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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

Abstract Background Our aim was to compare the diagnostic performance of digital breast tomosynthesis (DBT)-galactography with that of full-field digital (FFD)-galactography for detecting intraductal breast lesions using an intra-individual design. Methods Forty-nine consecutive patients with spontaneous, unilateral, single-pore nipple discharge and inconclusive FFD mammography and ultrasonography underwent galactography with a “COMBO” technique combining FFD- and DBT-galactography acquisitions. Examinations were independently analysed by two breast radiologists with 10-year experience. Sensitivity, specificity, and accuracy for both FFD- and DBT-galactography were calculated having histological examinations of surgical specimens as a reference standard. Data were presented as percentages with their 95% confidence intervals (CI). McNemar test was used. Interobserver agreement was assessed by using Cohen κ test for both techniques. Results Sensitivity was 41/43 (95%, 95% CI 84.2–99.4) for DBT-galactography and 33/43 (77%, 95% CI 61.4–88.2) for FFD-galactography (p = 0.008), specificity 6/6 (100%, 95% CI 54.1–100.0) for both imaging tools, accuracy 47/49 (96%, 95% CI 86.0–99.5) and 39/49 (80%, 95% CI 65.7–89.8) (p = 0.038), respectively. The inter-observer agreement was 0.86 for DBT-galactography and 0.78 for FFD-galactography. The AGD resulted to 1.94 ± 0.64 for the combined technique. Conclusion DBT-galactography showed a significantly higher sensitivity and accuracy than FFD-galactography for the identification of the intraductal findings, improving the possibility of a reliable diagnosis in patients with pathologic nipple-discharge.

Abstract Background Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features. Results For each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance. Conclusions The best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters.

Abstract Background The automatic segmentation of kidneys in medical images is not a trivial task when the subjects undergoing the medical examination are affected by Autosomal Dominant Polycystic Kidney Disease (ADPKD). Several works dealing with the segmentation of Computed Tomography images from pathological subjects were proposed, showing high invasiveness of the examination or requiring interaction by the user for performing the segmentation of the images. In this work, we propose a fully-automated approach for the segmentation of Magnetic Resonance images, both reducing the invasiveness of the acquisition device and not requiring any interaction by the users for the segmentation of the images. Methods Two different approaches are proposed based on Deep Learning architectures using Convolutional Neural Networks (CNN) for the semantic segmentation of images, without needing to extract any hand-crafted features. In details, the first approach performs the automatic segmentation of images without any procedure for pre-processing the input. Conversely, the second approach performs a two-steps classification strategy: a first CNN automatically detects Regions Of Interest (ROIs); a subsequent classifier performs the semantic segmentation on the ROIs previously extracted. Results Results show that even though the detection of ROIs shows an overall high number of false positives, the subsequent semantic segmentation on the extracted ROIs allows achieving high performance in terms of mean Accuracy. However, the segmentation of the entire images input to the network remains the most accurate and reliable approach showing better performance than the previous approach. Conclusion The obtained results show that both the investigated approaches are reliable for the semantic segmentation of polycystic kidneys since both the strategies reach an Accuracy higher than 85%. Also, both the investigated methodologies show performances comparable and consistent with other approaches found in literature working on images from different sources, reducing both the invasiveness of the analyses and the interaction needed by the users for performing the segmentation task.

Abstract Radiation therapy represents an important approach in the therapeutic management of children and adolescents with malignant tumors and its application with modern techniques – including Proton Beam Therapy (PBT) – is of great interest. In particular, potential radiation-induced injuries and secondary malignancies – also associated to the prolonged life expectancy of patients – are still questions of concern that increase the debate on the usefulness of PBT in pediatric treatments. This paper presents a literary review of current applications of PBT in non-Central Nervous System pediatric tumors (such as retinoblastoma, Hodgkin Lymphoma, Wilms tumor, bone and soft tissues sarcomas). We specifically reported clinical results achieved with PBT and dosimetric comparisons between PBT and the most common photon-therapy techniques. The analysis emphasizes that PBT minimizes radiation doses to healthy growing organs, suggesting for reduced risks of late side-effects and radiation-induced secondary malignancies. Extended follow up and confirms by prospective clinical trials should support the effectiveness and long-term tolerance of PBT in the considered setting.

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.

Ovarian cancer (OC) is the second most common gynecological malignancy, accounting for about 14,000 deaths in 2020 in the US. The recognition of tools for proper screening, early diagnosis, and prognosis of OC is still lagging. The application of methods such as radiomics to medical images such as ultrasound scan (US), computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) in OC may help to realize so-called “precision medicine” by developing new quantification metrics linking qualitative and/or quantitative data imaging to achieve clinical diagnostic endpoints. This narrative review aims to summarize the applications of radiomics as a support in the management of a complex pathology such as ovarian cancer. We give an insight into the current evidence on radiomics applied to different imaging methods.

