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Objectives: To clarify the heterogeneous development of liver fibrosis in patients with chronic hepatitis C (CHC) using extracellular volume fraction (ECV) map obtained from routine clinical CT data.
Methods: Between November 2012 and July 2020, patients with CHC were retrospectively recruited who had undergone four-phase CT and MR elastography (MRE) within one year. Patients were divided into 4 grades to represent different cirrhotic/fibrotic stage, using two different methods; one based on liver stiffness measured by MRE (MRE model), and the other by mALBI grades (mALBI model). Liver was anatomically divided into 16 sections, namely peripheral and central areas of each segment. ECV map was generated according to the previously reported method, and ECV was measured for the 16 sections. Estimated pathological fibrosis grade was assigned for each section based on the previously reported data.
Results: There were 150 patients available. In each anatomical section, ECV significantly increases as cirrhotic /fibrotic stage progresses. The peripheral areas of segments 4,5 and 8 were the earliest to show F2 or F3-equivalent ECV (p < 0.05), followed by central areas or other segments. The central areas of segments 6 and 7 were the last to be involved by fibrosis both in MRE and mALBI models, finally almost all sections showing F4-equivalent ECV at the end stage fibrosis.
Conclusion: Fibrosis starts at the peripheral areas of segments 4, 5, and 8, and spreads towards other parts of the liver, with the central areas of segments 6 and 7 being the last, in patients with CHC.
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http://dx.doi.org/10.1016/j.ejrad.2024.111845 | DOI Listing |
Biometrics
October 2024
Evidence Generation and Advanced Analytics Biogen Digital Health, Biogen, Cambridge, MA 02142, United States.
In many clinical contexts, the event of interest could occur multiple times for the same patient. Considerable advancement has been made on developing recurrent event models based on or that use biomarker information. However, less attention has been given to evaluating the prognostic accuracy of a biomarker or a composite score obtained from a fitted recurrent event-rate model.
View Article and Find Full Text PDFHum Brain Mapp
December 2024
SEB Centre for Brain Resilience & Recovery, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Canada.
White matter hyperintensities (WMH) of presumed vascular origin are a magnetic resonance imaging (MRI)-based biomarker of cerebral small vessel disease (CSVD). WMH are associated with cognitive decline and increased risk of stroke and dementia, and are commonly observed in aging, vascular cognitive impairment, and neurodegenerative diseases. The reliable and rapid measurement of WMH in large-scale multisite clinical studies with heterogeneous patient populations remains challenging, where the diversity of imaging characteristics across studies adds additional complexity to this task.
View Article and Find Full Text PDFFront Oncol
December 2024
Department of Gastroenterology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
Gastric mixed adenoneuroendocrine carcinoma (MANEC) is a rare and highly aggressive malignancy characterized by both exocrine and neuroendocrine components. Treatment options for metastatic cases are limited, with typical therapeutic approaches involving a combination of chemotherapy and immunotherapy. A 68-year-old male with metastatic gastric MANEC was treated with targeted therapy, immunotherapy, and chemotherapy, including S-1, apatinib, cadonilimab, and paclitaxel.
View Article and Find Full Text PDFFront Med (Lausanne)
December 2024
Department of Immunology and Infectious Disease, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.
Background And Hypothesis: A static predictive model relying solely on baseline clinicopathological data cannot capture the heterogeneity in predictor trajectories observed in the progression of chronic kidney disease (CKD). To address this, we developed and validated a dynamic survival prediction model using longitudinal clinicopathological data to predict end-stage kidney disease (ESKD), with death as a competing risk.
Methods: We trained a sequence of random survival forests using a landmarking approach and optimized the model with a pre-specified prediction horizon of 5 years.
Front Public Health
December 2024
Department of Obstetrics, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Background: This study aimed to identify the types of quality of life (QoL) based on the five dimensions of the EQ-5D and predict factors affecting QoL.
Methods: A multistage stratified cluster sampling survey was conducted among the staff of 12 general hospitals, 1,965 nurses completed the survey, and the data were analyzed using SPSS 26.0 and Mplus 8.
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