An ECG-Based Model for Left Ventricular Hypertrophy Detection: A Machine Learning Approach.

Despite the high incidence of left ventricular hypertrophy (LVH), clinical LVH-electrocardiography (ECG) criteria remain unsatisfactory due to low sensitivity. We propose an automatic LVH detection method based on ECG-extracted features and machine learning. ECG features were automatically extracted from two publicly available databases: PTB-XL with 2181 LVH and 9001 controls, and Georgia with 1012 LVH and 1387 controls.

Predicting vulnerable coronary arteries: A combined radiomics-biomechanics approach.

Background And Objective: Nowadays, vulnerable coronary plaque detection from coronary computed tomography angiography (CCTA) is suboptimal, although being crucial for preventing major adverse cardiac events. Moreover, despite the suggestion of various vulnerability biomarkers, encompassing image and biomechanical factors, accurate patient stratification remains elusive, and a comprehensive approach integrating multiple markers is lacking. To this aim, this study introduces an innovative approach for assessing vulnerable coronary arteries and patients by integrating radiomics and biomechanical markers through machine learning methods.

Radiomic features of primary retroperitoneal sarcomas: a prognostic study.

Background: Risk-stratification of patients with retroperitoneal sarcomas (RPS) relies on validated nomograms, such as Sarculator. This retrospective study investigated whether radiomic features extracted from computed tomography (CT) imaging could i) enhance the performance of Sarculator and ii) identify G3 dedifferentiated liposarcoma (DDLPS) or leiomyosarcoma (LMS), which are currently consider in a randomized clinical trial testing neoadjuvant chemotherapy.

Methods: Patients with primary localized RPS treated with curative-intent surgery (2011-2015) and available pre-operative CT imaging were included.

Positive association of the thigh muscle thickness to intracellular-water ratio with handgrip strength, but not with the risk of mortality in hospitalized cancer patients: a longitudinal study.

This study explored the relationship between handgrip strength, muscle thickness, and the intracellular water ratio (MT/ICW) in cancer patients. It aimed to identify a cut-off point for the MT/ICW ratio that might influence survival. Conducted as an exploratory, longitudinal study in a public hospital, it included patients from 2018 to 2022, with follow-up until August 31, 2023.

Lung nodule classification using radiomics model trained on degraded SDCT images.

Background And Objective: Low-dose computed tomography (LDCT) screening has shown promise in reducing lung cancer mortality; however, it suffers from high false positive rates and a scarcity of available annotated datasets. To overcome these challenges, we propose a novel approach using synthetic LDCT images generated from standard-dose CT (SDCT) scans from the LIDC-IDRI dataset. Our objective is to develop and validate an interpretable radiomics-based model for distinguishing likely benign from likely malignant pulmonary nodules.