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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Intra-arterial therapies are the standard of care for patients with hepatocellular carcinoma who cannot undergo surgical resection. A method for predicting response to these therapies is proposed. The technique uses pre-procedural clinical, demographic, and imaging information to train machine learning models capable of predicting response prior to...
8.6K
Intraoperative Gastroscopy for Tumor Localization in Laparoscopic Surgery for Gastric Adenocarcinoma10:31

Intraoperative Gastroscopy for Tumor Localization in Laparoscopic Surgery for Gastric Adenocarcinoma

In early gastric cancer, the aim of surgery is to precisely remove the distal stomach including the primary tumor. To do this, accurate localization of the tumor is crucial, especially in totally laparoscopic surgery. This protocol describes a procedure for intraoperative gastroscopy in totally laparoscopic subtotal...
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Robotics in Surgery: A Modular Robotic Platform Driven Gastric Wedge Resection07:27

Robotics in Surgery: A Modular Robotic Platform Driven Gastric Wedge Resection

Robotically assisted surgery has become highly popular in recent years. Presented here is the standard care for upper gastrointestinal procedures, including a demonstration of a robotic-assisted gastric wedge resection using a modular robotic...
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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

This study evaluates prognostic systems for colorectal signet-ring cell carcinoma patients using machine learning models and competing risk analyses. It identifies log odds of positive lymph nodes as a superior predictor compared to pN staging, demonstrating strong predictive performance and aiding clinical decision-making through robust survival prediction...
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

This protocol was designed to train a machine learning algorithm to use a combination of imaging parameters derived from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a rat model of breast cancer bone metastases to detect early metastatic disease and predict subsequent progression to...
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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

The study found that male gender, poor tumor grade, and advanced Tumor Node Metastasis stage were associated with poorer cancer-specific survival (CSS) in multiple primary colorectal cancer (MPCC) patients after surgery. We developed a nomogram to predict the CSS of MPCC patients and contribute to clinical treatment...
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