We have found that the MIB-1 grade, based on tumor differentiation/histological type, necrosis and Ki-67 (MIB-1) score, is a valid and reproducible prognostic system for adult soft-tissue sarcomas. However, there are limited data available on Ki-67 labeling indices (LI) from adult soft-tissue sarcomas for testing the validity of quantitative image analysis. In this study, the records of 146 adult patients with soft-tissue sarcomas of the extremities and trunk were retrieved, and MIB-1 immunostaining was carried out for the grading. The counted MIB-1 LI values and the scores estimated from microscopic observation were defined as the gold standard. The correlation between MIB-1 LI as assessed by computer-assisted image analysis and by microscopic observation was determined. The image analysis -based MIB-1 LI was highly correlated with the microscopic observation-based MIB-1 LI (r = 0.87, 95% confidence interval (CI) = 0.82-0.92). In addition, agreement between the MIB-1 scores was very high (kappa statistic = 0.83, 95% CI = 0.75-0.91), as was the percentage agreement (89%, 95% CI = 82.8-93.6%) between the results from image analysis and microscopic observation. We conclude that quantitative immunohistochemical evaluation of MIB-1 LI by image analysis enables pathologists to improve interobserver agreement in the assessment of MIB-1 score, and can help to objectively assign the correct histological grade to cases of adult soft-tissue sarcoma, resulting in optimal clinical management.
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http://dx.doi.org/10.1046/j.1440-1827.2002.01378.x | DOI Listing |
Background: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and validate an explainable machine learning model to evaluate overall health status in patients with comorbid CHD and depression.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Neurology, West China Hospital, Sichuan University, Chengdu, China.
Background: Despite the increasing popularity of electronic devices, the longitudinal effects of daily prolonged electronic device usage on brain health and the aging process remain unclear.
Objective: The aim of this study was to investigate the impact of the daily use of mobile phones/computers on the brain structure and the risk of neurodegenerative diseases.
Methods: We used data from the UK Biobank, a longitudinal population-based cohort study, to analyze the impact of mobile phone use duration, weekly usage time, and playing computer games on the future brain structure and the future risk of various neurodegenerative diseases, including all-cause dementia (ACD), Alzheimer disease (AD), vascular dementia (VD), all-cause parkinsonism (ACP), and Parkinson disease (PD).
JMIR Med Inform
January 2025
Institute of History and Ethics in Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Background: In data-sparse areas such as health care, computer scientists aim to leverage as much available information as possible to increase the accuracy of their machine learning models' outputs. As a standard, categorical data, such as patients' gender, socioeconomic status, or skin color, are used to train models in fusion with other data types, such as medical images and text-based medical information. However, the effects of including categorical data features for model training in such data-scarce areas are underexamined, particularly regarding models intended to serve individuals equitably in a diverse population.
View Article and Find Full Text PDFInvest Radiol
January 2025
From the Department of Radiology, Ulsan University Hospital, Ulsan, Republic of Korea (T.Y.L.); Department of Radiology, University of Ulsan College of Medicine, Seoul, Republic of Korea (T.Y.L.); Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea (J.H.Y., H.K., J.M.L.); Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (J.H.Y., S.H.P., J.M.L.); Department of Radiology, Inje University Busan Paik Hospital, Busan, Republic of Korea (J.Y.P.); Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea (S.H.P.); Department of Radiology, Hanyang University College of Medicine, Seoul, Republic of Korea (C.L.); Division of Biostatistics, Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Republic of Korea (Y.C.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (J.M.L.).
Objective: The aim of this study was to intraindividually compare the conspicuity of focal liver lesions (FLLs) between low- and ultra-low-dose computed tomography (CT) with deep learning reconstruction (DLR) and standard-dose CT with model-based iterative reconstruction (MBIR) from a single CT using dual-split scan in patients with suspected liver metastasis via a noninferiority design.
Materials And Methods: This prospective study enrolled participants who met the eligibility criteria at 2 tertiary hospitals in South Korea from June 2022 to January 2023. The criteria included (a) being aged between 20 and 85 years and (b) having suspected or known liver metastases.
PLoS One
January 2025
Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
Background: The relationships between pectoralis muscle parameters and outcomes in patients with coronavirus disease 2019 (COVID-19) remain uncertain.
Methods: We systematically searched PubMed, Embase, Web of Science and the Cochrane Library from 1 January 2019 to 1 May 2024 to identify non-overlapping studies evaluating pectoralis muscle-associated index on chest CT scan with clinical outcome in COVID-19 patients. Random-effects and fixed-effects meta-analyses were performed, and heterogeneity between studies was quantified using the I2 statistic.
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