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Machine-Learning Based Computed Tomography Radiomics Nomgram For Predicting Perineural Invasion In Gastric Cancer.
Curr Med Imaging
January 2025
Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.
Objective: The aim of this study was to develop and validate predictive models for perineural invasion (PNI) in gastric cancer (GC) using clinical factors and radiomics features derived from contrast-enhanced computed tomography (CE-CT) scans and to compare the performance of these models.
Methods: This study included 205 GC patients, who were randomly divided into a training set (n=143) and a validation set (n=62) in a 7:3 ratio. Optimal radiomics features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm.
Performance of an Algorithm Grading Surgery-Related Adverse Events According to the Clavien-Dindo Classification.
Ann Surg
January 2025
Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
Objective: To assess performance of an algorithm for automated grading of surgery-related adverse events (AEs) according to Clavien-Dindo (C-D) classification.
Summary Background Data: Surgery-related AEs are common, lead to increased morbidity for patients, and raise healthcare costs. Resource-intensive manual chart review is still standard and to our knowledge algorithms using electronic health record (EHR) data to grade AEs according to C-D classification have not been explored.
A Mitochondria-Related Signature in Diffuse Large B-Cell Lymphoma: Prognosis, Immune and Therapeutic Features.
Cancer Med
January 2025
Department of Pharmacology, College of Pharmacy, Jinan University, Guangzhou, China.
Background: Distinctive heterogeneity characterizes diffuse large B-cell lymphoma (DLBCL), one of the most frequent types of non-Hodgkin's lymphoma. Mitochondria have been demonstrated to be closely involved in tumorigenesis and progression, particularly in DLBCL.
Objective: The purposes of this study were to identify the prognostic mitochondria-related genes (MRGs) in DLBCL, and to develop a risk model based on MRGs and machine learning algorithms.
Introduction: This study aimed to identify cognitive tests that optimally relate to tau positron emission tomography (PET) signal in the inferior temporal cortex (ITC), a neocortical region associated with early tau accumulation in Alzheimer's disease (AD).
Methods: We analyzed cross-sectional data from the harvard aging brain study (HABS) (= 128) and the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) study (= 393). We used elastic net regression to identify the most robust cognitive correlates of tau PET signal in the ITC.
Robustness of machine learning predictions for Fe-Co-Ni alloys prepared by various synthesis methods.
iScience
January 2025
School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Developing high-performance alloys is essential for applications in advanced electromagnetic energy conversion devices. In this study, we assess Fe-Co-Ni alloy compositions identified in our previous work through a machine learning (ML) framework, which used both multi-property ML models and multi-objective Bayesian optimization to design compositions with predicted high values of saturation magnetization, Curie temperature, and Vickers hardness. Experimental validation was conducted on two promising compositions synthesized using three different methods: arc melting, ball milling followed by spark plasma sintering (SPS), and chemical synthesis followed by SPS.
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