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18F-Sodium Fluoride PET/CT as a Tool to Assess Enthesopathies in X-Linked Hypophosphatemia.

Calcif Tissue Int

January 2025

Endocrinology Department, School of Medicine, Pontificia Universidad Católica de Chile, Av. Diagonal Paraguay 262, Cuarto Piso, Santiago, Chile.

X-linked hypophosphatemia (XLH) is a rare metabolic disorder characterized by elevated FGF23 and chronic hypophosphatemia, leading to impaired skeletal mineralization and enthesopathies that are associated with pain, stiffness, and diminished quality of life. The natural history of enthesopathies in XLH remains poorly defined, partly due to absence of a sensitive quantitative tool for assessment and monitoring. This study investigates the utility of 18F-NaF PET/CT scans in characterizing enthesopathies in XLH subjects.

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Objectives: To conduct a meta-analysis of the diagnostic performance of non-contrast magnetic resonance pulmonary angiography (NC-MRPA) and ventilation-perfusion (V/Q) scintigraphy for the detection of acute pulmonary embolism (PE).

Materials And Methods: Systematic searches of electronic databases were conducted from 2000 to 2024. Primary outcomes were per-patient sensitivity and specificity of NC-MRPA and V/Q scintigraphy.

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We intended to investigate the potential of several transitional zone (TZ) volume-related variables for the detection of clinically significant prostate cancer (csPCa) among lesions scored as Prostate Imaging Reporting and Data System (PI-RADS) category 3. Between September 2018 and August 2023, patients who underwent mpMRI examination and scored as PI-RADS 3 were queried from our institution. The diagnostic performances of prostate-specific antigen density (PSAD), TZ-adjusted PSAD (TZPSAD), and TZ-ratio (TZ volume/whole gland prostate volume) were analyzed.

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Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.

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The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.

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