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

Recruiting

• Conditions: Lymph Nodes With Tumor Metastasis

• Registration Number: NCT06810349
• Lead Sponsor: West China Hospital

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2024-11-11
• Status Verified: 2025-01
• Study First Submitted: 2025-01-24
• Study First Submitted QC: 2025-01-30
• Last Update Submitted: 2025-01-30
• Study First Posted: 2025-02-05
• Last Update Posted: 2025-02-05
• Primary Completion: 2025-12
• Study Completion: 2025-12

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 10000

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Model performance metrics	1 year	Model performance was evaluated by Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, Sensitivity and Specificity.

Trial Locations

Locations (1)

West China Hospital of Sichuan University; 🇨🇳 Chengdu, Sichuan, China