Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer
NCT ID: NCT05925751
Last Updated: 2023-06-29
Study Results
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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UNKNOWN
100 participants
OBSERVATIONAL
2023-05-01
2023-10-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Interventions
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CT/PET/WSI-based Deep Learning Signature
CT/PET/WSI-based Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer
Eligibility Criteria
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Inclusion Criteria
2. Patients who underwent curative surgery after neoadjuvant chemoimmunotherapy for NSCLC;
3. Obtained written informed consent.
Exclusion Criteria
2. Pathological N3 disease.
20 Years
75 Years
ALL
No
Sponsors
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Ningbo No.2 Hospital
OTHER
Zunyi Medical College
OTHER
The First Affiliated Hospital of Nanchang University
OTHER
Shanghai Pulmonary Hospital, Shanghai, China
OTHER
Responsible Party
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Chang Chen
Professor
Locations
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Affiliated Hospital of Zunyi Medical University
Zunyi, Guizhou, China
The First Affiliated Hospital of Nanchang University
Nanchang, Jiangxi, China
Ningbo HwaMei Hospital
Ningbo, Zhejiang, China
Countries
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Facility Contacts
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Yongxiang Song, Dr
Role: primary
Bentong Yu, Dr
Role: primary
Minglei Yang, Dr
Role: primary
Other Identifiers
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DLCPR
Identifier Type: -
Identifier Source: org_study_id