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Background and Aims: Several computed tomographic (CT) imaging features have been proposed to describe the infection of postoperative abdominal fluid collections; however, these features are vague, and there is a significant overlap between infected and non-infected collections. We assessed the role of textural parameters as additional diagnostic tools for distinguishing between infected and non-infected peritoneal collections in patients operated for gastric cancer. Methods: From 527 patients operated for gastric cancer, we retrospectively selected 82 cases with intraperitoneal collections who underwent CT exams. The fluid component was analyzed through a novel method (texture analysis); different patterns of pixel intensity and distribution were extracted and processed through a dedicated software (MaZda). A univariate analysis comparing the parameters of texture analysis between the two groups was performed. Afterwards, a multivariate analysis was performed for the univariate statistically significant parameters. Results: The study included 82 patients with bacteriologically verified infected (n=40) and noninfected (n=42) intraperitoneal effusions. The univariate analysis evidenced statistically significant differences between all the parameters involved. The multivariate analysis highlighted 10 parameters as being statistically significant, adjusted to Bonferroni correction. Conclusions: Our evidence supports the fact that textural analysis can be used as a complementary diagnostic tool for the detection of infected fluid collections after gastric cancer surgery. Further studies are required to validate the accuracy of this method.

Introduction: Prostate magnetic resonance imaging (MRI) has been recently integrated into the pathway of diagnosis of prostate cancer (PCa). However, the lack of an optimal contrast-to-noise ratio hinders automatic recognition of suspicious lesions, thus developing a solution for proper delimitation of the tumour and its separation from the healthy parenchyma, which is of primordial importance. Method: As a solution to this unmet medical need, we aimed to develop a decision support system based on artificial intelligence, which automatically segments the prostate and any suspect area from the 3D MRI images. We assessed retrospective data from all patients diagnosed with PCa by MRI-US fusion prostate biopsy, who underwent prostate MRI in our department due to a clinical or biochemical suspicion of PCa (n=33). All examinations were performed using a 1.5 Tesla MRI scanner. All images were reviewed by two radiologists, who performed manual segmentation of the prostate and all lesions. A total of 145 augmented datasets were generated. The performance of our fully automated end-to-end segmentation model based on a 3D UNet architecture and trained in two learning scenarios (on 14 or 28 patient datasets) was evaluated by two loss functions. Results: Our model had an accuracy of over 90% for automatic segmentation of prostate and PCa nodules, as compared to manual segmentation. We have shown low complexity networks, UNet architecture with less than five layers, as feasible and to show good performance for automatic 3D MRI image segmentation. A larger training dataset could further improve the results. Conclusion: Therefore, herein, we propose a less complex network, a slim 3D UNet with superior performance, being faster than the original five-layer UNet architecture.

Background The magnetic resonance (MRI) diagnosis of chronic prostatitis (CP) is insufficiently evaluated. Purpose To evaluate the MRI appearance of CP in young patients by comparing it to individuals with non-prostatic related pathology. Material and Methods The study included 47 patients with prostatitis‐like symptoms evaluated by urologists and referred to pelvic MRI examination (mean age=40.23±7 years; age range=23–49 years) and 93 age-matched individuals with non-prostatic related pathology (mean age=37.5±7 years; age range=21–49 years). All MRI examinations were performed on a 1.5-T machine using a prostate-specific protocol for the prostatitis group and different protocols that included high-resolution small field of view T2-weighted (T2WI) and diffusion-weighted imaging (DWI), for the control group, depending on the clinical indication. Results Four different T2WI intensity patterns were observed: hyperintense homogenous; slightly to moderate homogenous hypointense; inhomogeneous; and marked hypointense. We found statistically significant differences between the two analyzed groups regarding mean ADC values ( P2=0.67; P28 years, an inhomogeneous or marked hypointense T2WI intensity pattern (types 3 and 4), an ADC value ≤1250, and the presence of dilated venous plexus are able to predict CP with an AUC of 93% (sensitivity=85.1%, specificity=88.4%). Conclusion MR parameters like T2WI intensity patterns, ADC values, and venous plexus appearance are promising non-invasive tools in the challenging environment of CP diagnosis.

