Photoacoustic Imaging: Hybrid Technology for Small Animals Holding Potentials Translatable for Clinical applications

Photoacoustic imaging is an emerging modality that exploits the photoacoustic effect to combine the high contrast of optical imaging with the spatial resolution and penetration depth of ultrasound. A key feature of PA imaging methods is that they exploit optical contrast but employ US detection principles. The PA effect offers a way to take advantage of the ability of light to penetrate into the body and let us defeat light diffusion by using US waves to see the penetrating light. The main advantage of this hybrid approach is that the optical properties of biological tissue, including high contrast and spectral specificity, are encoded in an ultrasound signal. Resolutions of better than 1 mm can be obtained at depths measured in centimeters (up to 7) and not in millimeters, depending on the laser wavelength and transducer frequency used, opening up entirely new regimens of optical imaging. From a clinical standpoint, PA imaging is complementary in nature and synergetic with US and a combined US and PA imaging system can be easily implemented due to the presence of a shared detector and associated electronics. Furthermore, such a system will be readily accepted by clinicians familiar with US imaging

Pituitary function and morphology in Fabry disease.

Endocrine abnormalities are known to affect patients with Fabry disease (FD). Pituitary gland theoretically represents an ideal target for FD because of high vascularization and low proliferation rate. We explored pituitary morphology and function in a cohort of FD patients through a prospectic, monocentric study at an Academic Tertiary Center. The study population included 28 FD patients and 42 sex and age-matched normal subjects. The protocol included a contrast enhancement pituitary MRI, the assessment of pituitary hormones, anti-pituitary, and anti-hypothalamus antibodies. At pituitary MRI, an empty sella was found in 11 (39%) FD patients, and in 2 (5%) controls (p < 0.001). Pituitary volume was significantly smaller in FD than in controls (p < 0.001). Determinants of pituitary volume were age and alpha-galactosidase enzyme activity. Both parameters resulted independently correlated at multivariate analysis. Pituitary function was substantially preserved in FD patients. Empty sella is a common finding in patients with FD. The major prevalence in the elderly supports the hypothesis of a progressive pituitary shrinkage overtime. Pituitary function seems not to be impaired in FD. An endocrine workup with pituitary hormone assessment should be periodically performed in FD patients, who are already at risk of cardiovascular complications

A longitudinal study on BIO14.6 hamsters with dilated cardiomyopathy: micro-echocardiographic evaluation

<p>Abstract</p> <p>Background</p> <p>In recent years, several new technologies for small-animal imaging have been developed. In particular, the use of ultrasound in animal imaging has focused on the investigation of accessible biological structures such as the heart, of which it provides a morphological and functional assessment. The purpose of this study was to investigate the role of micro-ultrasonography (μ-US) in a longitudinal study on BIO14.6 cardiomyopathic hamsters treated with gene therapy.</p> <p>Methods</p> <p>Thirty hamsters were divided into three groups (n = 10): Group I, untreated BIO 14.6 hamsters; Group II, BIO 14.6 hamsters treated with gene therapy; Group III, untreated wild type (WT) hamsters. All hamsters underwent serial μ-US sessions and were sacrificed at predetermined time points.</p> <p>Results</p> <p>μ-US revealed: in Group I, progressive dilation of the left ventricle with a change in heart morphology from an elliptical to a more spherical shape, altered configuration of the mitral valve and subvalvular apparatus, and severe reduction in ejection fraction; in Group II, mild decrease in contractile function and ejection fraction; in Group III, normal cardiac chamber morphology and function. There was a negative correlation between the percentage of fibrosis observed at histology and the ejection fraction obtained on μ-echocardiography (Spearman r: -0.839; p < 0.001).</p> <p>Conclusions</p> <p>Although histological examination remains indispensable for a conclusive diagnosis, high-frequency μ-echocardiography, thanks to the high spatial and contrast resolution, can be considered sufficient for monitoring therapeutic efficacy and/or the progression of dilated cardiomyopathy, providing an alternative tool for repeatable and noninvasive evaluation.</p

Diffusion-weighted imaging and apparent diffusion coefficient mapping of head and neck lymph node metastasis: a systematic review

Aim: Head and neck squamous cell cancer (HNSCC) is the ninth most common tumor worldwide. Neck lymph node (LN) status is the major indicator of prognosis in all head and neck cancers, and the early detection of LN involvement is crucial in terms of therapy and prognosis. Diffusion-weighted imaging (DWI) is a non- invasive imaging technique used in magnetic resonance imaging (MRI) to characterize tissues based on the displacement motion of water molecules. This review aims to provide an overview of the current literature concerning quantitative diffusion imaging for LN staging in patients with HNSCC. Methods: This systematic review performed a literature search on the PubMed database (https://pubmed.ncbi.nlm.nih.gov/) for all relevant, peer-reviewed literature on the subject following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) criteria, using the keywords: DWI, MRI, head and neck, staging, lymph node. Results: After excluding reviews, meta-analyses, case reports, and bibliometric studies, 18 relevant papers out of the 567 retrieved were selected for analysis. Conclusions: DWI improves the diagnosis, treatment planning, treatment response evaluation, and overall management of patients affected by HNSCC. More robust data to clarify the role of apparent diffusion coefficient (ADC) and DWI parameters are needed to develop models for prognosis and prediction in HNSCC cancer using MRI

