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.

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.

Inclusion Criteria:

• 1.Patients who were confirmed with intrahepatic cholangiocarcinoma 2. curative-intent hepatectomy 3. Without concurrent extrahepatic disease

Exclusion Criteria:

• 1.Patients lacking complete pathology information, 2. Patients who didn't get curative resection 3.Patients with concurrent extrahepatic disease or had missing follow-up data