Introduction: Lateral pelvic lymph node dissection (LPLND) is a technically challenging procedure and its learning curve has not been analysed against an oncologically relevant outcome. The purpose of the study was to determine the learning curve for LPLND in rectal cancers using nodal retrieval as performance measure.
Methods: Consecutive LPLND for rectal adenocarcinomas from a single institution were retrospectively analysed. Cumulative sum (CUSUM) control charts were used to detect difference in performance with respect to lymph node yield. Negative binomial regression was used to determine factors influencing nodal harvest using Incidence Risk Ratios (IRR). Separate CUSUM curves were generated for open and minimally invasive surgeries (MIS).
Results: One-hundred and twenty patients were included and all received preoperative radiation. MIS was used in 53.3%. Median lymph node yield was 6 with 20% nodal positivity. Increasing experience (IRR - 1.196) and MIS (IRR - 1.586) were the only factors that influenced nodal harvest. CUSUM charts revealed that learning curve was achieved after the 83rd case overall and after the 19 operations in MIS. There was a 20% increase in nodal yield after every 30 MIS LPLND performed.
Conclusions: Learning curve for LPLND is relatively long and only increasing experience and minimally invasive operations increased nodal yield.
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http://dx.doi.org/10.1016/j.ejso.2021.12.003 | DOI Listing |
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.
Diagn Interv Radiol
January 2025
Huadong Hospital, Fudan University, Department of Thoracic Surgery, Shanghai, China.
Purpose: Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomarkers to accurately predict its therapeutic efficacy. The present study aimed to construct individualized models through automatic machine learning (autoML) to predict the efficacy of immunotherapy in patients with inoperable advanced NSCLC.
Methods: A total of 63 eligible participants were included and randomized into training and validation groups.
Liver Int
February 2025
Liver Research Center, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Background And Aim: Discriminating between idiosyncratic drug-induced liver injury (DILI) and autoimmune hepatitis (AIH) is critical yet challenging. We aim to develop and validate a machine learning (ML)-based model to aid in this differentiation.
Methods: This multicenter cohort study utilised a development set from Beijing Friendship Hospital, with retrospective and prospective validation sets from 10 tertiary hospitals across various regions of China spanning January 2009 to May 2023.
Bioelectromagnetics
January 2025
Department of Electrical Engineering and ITEMS, University of Southern California, Los Angeles, California, USA.
As the clinical applicability of peripheral nerve stimulation (PNS) expands, the need for PNS-specific safety criteria becomes pressing. This study addresses this need, utilizing a novel machine learning and computational bio-electromagnetics modeling platform to establish a safety criterion that captures the effects of fields and currents induced on axons. Our approach is comprised of three steps: experimentation, model creation, and predictive simulation.
View Article and Find Full Text PDFWorld J Gastrointest Oncol
January 2025
Department of Hepatobiliary and Pancreaticosplenic Surgery, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou 434100, Hubei Province, China.
Background: The liver, as the main target organ for hematogenous metastasis of colorectal cancer, early and accurate prediction of liver metastasis is crucial for the diagnosis and treatment of patients. Herein, this study aims to investigate the application value of a combined machine learning (ML) based model based on the multiparameter magnetic resonance imaging for prediction of rectal metachronous liver metastasis (MLM).
Aim: To investigate the efficacy of radiomics based on multiparametric magnetic resonance imaging images of preoperative first diagnosed rectal cancer in predicting MLM from rectal cancer.
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