AIMC Topic: Hepatectomy

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Radiomics analysis based on dynamic contrast-enhanced MRI for predicting early recurrence after hepatectomy in hepatocellular carcinoma patients.

Scientific reports Jul 1, 2025
This study aimed to develop a machine learning model based on Magnetic Resonance Imaging (MRI) radiomics for predicting early recurrence after curative surgery in patients with hepatocellular carcinoma (HCC).A retrospective analysis was conducted on ...
Humans Machine Learning Retrospective Studies Female Aged Liver Neoplasms Adult Carcinoma, Hepatocellular Radiomics Contrast Media Male Magnetic Resonance Imaging Middle Aged Neoplasm Recurrence, Local ROC Curve Hepatectomy
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Interpretable machine learning model for predicting post-hepatectomy liver failure in hepatocellular carcinoma.

Scientific reports May 3, 2025
Post-hepatectomy liver failure (PHLF) is a severe complication following liver surgery. We aimed to develop a novel, interpretable machine learning (ML) model to predict PHLF. We enrolled 312 hepatocellular carcinoma (HCC) patients who underwent hepa...
ROC Curve Liver Neoplasms Carcinoma, Hepatocellular Hepatectomy Liver Failure Aged Humans Adult Machine Learning Male Algorithms Middle Aged Prospective Studies Postoperative Complications Female
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Controlling nutritional status score predicts posthepatectomy liver failure: an online interpretable machine learning prediction model.

European journal of gastroenterology & hepatology Mar 5, 2025
BACKGROUND AND AIMS: Posthepatectomy liver failure (PHLF) remains a severe complication after hepatectomy for hepatocellular carcinoma (HCC) and accurate preoperative evaluation and predictive measures are urgently needed. We investigated the impact ...
Algorithms Female Risk Factors Aged Carcinoma, Hepatocellular Humans Male Predictive Value of Tests Middle Aged Machine Learning Hepatectomy Nutrition Assessment Nutritional Status Risk Assessment Liver Failure Postoperative Complications Liver Neoplasms
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Predicting the complexity of minimally invasive liver resection for hepatocellular carcinoma using machine learning.

HPB : the official journal of the International Hepato Pancreato Biliary Association Mar 4, 2025
BACKGROUND: Despite technical advancements, minimally invasive liver surgery (MILS) for hepatocellular carcinoma (HCC) remains challenging. Nonetheless, effective tools to assess MILS complexity are still lacking. Machine learning (ML) models could i...
Tumor Burden Hepatectomy Liver Neoplasms Carcinoma, Hepatocellular Treatment Outcome Predictive Value of Tests Minimally Invasive Surgical Procedures Risk Factors Aged Databases, Factual Male Retrospective Studies Middle Aged Female Risk Assessment Humans Machine Learning Decision Support Techniques
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Indication model for laparoscopic repeat liver resection in the era of artificial intelligence: machine learning prediction of surgical indication.

HPB : the official journal of the International Hepato Pancreato Biliary Association Mar 4, 2025
BACKGROUND: Laparoscopic repeat liver resection (LRLR) is still a challenging technique and requires a careful selection of indications. However, the current difficulty scoring system is not suitable for selecting indications. The purpose of this stu...
Conversion to Open Surgery Risk Assessment Liver Neoplasms Laparoscopy Hepatectomy Reoperation Humans Machine Learning Retrospective Studies Female Risk Factors Aged Male Middle Aged
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Developing a Novel Artificial Intelligence Framework to Measure the Balance of Clinical Versus Nonclinical Influences on Posthepatectomy Length of Stay.

Annals of surgical oncology Feb 5, 2025
BACKGROUND: Length of stay (LOS) is a key indicator of posthepatectomy care quality. While clinical factors influencing LOS are identified, the balance between clinical and nonclinical influences remains unquantified. We developed an artificial intel...
Prognosis Female Aged Male Middle Aged Postoperative Complications Length of Stay Liver Neoplasms Follow-Up Studies Hepatectomy Humans Artificial Intelligence
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Preoperative blood and CT-image nutritional indicators in short-term outcomes and machine learning survival framework of intrahepatic cholangiocarcinoma.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology Jan 30, 2025
BACKGROUND&AIMS: Intrahepatic cholangiocarcinoma (iCCA) is aggressive with limited treatment and poor prognosis. Preoperative nutritional status assessment is crucial for predicting outcomes in patients. This study aimed to compare the predictive cap...
Humans Machine Learning Prognosis Retrospective Studies Middle Aged Nutrition Assessment Postoperative Complications Neoplasm Recurrence, Local Bilirubin Tomography, X-Ray Computed Female Aged Survival Rate Male Serum Albumin Cholangiocarcinoma Subcutaneous Fat Hepatectomy Bile Duct Neoplasms Nutritional Status Preoperative Period Intra-Abdominal Fat
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Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patients.

BMC surgery Jan 15, 2025
OBJECTIVE: The objective of this study was to develop and validate a clinically applicable nomogram for predicting the risk of delirium following hepatectomy.
Nomograms Delirium Hepatectomy Adult Male Middle Aged Postoperative Complications ROC Curve Machine Learning Algorithms Female Risk Factors Aged Humans
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Construction of a random survival forest model based on a machine learning algorithm to predict early recurrence after hepatectomy for adult hepatocellular carcinoma.

BMC cancer Dec 25, 2024
BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) exhibits a propensity for early recurrence following liver resection, resulting in a bleak prognosis. At present, majority of the predictive models for the early postoperative recurrence of HCC rely...
Adult Male Middle Aged Humans Machine Learning Carcinoma, Hepatocellular Prognosis ROC Curve Algorithms Liver Neoplasms Neoplasm Recurrence, Local Retrospective Studies Hepatectomy Female Proportional Hazards Models Risk Factors Aged
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Machine learning based prediction model for bile leak following hepatectomy for liver cancer.

HPB : the official journal of the International Hepato Pancreato Biliary Association Dec 20, 2024
OBJECTIVE: We sought to develop a machine learning (ML) preoperative model to predict bile leak following hepatectomy for primary and secondary liver cancer.
Risk Assessment Female Humans Anastomotic Leak Liver Neoplasms Machine Learning Risk Factors Carcinoma, Hepatocellular Cholangiocarcinoma Decision Support Techniques Predictive Value of Tests Aged Databases, Factual Hepatectomy Male Retrospective Studies Middle Aged
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