Purpose: To examine the diagnostic performance of PI-RADSv2 T2w and diffusion weighted imaging (DWI) based lexicon descriptors, inter-observer agreement for descriptor assignment and diagnostic accuracy of the PI-RADSv2 assessment categories for multiparametric prostate MRI.
Materials And Methods: 176 lesions in 79 consecutive patients are analyzed, lesions are histopathologically verified by MRI-ultrasound fusion biopsy. All lesions are rated according to the PI-RADSv2 lexicon, descriptors for T2w and DWI sequences and resulting assessment categories are assigned by two independent blinded radiologists. We perform receiver-operating-characteristic analysis using the assessment categories. To analyze inter-observer agreement, we calculate weighted kappa values for assessment category assignment and unweighted kappa values for descriptor assignment.
Results: PI-RADSv2 assessment categories yield an area under the curve of 0.76/0.74 (radiologist 1/radiologist 2), P >0.05. Weighted kappa for agreement is 0.601 in the peripheral zone and 0.580 in the transition zone. We detect a difference in the cancer rate for PI-RADSv2 category 3 between peripheral zone (32%) and transition zone (12%), P <0.05. We obtain moderate agreement at most for descriptor assignment with kappa values ranging from 0.082 (T2w shape in the transition zone) to 0.407 (T2w signal intensity in the peripheral zone) and 0.493 (ADC pattern in the peripheral zone). Our analysis corroborates typical descriptors for benign/malignant lesions, but also reveals insights into potential pitfalls - T2w wedge shaped lesions in the peripheral zone have a considerable cancer rate, despite being labelled category 2 in the lexicon.
Conclusion: Agreement for descriptor assignment in the PI-RADSv2 lexicon is at most moderate in our study. Typical descriptors for benign and malignant lesions are validated, whereas the discriminatory power of some descriptors is challenged. The difference in the cancer rate for PI-RADSv2 category 3 between peripheral zone and transition zone should be considered when management recommendations are linked to assessment categories in the future.
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http://dx.doi.org/10.1016/j.ejrad.2017.05.015 | DOI Listing |
Lipids Health Dis
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Department of Orthopedics, The 921st Hospital of the People's Liberation Army, The Second Affiliated Hospital of Hunan Normal University, Changsha, 410003, People's Republic of China.
Background: The metabolic score for visceral fat (METS-VF) is a recently identified index for evaluating visceral fat, also referred to as abdominal obesity. The skeletal muscle mass index (SMI) serves as a critical measure for assessing muscle mass and sarcopenia. Both obesity and the reduction of muscle mass can significantly affect human health.
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Bangkok Hospital Dental Center Holistic Care and Dental Implant, Bangkok Hospital, Bangkok, 10310, Thailand.
Background: Assessing the difficulty of impacted lower third molar (ILTM) surgical extraction is crucial for predicting postoperative complications and estimating procedure duration. The aim of this study was to evaluate the effectiveness of a convolutional neural network (CNN) in determining the angulation, position, classification and difficulty index (DI) of ILTM. Additionally, we compared these parameters and the time required for interpretation among deep learning (DL) models, sixth-year dental students (DSs), and general dental practitioners (GPs) with and without CNN assistance.
View Article and Find Full Text PDFBMC Psychiatry
January 2025
West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
The current DSM-oriented diagnostic paradigm has introduced the issue of heterogeneity, as it fails to account for the identification of the neurological processes underlying mental illnesses, which affects the precision of treatment. The Research Domain Criteria (RDoC) framework serves as a recognized approach to addressing this heterogeneity, and several assessment and translation techniques have been proposed. Among these methods, transforming RDoC scores from electronic medical records (EMR) using Natural Language Processing (NLP) has emerged as a suitable technique, demonstrating clinical effectiveness.
View Article and Find Full Text PDFTher Innov Regul Sci
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Bayer AG, Pharmaceuticals R&D, Bayer AG, Muellerstr. 178, 13342, Berlin, Germany.
Medicine is increasingly supported by software, with digital health technologies offering innovative ways to capture insights and drive therapies. Globally, medical device software must follow regulatory processes based on risk classification. The introduction of MDR represents a significant shift in risk-based classification for Medical Devices in Europe, including classification Rule 11 for software, which has caused significant discussions among European regulators.
View Article and Find Full Text PDFInt J Obes (Lond)
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Department of Biosciences, COMSATS University Islamabad, Park Road Tarlai, Islamabad, 45550, Pakistan.
Background: Obesity plays a crucial role in the development of metabolic disorders including diabetes, coronary and renal diseases. There are several factors involved in the pathology of obesity, including chronic inflammation and exposure to environmental contaminants. Recently, the cholinergic co-hydrolyzing enzyme BChE has been associated with clinical conditions such as diabetes and obesity.
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