D-Lung: An Analytics Platform for Lung Cancer Based on Deep Learning Technology

Lung Cancer Clinical Trial

Official title:

D-Lung: An Analytics Platform for Primary Lung Cancer Screening, Diagnosis and Management Based on Deep Learning Technology

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04036903
• Other study ID #: 2019.306
• Status: Completed
• Start date: July 1, 2018
• Est. completion date: June 30, 2020

Study information

• Verified date: February 2023
• Source: Chinese University of Hong Kong
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Lung cancer is one of main cause of cancer death in worldwide, characterized of low 5-year survival rate of less than 20%. Pulmonary nodule is considered as the typical imaging manifestation in early stage of lung cancer. The National Lung Screen Trial has demonstrated that the mortality rates could decline greatly, by the utility of low-dose helical computed tomography for screen of pulmonary nodules. Thus, automatic detection, diagnosis and management of pulmonary nodules, play the vital roles in computer-aided lung cancer screening and early intervention.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 130
• Est. completion date: June 30, 2020
• Est. primary completion date: June 30, 2020
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

• Subjects with suspicious lung nodules.

• Thin-layer thoracic CT and pathology examination have been performed for suspicious lung nodules.

Exclusion Criteria:

• Subjects with accompanied lesions on CT images that may interfere to lung nodules analysis

Related Conditions & MeSH terms

• Lung Cancer
• Lung Neoplasms

Intervention

Radiation:
• computed tomography
thoracic CT examinations for diagnosis, and/or follow-up.

Locations

Country	Name	City	State
Hong Kong	The Chinese University of Hong Kong, Prince of Wale Hospital	Hong Kong	Shatin

Sponsors (2)

Lead Sponsor	Collaborator
Chinese University of Hong Kong	Department of Computer Science & Engineering, CUHK

Country where clinical trial is conducted

Hong Kong, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	accuracy	proportion of true results(both true positives and true negatives) among whole instances	2 years
Primary	sensitivity	true positive rate in percentage(%) derived by ROC analysis	2 years
Primary	specificity	true negative rate in percentage (%) derived by ROC analysis	2 years
Primary	area under curve (AUC)	area under ROC curve in percentage (%)	2 years
Secondary	average number of false positives per scan (FPs/scan)	FPs/scan in number (N) based on free-response receiver operating characteristic (FROC) analysis	2 years
Secondary	competition performance metric (CPM)	Competitive performance metric (CPM) is a criterion used for CAD system evaluation. Based on FROC paradigm, CPM score is computed as an average sensitivity at seven predefined average false positive rates. CPM score ranges from 0 to 1, with higher CPM score indicating better CAD performance.	2 years