Deep Learning Model for Pure Solid Nodules Classification

Lung Cancer Clinical Trial

Official title:

Deep Learning Model Supplementary PET-CT as a More Effectively Diagnostic Method for Pure Solid Nodules Classification: a Multicenter Observational Study

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05542992
• Other study ID #: L21-022
• Status: Recruiting
• Start date: January 1, 2022
• Est. completion date: December 31, 2023

Study information

• Verified date: September 2022
• 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 compare the predictive performance of a CT-based deep learning model for pure-solid nodules classification and compared with the tumor maximum standardized uptake value on PET in a multicenter prospective cohort.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 260
• Est. completion date: December 31, 2023
• Est. primary completion date: December 31, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 75 Years

Eligibility

Inclusion Criteria:

• Participants scheduled for surgery for radiological finding of pulmonary pure-solid lesions from the preoperative thin-section CT scans;

• The maximum short-axis diameter of lymph nodes less than 3 cm on CT scan;

• Age ranging from 18-75 years;

• definied pathological examination report available;

• Obtained written informed consent.

Exclusion Criteria:

• Multiple lung lesions;

• Poor quality of CT images;

• Participants with incomplete clinical information;

• Participants who have received neoadjuvant therapy before initial CT evaluation.

Related Conditions & MeSH terms

• Lung Cancer

Intervention

Diagnostic Test:
• CT-based deep learning model
CT-based deep learning model for pure-solid nodules classifications

Locations

Country	Name	City	State
China	Lanzhou	China, Gansu
China	Zunyi	China, Guizhou
China	Nanchang	China, Jiangxi
China	Ningbo	China, Zhejiang
China	Shanghai Pulmonary Hospital	Yangpu	Shanghai

Sponsors (5)

Lead Sponsor	Collaborator
Chang Chen	Ningbo HwaMei Hospital, Zhejiang, China, The First Affiliated Hospital of Nanchang University, Jiangxi, China, The First Hospital of Lanzhou University, Gansu, China, Zunyi Medical College

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	AUC	Area under the curve of the receiver operating characteristic	2022.01-2023.12
Secondary	Accuracy	Ratio of the number of correctly classified samples to the total number of samples	2022.01-2023.12
Secondary	sensitivity	The probability of detecting a positive test in the population with the gold standard for disease (positive)	2022.01-2023.12
Secondary	Specificity	Odds of detecting a negative test in a population judged disease-free (negative) by the gold standard	2022.01-2023.12
Secondary	PPV	Positive predictive value	2022.01-2023.12
Secondary	NPV	Negative predictive value	2022.01-2023.12