Background: Intraperitoneal fluid accumulations are a common matter in current clinical practice, being encountered by most medical and surgical fields. Objective: To assess ascites fluid with attenuation values in the form of Hounsfield units (HU) in order to determine a non-invasive differentiation criterion for the diagnosis of intraperitoneal collections. Methods: Sixty patients with known intra-peritoneal collections who underwent computed tomography (CT) for reasons such as tumor staging, post-surgical follow-up or other indications, were retrospectively included in this study. All subjects had a final pathological analysis of the fluid collections. Two radiologists measured the attenuation values for each collection. The averaged values were used for comparing benign and malignancy-related ascites (MRA), bland and hemorrhagic ascites and infected and noninfected fluid collections by consuming the Mann–Whitney U test. Also, the receiver operating characteristic analysis was performed for the statistically significant results (P Results: Attenuation values could differentiate between benign and MRA (P=0.04; AUC=0.656; sensitivity, 65.52%; specificity, 71.43%) but failed to distinguish between bland ascites and ascites with hemorrhagic component (P=0.85), and between infected and noninfected fluid collections (P=0.47). Conclusion: Although the results are statistically significant, the substrate of differentiation between benign and MRA ascites cannot be clearly stated. As being the first study to investigate this issue, it opens the way for other researches in the field to determine the dynamics of imaging quantitative measurements according to the fluid’s pathological features.

(1) Introduction: Multiparametric magnetic resonance imaging (mpMRI) is the main imagistic tool employed to assess patients suspected of harboring prostate cancer (PCa), setting the indication for targeted prostate biopsy. However, both mpMRI and targeted prostate biopsy are operator dependent. The past decade has been marked by the emerging domain of radiomics and artificial intelligence (AI), with extended application in medical diagnosis and treatment processes. (2) Aim: To present the current state of the art regarding decision support tools based on texture analysis and AI for the prediction of aggressiveness and biopsy assistance. (3) Materials and Methods: We performed literature research using PubMed MeSH, Scopus and WoS (Web of Science) databases and screened the retrieved papers using PRISMA principles. Articles that addressed PCa diagnosis and staging assisted by texture analysis and AI algorithms were included. (4) Results: 359 papers were retrieved using the keywords “prostate cancer”, “MRI”, “radiomics”, “textural analysis”, “artificial intelligence”, “computer assisted diagnosis”, out of which 35 were included in the final review. In total, 24 articles were presenting PCa diagnosis and prediction of aggressiveness, 7 addressed extracapsular extension assessment and 4 tackled computer-assisted targeted prostate biopsies. (5) Conclusions: The fusion of radiomics and AI has the potential of becoming an everyday tool in the process of diagnosis and staging of the prostate malignancies.

The classic ultrasonographic differentiation between benign and malignant adnexal masses encounters several limitations. Ultrasonography-based texture analysis (USTA) offers a new perspective, but its role has been incompletely evaluated. This study aimed to further investigate USTA’s capacity in differentiating benign from malignant adnexal tumors, as well as comparing the workflow and the results with previously-published research. A total of 123 adnexal lesions (benign, 88; malignant, 35) were retrospectively included. The USTA was performed on dedicated software. By applying three reduction techniques, 23 features with the highest discriminatory potential were selected. The features’ ability to identify ovarian malignancies was evaluated through univariate, multivariate, and receiver operating characteristics analyses, and also by the use of the k-nearest neighbor (KNN) classifier. Three parameters were independent predictors for ovarian neoplasms (sum variance, and two variations of the sum of squares). Benign and malignant lesions were differentiated with 90.48% sensitivity and 93.1% specificity by the prediction model (which included the three independent predictors), and with 71.43–80% sensitivity and 87.5–89.77% specificity by the KNN classifier. The USTA shows statistically significant differences between the textures of the two groups, but it is unclear whether the parameters can reflect the true histopathological characteristics of adnexal lesions.

