Gastroenterology

Machine Learning as a Diagnostic and Prognostic Tool for Predicting Thrombosis in Cancer Patients: A Systematic Review.

Khorana score (KS) is an established risk assessment model for predicting cancer-associated thrombosis. However, it ignores several risk factors and has poor predictability in some cancer types. Machine learning (ML) is a novel technique used for the diagnosis and prognosis of several diseases, including cancer-associated thrombosis, when trained on specific diagnostic modalities. Consolidating th...

Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma.

BACKGROUND/AIMS: The performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups.

Long-term quality of life after hybrid robot-assisted and open Ivor Lewis esophagectomy for esophageal cancer in a single center: a comparative analysis.

PURPOSE: Due to improved survival of esophageal cancer patients, long-term quality of life (QoL) is increasingly gaining importance. The aim of this s...

Establishment of a prognostic model for gastric cancer patients who underwent radical gastrectomy using machine learning: a two-center study.

OBJECTIVE: Gastric cancer is a prevalent gastrointestinal malignancy worldwide. In this study, a prognostic model was developed for gastric cancer pat...

Exploring tumor heterogeneity in colorectal liver metastases by imaging: Unsupervised machine learning of preoperative CT radiomics features for prognostic stratification.

OBJECTIVES: This study aimed to investigate tumor heterogeneity of colorectal liver metastases (CRLM) and stratify the patients into different risk gr...

Deep learning-based compressed SENSE improved diffusion-weighted image quality and liver cancer detection: A prospective study.

PURPOSE: To assess whether diffusion-weighted imaging (DWI) with Compressed SENSE (CS) and deep learning (DL-CS-DWI) can improve image quality and les...

Deep causal learning for pancreatic cancer segmentation in CT sequences.

Segmenting the irregular pancreas and inconspicuous tumor simultaneously is an essential but challenging step in diagnosing pancreatic cancer. Current...

S2DA-Net: Spatial and spectral-learning double-branch aggregation network for liver tumor segmentation in CT images.

Accurate liver tumor segmentation is crucial for aiding radiologists in hepatocellular carcinoma evaluation and surgical planning. While convolutional...

A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features.

BACKGROUND: Positron emission tomography (PET) is extensively employed for diagnosing and staging various tumors, including liver cancer, lung cancer,...

Cascade-EC Network: Recognition of Gastrointestinal Multiple Lesions Based on EfficientNet and CA_stm_Retinanet.

Capsule endoscopy (CE) is non-invasive and painless during gastrointestinal examination. However, capsule endoscopy can increase the workload of image...

Therapeutic endoscopy: Recent updates and future directions.

The landscape of therapeutic endoscopy has undergone a remarkable evolution over the past few decades, carving out a niche that merges innovative tech...

Duodenal papilla radiomics-based prediction model for post-ERCP pancreatitis using machine learning: a retrospective multicohort study.

BACKGROUND AND AIMS: The duodenal papillae are the primary and essential pathway for ERCP, greatly determining its complexity and outcome. We investig...

A muti-modal feature fusion method based on deep learning for predicting immunotherapy response.

Immune checkpoint therapy (ICT) has greatly improved the survival of cancer patients in the past few years, but only a small number of patients respon...

Multistep validation of a post-ERCP pancreatitis prediction system integrating multimodal data: a multicenter study.

BACKGROUND AND AIMS: The impact of various categories of information on the prediction of post-ERCP pancreatitis (PEP) remains uncertain. We comprehen...

Rapid Endoscopic Diagnosis of Benign Ulcerative Colorectal Diseases With an Artificial Intelligence Contextual Framework.

BACKGROUND & AIMS: Benign ulcerative colorectal diseases (UCDs) such as ulcerative colitis, Crohn's disease, ischemic colitis, and intestinal tubercul...

Precise highlighting of the pancreas by semantic segmentation during robot-assisted gastrectomy: visual assistance with artificial intelligence for surgeons.

BACKGROUND: A postoperative pancreatic fistula (POPF) is a critical complication of radical gastrectomy for gastric cancer, mainly because surgeons oc...

Development and validation of an imageless machine-learning algorithm for the initial screening of prostate cancer.

PURPOSE: Prostate specific antigen (PSA) testing is a low-cost screening method for prostate cancer (PCa). However, its accuracy is limited. While pro...

Automated detection of small bowel lesions based on capsule endoscopy using deep learning algorithm.

BACKGROUND: In order to overcome the challenges of lesion detection in capsule endoscopy (CE), we improved the YOLOv5-based deep learning algorithm an...

Deep-learning-based image super-resolution of an end-expandable optical fiber probe for application in esophageal cancer diagnostics.

SIGNIFICANCE: Endoscopic screening for esophageal cancer (EC) may enable early cancer diagnosis and treatment. While optical microendoscopic technolog...

Long-term outcomes of robot-assisted laparoscopic surgery versus conventional laparoscopic surgery for rectal cancer: single-center, retrospective, propensity score analyses.

Although the short-term outcomes of robot-assisted laparoscopic surgery (RALS) for rectal cancer are well known, the long-term oncologic outcomes of R...