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

Launched by WEST CHINA HOSPITAL · Jan 30, 2025

Keywords

ClinConnect Summary

This clinical trial is studying a new method to help doctors identify the primary source of tumors that have spread to lymph nodes. Researchers are developing a computer program that uses images of cells taken from lymph nodes (called cytology) to predict where the original tumor might be located, even if it is unknown. They will test the accuracy of this program against the diagnoses made by experienced doctors to see how well it works.

To participate in this trial, individuals need to have clinical data that includes details like their age, sex, medical history, and the type of tumor. They must also have undergone certain medical procedures within specific hospitals during set time periods. Participants can expect to contribute to important research that could improve how doctors diagnose tumors in the future. It's important to note that only high-quality images and data will be used for the study, as poor-quality information could affect the results.

Gender

ALL

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.