Nuclear grade is important for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). This study aimed to determine the ability of preoperative four-phase multiphasic multidetector computed tomography (MDCT)-based radiomics features to predict the WHO/ISUP nuclear grade. In all 102 patients with histologically confirmed ccRCC, the training set (n = 62) and validation set (n = 40) were randomly assigned. In both datasets, patients were categorized according to the WHO/ISUP grading system into low-grade ccRCC (grades 1 and 2) and high-grade ccRCC (grades 3 and 4). The feature selection process consisted of three steps, including least absolute shrinkage and selection operator (LASSO) regression analysis, and the radiomics scores were developed using 48 radiomics features (10 in the unenhanced phase, 17 in the corticomedullary (CM) phase, 14 in the nephrographic (NP) phase, and 7 in the excretory phase). The radiomics score (Rad-Score) derived from the CM phase achieved the best predictive ability, with a sensitivity, specificity, and an area under the curve (AUC) of 90.91%, 95.00%, and 0.97 in the training set. In the validation set, the Rad-Score derived from the NP phase achieved the best predictive ability, with a sensitivity, specificity, and an AUC of 72.73%, 85.30%, and 0.84. We constructed a complex model, adding the radiomics score for each of the phases to the clinicoradiological characteristics, and found significantly better performance in the discrimination of the nuclear grades of ccRCCs in all MDCT phases. The highest AUC of 0.99 (95% CI, 0.92&ndash, 1.00, p &lt, 0.0001) was demonstrated for the CM phase. Our results showed that the MDCT radiomics features may play a role as potential imaging biomarkers to preoperatively predict the WHO/ISUP grade of ccRCCs.

High-grade gliomas (HGGs) and solitary brain metastases (BMs) have similar imaging appearances, which often leads to misclassification. In HGGs, the surrounding tissues show malignant invasion, while BMs tend to displace the adjacent area. The surrounding edema produced by the two cannot be differentiated by conventional magnetic resonance (MRI) examinations. Forty-two patients with pathology-proven brain tumors who underwent conventional pretreatment MRIs were retrospectively included (HGGs, n = 16, BMs, n = 26). Texture analysis of the peritumoral zone was performed on the T2-weighted sequence using dedicated software. The most discriminative texture features were selected using the Fisher and the probability of classification error and average correlation coefficients. The ability of texture parameters to distinguish between HGGs and BMs was evaluated through univariate, receiver operating, and multivariate analyses. The first percentile and wavelet energy texture parameters were independent predictors of HGGs (75&ndash, 87.5% sensitivity, 53.85&ndash, 88.46% specificity). The prediction model consisting of all parameters that showed statistically significant results at the univariate analysis was able to identify HGGs with 100% sensitivity and 66.7% specificity. Texture analysis can provide a quantitative description of the peritumoral zone encountered in solitary brain tumors, that can provide adequate differentiation between HGGs and BMs.

The imaging diagnosis of malignant ovarian cysts relies on their morphological features, which are not always specific to malignancy. The histological analysis of these cysts shows specific fluid characteristics, which cannot be assessed by conventional imaging techniques. This study investigates whether the texture-based radiomics analysis (TA) of magnetic resonance (MRI) images of the fluid content within ovarian cysts can function as a noninvasive tool in differentiating between benign and malignant lesions. Twenty-eight patients with benign (n = 15) and malignant (n = 13) ovarian cysts who underwent MRI examinations were retrospectively included. TA of the fluid component was undertaken on an axial T2-weighted sequence. A comparison of resulted parameters between benign and malignant groups was undertaken using univariate, multivariate, multiple regression, and receiver operating characteristics analyses, with the calculation of the area under the curve (AUC). The standard deviation of pixel intensity was identified as an independent predictor of malignant cysts (AUC = 0.738, sensitivity, 61.54%, specificity, 86.67%). The prediction model was able to identify malignant lesions with 84.62% sensitivity and 80% specificity (AUC = 0.841). TA of the fluid contained within the ovarian cysts can differentiate between malignant and benign lesions and potentially act as a noninvasive tool augmenting the imaging diagnosis of ovarian cystic lesions.

Background and Objectives: To assess ovarian cysts with texture analysis (TA) in magnetic resonance (MRI) images for establishing a differentiation criterion for endometriomas and functional hemorrhagic cysts (HCs) that could potentially outperform their classic MRI diagnostic features. Materials and Methods: Forty-three patients with known ovarian cysts who underwent MRI were retrospectively included (endometriomas, n = 29, HCs, n = 14). TA was performed using dedicated software based on T2-weighted images, by incorporating the whole lesions in a three-dimensional region of interest. The most discriminative texture features were highlighted by three selection methods (Fisher, probability of classification error and average correlation coefficients, and mutual information). The absolute values of these parameters were compared through univariate, multivariate, and receiver operating characteristic analyses. The ability of the two classic diagnostic signs (&ldquo, T2 shading&rdquo, and &ldquo, T2 dark spots&rdquo, ) to diagnose endometriomas was assessed by quantifying their sensitivity (Se) and specificity (Sp), following their conventional assessment on T1-and T2-weighted images by two radiologists. Results: The diagnostic power of the one texture parameter that was an independent predictor of endometriomas (entropy, 75% Se and 100% Sp) and of the predictive model composed of all parameters that showed statistically significant results at the univariate analysis (100% Se, 100% Sp) outperformed the ones shown by the classic MRI endometrioma features (&ldquo, 75.86% Se and 35.71% Sp, &ldquo, 55.17% Se and 64.29% Sp). Conclusion: Whole-lesion MRI TA has the potential to offer a superior discrimination criterion between endometriomas and HCs compared to the classic evaluation of the two lesions&rsquo, MRI signal behaviors.

Pathological analysis of ovarian cysts shows specific fluid characteristics that cannot be standardly evaluated on computer tomography (CT) examinations. This study aimed to assess the ovarian cysts&rsquo, fluid attenuation values on the native (Np), arterial (Ap), and venous (Vp) contrast phases of seventy patients with ovarian cysts who underwent CT examinations and were retrospectively included in this study. Patients were divided according to their final diagnosis into the benign group (n = 32) and malignant group (n = 38, of which 27 were primary and 11 were secondary lesions). Two radiologists measured the fluid attenuation values on each contrast phase, and the average values were used to discriminate between benign and malignant groups and primary tumors and metastases via univariate, multivariate, multiple regression, and receiver operating characteristics analyses. The Ap densities (p = 0.0002) were independently associated with malignant cysts. Based on the densities measured on all three phases, neoplastic lesions could be diagnosed with 89.47% sensitivity and 62.5% specificity. The Np densities (p = 0.0005) were able to identify metastases with 90.91% sensitivity and 70.37% specificity, while the combined densities of all three phases diagnosed secondary lesions with 72.73% sensitivity and 92.59% specificity. The ovarian cysts&rsquo, fluid densities could function as an adjuvant criterion to the classic CT evaluation of ovarian cysts.

The morphological changes advocating for peritoneal carcinomatosis are inconsistent and may be visible only in later stages of the disease. However, malignant ascites represents an early sign, and this fluid exhibits specific histological characteristics. This study aimed to quantify the fluid properties on computed tomography (CT) images of intraperitoneal effusions through texture analysis and evaluate its utility in differentiating benign and malignant collections. Fifty-two patients with histologically proven benign (n=29) and malignant (n=23) intraperitoneal effusions who underwent CT examinations were retrospectively included. Texture analysis of the fluid component was performed on the non-enhanced phase of each examination using dedicated software. Fisher and the probability of classification error and average correlation coefficients were used to select two sets of ten texture features, whose ability to distinguish between the two types of collections were tested using a k-nearest-neighbor classifier. Also, each of the selected feature’s diagnostic power was assessed using univariate and receiver operating characteristics analysis with the calculation of the area under the curve. The k-nearest-neighbor classifier was able to distinguish between the two entities with 71.15% accuracy, 73.91% sensitivity, and 68.97% specificity. The highest-ranked texture parameter was Inverse Difference Moment (p=0.0023; area under the curve=0.748), based on which malignant collections could be diagnosed with 95.65% sensitivity and 44.83% specificity. Although successful, the texture assessment of benign and malignant collections most likely does not reflect the cytological differences between the two groups.

The PI3K/AKT/mTOR signaling pathway is significantly activated in rheumatoid arthritis. In addition, somatic activating mutations of the PI3K/AKT/mTOR pathway may result in PIK3CA-related overgrowth spectrum diseases, including CLOVES (Congenital Lipomatous Overgrowth, Vascular malformation, Epidermal nevi, Skeletal abnormalities/Scoliosis) syndrome. We describe the case of a young female patient, with anti-citrullinated peptide antibodies-positive rheumatoid arthritis, referred for persistent finger pain and stiffness. Examination revealed discrete macrodactyly involving two fingers, scoliosis, asymmetrical calves, venectasias, a shoulder nevus and triangular feet with a “sandal gap” between two toes. These mild dysmorphic features with early-onset and the history of surgeries for thoracic lipoma and venous malformation were strongly suggestive of CLOVES syndrome. Confirmatory mutation analysis was not performed, as blood or saliva testing is not contributive for tissue-specific localized effects in the PIK3CA-related overgrowth spectrum. Nevertheless, lack of detection of a PIK3CA mutation does not exclude the diagnosis in patients fulfilling clinical criteria. Due to the patient’s wish to plan a pregnancy, therapy consisted in sulfasalazine and hydroxychloroquine, along with orthotic correction of leg length discrepancy. Overgrowth syndromes and arthritis may share common pathways. Mild macrodactyly should be differentiated from dactylitis. Diagnosing patients with minimal dysmorphic features within the PI3K-related overgrowth spectrum may help design better care strategies, in the quest for personalized medicine.

To quantify specific characteristics of different types of ascitic fluid on magnetic resonance (MR) images and to determine their utility for computer-assisted lesion classification.The MR images of 48 patients with intra-abdominal fluid were retrospectively analyzed. Patients were grouped according to the underlying disease and pathological outcomes. The fluid texture was analyzed on Breath Hold Axial T2 FatSat FIESTA sequence, using MaZda software. Most discriminative texture features for the classification of different types of ascites were selected based on Fisher coefficients (F) and the probability of classification error and average correlation coefficients (POE+ACC). Computer-assisted classification based on k-nearest-neighbor (k-NN) and artificial neural network (ANN) was performed and then accuracy, sensitivity and specificity were calculated.Adequate discriminative power for differentiating benign ascites from malignant ascites was achieved for two textural features, namely the Run Length Nonuniformity computed from both vertical and horizontal directions with 91.84% accuracy (sensitivity 100%; specificity 42.86%), and ten features for differentiating bland from hemorrhagic fluid with 90.00% accuracy (sensitivity 92.31%; specificity 85.71%), both for the ANN classifier.Texture analysis revealed several differences in signal characteristics of benign and malignant ascites. Computer-assisted pattern recognition algorithms may aid in the differential diagnosis of ascites types, especially in the early stages when there are few peritoneal modifications or when the cause is difficult to find.

Locally advanced rectal cancer (LARC) response to neoadjuvant chemoradiotherapy (nCRT) is very heterogeneous and up to 30% of patients are considered non-responders, presenting no tumor regression after nCRT. This study aimed to determine the ability of pre-treatment T2-weighted based radiomics features to predict LARC non-responders. A total of 67 LARC patients who underwent a pre-treatment MRI followed by nCRT and total mesorectal excision were assigned into training (n = 44) and validation (n = 23) groups. In both datasets, the patients were categorized according to the Ryan tumor regression grade (TRG) system into non-responders (TRG = 3) and responders (TRG 1 and 2). We extracted 960 radiomic features/patient from pre-treatment T2-weighted images. After a three-step feature selection process, including LASSO regression analysis, we built a radiomics score with seven radiomics features. This score was significantly higher among non-responders in both training and validation sets (p < 0.001 and p = 0.03) and it showed good predictive performance for LARC non-response, achieving an area under the curve (AUC) = 0.94 (95% CI: 0.82–0.99) in the training set and AUC = 0.80 (95% CI: 0.58–0.94) in the validation group. The multivariate analysis identified the radiomics score as an independent predictor for the tumor non-response (OR = 6.52, 95% CI: 1.87–22.72). Our results indicate that MRI radiomics features could be considered as potential imaging biomarkers for early prediction of LARC non-response to neoadjuvant treatment.

