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Completed

NCT Number: NCT04036903

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

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.

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Key information

Conditions

Lung Cancer Lung Diseases Lung Neoplasms Neoplasms Neoplasms by Site Respiratory Tract Diseases Respiratory Tract Neoplasms Thoracic Neoplasms

Sex eligibility

All sexes

Study type

Observational

Primary location

The Chinese University of Hong Kong, Prince of Wale Hospital

Hong Kong, Shatin

Who can participate

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

computed tomography

Radiation

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

Primary outcomes

  1. accuracy

    Time frame: 2 years

    proportion of true results(both true positives and true negatives) among whole instances

  2. sensitivity

    Time frame: 2 years

    true positive rate in percentage(%) derived by ROC analysis

  3. specificity

    Time frame: 2 years

    true negative rate in percentage (%) derived by ROC analysis

  4. area under curve (AUC)

    Time frame: 2 years

    area under ROC curve in percentage (%)

Secondary outcomes

  1. average number of false positives per scan (FPs/scan)

    Time frame: 2 years

    FPs/scan in number (N) based on free-response receiver operating characteristic (FROC) analysis

  2. competition performance metric (CPM)

    Time frame: 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.

Sponsors and collaborators

Lead sponsor

Chinese University of Hong Kong

Other

Collaborators

  • Department of Computer Science & Engineering, CUHK

Registry information

Official study title

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

Important dates

Study start
2018
Primary completion
2020
Study completion
2020
First posted
Jul 30, 2019
Registry last updated
Feb 8, 2023

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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