Despite several studies having identified factors associated with successful treatment outcomes in locally advanced cervical cancer, there is the lack of accurate predictive modeling for progression-free survival (PFS) in patients who undergo radical hysterectomy after neoadjuvant chemotherapy (NACT). Here we investigated whether machine learning (ML) may have the potential to provide a tool to predict neoadjuvant treatment response as PFS. In this retrospective observational study, we analyzed patients with locally advanced cervical cancer (FIGO stages IB2, IB3, IIA1, IIA2, IIB, and IIIC1) who were followed in a tertiary center from 2010 to 2018. Demographic and clinical characteristics were collected at either treatment baseline or at 24-month follow-up. Furthermore, we recorded data about magnetic resonance imaging (MRI) examinations and post-surgery histopathology. Proper feature selection was used to determine an attribute core set. Three different machine learning algorithms, namely Logistic Regression (LR), Random Forest (RFF), and K-nearest neighbors (KNN), were then trained and validated with 10-fold cross-validation to predict 24-month PFS. Our analysis included n. 92 patients. The attribute core set used to train machine learning algorithms included the presence/absence of fornix infiltration at pre-treatment MRI as well as of either parametrium invasion and lymph nodes involvement at post-surgery histopathology. RFF showed the best performance (accuracy 82.4%, precision 83.4%, recall 96.2%, area under receiver operating characteristic curve (AUROC) 0.82). We developed an accurate ML model to predict 24-month PFS.

Contrast-Enhanced Spectral Mammography (CESM) is a recently introduced mammographic method with characteristics particularly suitable for breast cancer radiomic analysis. This work aims to evaluate radiomic features for predicting histological outcome and two cancer molecular subtypes, namely Human Epidermal growth factor Receptor 2 (HER2)-positive and triple-negative. From 52 patients, 68 lesions were identified and confirmed on histological examination. Radiomic analysis was performed on regions of interest (ROIs) selected from both low-energy (LE) and ReCombined (RC) CESM images. Fourteen statistical features were extracted from each ROI. Expression of estrogen receptor (ER) was significantly correlated with variation coefficient and variation range calculated on both LE and RC images; progesterone receptor (PR) with skewness index calculated on LE images; and Ki67 with variation coefficient, variation range, entropy and relative smoothness indices calculated on RC images. HER2 was significantly associated with relative smoothness calculated on LE images, and grading tumor with variation coefficient, entropy and relative smoothness calculated on RC images. Encouraging results for differentiation between ER+/ER−, PR+/PR−, HER2+/HER2−, Ki67+/Ki67−, High-Grade/Low-Grade and TN/NTN were obtained. Specifically, the highest performances were obtained for discriminating HER2+/HER2− (90.87%), ER+/ER− (83.79%) and Ki67+/Ki67− (84.80%). Our results suggest an interesting role for radiomics in CESM to predict histological outcomes and particular tumors’ molecular subtype.

Purpose: To evaluate the role of multidetector computed tomography (MDCT) in recognizing the complications of extracorporeal shock wave lithotripsy (ESWL) and providing a prognostic grading system for the therapeutic approach. Materials and Methods: A total of 43 patients who underwent ESWL because of urinary stone disease were assessed by 320-row MDCT examination before and after ESWL. Pre-ESWL CT unenhanced scans were performed for diagnosing stone disease. Post-ESWL CT scans were acquired before and after intravenous injection of contrast medium searching for peri-renal fluid collection or hyper-density, pyelic or ureteral wall thickening, blood clots in the urinary tract, peri- or intra-renal hematoma or abscess, active bleeding. A severity grading system of ESWL complications was established. Results: Patients were affected by renal (n = 36) or ureteral (n = 7) lithiasis. Post-ESWL CT examination detected small fluid collections and hyper-density of peri-renal fat tissue in 35/43 patients (81%), pyelic or ureteral wall thickening in 2/43 (4%), blood clots in the urinary tract in 9/43 (21%), renal abscesses or hematomas with a diameter of

Objectives. To assess the potential of CT for characterizing small renal tumors. Methods. 76 patients with

