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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

10000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-11-11

Study Completion Date

2025-12-31

Brief Summary

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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.

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Detailed Description

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Conditions

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Lymph Nodes With Tumor Metastasis

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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a training cohort and a validation cohort

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.

No interventions assigned to this group

Eligibility Criteria

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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.

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes