Case report: Coxiella burnetii endocarditis in the absence of evident exposure.

Q fever is a worldwide zoonotic disease caused by Coxiella burnetii. In humans, it can manifest clinically as an acute or chronic disease and endocarditis, the most frequent complication of chronic Q fever is associated with the greatest morbidity and mortality. We report a severe case of endocarditis in a 55-year-old man with a history of aortic valve replacement affected by monoclonal gammopathy of undetermined significance (MGUS), and living in a non-endemic area for C. burnetii. After two episodes of fever of unknown origin (FUO), occurring 2 years apart and characterized by negative blood cultures, a serological diagnosis of Q fever endocarditis was performed even though the patient did not refer to possible past exposure to C. burnetii. Since people with preexisting valvular heart disease, when infected with C. burnetii, have reported a 40% risk of Q fever endocarditis, clinicians should maintain a high index of suspicion for infective endocarditis in all patients with FUO even when the exposure to C. burnetii appears to be unlikely.

Radiation-Free Microwave Technology for Breast Lesion Detection Using Supervised Machine Learning Model.

Mammography is the gold standard technology for breast screening, which has been demonstrated through different randomized controlled trials to reduce breast cancer mortality. However, mammography has limitations and potential harms, such as the use of ionizing radiation. To overcome the ionizing radiation exposure issues, a novel device (i.e. MammoWave) based on low-power radio-frequency signals has been developed for breast lesion detection. The MammoWave is a microwave device and is under clinical validation phase in several hospitals across Europe. The device transmits non-invasive microwave signals through the breast and accumulates the backscattered (returned) signatures, commonly denoted as the S21 signals in engineering terminology. Backscattered (complex) S21 signals exploit the contrast in dielectric properties of breasts with and without lesions. The proposed research is aimed to automatically segregate these two types of signal responses by applying appropriate supervised machine learning (ML) algorithm for the data emerging from this research. The support vector machine with radial basis function has been employed here. The proposed algorithm has been trained and tested using microwave breast response data collected at one of the clinical validation centres. Statistical evaluation indicates that the proposed ML model can recognise the MammoWave breasts signal with no radiological finding (NF) and with radiological findings (WF), i.e., may be the presence of benign or malignant lesions. A sensitivity of 84.40% and a specificity of 95.50% have been achieved in NF/WF recognition using the proposed ML model.

Bone Health Care Pathway for Non-metastatic Prostate Cancer Patients on Radiation and Androgen Deprivation Therapy.

BACKGROUND/AIM: Survival rates of prostate cancer (PCa) patients have improved considerably as a result of earlier diagnosis and therapies, including radiotherapy (RT) and androgen deprivation therapy (ADT). Patients on ADT develop cancer treatment-induced bone loss (CTIBL) and a high risk of fragility fractures. Bone health (BH) assessment is strongly recommended, together with timely initiation of treatments, to counteract CTIBL and preserve bone strength. Therefore, we decided to develop an interdisciplinary pathway of care (IPC) dedicated to non-metastatic PCa patients on long-term ADT and RT. PATIENTS AND METHODS: An interdisciplinary team allocated resources to support an IPC to manage patients' CTIBL and prevent fragility fractures. The team provided a diagnostic and therapeutic workflow according to patients' and professional perspectives, consistent with recommendations and healthcare policies. The hospital's quality department certified the IPC, the Ethical Committee approved procedures over the workflow. The Fracture Liaison Service (FLS) standards inspired services and professionals' activities and interactions. RESULTS: Preliminary data support the feasibility of the IPC from professionals' and patients' perspectives. Median age of the enrolled patients was 75 years, more than a half (58.9%) had low grade osteopenia or normal BMD (T-score ≥-1.5 standard deviation, SD), while 23.5% and 17.6% had osteoporosis and osteopenia, respectively. The IPC meets the requirements of a FLS concerning crucial indicators. CONCLUSION: Our IPC was a suitable approach to assure timely identification, assessment, initiation, and monitoring of adherence to anti-fracture treatments among non-metastatic PCa patients on long-term ADT and RT. Further data are required to show its effectiveness on fragility fracture prevention.

Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network.

