based Deep Learning Signature
Deep Learning Signature Based on PET-CT for Predicting the Aggressive Histological Pattern in Resected Non-small Cell Lung Cancer

Locations

Country	Name	City	State
China	The First Affiliated Hospital of Nanchang University	Nanchang	Jiangxi
China	Ningbo HwaMei Hospital	Ningbo	Zhejiang
China	Affiliated Hospital of Zunyi Medical University	Zunyi	Guizhou

Sponsors (4)

Lead Sponsor	Collaborator
Shanghai Pulmonary Hospital, Shanghai, China	Ningbo HwaMei Hospital, Zhejiang, China, The First Affiliated Hospital of Nanchang University, Jiangxi, China, Zunyi Medical College

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	Specificity	The specificity 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.	2023.5.1-2023.10.31
Other	Positive predictive value	The positive predictive value 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.	2023.5.1-2023.10.31
Other	Negative predictive value	The negative predictive value 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.	2023.5.1-2023.10.31
Other	Accuracy	The accuracy 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.	2023.5.1-2023.10.31
Primary	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.	2023.5.1-2023.10.31
Secondary	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.	2023.5.1-2023.10.31