Search

Abstract Background Contrast-enhanced mammography (CEM) is a promising technique. We evaluated the diagnostic potential of CEM performed immediately after contrast-enhanced computed tomography (CE-CT). Methods Fifty patients with breast cancer underwent first CE-CT and then CEM without additional contrast material injection. Two independent radiologists evaluated CEM images. The sensitivity of CEM for detecting index and additional malignant lesions was compared with that of mammography/ultrasonography by the McNemar test, using histopathology as a reference standard. Interobserver agreement for detection of malignant lesions, for classifying index tumors, and for evaluating index tumor size and extent was assessed using Cohen κ. Pearson correlation was used for correlating index tumor size/extent at CEM or mammography/ultrasonography with histopathology. Results Of the 50 patients, 30 (60%) had unifocal disease while 20 (40%) had multicentric or multifocal disease; 5 of 20 patients with multicentric disease (25%) had bilateral involvement, for a total of 78 malignant lesions, including 72 (92%) invasive ductal and 6 (8%) invasive lobular carcinomas. Sensitivity was 63/78 (81%, 95% confidence interval 70.27–88.82) for unenhanced breast imaging and 78/78 (100%, 95.38–100) for CEM (p

IntroductionBody image concerns related to breast cancer surgery may challenge patients’ quality of life and their treatment outcomes, thus representing a key aspect to be assessed in the psycho-oncological settings. The present longitudinal study is aimed to (1) investigate the association between preoperative body image and postoperative psychological symptoms in breast cancer patients; (2) explore the impact of pre−/post-surgery variation in body image on psychological symptomatology.MethodsN = 72 women undergoing breast cancer surgery were preoperatively screened (T1) using the Body Uneasiness Test (BUT) and were assessed postoperatively (T2) using the Symptom Checklist-90 Revised (SCL-90-R) and re-administered the BUT. Spearman’s correlation was used to investigate the relationship between age, preoperative body image and postoperative psychological symptoms, and variation in body image. To predict post-surgical psychological symptomatology, two separated multiple regression models were used to evaluate preoperative body image and its variation after surgery controlling for covariates (i.e., education; intervention type). P significance was set as 0.05 for all analyses and adjusted for multiple comparisons.ResultsAt T1, anxiety in relation to body image scores emerged as the most frequently experienced psychological symptomatology after surgery (all adjusted p 0.05). Pre-surgery body avoidance was significantly associated with post-intervention psychological symptoms (SOMβ = 0.453, p = 0.0001; DEPβ = 0.507, p = 0.0001; AXβ = 0.459, p = 0.0001; HOSβ = 0.410, p=. 0001). However, increased weight phobia between pre- and post-surgery was statistically associated with increased somatization, anxiety, depression and hostility at T2 (βSOM = 0.439, p = 0.0001; βDEP = 0.454, p = 0.0001; βANX = 0.471, p = 0.0001).DiscussionOverall, pre−/post-intervention body concerns were significantly associated with primary psychological symptoms in breast cancer patients undergoing surgery. Higher levels of body avoidance and weight phobia were significantly associated with the primary psychological dimensions assessed. As body concerns might act as quality-of-life predictors, their evaluation is crucial in fostering patients’ well-being and treatment adherence.

Abstract Background Psycho-oncology literature pointed out that individual health outcomes may depend on patients’ propensity to adopt approach or, conversely, avoidant coping strategies. Nevertheless, coping factors associated with postoperative distress remain unclear, unfolding the lack of tailored procedures to help breast cancer patients manage the psychological burden of scheduled surgery. In view of this, the present study aimed at investigating: 1. pre-/post-surgery distress variations occurring among women diagnosed with breast cancer; 2. the predictivity of approach and avoidant coping strategies and factors in affecting post-surgery perceived distress. Methods N = 150 patients (mean age = 59.37; SD = ± 13.23) scheduled for breast cancer surgery were administered a screening protocol consisting of the Distress Thermometer (DT) and the Brief-COPE. The DT was used to monitor patients’ distress levels before and after surgery (± 7 days), whereas the Brief-COPE was adopted only preoperatively to evaluate patients’ coping responses to the forthcoming surgical intervention. Non-parametric tests allowed for the detection of pre-/post-surgery variations in patients’ perceived distress. Factor analysis involved the extraction and rotation of principal components derived from the Brief-COPE strategies. The predictivity of such coping factors was investigated through multiple regression (Backward Elimination). Results The Wilcoxon Signed-Rank Test yielded a significant variation in DT mean scores (TW = -5,68

Computer-aided diagnosis (CAD) systems can help radiologists in numerous medical tasks including classification and staging of the various diseases. The 3D tomosynthesis imaging technique adds value to the CAD systems in diagnosis and classification of the breast lesions. Several convolutional neural network (CNN) architectures have been proposed to classify the lesion shapes to the respective classes using a similar imaging method. However, not only is the black box nature of these CNN models questionable in the healthcare domain, but so is the morphological-based cancer classification, concerning the clinicians. As a result, this study proposes both a mathematically and visually explainable deep-learning-driven multiclass shape-based classification framework for the tomosynthesis breast lesion images. In this study, authors exploit eight pretrained CNN architectures for the classification task on the previously extracted regions of interests images containing the lesions. Additionally, the study also unleashes the black box nature of the deep learning models using two well-known perceptive explainable artificial intelligence (XAI) algorithms including Grad-CAM and LIME. Moreover, two mathematical-structure-based interpretability techniques, i.e., t-SNE and UMAP, are employed to investigate the pretrained models’ behavior towards multiclass feature clustering. The experimental results of the classification task validate the applicability of the proposed framework by yielding the mean area under the curve of 98.2%. The explanability study validates the applicability of all employed methods, mainly emphasizing the pros and cons of both Grad-CAM and LIME methods that can provide useful insights towards explainable CAD systems.

On 12 March 2020, the World Health Organization (WHO) declared the novel Coronavirus (CoV) disease a global Pandemic and an emerging risk. In order to understand patterns that are typical in COVID-19 pneumonia and track the evolution of the disease, the role of the chest computed tomography (CT) is pivotal. The impact of the illness as well as the efficiency of the therapy are also monitored carefully when performing this imaging exam. Coronaviruses, specifically CoV-2, as RNA viruses, have a tendency to frequently change their genome, giving the virus beneficial characteristics such as greater transmissibility, pathogenicity and the possibility to escape the previously acquired immunity. Therefore, genome evaluation became an extremely important routine practice worldwide. In particular, in Italy, four variants have been recognised and each of them represent a specific temporal wave of the disease. Hence, our goal was to describe imaging findings of COVID-19 pneumonia, specifically its most typical imaging identified during the period of our study, and to assess whether or not SARS-CoV-2 variants determine different CT patterns. Our analyses revealed that the SARS-CoV-2 genotype seems not to interfere with the severity of CT patterns and, in particular, bilateral Ground Glass Opacities (GGOs) are the most frequent findings in all COVID-19 waves.