Over the past decade, there has been a considerable increase in the utilization of cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) in the management of patients with peritoneal metastases. This is due to improved safety and favorable oncologic outcomes, including curative potential. CRS/HIPEC has a steep learning curve and requires familiarity with peritonectomy procedures. This review will outline the technical aspects and learning curve of CRS/HIPEC.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/jso.25939 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Learning curve and proficiency assessment for gynecological brachytherapy amongst radiation oncology trainees in India: Results from a prospective study.
Brachytherapy
January 2025
Department of Radiation Oncology and Medical Physics, Advanced Centre for Treatment Research and Education in Cancer, Tata Memorial Centre, Homi Bhabha National Institute, Navi Mumbai, India. Electronic address:
Purpose: The quality of cervical cancer intracavitary brachytherapy (ICBT) depends on the training and experience of the radiation oncologist (RO). The present study was performed to establish primary learning curve for ICBT.
Materials And Methods: Forty-three skill parameters were identified for performing ICBT and were included for Brachytherapy Proficiency Assessment and Scoring System (Brachy-PASS) questionnaire.
Voice Quality as Digital Biomarker in Bipolar Disorder: A Systematic Review.
J Voice
January 2025
Department of Surgery, UMONS Research Institute for Health Sciences and Technology, University of Mons (UMons), Mons, Belgium; Division of Laryngology and Bronchoesophagology, Department of Otolaryngology Head Neck Surgery, EpiCURA Hospital, Baudour, Belgium; Department of Otolaryngology-Head and Neck Surgery, Foch Hospital, School of Medicine, UFR Simone Veil, Université Versailles Saint-Quentin-en-Yvelines (Paris Saclay University), Paris, France; Department of Otolaryngology, Elsan Hospital, Paris, France. Electronic address:
Background: Voice analysis has emerged as a potential biomarker for mood state detection and monitoring in bipolar disorder (BD). The systematic review aimed to summarize the evidence for voice analysis applications in BD, examining (1) the predictive validity of voice quality outcomes for mood state detection, and (2) the correlation between voice parameters and clinical symptom scales.
Methods: A PubMed, Scopus, and Cochrane Library search was carried out by two investigators for publications investigating voice quality in BD according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statements.
EEG microstate analysis and machine learning classification in patients with obsessive-compulsive disorder.
J Psychiatr Res
January 2025
Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China. Electronic address:
Background: Microstate characterization of electroencephalogram (EEG) is a data-driven approach to explore the functional changes and interrelationships of multiple brain networks on a millisecond scale. This study aimed to explore the pathological changes of whole-brain functional networks in patients with obsessive-compulsive disorders (OCD) through microstate analysis and further to explore its potential value as an auxiliary diagnostic index.
Methods: Forty-eight OCD patients (33 with more than moderate anxiety symptoms, 15 with mild anxiety symptoms) and 52 healthy controls (HCs) were recruited.
Implementing the discontinuous-Galerkin finite element method using graph neural networks with application to diffusion equations.
Neural Netw
December 2024
Department of Earth Science and Engineering, Imperial College London, Prince Consort Road, London SW7 2BP, UK; Centre for AI-Physics Modelling, Imperial-X, White City Campus, Imperial College London, W12 7SL, UK.
Machine learning (ML) has benefited from both software and hardware advancements, leading to increasing interest in capitalising on ML throughout academia and industry. There have been efforts in the scientific computing community to leverage this development via implementing conventional partial differential equation (PDE) solvers with machine learning packages, most of which rely on structured spatial discretisation and fast convolution algorithms. However, unstructured meshes are favoured in problems with complex geometries.
View Article and Find Full Text PDFEnhancing Small-for-Gestational-Age Prediction: Multi-Country Validation of Nuchal Thickness, Estimated Fetal Weight, and Machine Learning Models.
Prenat Diagn
January 2025
Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.
Objective: The first objective is to develop a nuchal thickness reference chart. The second objective is to compare rule-based algorithms and machine learning models in predicting small-for-gestational-age infants.
Method: This retrospective study involved singleton pregnancies at University Malaya Medical Centre, Malaysia, developed a nuchal thickness chart and evaluated its predictive value for small-for-gestational-age using Malaysian and Singapore cohorts.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!