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

NCT ID: NCT04036903

Last Updated: 2023-02-08

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

130 participants

Study Classification

OBSERVATIONAL

Study Start Date

2018-07-01

Study Completion Date

2020-06-30

Brief Summary

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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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Detailed Description

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Conditions

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Lung Cancer

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

RETROSPECTIVE

Interventions

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computed tomography

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

Intervention Type RADIATION

Eligibility Criteria

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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
Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Department of Computer Science & Engineering, CUHK

UNKNOWN

Sponsor Role collaborator

Chinese University of Hong Kong

OTHER

Sponsor Role lead

Responsible Party

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Professor Winnie W.C. Chu

Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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The Chinese University of Hong Kong, Prince of Wale Hospital

Hong Kong, Shatin, Hong Kong

Site Status

Countries

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Hong Kong

Other Identifiers

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2019.306

Identifier Type: -

Identifier Source: org_study_id

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