Clinical trial NCT06810349: Predicting Tumor Origin Based on Deep Learning of Lymph Node Puncture Cytology
Official title: A Multicenter Study on Predicting Tumor Origin Based on Deep Learning of Lymph Node Puncture Cytology
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Lymph Nodes With Tumor Metastasis
Keywords: Primary unknown tumor origin
Deep learning
Cytopathology
Lead sponsor: West China Hospital
Sponsor class: OTHER
First posted: 2025-02-05
Enrollment: 10000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Lymph node cytology smear imaging data and clinical data in the training cohort were obtained from West China Hospital of Sichuan University (October 1, 2008 to August 31, 2024).

Lymph node cytology smear imaging data and clinical data in the validation cohort were obtained from the Department of Pathology of the First Affiliated Hospital of Zhengzhou University, Sichuan Cancer Hospital, and Cancer Hospital of the Chinese Academy of Medical Sciences (January 1, 2020 to August 31, 2024).

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.


Eligibility 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.

Primary outcomes:
measure: Model performance metrics
timeFrame: 1 year
Secondary outcomes:
Other outcomes:
Locations:
city: Chengdu
country: China
facility: West China Hospital of Sichuan University
state: Sichuan
Study officials:
References: