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Imaging-based Deep Learning for Lung Cancer Diagnosis and Staging

The Role of CNN Architecture-based Transfer Learning of Medical Imaging in Lung Cancer Diagnosis and Staging

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT04000620
Enrollment
500
Registered
2019-06-27
Start date
2018-05-01
Completion date
2024-05-31
Last updated
2021-11-16

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

Conditions

Lung Cancer

Brief summary

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

Radiologist

Interventions

PROCEDUREsurgery

treatment intent surgery

PROCEDUREpunture

diagnostic punture

Sponsors

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

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
No

Inclusion criteria

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

Exclusion criteria

* A history of other malignancies

Design outcomes

Primary

MeasureTime frame
pathologic result revealed cancer cell involvement in lesion1 month after the pathologic test

Countries

China

Contacts

Primary ContactZhilei Lv, MD
794646434@qq.com86-15107177084

Outcome results

None listed

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