MammoWave is a microwave imaging device for breast lesion detection, employing two antennas which rotate azimuthally (horizontally) around the breast. The antennas operate in the 1-9 GHz band and are set in free space, i.e., pivotally, no matching liquid is required. Microwave images, subsequently obtained through the application of Huygens Principle, are intensity maps, representing the homogeneity of the dielectric properties of the breast tissues under test. In this paper, MammoWave is used to realise tissues dielectric differences and localise lesions by segmenting microwave images adaptively employing pulse coupled neural network (PCNN). Subsequently, a non-parametric thresholding technique is modelled to differentiate between breasts having no radiological finding (NF) or benign (BF) and breasts with malignant finding (MF). Resultant findings verify that automated breast lesion localization with microwave imaging matches the gold standard achieving 81.82% sensitivity in MF detection. The proposed method is tested on microwave images acquired from a feasibility study performed in Foligno Hospital, Italy. This study is based on 61 breasts from 35 patients; performance may vary with larger number of datasets and will be subsequently investigated.

Radial Basis Function for Breast Lesion Detection from MammoWave Clinical Data.

Recently, a novel microwave apparatus for breast lesion detection (MammoWave), uniquely able to function in air with 2 antennas rotating in the azimuth plane and operating within the band 1-9 GHz has been developed. Machine learning (ML) has been implemented to understand information from the frequency spectrum collected through MammoWave in response to the stimulus, segregating breasts with and without lesions. The study comprises 61 breasts (from 35 patients), each one with the correspondent output of the radiologist's conclusion (i.e., gold standard) obtained from echography and/or mammography and/or MRI, plus pathology or 1-year clinical follow-up when required. The MammoWave examinations are performed, recording the frequency spectrum, where the magnitudes show substantial discrepancy and reveals dissimilar behaviours when reflected from tissues with/without lesions. Principal component analysis is implemented to extract the unique quantitative response from the frequency response for automated breast lesion identification, engaging the support vector machine (SVM) with a radial basis function kernel. In-vivo feasibility validation (now ended) of MammoWave was approved in 2015 by the Ethical Committee of Umbria, Italy (N. 6845/15/AV/DM of 14 October 2015, N. 10352/17/NCAV of 16 March 2017, N 13203/18/NCAV of 17 April 2018). Here, we used a set of 35 patients. According to the radiologists conclusions, 25 breasts without lesions and 36 breasts with lesions underwent a MammoWave examination. The proposed SVM model achieved the accuracy, sensitivity, and specificity of 91%, 84.40%, and 97.20%. The proposed ML augmented MammoWave can identify breast lesions with high accuracy.

Magnetic resonance imaging of pure ovarian dysgerminoma: a series of eight cases.

BACKGROUND: Imaging findings have a prominent role in early and correct identification of ovarian dysgerminoma, the most common ovarian malignant germ cell tumor (OMGCT). Despite Computed Tomography (CT) is widely used, Magnetic Resonance Imaging (MRI) has proved to be superior in adnexal masses characterization. Limited data and small series are available concerning MRI aspects of dysgerminoma. CASE PRESENTATION: From January 2012 to December 2018, a database of solid ovarian masses was retrospectively reviewed. Eight patients with histologically proven pure ovarian dysgerminoma and complete imaging available were identified and analyzed. Imaging findings were evaluated separately by two radiologists expert in female genito-urinary MRI. CONCLUSIONS: MRI findings of a lobulated, purely solid, encapsulated mass with hyper-intensity of lobules and hypo-intensity of septa on T2w images contribute to differentiate dysgerminomas from other ovarian neoplasms.

Breast lesion detection through MammoWave device: Empirical detection capability assessment of microwave images' parameters.

MammoWave is a microwave imaging device for breast lesions detection, which operates using two (azimuthally rotating) antennas without any matching liquid. Images, subsequently obtained by resorting to Huygens Principle, are intensity maps, representing the homogeneity of tissues' dielectric properties. In this paper, we propose to generate, for each breast, a set of conductivity weighted microwave images by using different values of conductivity in the Huygens Principle imaging algorithm. Next, microwave images' parameters, i.e. features, are introduced to quantify the non-homogenous behaviour of the image. We empirically verify on 103 breasts that a selection of these features may allow distinction between breasts with no radiological finding (NF) and breasts with radiological findings (WF), i.e. with lesions which may be benign or malignant. Statistical significance was set at p<0.05. We obtained single features Area Under the receiver operating characteristic Curves (AUCs) spanning from 0.65 to 0.69. In addition, an empirical rule-of-thumb allowing breast assessment is introduced using a binary score S operating on an appropriate combination of features. Performances of such rule-of-thumb are evaluated empirically, obtaining a sensitivity of 74%, which increases to 82% when considering dense breasts only.

