Deep Learning Model for Pure Solid Nodules Classification
Deep Learning Model Supplementary PET-CT as a More Effectively Diagnostic Method for Pure Solid Nodules Classification: a Multicenter Observational Study
1 other identifier
observational
260
1 country
5
Brief Summary
The purpose of this study is to compare the predictive performance of a CT-based deep learning model for pure-solid nodules classification and compared with the tumor maximum standardized uptake value on PET in 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 Jan 2022
5 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
January 1, 2022
CompletedFirst Submitted
Initial submission to the registry
September 13, 2022
CompletedFirst Posted
Study publicly available on registry
September 16, 2022
CompletedPrimary Completion
Last participant's last visit for primary outcome
December 31, 2023
CompletedStudy Completion
Last participant's last visit for all outcomes
December 31, 2023
CompletedSeptember 16, 2022
September 1, 2022
2 years
September 13, 2022
September 14, 2022
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
AUC
Area under the curve of the receiver operating characteristic
2022.01-2023.12
Secondary Outcomes (5)
Accuracy
2022.01-2023.12
sensitivity
2022.01-2023.12
Specificity
2022.01-2023.12
PPV
2022.01-2023.12
NPV
2022.01-2023.12
Interventions
CT-based deep learning model for pure-solid nodules classifications
Eligibility Criteria
Patients with pulmonary radiological pure-solid nodules with size less than 3cm
You may qualify if:
- Participants scheduled for surgery for radiological finding of pulmonary pure-solid lesions from the preoperative thin-section CT scans;
- The maximum short-axis diameter of lymph nodes less than 3 cm on CT scan;
- Age ranging from 18-75 years;
- definied pathological examination report available;
- Obtained written informed consent.
You may not qualify if:
- Multiple lung lesions;
- Poor quality of CT images;
- Participants with incomplete clinical information;
- Participants who have received neoadjuvant therapy before initial CT evaluation.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Chang Chenlead
- Ningbo No.2 Hospitalcollaborator
- Zunyi Medical Collegecollaborator
- The First Affiliated Hospital of Nanchang Universitycollaborator
- The First Hospital of Lanzhou University, Gansu, Chinacollaborator
Study Sites (5)
Shanghai Pulmonary Hospital
Yangpu, Shanghai Municipality, China
Lanzhou
China, Gansu, China
Zunyi
China, Guizhou, China
Nanchang
China, Jiangxi, China
Ningbo
China, Zhejiang, China
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR INVESTIGATOR
- PI Title
- Professor
Study Record Dates
First Submitted
September 13, 2022
First Posted
September 16, 2022
Study Start
January 1, 2022
Primary Completion
December 31, 2023
Study Completion
December 31, 2023
Last Updated
September 16, 2022
Record last verified: 2022-09
Data Sharing
- IPD Sharing
- Will not share