Deep Learning Signature for Predicting Aggressive Histological Pattern in Resected Non-small Cell Lung Cancer
Positron Emission Tomography/ Computed Tomography (PET/CT) Based Deep Learning Signature for Predicting Aggressive Histological Pattern in Resected Non-small Cell Lung Cancer
1 other identifier
observational
1,500
1 country
3
Brief Summary
The purpose of this study is to evaluate the performance of a PET/ CT-based deep learning signature for predicting aggressive histological pattern in resected non-small cell lung cancer based on a multicenter prospective cohort.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started May 2023
Shorter than P25 for all trials
3 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
May 1, 2023
CompletedFirst Submitted
Initial submission to the registry
May 12, 2023
CompletedFirst Posted
Study publicly available on registry
June 29, 2023
CompletedPrimary Completion
Last participant's last visit for primary outcome
October 31, 2023
CompletedStudy Completion
Last participant's last visit for all outcomes
October 31, 2023
CompletedJune 29, 2023
June 1, 2023
6 months
May 12, 2023
June 27, 2023
Conditions
Outcome Measures
Primary Outcomes (1)
Area under the receiver operating characteristic curve
The area under the receiver operating characteristic curve (ROC) of the deep learning model in predicting the presence or absence of the aggressive histological pattern. The aggressive histological pattern includes spread through air space (STAS), visceral pleural invasion (VPI), and lymphovascular invasion (LVI). And the model will output all predictive values (presence or absence) of the three kinds of aggressive histological patterns.
2023.5.1-2023.10.31
Secondary Outcomes (1)
Sensitivity
2023.5.1-2023.10.31
Other Outcomes (4)
Specificity
2023.5.1-2023.10.31
Positive predictive value
2023.5.1-2023.10.31
Negative predictive value
2023.5.1-2023.10.31
- +1 more other outcomes
Interventions
Deep Learning Signature Based on PET-CT for Predicting the Aggressive Histological Pattern in Resected Non-small Cell Lung Cancer
Eligibility Criteria
Resected Stage I-III Non-small Cell Lung Cancer
You may qualify if:
- (1) Participants scheduled for surgery for radiological finding of pulmonary lesions from the preoperative thin-section CT scans; (2) Pathological confirmation of primary NSCLC; (3) Age ranging from 20-75 years; (4) Obtained written informed consent.
You may not qualify if:
- (1) Multiple lung lesions; (2) Poor quality of PET-CT images; (3) Participants with incomplete clinical information; (4) Participants who have received neoadjuvant therapy.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Shanghai Pulmonary Hospital, Shanghai, Chinalead
- Ningbo No.2 Hospitalcollaborator
- Zunyi Medical Collegecollaborator
- The First Affiliated Hospital of Nanchang Universitycollaborator
Study Sites (3)
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
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Professor
Study Record Dates
First Submitted
May 12, 2023
First Posted
June 29, 2023
Study Start
May 1, 2023
Primary Completion
October 31, 2023
Study Completion
October 31, 2023
Last Updated
June 29, 2023
Record last verified: 2023-06
Data Sharing
- IPD Sharing
- Will not share