Machine Learning Approaches for Automated Lesion Detection in Microwave Breast Imaging Clinical Data.

Breast lesion detection employing state of the art microwave systems provide a safe, non-ionizing technique that can differentiate healthy and non-healthy tissues by exploiting their dielectric properties. In this paper, a microwave apparatus for breast lesion detection is used to accumulate clinical data from subjects undergoing breast examinations at the Department of Diagnostic Imaging, Perugia Hospital, Perugia, Italy. This paper presents the first ever clinical demonstration and comparison of a microwave ultra-wideband (UWB) device augmented by machine learning with subjects who are simultaneously undergoing conventional breast examinations. Non-ionizing microwave signals are transmitted through the breast tissue and the scattering parameters (S-parameter) are received via a dedicated moving transmitting and receiving antenna set-up. The output of a parallel radiologist study for the same subjects, performed using conventional techniques, is taken to pre-process microwave data and create suitable data for the machine intelligence system. These data are used to train and investigate several suitable supervised machine learning algorithms nearest neighbour (NN), multi-layer perceptron (MLP) neural network, and support vector machine (SVM) to create an intelligent classification system towards supporting clinicians to recognise breasts with lesions. The results are rigorously analysed, validated through statistical measurements, and found the quadratic kernel of SVM can classify the breast data with 98% accuracy.

CT-Guided Percutaneous Trans-scapular Lung Biopsy in the Diagnosis of Peripheral Pulmonary Lesion Nodules of the Superior Lobes Using Large Needles.

PURPOSE: CT-guided percutaneous transthoracic lung needle biopsy (PLB) is an effective procedure for obtaining cyto-histological diagnoses of peripheral lesions. However, upper postero-lateral lung nodules (UPLN) may be difficult to reach using a standard intercostal either anterior or lateral approach or when the nodule is just behind a rib or scapula. We evaluated the feasibility and effectiveness of trans-scapular approach (tPLB) in these patients using large-core needles. METHODS: We retrospectively collected the data of 11 consecutive patients (mean age 74.6 years, SD 5.9) among those scheduled to undergo CT-guided PLB over the period February 2015 to February 2017. In these patients, the presence of a UPLN required a tPLB using a co-axial technique and large full-core needles (15G for the scapular piercing and 18-19G for tissue sampling). All patients were assessed by telephone at 24 h, 78 h and at an office visit at 30 days after the procedure to evaluate post-procedural pain (VAS score) and shoulder mobility. RESULTS: Ten of the eleven samples were diagnostic. No major complications were encountered. Three patients developed a pneumothorax, but none required pleural drainage. All the patients confirmed the absence of pain at 24-72 h and 30 days, reporting a VAS score less than 1, without any shoulder mobility limitation. CONCLUSION: tPLB seems to be an effective and feasible procedure, accompanied by a low risk of pneumothorax in UPLN cases.

Lung ultrasound in the diagnosis of stroke-associated pneumonia.

Pneumonia is a common complication in stroke patients; it is associated with an adverse outcome, prolonged hospital stay and increased health costs. The aim of this study was to assess the ability of lung ultrasound to rule in or rule out pneumonia in patients with stroke. Patients with ischemic or hemorrhagic stroke were included in the study if they had a clinical suspicion of pneumonia. Lung ultrasound imaging and chest X-ray studies were performed within 24 h from the onset of symptoms of pneumonia; the sonographer and radiologists were unaware of the chest X-ray study and ultrasound imaging results, respectively. In case of discordant results, lung computed tomography (CT) was performed if a definitive diagnosis was required to optimize clinical management. Seventy patients were included in the study. Among 24 patients with unilaterally positive chest X-ray studies, the ultrasound imaging was unilaterally positive in 19 cases (79.2 %), bilaterally positive in 3 cases (12.5 %) and negative in 2 cases (8.3 %). Among 44 patients with negative chest X-ray studies, ultrasound imaging was unilaterally positive in 17 cases (38.6 %), bilaterally positive in 2 cases (4.6 %), negative in 19 cases (43.2 %) and non-conclusive in 6 cases (13.6 %). A lung CT scan was performed in 9 of the 21 discordant cases, and it always confirmed the ultrasound imaging results. Ultrasound imaging and chest X-ray studies were concordant in 42 out of 63 cases, 66.7 % (Pearson χ(2) = 11.97, p = 0.001). In conclusions, this study shows the utility of LUS imaging to rule in or rule out pneumonia in patients with stroke. We believe that lung ultrasound imaging can help clinicians in the diagnosis of stroke-associated pneumonia.

