Pathological Classification of Pulmonary Nodules in Images Using Deep Learning
1 other identifier
observational
2,000
1 country
2
Brief Summary
This study aimed to develop a deep-learning model to automatically classify pulmonary nodules based on white-light images and to evaluate the model performance. Besides, suitable operation could be chosen with the help of this model, which could shorten the time of surgery.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jun 2020
Typical duration for all trials
2 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
June 1, 2020
CompletedFirst Submitted
Initial submission to the registry
January 5, 2022
CompletedFirst Posted
Study publicly available on registry
February 3, 2022
CompletedPrimary Completion
Last participant's last visit for primary outcome
June 1, 2022
CompletedStudy Completion
Last participant's last visit for all outcomes
January 1, 2023
CompletedFebruary 3, 2022
January 1, 2022
2 years
January 5, 2022
January 23, 2022
Conditions
Keywords
Outcome Measures
Primary Outcomes (2)
1. Pathological subtype
According to WHO classification of pulmonary tumors in 2020, this study classify pulmonary tumors into adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC). We would collect the reports of pathological type of pulmonary nodules after surgery.
through study completion, an average of 2 year
Area Under the Curve (AUC)
The area under the ROC curve based the predicton efficency of model
through study completion, an average of 2 year
Interventions
Whether apply gross pathologic photo based deep learning model to predict pathologic subtype
Eligibility Criteria
Patients in Guangdong Provincial People's hospital from June 30, 2020 to September 15, 2021.
You may qualify if:
- Male or female，18 years and older.
- Patients haven't undergone any therapy.
- The pulmonary nodules were confirmed AIS, MIA or IAC.
- The sizes of pulmonary nodules were less than 3cm.
- The images were jpg format.
You may not qualify if:
- Suffering from other tumor disease before or at the same time.
- Images with poor quality or low resolution that precluded proper classification.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (2)
Guagndong Provincial People's Hospital
Guangzhou, Guangdong, 510000, China
Jiangxi Cancer Hospital
Nanchang, Jiangxi, 330000, China
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Haiyu Zhou
Guangdong Provincial People's Hospital
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- OTHER
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- vice-president
Study Record Dates
First Submitted
January 5, 2022
First Posted
February 3, 2022
Study Start
June 1, 2020
Primary Completion
June 1, 2022
Study Completion
January 1, 2023
Last Updated
February 3, 2022
Record last verified: 2022-01