The image biomarker standardization initiative: Standardized convolutional filters for reproducible radiomics and enhanced clinical insights

Standardizing convolutional filters that enhance specific structures and patterns in medical imaging enables reproducible radiomics analyses, improving consistency and reliability for enhanced clinical insights. Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking

Causes of discharge against medical advice in hospitals affiliated with mazandaran university of medical sciences, 2014

Background and purpose: Discharge against medical advice (AMA) has negative impacts on treatment outcomes and health care resources. Also, physicians and hospital administrators get involved in litigation. The aim of this study was to investigate the causes of AMA discharges in different wards and emergency departments in teaching and nonteaching hospitals affiliated with Mazandaran University of Medical Sciences. Materials and methods: This cross-sectional study was performed investigating the AMA leaves within 6 months in 2014. Data was obtained from the Department of Medical Records and the Office of Quality Improvement. Data was recorded in a questionnaire consisting of two parts. Part I included demographic information and Part II was about the reasons of discharge against medical advice. Data was aggregated by expert staff in Office of Quality Improvement. SPSS V.18 was applied for data analysis. Results: The prevalence of AMA leaves in emergency department and other wards in teaching hospitals affiliated with Mazandaran University of Medical Sciences was 3.81 and 4.38, respectively. These figures in nonteaching hospitals were 8.38 and 1.59, respectively. The most common causes of discharge against medical advice were partial recovery, lack of trust in service quality, feeling uncomfortable, and emotional reasons. Conclusion: To reduce the rate of AMA leaves and its consequences, health care staff should be supportive towards the patients and communicate effectively in order to build their confidence. © 2016, Mazandaran University of Medical Sciences. All rights reserved

Robust identification of Parkinson's disease subtypes using radiomics and hybrid machine learning

Objectives: It is important to subdivide Parkinson's disease (PD) into subtypes, enabling potentially earlier disease recognition and tailored treatment strategies. We aimed to identify reproducible PD subtypes robust to variations in the number of patients and features. Methods: We applied multiple feature-reduction and cluster-analysis methods to cross-sectional and timeless data, extracted from longitudinal datasets (years 0, 1, 2 & 4; Parkinson's Progressive Marker Initiative; 885 PD/163 healthy-control visits; 35 datasets with combinations of non-imaging, conventional-imaging, and radiomics features from DAT-SPECT images). Hybrid machine-learning systems were constructed invoking 16 feature-reduction algorithms, 8 clustering algorithms, and 16 classifiers (C-index clustering evaluation used on each trajectory). We subsequently performed: i) identification of optimal subtypes, ii) multiple independent tests to assess reproducibility, iii) further confirmation by a statistical approach, iv) test of reproducibility to the size of the samples. Results: When using no radiomics features, the clusters were not robust to variations in features, whereas, utilizing radiomics information enabled consistent generation of clusters through ensemble analysis of trajectories. We arrived at 3 distinct subtypes, confirmed using the training and testing process of k-means, as well as Hotelling's T2 test. The 3 identified PD subtypes were 1) mild; 2) intermediate; and 3) severe, especially in terms of dopaminergic deficit (imaging), with some escalating motor and non-motor manifestations. Conclusion: Appropriate hybrid systems and independent statistical tests enable robust identification of 3 distinct PD subtypes. This was assisted by utilizing radiomics features from SPECT images (segmented using MRI). The PD subtypes provided were robust to the number of the subjects, and features. © 2020 Elsevier Lt

Effects of aging and coronary artery disease on sympathetic neural recruitment strategies during end-inspiratory and end-expiratory apnea

© 2016 the American Physiological Society. In response to acute physiological stress, the sympathetic nervous system modifies neural outflow through increased firing frequency of lowerthreshold axons, recruitment of latent subpopulations of higherthreshold axons, and/or acute modifications of synaptic delays. Aging and coronary artery disease (CAD) often modify efferent muscle sympathetic nerve activity (MSNA). Therefore, we investigated whether CAD (n = 14; 61 ± 10 yr) and/or healthy aging without CAD (OH; n = 14; 59 ± 9 yr) modified these recruitment strategies that normally are observed in young healthy (YH; n = 14; 25 ± 3 yr) individuals. MSNA (microneurography) was measured at baseline and during maximal voluntary end-inspiratory (EI) and end-expiratory (EE) apneas. Action potential (AP) patterns were studied using a novel AP analysis technique. AP frequency increased in all groups during both EI-and EE-apnea (all P \u3c 0.05). The mean AP content per integrated burst increased during EI-and EE-apnea in YH (EI: Δ6 ± 4 APs/burst; EE: Δ10 ± 6 APs/burst; both P \u3c 0.01) and OH (EI: Δ3 ± 3 APs/burst; EE: Δ4 ± 5 APs/burst; both P \u3c 0.01), but not in CAD (EI: Δ1 ± 3 APs/burst; EE: Δ2 ± 3 APs/burst; both P = NS). When APs were binned into “clusters” according to peak-to-peak amplitude, total clusters increased during EI-and EE-apnea in YH (EI: Δ5 ± 2; EE: Δ6 ± 4; both P \u3c 0.01), during EI-apnea only in OH (EI: Δ1 ± 2; P \u3c 0.01; EE: Δ1 ± 2; P = NS), and neither apnea in CAD (EI: Δ –2 ± 2; EE: Δ –1 ± 2; both P = NS). In all groups, the AP cluster size-latency profile was shifted downwards for every corresponding cluster during EI-and EE-apnea (all P \u3c 0.01). As such, inherent dysregulation exists within the central features of apnea-related sympathetic outflow in aging and CAD

Menstrual cycle and sex effects on sympathetic responses to acute chemoreflex stress

© 2015 the American Physiological Society. This study aimed to examine the effects of sex (males vs. females) and sex hormones (menstrual cycle phases in women) on sympathetic responsiveness to severe chemoreflex activation in young, healthy individuals. Muscle sympathetic nerve activity (MSNA) was measured at baseline and during rebreathing followed by a maximal end-inspiratory apnea. In women, baseline MSNA was greater in the midluteal (ML) than early-follicular (EF) phase of the menstrual cycle. Baseline MSNA burst incidence was greater in men than women, while burst frequency and total MSNA were similar between men and women only in the ML phase. Chemoreflex activation evoked graded increases in MSNA burst frequency, amplitude, and total activity in all participants. In women, this sympathoexcitation was greater in the EF than ML phase. The sympathoexcitatory response to chemoreflex stimulation of the EF phase in women was also greater than in men. Nonetheless, changes in total peripheral resistance were similar between sexes and menstrual cycle phases. This indicates that neurovascular transduction was attenuated during the EF phase during chemoreflex activation, thereby offsetting the exaggerated sympathoexcitation. Chemoreflex-induced increases in mean arterial pressure were similar across sexes and menstrual cycle phases. During acute chemoreflex stimulation, reduced neurovascular transduction could provide a mechanism by which apnea-associated morbidity might be attenuated in women relative to men