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

NCT06290739 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 483

Last updated 2024-11-22

No results posted yet for this study

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

PROCEDURE

lymph nodes dissection

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

Sponsors & Collaborators

  • West China Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2024-02-07
Primary Completion
2024-11-20
Completion
2024-11-20

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06290739 on ClinicalTrials.gov