Predicting Tumor Origin Based on Deep Learning of Lymph Node Puncture Cytology

Sichuan University

Status: Enrolling

Conditions: Lymph Nodes With Tumor Metastasis

Study type: Observational

Funder types: Other

Identifiers

NCT06810349

2024 Audit No. (1041)

Details and patient eligibility

About

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.

Enrollment: 10,000 estimated patients

Sex: All

Volunteers: Accepts Healthy Volunteers

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.

Trial design

10,000 participants in 1 patient group

a training cohort and a validation cohort

Description:

Training cohort: Lymph node cytology smear imaging data and corresponding clinical data from October 1, 2008-August 31, 2024 in West China Hospital of Sichuan University. Validation cohort: Lymph node cytology smear imaging data and corresponding clinical data from January 1, 2020-August 31, 2024 in the First Affiliated Hospital of Zhengzhou University, Sichuan Provincial Cancer Hospital, and the Cancer Hospital of China Academy of Medical Sciences.