NCT06290739

A Machine-learning Model to Predict Lymph Node Metastasis of Intrahepatic Cholangiocarcinoma

Development and Validation of a Machine-learning Model to Predict Lymph Node Metastasis of Intrahepatic Cholangiocarcinoma: a Retrospective Cohort Study.

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 483 (actual)
• Sponsor: West China Hospital · Academic / Other
• Sex: All
• Age: 18 Years – 80 Years
• Healthy volunteers: Accepted

Summary

The object of this study is to develop a model for prediction of lymph node metastasis among intrahepatic cholangiocarcinoma (ICC) patients. Intrahepatic cholangiocarcinoma is the second most common kind of primary liver cancer, accounting for approximately 10%-15%. There is a lack of agreement regarding the necessity of performing lymph node dissection (LND) in patients with ICC. Currently, the percentage of LND is below 50%, and the rate of sufficient LND (≥6) has plummeted to less than 20%. Consequently, a large proportion of patients are unable to acquire LN status, which hinders the following systematic treatment strategies after surgery:. Therefore, our objective is to construct a LN metastasis model utilizing machine learning techniques, including patients' clinical data and pathology information, with the goal of offering a reference for patients who have not undergone LND or have had inadequate LND.

Conditions

• Intrahepatic Cholangiocarcinoma
• Machine Learning

Interventions

Type	Name	Description
PROCEDURE	lymph nodes dissection	Whether lymph nodes dissection should be performed on curative-intent hepatectomy for intrahepatic cholangiocarcinoma is still debated.

Timeline

• Start date: 2024-02-07
• Primary completion: 2024-11-20
• Completion: 2024-11-20
• First posted: 2024-03-04
• Last updated: 2024-11-22

Locations

1 site across 1 country: China