[Single cardiac metastasis from colorectal cancer: an unusual localization]. / Singola metastasi cardiaca da adenocarcinoma intestinale: una rara localizzazione.

Metastasis to the heart from malignancy is a frequent but underestimated event. Tumors that are located in the mediastinum, such as pleural mesothelioma, are more frequently associated with cardiac colonization. Few reports have described metastasis from colon adenocarcinoma, which usually colonizes liver and lungs. Moreover, intracardiac localization is more common for primary cardiac neoplasms than for metastasis. We present an unusual case of a patient operated for colon adenocarcinoma who exhibited a single intracardiac secondary localization. Although the mass was huge, the patient was completely asymptomatic. Strict oncologic follow-up facilitates an early identification of the lesion, which could then be promptly resected.

Multidetector CT scan for acute pulmonary embolism: embolic burden and clinical outcome.

BACKGROUND: In patients with acute pulmonary embolism (PE), the correlation between the embolic burden assessed by multidetector CT (MDCT) scan and clinical outcomes remains unclear. Patients with symptomatic acute PE diagnosed based on MDCT angiography were included in a multicenter study aimed at assessing the prognostic role of the embolic burden evaluated with MDCT scan. METHODS: Embolic burden was assessed as (1) localization of the emboli as central (saddle or at least one main pulmonary artery), lobar, or distal (segmental or subsegmental arteries) and (2) the obstruction index by the scoring system of Qanadli. The primary outcome was 30-day all-cause death or clinical deterioration. Predictors of all-cause death or clinical deterioration were identified by Cox regression statistics. RESULTS: Overall, 579 patients were included in the study; 60 (10.4%) died or had clinical deterioration at 30 days. Central localization of emboli was not associated with all-cause death or clinical deterioration (hazard ratio [HR], 2.42; 95% CI, 0.77-7.59; P 5 .13). However, in 516 hemodynamically stable patients, central localization of emboli (HR, 8.3; 95% CI, 1.0-67; P 5 .047) was an independent predictor of all-cause death or clinical deterioration, whereas distal emboli were inversely associated with these outcome events (HR, 0.12; 95% CI, 0.015-0.97; P 5 .047). No correlation was found between obstruction index (evaluated in 448 patients) and all-cause death or clinical deterioration in the overall study population and in the hemodynamically stable patients. CONCLUSIONS: In hemodynamically stable patients with acute PE, central emboli are associated with an increased risk for all-cause death or clinical deterioration. This risk is low in patients with segmental or subsegmental PE.

Multidetector computed tomography for acute pulmonary embolism: diagnosis and risk stratification in a single test.

AIMS: In patients with acute pulmonary embolism (PE), right ventricular dysfunction at echocardiography is associated with increased in-hospital mortality. The aims of this study in patients with acute PE were to identify a sensitive and simple criterion for right ventricular dysfunction at multidetector computed tomography (MDCT) using echocardiography as the reference standard and to evaluate the predictive value of the identified MDCT criterion for in-hospital death or clinical deterioration. METHODS AND RESULTS: Right ventricular dysfunction at MDCT was defined as the right-to-left ventricular dimensional ratio and was centrally assessed by a panel unaware of clinical and echocardiographic data. A right-to-left ventricular dimensional ratio ≥0.9 at MDCT had a 92% sensitivity for right ventricular dysfunction [95% confidence interval (CI) 88-96]. Overall, 457 patients were included in the outcome study: 303 had right ventricular dysfunction at MDCT. In-hospital death or clinical deterioration occurred in 44 patients with and in 8 patients without right ventricular dysfunction at MDCT (14.5 vs. 5.2%; P< 0.004). The negative predictive value of right ventricular dysfunction for death due to PE was 100% (95% CI 98-100). Right ventricular dysfunction at MDCT was an independent predictor for in-hospital death or clinical deterioration in the overall population [hazard ratio (HR) 3.5, 95% CI 1.6-7.7; P= 0.002] and in haemodynamically stable patients (HR 3.8, 95% CI 1.3-10.9; P= 0.007). CONCLUSION: In patients with acute PE, MDCT might be used as a single procedure for diagnosis and risk stratification. Patients without right ventricular dysfunction at MDCT have a low risk of in-hospital adverse outcome.