A Machine-learning Model to Predict Lymph Node Metastasis of Intrahepatic Cholangiocarcinoma
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.
- EligibilityCriteria: 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 - HealthyVolunteers: Accepts Healthy Volunteers - Gender: All - MinimumAge: 18 Years - MaximumAge: 80 Years - StdAgeList: Adult, Older Adult - StudyPopulation: The data was collected from patients who underwent curative-intent hepatectomy and were diagnosed with ICC pathologically. The data was collected from the hepato-biliary and pancreatic department of West China Hospital, SCU, between the periods of January 2010 to December 2016 and January 2019 to October 2023. - SamplingMethod: Probability Sample