Predicting Tumor Origin Based on Deep Learning of Lymph Node Puncture Cytology
Recruiting now
Conditions studied: Lymph Nodes With Tumor Metastasis
In brief
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.
Key facts
- Study ID
- NCT06810349
- Run by
- West China Hospital
- People needed
- 10000
- Starts
- 2024-11-11
- Expected to finish
- 2025-12-01
- Last updated by the study team
- 2025-02-05
Who can join
Age: any. Sex: any. Healthy volunteers: accepted.
You may qualify if…
- 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.
You may not qualify if…
- 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 is running
- West China Hospital of Sichuan University — Chengdu, Sichuan, China (enrolling)
Full record on ClinicalTrials.gov
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