Background: Endometriomas and functional hemorrhagic cysts (FHCs) are a common gynecological encounter. Objective: This study aimed to assess the diagnostic efficiency of magnetic resonance imaging (MRI) using signal intensity measurements in differentiating endometriomas from FHCs. Methods: Forty-six patients who underwent pelvic MRI examinations (endometriomas, n=28; FHCs, n=18) were retrospectively included. The “T2 shading” sign was evaluated subjectively and quantitatively by measuring the T1-T2 signal intensity difference and calculating the percentage of signal decrease between T1 and T2-weighted sequences. The resulted values, along with the measurement of the Apparent Diffusion Coefficient (ADC) and the signal intensity on three diffusion- weighted sequences (DWI) (b50, b400, and b800), were compared between groups by using the Mann–Whitney U test. Also, the receiver operating characteristic analysis was performed for the statistically significant results (P Results: The two quantitative assessment methods showed similar efficiency in detecting endometriomas (P0.86), outperforming the classic subjective evaluation of the “T2 shading” sign (sensitivity, 92.86%; specificity, 66.67%). ADC (P=0.52) and DWI measurements (P=0.49, P=0.74, and P=0.78) failed to distinguish between the two entities. Conclusion: The quantitative analysis and interpretation of the “T2 shading” sign can significantly improve the differential diagnosis between endometriomas and FHCs.

Aim: Torsion of the testicular appendages represents the most common cause of an acute scrotum in prepubertal boys. Its sonographic appearances on gray-scale US and color Doppler US have already been presented in several studies. The aim of this analysis was to expand those already established techniques with strain elastography and thus present typical features of this entity on multiparametric US.Material and methods: Retrospective analysis of all patients presented to the urological department with an acute scrotum between January 2018 and July 2020 identified eleven patients 6-17 years old (mean, 11.1 years), discharged with the diagnosis torsion of the testicular appendages that were examined with a high-end ultrasound device. Results: On gray-scale US all patients showed a round lesion with heterogenous echotexture adjacent to the upper pole of the testis/epididymis with a diameter of 4 to 11.1 mm (mean, 7.7 mm). Scrotal skin thickening and a concomitant hydrocele were found in 9 (81.8%) and 7 (63.6%) cases, respectively. On color Doppler images, all torsed appendages were avascular and in 9 (81.8%) patients we observed hyperemia of the adjacent epididymis. Strain elastography showed increased tissue stiffness in all documented images.Conclusion: Torsion of the testicular appendages has a set of features on multiparametric US. Awareness of this features can facilitate diagnosis of torsion of the testicular appendages and reduce unnecessary surgicalscrotal exploration or unwarranted antibiotic treatment.

The ultrasonographic (US) features of endometriomas and hemorrhagic ovarian cysts (HOCs) are often overlapping. With the emergence of new computer-aided diagnosis techniques, this is the first study to investigate whether texture analysis (TA) could improve the discrimination between the two lesions in comparison with classic US evaluation. Fifty-six ovarian cysts (endometriomas, 30, HOCs, 26) were retrospectively included. Four classic US features of endometriomas (low-level internal echoes, perceptible walls, no solid components, and less than five locules) and 275 texture parameters were assessed for every lesion, and the ability to identify endometriomas was evaluated through univariate, multivariate, and receiver operating characteristics analyses. The sensitivity (Se) and specificity (Sp) were calculated with 95% confidence intervals (CIs). The texture model, consisting of seven independent predictors (five variations of difference of variance, image contrast, and the 10th percentile, 100% Se and 100% Sp), was able to outperform the ultrasound model composed of three independent features (low-level internal echoes, perceptible walls, and less than five locules, 74.19% Se and 84.62% Sp) in the diagnosis of endometriomas. The TA showed statistically significant differences between the groups and high diagnostic value, but it remains unclear if the textures reflect the intrinsic histological characteristics of the two lesions.

