Forefoot plantar multilobular noninfiltrating angiolipoma: a case report and review of the literature

<p>Abstract</p> <p>Background</p> <p>Soft tissue tumors of the feet are uncommon and there have been very few reports of large series in the literature. These tumors continue to present the clinician with one of the most difficult problems in medicine.</p> <p>Case presentation</p> <p>We present a case of a large multilobular noninfiltrating angiolipoma at the plantar surface of the forefoot. Only three cases occurring at the foot have been previously described. We report this new case due to unusual location of the tumor, the long duration (25 years) of its existence and the unique surgical approach for the tumor excision.</p> <p>Conclusion</p> <p>Surgical excision is the treatment of choice and adjuvant radiotherapy is indicated in select cases.</p

Automated segmentation and classification of the atherosclerotic carotid plaque in ultrasound videos

The automated and reliable delineation of atherosclerotic carotid plaques in ultrasound (CUS) videos is of significant clinical relevance for management of the disease and the prediction of future stroke events. To facilitate stroke risk assessment, in this study, we propose an integrated software system for the automated segmentation and classification of atherosclerotic carotid plaques in longitudinal CUS videos, which was evaluated using 10 CUS videos, from 10 patients (5 Asymptomatic, AS, and 5 Symptomatic, SY). The proposed methodology involves the following steps: a) CUS video frame (VF) resolution and intensity normalization, b) speckle reduction filtering, c) Motion-mode state-based cardiac cycle (CC) identification, d) deep learning (DL)-based plaque segmentation, e) extraction and selection of plaque region of interest (ROI)-specific textural features, and f) machine learning (ML)-based plaque classification. Initially, one CC (cardiac diastole-systole-diastole) was selected per CUS video, and the CC's consecutive VFs were identified and reduced in number to exclude redundant VFs. All standardized VFs per patient were extracted, cropped and resized to mainly accommodate the ROI and were fed into a priorly trained and evaluated 2-dimensional DL plaque segmentation model. For each VF, the DL-based segmented plaque ROI was projected onto its primary resolution-normalized VF counterpart, from which textural and amplitude modulation-frequency modulation (AM-FM) plaque ROI features were extracted. Statistical analysis on the total AS and SY VFs was used for feature selection. We identified 2 plaque-originating AM-FM features, which exhibited statistically significant differences between the AS and SY standardized VFs (p<0.05), followed by 3 textural features (p<0.05). To finalize our system, in a future study, the strong AM-FM AS/SY descriptors, identified here, will be evaluated alone or in combinations with other plaque-descriptive features, in machine learning (ML)-based plaque classification, using a larger CUS video sample

Deep Learning-Based Segmentation of the Atherosclerotic Carotid Plaque in Ultrasonic Images

Deep Learning-Based Segmentation of the Atherosclerotic Carotid Plaque in Ultrasonic Images, vol. 652 IFIP, pp. 187 - 198Early stroke risk stratification in individuals with carotid atherosclerosis is of great importance, especially in high-risk asymptomatic (AS) cases. In this study, we present a new computer-aided diagnostic (CAD) system for the automated segmentation of the atherosclerotic plaque in carotid ultrasound (US) images and the extraction of a refined set of ultrasonic features to robustly characterize plaques in carotid US images and videos (AS vs symptomatic (SY)). So far, we trained a UNet model (16 to 256 neurons in the contracting path; the reverse, for the expanding path), starting from a dataset of 201 (AS = 109 and SY = 92) carotid US videos of atherosclerotic plaques, from which their first frames were extracted to prepare three subsets, a training, an internal validation, and final evaluation set, with 150, 30 and 15 images, respectively. The automated segmentations were evaluated based on manual segmentations, performed by a vascular surgeon. To assess our model’s capacity to segment plaques in previously unseen images, we calculated 4 evaluation metrics (mean ± std). The evaluation of the proposed model yielded a 0.736 ± 0.10 Dice similarity score (DSC), a 0.583 ± 0.12 intersection of union (IoU), a 0.728 ± 0.10 Cohen’s Kappa coefficient (KI) and a 0.65 ± 0.19 Hausdorff distance. The proposed segmentation workflow will be further optimized and evaluated, using a larger dataset and more neurons in each UNet layer, as in the original model architecture. Our results are close to others published in relevant studies

