West China Hospital of Sichuan University
Chengdu, Sichuan, 610041, China
Location status: Recruiting
Recruiting
NCT Number: NCT06810349
Predicting Tumor Origin Based on Deep Learning of Lymph Node Puncture Cytology
In this study, the investigators aimed to construct a deep learning diagnostic model that uses cytological images to predict primary unknown tumor origins in patients with tumors combined with lymph node metastases. After the model is constructed, the model will be validated by a large-scale test set to test the model performance. The investigators also propose to compare the performance of the constructed model in diagnosing cytology smears compared to human pathologists.
Recruiting
Interested in participating?
Request InfoKey information
Conditions
Sex eligibility
All sexes
Study type
Observational
Primary location
Who can participate
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
- From West China Hospital of Sichuan University (October 1, 2008-August 31, 2024) with corresponding clinical data, including age, sex, specimen puncture site, pathologic diagnosis, pathologic type, whether immunocytochemistry was added, clinical diagnosis, lesion site, co-morbidities, history of malignancy, treatment modality, occurrence of postoperative complications, total number of days of hospitalization postoperatively, and survival time;
- From the Department of Pathology of the First Affiliated Hospital of Zhengzhou University, the Sichuan Provincial Cancer Hospital, and the Cancer Hospital of the Chinese Academy of Medical Sciences (January 1, 2020-August 31, 2024) with corresponding clinical data, including age, sex, specimen puncture site, pathologic diagnosis, pathologic type, whether immunocytochemistry was added, clinical diagnosis, lesion site, co-morbidities, history of malignancy, treatment modality, occurrence of postoperative complications, total number of days of hospitalization postoperatively, and survival time.
Exclusion criteria
- Images lacking any supporting clinical or pathologic evidence to support a primary origin and its corresponding clinical information;
- Blank, poorly focused, and low-quality images containing severe artifacts and their corresponding clinical information.
Treatment and study plan
Primary outcomes
-
Model performance metrics
Time frame: 1 year
Model performance was evaluated by Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, Sensitivity and Specificity.
Study contacts
Contact information is provided by the study sponsor or research team.
Sponsors and collaborators
Lead sponsor
West China Hospital
Other
Registry information
Official study title
A Multicenter Study on Predicting Tumor Origin Based on Deep Learning of Lymph Node Puncture Cytology
Important dates
- Study start
- 2024
- Primary completion
- 2025
- Study completion
- 2025
- First posted
- Feb 5, 2025
- Registry last updated
- Feb 5, 2025
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.