Background and Objective: The imaging differentiation of benign from malignant intraperitoneal collections (IPCs) relies on the tumoral morphological modifications of the peritoneum, which are not always advocating for malignancy. We aimed to assess ascitic fluid with the apparent diffusion coefficient (ADC) to determine non-invasive, stand-alone, differentiation criteria for benign and malignant intraperitoneal effusions. Materials and Methods: Sixty-one patients with known IPCs who underwent magnetic resonance examinations for reasons such as tumor staging, undetermined abdominal mass and disease follow up were retrospectively included in this study. All subjects had a final diagnosis of the fluid based on pathological examinations, which were divided into benign (n = 37) and malignant (n = 24) IPCs groups. ADC values were measured separately by two radiologists, and the average values were used for comparing the two groups by consuming the independent samples t-test. The receiver operating characteristic analysis was performed to test the ADC values&rsquo, diagnostic ability to distinguish malignant from benign collections. Results: The differentiation between benign and malignant IPCs based on ADC values was statistically significant (p = 0.0034). The mean ADC values were higher for the benign (3.543 &times, 10&minus, 3 mm2/s) than for the malignant group (3.057 &times, 3 mm2/s). The optimum ADC cutoff point for the diagnosis of malignant ascites was &lt, 3.241 &times, 3 mm2/s, with a sensitivity of 77.78% and a specificity of 80%. Conclusions: ADC represents a noninvasive and reproducible imaging parameter that may help to assess intraperitoneal collections. Although successful in distinguishing malignant from benign IPCs, further research must be conducted in order to certify if the difference in ADC values is a consequence of the physical characteristics of the ascitic fluids or their appurtenance to a certain histopathological group.

ObjectivesThis study aimed to evaluate the association between cardiovascular risk factors and Coronary Artery Disease—Reporting and Data System (CAD-RADS) score in the Romanian population. CAD-RADS is a new, standardised method to assess coronary artery disease (CAD) using coronary CT angiography (CCTA).DesignA cross-sectional observational, patient-based study.SettingReferred imaging centre for CAD in Transylvania, Romania.ParticipantsWe retrospectively reviewed 674 patients who underwent CCTA between January 2017 and August 2018. The exclusion criteria included: previously known CAD, defined as prior myocardial infarction, percutaneous coronary intervention or coronary artery bypass graft surgery (n=91), cardiac CT for other than evaluation of possible CAD (n=85), significant arrhythmias compromising imaging quality (n=23). Finally, 475 patients fulfilled the inclusion criteria.MethodsDemographical, clinical and CCTA characteristics of the patients were obtained. CAD was evaluated using CAD-RADS score. Obstructive CAD was defined as ≥50% stenosis of ≥1 coronary segment on CCTA.ResultsWe evaluated the association between risk factors and CAD-RADS score in univariate and multivariable analysis. We divided the patients into two groups according to the CAD-RADS system: group 1: CAD-RADS score between 0 and 2 (stenosis ConclusionThis study demonstrated a significant association between multiple cardiovascular risk factors and a higher coronary atherosclerotic burden assessed using CAD-RADS system in the Romanian population.

