Deep Learning Signature for Predicting the Novel Grading System of Clinical Stage I Lung Adenocarcinoma
Deep Learning Signature Based on PET-CT Images for Predicting the Novel Grading System of Clinical Stage I Lung Adenocarcinoma
1 other identifier
observational
600
1 country
4
Brief Summary
The purpose of this study is to evaluate the performance of a PET/ CT-based deep learning signature for predicting the grade 3 tumors based on the novel grading system in clinical stage stage I lung adenocarcinoma 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 Nov 2022
Shorter than P25 for all trials
4 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
November 1, 2022
CompletedFirst Submitted
Initial submission to the registry
February 7, 2023
CompletedFirst Posted
Study publicly available on registry
February 21, 2023
CompletedPrimary Completion
Last participant's last visit for primary outcome
April 30, 2023
CompletedStudy Completion
Last participant's last visit for all outcomes
April 30, 2023
CompletedFebruary 21, 2023
February 1, 2023
6 months
February 7, 2023
February 12, 2023
Conditions
Outcome Measures
Primary Outcomes (1)
Area under the receiver operating characteristic curve
Area under the receiver operating characteristic curve
2022.11-2023.4
Secondary Outcomes (5)
Sensitivity
2022.11-2023.4
Specificity
2022.11-2023.4
Positive predictive value
2022.11-2023.4
Negative predictive value
2022.11-2023.4
Accuracy
2022.11-2023.4
Interventions
Radiomics Signature Based on PET-CT for Predicting the Novel Grading System of Clinical Stage I Lung Adenocarcinoma
Eligibility Criteria
Clinical Stage I Lung Adenocarcinoma
You may qualify if:
- (1) Participants scheduled for surgery for radiological finding of pulmonary lesions from the preoperative thin-section CT scans; (2) The maximum diameter of lesion less than 4 cm on CT scans; (3) The maximum short axis diameter of lymph nodes less than 1 cm on CT scan; (4) The SUVmax of hilar and mediastinal lymph nodes less than 2.5; (5) Pathological confirmation of primary lung adenocarcinoma; (5) Age ranging from 20-75 years; (6) 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) Mucinous adenocarcinomas; (5) 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 (4)
Affiliated Hospital of Zunyi Medical University
Zunyi, Guizhou, China
The First Affiliated Hospital of Nanchang University
Nanchang, Jiangxi, China
Shanghai Pulmonary Hospital
Yangpu, Shanghai Municipality, 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
February 7, 2023
First Posted
February 21, 2023
Study Start
November 1, 2022
Primary Completion
April 30, 2023
Study Completion
April 30, 2023
Last Updated
February 21, 2023
Record last verified: 2023-02