Purpose. To evaluate the accuracy of the washout in the differential diagnosis between adenomas and nonadenomas and to compare the obtained results in delayed CT scans at 5, 10 and 15 minutes. Methods. Fifty patients with adrenal masses were prospectively evaluated. CT scans were performed by using a 320-row MDCT device, before and after injection of contrast material. In 25 cases, delayed scans were performed at 5′ and 10′ (group 1), while in the remaining 25, at 5′ and 15′ (group 2). Absolute and relative wash-out percentage values (APW and RPW) were calculated. Results. Differential diagnosis between adenomas and nonadenomas was obtained in 48/50 (96%) cases, with sensitivity, specificity, and accuracy values of 96%, 95%, and 96%, respectively. In group 1, APW and RPW values were, respectively, 69.8% and 67.2% at 5′ and 75.9% and 73.5% at 10′ for adenomas and 25.1% and 15.8% at 5′ and 33.5% and 20.5% at 10′ for nonadenomas. In group 2, APW and RPW values were 63% and 54.6% at 5′ and 73.8% and 65.5% at 15′ for adenomas and 22% and 12.5% at 5′ and 35.5% and 19.9% at 15′ for nonadenomas. Conclusions. The evaluation of the wash-out values in CT scans performed at 5′, 10′, and 15′ provides comparable diagnostic results. CT scans performed at 5′ are, therefore, to be preferred, since they reduce the examination time and patient discomfort.

Aim: To evaluate the role of computed tomography (CT) for diagnosing traumatic injuries of the pancreas and guiding the therapeutic approach., Methods: CT exams of 6740 patients admitted to our Emergency Department between May 2005 and January 2013 for abdominal trauma were retrospectively evaluated. Patients were identified through a search of our electronic archive system by using such terms as "pancreatic injury", "pancreatic contusion", "pancreatic laceration", "peri-pancreatic fluid", "pancreatic active bleeding". All CT examinations were performed before and after the intravenous injection of contrast material using a 16-slice multidetector row computed tomography scanner. The data sets were retrospectively analyzed by two radiologists in consensus searching for specific signs of pancreatic injury (parenchymal fracture and laceration, focal or diffuse pancreatic enlargement/edema, pancreatic hematoma, active bleeding, fluid between splenic vein and pancreas) and non-specific signs (inflammatory changes in peri-pancreatic fat and mesentery, fluid surrounding the superior mesenteric artery, thickening of the left anterior renal fascia, pancreatic ductal dilatation, acute pseudocyst formation/peri-pancreatic fluid collection, fluid in the anterior and posterior pararenal spaces, fluid in transverse mesocolon and lesser sac, hemorrhage into peri-pancreatic fat, mesocolon and mesentery, extraperitoneal fluid, intra-peritoneal fluid)., Results: One hundred and thirty-six/Six thousand seven hundred and forty (2%) patients showed CT signs of pancreatic trauma. Eight/one hundred and thirty-six (6%) patients underwent surgical treatment and the pancreatic injures were confirmed in all cases. Only in 6/8 patients treated with surgical approach, pancreatic duct damage was suggested in the radiological reports and surgically confirmed in all cases. In 128/136 (94%) patients who underwent non-operative treatment CT images showed pancreatic edema in 97 patients, hematoma in 31 patients, fluid between splenic vein and pancreas in 113 patients. Non-specific CT signs of pancreatic injuries were represented by peri-pancreatic fat stranding and mesentery fluid in 89% of cases, thickening of the left anterior renal fascia in 65%, pancreatic ductal dilatation in 18%, acute pseudocyst/peri-pancreatic fluid collection in 57%, fluid in the pararenal spaces in 45%, fluid in transverse mesocolon and lesser sac in 29%, hemorrhage into peri-pancreatic fat, mesocolon and mesentery in 66%, extraperitoneal fluid in 66%, intra-peritoneal fluid in 41% cases., Conclusion: CT represents an accurate tool for diagnosing pancreatic trauma, provides useful information to plan therapeutic approach with a detection rate of 75% for recognizing ductal lesions.

Acute intestinal ischemia is an abdominal emergency occurring in nearly 1% of patients presenting with acute abdomen. The causes can be occlusive or non occlusive. Early diagnosis is important to improve survival rates. In most cases of late or missed diagnosis, the mortality rate from intestinal infarction is very high, with a reported value ranging from 60% to 90%. Multi-detector computed tomography (MDCT) is a fundamental imaging technique that must be promptly performed in all patients with suspected bowel ischemia. Thanks to the new dedicated reconstruction program, its diagnostic potential is much improved compared to the past and currently it is superior to that of any other noninvasive technique. The increased spatial and temporal resolution, high-quality multi-planar reconstructions, maximum intensity projections, vessel probe, surface-shaded volume rending and tissue transition projections make MDCT the gold standard for the diagnosis of intestinal ischemia, with reported sensitivity, specificity, positive and negative predictive values of 64%-93%, 92%-100%, 90%-100% and 94%-98%, respectively. MDCT contributes to appropriate treatment planning and provides important prognostic information thanks to its ability to define the nature and extent of the disease. The purpose of this review is to examine the diagnostic and prognostic role of MDCT in bowel ischemia with special regard to the state of art new reconstruction software.