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 Liver Neoplasms Aged Carcinoma, Hepatocellular Magnetic Resonance Imaging Contrast Media Neoplasm Recurrence, Local Adult Hepatectomy Radiomics Male Middle Aged ROC Curve
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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 Aged Carcinoma, Hepatocellular Adult Hepatectomy Humans Male Liver Failure Machine Learning Middle Aged Algorithms Postoperative Complications Prospective Studies 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 ...
Carcinoma, Hepatocellular Algorithms Predictive Value of Tests Female Humans Hepatectomy Risk Factors Middle Aged Machine Learning Nutritional Status Aged Nutrition Assessment Liver Failure Male Risk Assessment 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 Databases, Factual Retrospective Studies Liver Neoplasms Female Carcinoma, Hepatocellular Risk Factors Humans Treatment Outcome Aged Predictive Value of Tests Machine Learning Male Minimally Invasive Surgical Procedures Decision Support Techniques Middle Aged Risk Assessment
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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 Hepatectomy Subcutaneous Fat 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.
Machine Learning Algorithms Female Risk Factors Aged Nomograms Delirium Hepatectomy Humans Adult Male Middle Aged Postoperative Complications ROC Curve
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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 Humans Middle Aged Carcinoma, Hepatocellular Machine Learning Prognosis ROC Curve Neoplasm Recurrence, Local Algorithms Liver Neoplasms 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 Risk Factors Machine Learning Liver Neoplasms Cholangiocarcinoma Aged Decision Support Techniques Carcinoma, Hepatocellular Male Databases, Factual Predictive Value of Tests Middle Aged Retrospective Studies Hepatectomy
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