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Imaging-based Deep Learning for Lung Cancer Diagnosis and Staging

NCT ID: NCT04000620

Last Updated: 2021-11-16

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

UNKNOWN

Total Enrollment

500 participants

Study Classification

OBSERVATIONAL

Study Start Date

2018-05-01

Study Completion Date

2024-05-31

Brief Summary

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Lung cancer diagnosis and staging are two fundamental and critical issue in clinical lung cancer management and therapeutic decision-making. Invasive procedures for pathologic analysis are gold standard for diagnosis and staging, however, invasive procedures related-complications are inevitable. Noninvasive medical imaging is a powerful tool, however there is almost no room for improvement just according to the experience of radiologist and clinician. The researchers will investigate the role of computer based deep learning of medical imaging in the diagnosis of lesion of lung, lymph node and other sites suspected with metastasis.

Detailed Description

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Radiologist

Conditions

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Lung Cancer

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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cancer cell involvement predicted by deep learning

the participants with lesions of lung, lymph node or other sites predicted as positive for cancer cell involvement by imaging based deep learning.

surgery

Intervention Type PROCEDURE

treatment intent surgery

punture

Intervention Type PROCEDURE

diagnostic punture

no cancer cell involvement predicted by deep learning

the participants with lesions of lung, lymph node or other sites predicted as negative for cancer cell involvement by imaging based deep learning.

surgery

Intervention Type PROCEDURE

treatment intent surgery

punture

Intervention Type PROCEDURE

diagnostic punture

Interventions

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surgery

treatment intent surgery

Intervention Type PROCEDURE

punture

diagnostic punture

Intervention Type PROCEDURE

Eligibility Criteria

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Inclusion Criteria

* Pathological diagnosis of lung cancer
* PET/CT or CT examination before any cancer-specific treatment

Exclusion Criteria

* A history of other malignancies
Minimum Eligible Age

18 Years

Maximum Eligible Age

75 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

OTHER

Sponsor Role lead

Responsible Party

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Yang Jin

Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Wuhan Union Hospital

Wuhan, Hubei, China

Site Status RECRUITING

Countries

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China

Central Contacts

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Zhilei Lv, MD

Role: CONTACT

Phone: 86-15107177084

Email: [email protected]

Facility Contacts

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Zhilei Lv, MD

Role: primary

Other Identifiers

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AFDSFA

Identifier Type: -

Identifier Source: org_study_id