Coronavirus covid-19 detection by means of explainable deep learning

: The coronavirus is caused by the infection of the SARS-CoV-2 virus: it represents a complex and new condition, considering that until the end of December 2019 this virus was totally unknown to the international scientific community. The clinical management of patients with the coronavirus disease has undergone an evolution over the months, thanks to the increasing knowledge of the virus, symptoms and efficacy of the various therapies. Currently, however, there is no specific therapy for SARS-CoV-2 virus, know also as Coronavirus disease 19, and treatment is based on the symptoms of the patient taking into account the overall clinical picture. Furthermore, the test to identify whether a patient is affected by the virus is generally performed on sputum and the result is generally available within a few hours or days. Researches previously found that the biomedical imaging analysis is able to show signs of pneumonia. For this reason in this paper, with the aim of providing a fully automatic and faster diagnosis, we design and implement a method adopting deep learning for the novel coronavirus disease detection, starting from computed tomography medical images. The proposed approach is aimed to detect whether a computed tomography medical images is related to an healthy patient, to a patient with a pulmonary disease or to a patient affected with Coronavirus disease 19. In case the patient is marked by the proposed method as affected by the Coronavirus disease 19, the areas symptomatic of the Coronavirus disease 19 infection are automatically highlighted in the computed tomography medical images. We perform an experimental analysis to empirically demonstrate the effectiveness of the proposed approach, by considering medical images belonging from different institutions, with an average time for Coronavirus disease 19 detection of approximately 8.9 s and an accuracy equal to 0.95

On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis

Considering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose a two-tiered method, based on formal methods (to the best of authors knowledge never previously introduced in this context), aimed to (i) detect whether the patient lungs are healthy or present a generic pulmonary infection; (ii) in the case of the previous tier, a generic pulmonary disease is detected to identify whether the patient under analysis is affected by the novel Coronavirus disease. The proposed approach relies on the extraction of radiomic features from medical images and on the generation of a formal model that can be automatically checked using the model checking technique. We perform an experimental analysis using a set of computed tomography medical images obtained by the authors, achieving an accuracy of higher than 81% in disease detection

Coronavirus covid-19 detection by means of explainable deep learning

Abstract The coronavirus is caused by the infection of the SARS-CoV-2 virus: it represents a complex and new condition, considering that until the end of December 2019 this virus was totally unknown to the international scientific community. The clinical management of patients with the coronavirus disease has undergone an evolution over the months, thanks to the increasing knowledge of the virus, symptoms and efficacy of the various therapies. Currently, however, there is no specific therapy for SARS-CoV-2 virus, know also as Coronavirus disease 19, and treatment is based on the symptoms of the patient taking into account the overall clinical picture. Furthermore, the test to identify whether a patient is affected by the virus is generally performed on sputum and the result is generally available within a few hours or days. Researches previously found that the biomedical imaging analysis is able to show signs of pneumonia. For this reason in this paper, with the aim of providing a fully automatic and faster diagnosis, we design and implement a method adopting deep learning for the novel coronavirus disease detection, starting from computed tomography medical images. The proposed approach is aimed to detect whether a computed tomography medical images is related to an healthy patient, to a patient with a pulmonary disease or to a patient affected with Coronavirus disease 19. In case the patient is marked by the proposed method as affected by the Coronavirus disease 19, the areas symptomatic of the Coronavirus disease 19 infection are automatically highlighted in the computed tomography medical images. We perform an experimental analysis to empirically demonstrate the effectiveness of the proposed approach, by considering medical images belonging from different institutions, with an average time for Coronavirus disease 19 detection of approximately 8.9 s and an accuracy equal to 0.95

Concurrent chemotherapy alone versus irreversible electroporation followed by chemotherapy on survival in patients with locally advanced pancreatic cancer

Pancreatic adenocarcinoma is one of the most fatal cancers, characterized by aggressive tumor growth and a short patient survival time between diagnosis and death. Safe and effective treatment options are limited, especially in cases when surgical resection is not possible. Irreversible electroporation (IRE) is a non-thermal ablation technique recently introduced in the treatment of pancreatic cancer. From 2013 to 2016, 29 cases of locally advanced pancreatic cancer (LAPC) treated with IRE were retrospectively analyzed and the median overall survival (OS) rates were compared with patients with the same diagnosis who received standard chemotherapy as reported in the literature. Literature was selected according to a predetermined protocol. Secondarily, preoperative and postoperative Karnofsky scores of the 29 IRE-treated patients were compared to determine improvement in quality-of-life. Median OS of IRE-treated patients was 14Â&nbsp;months (SE 11Â&nbsp;months, 95% CI range 9.86â18.14). For IRE-treated patients, the Karnofsky score increased from Tzero to T3m by a mean of 28.28 (SE 2.11, 95% CI range 23.95â32.60). In 27 patients, 6-month imaging follow-up showed a mean lesion volumetric decrease percentage of 40.32% (SE 2.76, 95% CI 34.63â46.01%). Treatment with IRE followed by chemotherapy substantially increases median OS rate and quality-of-life of LAPC-diagnosed patients when compared to patients treated with traditional methods, including chemotherapy. Further investigation of this multi-modal treatment is warranted