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

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

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04036903

Enrollment

130

Registered

2019-07-30

Start date

2018-07-01

Completion date

2020-06-30

Last updated

2023-02-08

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Lung Cancer

Keywords

Lung nodules, Deep learning

Brief 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.

Interventions

RADIATIONcomputed tomography

thoracic CT examinations for diagnosis, and/or follow-up.

Study design

Observational model

CASE_ONLY

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

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

Design outcomes

Primary

Measure	Time frame	Description
accuracy	2 years	proportion of true results(both true positives and true negatives) among whole instances
sensitivity	2 years	true positive rate in percentage(%) derived by ROC analysis
specificity	2 years	true negative rate in percentage (%) derived by ROC analysis
area under curve (AUC)	2 years	area under ROC curve in percentage (%)

Secondary

Measure	Time frame	Description
average number of false positives per scan (FPs/scan)	2 years	FPs/scan in number (N) based on free-response receiver operating characteristic (FROC) analysis
competition performance metric (CPM)	2 years	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.

Countries

Hong Kong

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026