Background And Purpose: (1) To establish a method to evaluate dosimetry at the time of primary prostate permanent implant (pPPI) using MRI of the shrunken prostate at the time of failure (tf). (2) To compare cold spot mapping with sextant-biopsy mapping at tf.
Material And Methods: Twenty-four patients were referred for biopsy-proven local failure (LF) after pPPI. Multiparametric MRI and combined-sextant biopsy with a central review of the pathology at tf were systematically performed. A model of the shrinking pattern was defined as a Volumetric Change Factor (VCF) as a function of time from time of pPPI (t0). An isotropic expansion to both prostate volume (PV) and seed position (SP) coordinates determined at tf was performed using a validated algorithm using the VCF.
Results: pPPI CT-based evaluation (at 4weeks) vs. MR-based evaluation: Mean D90% was 145.23±19.16Gy [100.0-167.5] vs. 85.28±27.36Gy [39-139] (p=0.001), respectively. Mean V100% was 91.6±7.9% [70-100%] vs. 73.1±13.8% [55-98%] (p=0.0006), respectively. Seventy-seven per cent of the pathologically positive sextants were classified as cold.
Conclusions: Patients with biopsy-proven LF had poorer implantation quality when evaluated by MRI several years after implantation. There is a strong relationship between microscopic involvement at tf and cold spots.
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http://dx.doi.org/10.1016/j.radonc.2013.10.028 | DOI Listing |
Sci Rep
January 2025
Department of Health Informatics, Institute of Public Health, College of Medicine & Health Sciences, University of Gondar, Gondar, Ethiopia.
United Nations is standing for Sustainable Development Goal (SDG) 6 sets the agenda to address worldwide inequality in accessing safe water and improved sanitation facilities for all by 2030. However, governments in Africa seem unable to address the issue water and of sanitation facilities, since there are problems like increasing costs of sustaining existing water sources and the requirement to deliver new facilities ahead of time. Hence, this study aimed to investigate unimproved water sources and sanitation facilities geographical variation in Ethiopia using EDHS 2019 datasets.
View Article and Find Full Text PDFFront Public Health
January 2025
Department of Internal Medicine, School of Medicine, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
Health Policy Plan
December 2024
Global Health Unit, Department of Health Sciences, University Medical Center Groningen, Groningen, the Netherlands.
While there is ample evidence of the overall reduction in perinatal and pregnancy-related mortality in Ethiopia, it remains uncertain if geographic disparities have diminished. This study aimed to investigate perinatal and pregnancy-related mortality spatial distributions, trends over time, and factors associated with the distribution in Ethiopia. We used data from Ethiopian Demographic and Health Surveys conducted in Ethiopia in 2000, 2005, 2011, and 2016.
View Article and Find Full Text PDFSensors (Basel)
November 2024
College of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China.
Based on the traditional saddle instrument, a portable roll-profile measuring device based on a contact sensor is designed and optimized. The positioning module is added via the machine vision method, which enables the automatic reading of measurement points. The measurement accuracy of the device is 1 μm.
View Article and Find Full Text PDFMed Phys
December 2024
Département de physique, de génie physique et d'optique, et Centre de recherche sur le cancer, Université Laval, Québec, Quebec, Canada.
Background: Recently, high-dose-rate (HDR) brachytherapy treatment plans generation was improved with the development of multicriteria optimization (MCO) algorithms that can generate thousands of pareto optimal plans within seconds. This brings a shift, from the objective of generating an acceptable plan to choosing the best plans out of thousands.
Purpose: In order to choose the best plans, new criteria beyond usual dosimetrics volumes histogram (DVH) metrics are introduced and a deep learning (DL) framework is added as an automatic plan selection algorithm.
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