Examining Voting Capacity in Older Adults with and without Cognitive Decline

Background: Nowadays, controversy exists regarding the stage of cognitive decline and/or dementia where voting capacity is diminished. Aim: To evaluate whether general cognitive status in advancing age predicts voting capacity in its specific aspects. Methods: The study sample comprised 391 people: 88 cognitively healthy older adults (CH), 150 people with Mild Cognitive Impairment (MCI), and 153 people with Alzheimer’s disease dementia (ADD). The assessment included CAT-V for the voting capacity and Mini Mental State Examination (MMSE) for general cognitive ability. ANOVAs and ROC curves were the tools of statistical analysis towards (a) indicating under which MMSE rate participants are incapable of voting and (b) whether the CAT-V total score can discriminate people with dementia (PwADD) from people without dementia (PwtD). Results: Out of the six CAT-V questions, one question was associated with a low MMSE cutoff score (19.50), having excellent sensitivity (92.5%) and specificity (77.20%), whilst the other five questions presented a higher MMSE cutoff score, with a good sensitivity (78.4% to 87.6%) and specificity (75.3% to 81.7%), indicating that voting difficulties are associated with cognitive status. Secondarily, the total CAT-V score discriminates PwADD from PwtD of 51–65 years (sensitivity 93.2%/specificity 100%—excellent), PwADD from PwtD of 66–75 years (sensitivity 73.3%/specificity 97.1%—good), PwADD from PwtD of 76–85 years (sensitivity 92.2%/specificity 64.7%—good), whilst for 86–95 years, a cutoff of 9.5 resulted in perfect sensitivity and specificity (100%). Conclusion: According to MMSE, PwADD have no full voting competence, whilst PwtD seem to have intact voting capacity. The calculated cut-off scores indicate that only people who score more than 28 points on the MMSE have voting capacity

Are There Any Cognitive and Behavioral Changes Potentially Related to Quarantine Due to the COVID-19 Pandemic in People with Mild Cognitive Impairment and AD Dementia? A Longitudinal Study

The aim of the study was to examine potential cognitive, mood (depression and anxiety) and behavioral changes that may be related to the quarantine and the lockdown applied during the COVID-19 pandemic in Greek older adults with mild cognitive impairment (MCI), and AD dementia in mild and moderate stages. Method: 407 older adults, diagnosed either with MCI or AD dementia (ADD), were recruited from the Day Centers of the Greek Association of Alzheimer Disease and Related Disorders (GAADRD). Neuropsychological assessment was performed at baseline (at the time of diagnosis) between May and July of 2018, as well as for two consecutive follow-up assessments, identical in period, in 2019 and 2020. The majority of participants had participated in non-pharmacological interventions during 2018 as well as 2019, whereas all of them continued their participation online in 2020. Results: Mixed measures analysis of variance showed that participants’ ‘deterioration difference—D’ by means of their performance difference in neuropsychological assessments between 2018–2019 (D1) and 2019–2020 (D2) did not change, except for the FUCAS, RAVLT, and phonemic fluency tests, since both groups resulted in a larger deterioration difference (D2) in these tests. Additionally, three path models examining the direct relationships between performance in tests measuring mood, as well as everyday functioning and cognitive measures, showed that participants’ worsened performance in the 2019 and 2020 assessments was strongly affected by NPI performance, in sharp contrast to the 2018 assessment. Discussion: During the lockdown period, MCI and ADD patients’ neuropsychological performance did not change, except from the tests measuring verbal memory, learning, and phonemic fluency, as well as everyday functioning. However, the natural progression of the MCI as well as ADD condition is the main reason for participants’ deterioration. Mood performance became increasingly closely related to cognition and everyday functioning. Hence, the role of quarantine and AD progression are discussed as potential factors associated with impairments