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

Brief summary (registry, in English)

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.

What you commit to

• Study type: Observational
• Participants enrolled: 483
• From 7 Feb 2024 to the primary completion date, 20 Nov 2024
• 1 site in 1 country
• Visits and tests: ask the trial team, the registry does not list them

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Who cannot take part

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Who the study is looking for

Age in the registry: 18 years to 80 years · Sex: All · Accepts healthy volunteers

Full criteria on ClinicalTrials.gov

Sites (1)

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