Using deep learning to identify tumor origins from lymph node samples
Official title: A Multicenter Study on Predicting Tumor Origin Based on Deep Learning of Lymph Node Puncture Cytology
Run by West China Hospital · NCT06810349 · Data updated 2026-11-12
This study is trying to see if a new computer program can help find out where tumors that spread to lymph nodes come from, and how it compares to the work of human doctors.
Quick facts
| Study type | Observational |
|---|---|
| Sex | All |
| Enrollment | 10000 (estimated) |
| Sponsor | West China Hospital Academic / other |
| Locations | 1 site (Chengdu, Sichuan) |
| Status | Recruiting |
| Started | November 11, 2024 |
| Main results expected | December 2025 |
| Study ends | December 2025 |
| Registry record updated | February 5, 2025 |
| Trial ID | NCT06810349 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates for this study are patients with lymph node metastases and unknown primary tumor origins who have provided relevant clinical data.
✕Probably not a fit
Patients with known primary tumor origins or those not undergoing lymph node puncture procedures may not benefit from this study.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This study aims to develop a deep learning model that analyzes cytological images from lymph node punctures to predict the origins of tumors that have metastasized to lymph nodes. The model will be constructed using data collected from patients at West China Hospital and will be validated with a large test set to assess its performance. Additionally, the study will compare the accuracy of the deep learning model against human pathologists in diagnosing these cytology smears.
Why it matters
Potential benefit: If successful, this approach could lead to more accurate and timely identification of tumor origins, improving treatment decisions for patients with unknown primary tumors.
How similar studies have gone: While the use of deep learning in medical diagnostics is gaining traction, this specific application in predicting tumor origins from lymph node cytology is relatively novel and has not been extensively tested.
Who can join: the full rules
Show full inclusion / exclusion criteria
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.
Where it's running
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West China Hospital of Sichuan University
Chengdu, Sichuan, China Recruiting
Study contacts
- Study coordinator: Jianyong Lei
- Email: guosiyin2000@163.com
- Phone: 02885423822
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.