Endorectal ultrasonography (ERUS) and high-resolution Magnetic Resonance Imaging (HR-MRI) are two frequently used techniques for the preoperative staging of rectal cancer to offer proper neoadjuvant or surgical treatment. Because tumor restaging after neoadjuvant therapy using ERUS and HR-MRI remains challenging the aim of this study is to determine which of the two imaging methods used in restaging rectal cancer has the highest accuracy.We included patients with rectal cancer who underwent ERUS and HR-MRI scans before and after neoadjuvant chemo-radiotherapy (n-CRT). The n-CRT was followed by imagistic restaging at 6 weeks after the last therapy session and by surgical resection. The pathology stage from the surgical sample was compared with the HR-MRI and ERUS restaging.Fifty-four patients underwent n-CRT and 47 were restaged by both ERUS and HR-MRI. ERUS was accurate in tumor restaging after n-CRT in 29 cases (61.7%) and HR-MRI in 32 cases (68%). Regarding lymphatic node status, ERUS was accurate for 34 patients (72.3%) and had an overall rate of over-staging of 12.8% and 14.9% of under-staging. HR-MRI was accurate for 30 patients (63.8%) in restaging the lymph nodes after n-CRT and had an overall rate of over-staging of 25.5% and 10.7% of under-staging.Restaging rectal cancer after n-CRT remains difficult because of radiotherapy tissue alteration, which results in low diagnostic accuracy for both methods.Endorectal Ultrasonography (ERUS), High-Resolution Magnetic Resonance Imaging (HR-MRI), Neoadjuvant Chemo-Radiotherapy, Rectal Cancer Restaging.L’ultrasonografia endorettale (ERUS) e la risonanza magnetica ad elevate risoluzione (HR-MRI) sono le due tecniche usate di frequente per la stadiazione preoperatoria del cancro del retto per permettere l’adeguato trattamento neoadiuvante o chirurgico. Dato che la restadiazione del tumore con ERSU e con HR-MRI dopo trattamento neoadiuvante rappresenta una problematica, lo scopo del nostro studio era quello di stabilire quale dei due metodi di indagine per imaging avesse la maggiore accuratezza per la restadiazione del cancro del retto. Lo studio ha preso in considerazione pazienti con cancro del retto sottoposti ad ERUS e a HR-MRI prima e dopo trattamento chemioterapico neoadiuvante (n-CRT). La restadiazione per imaging è stata eseguita 6 settimane dopo l’ultima sessione del trattamento chemioterapico e prima dell’intervento chirurgico. Lo studio anatomo-patologico del pezzo chirurgico è stato messo in relazione con i referti di restadiazione con ERUS e HR-MRI. Di 54 pazienti sottoposti a n-CRT, 47 sono stati restadiati sia con ERUS che con HR-MRI. ERUS si è dimostrata adeguata nella restadiazione del tumore dopo n- CRT in 29 casi (61,7%) e la HR-MRI in 32 casi (68%). Per quanto riguarda lo stato linfonodale ERUS è stata accurata in 34 pazienti (72,3%) con una incidenza di sopra-stadiazione del 12,8% e una di sottostadiazione del 14,9%. La HR.MRI si è dimostrata accurata nella restadiazione linfonodale dopo n-CRT in 30 pazienti (63,8%) con una incidenza di soprastadiazione del 25,5% e del 10,7% di sottostadiazione. In conclusione la restadiazione del cancro del retto dopo n-CRT rimane difficoltosa per il sovvertimento tissutale dovuto alle radiazioni, con la conseguente scarsa accuratezza diagnostica con entrambi i metodi.

Background High-grade gliomas (HGGs) and brain metastases (BMs) can display similar imaging characteristics on conventional MRI. In HGGs, the peritumoral edema may be infiltrated by the malignant cells, which was not observed in BMs. Purpose To determine whether the apparent diffusion coefficient values could differentiate HGGs from BMs. Material and Methods Fifty-seven patients underwent conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) before treatment. The minimum and mean ADC in the enhancing tumor (ADCmin, ADCmean) and the minimum ADC in the peritumoral region (ADCedema) were measured from ADC maps. To determine whether there was a statistical difference between groups, ADC values were compared. A receiver operating characteristic (ROC) curve analysis was used to determine the cutoff ADC value for distinguishing between HGGs and BMs. Results The mean ADCmin values in the intratumoral regions of HGGs were significantly higher than those in BMs. No differences were observed between groups regarding ADCmean values. The mean ADCmin values in the peritumoral edema of HGGs were significantly lower than those in BMs. According to ROC curve analysis, a cutoff value of 1.332 × 10−3 mm2/s for the ADCedema generated the best combination of sensitivity (95%) and specificity (84%) for distinguishing between HGGs and BMs. The same value showed a sensitivity of 95.6% and a specificity of 100% for distinguishing between GBMs and BMs. Conclusion ADC values from DWI were found to distinguish between HGGs and solitary BMs. The peritumoral ADC values are better than the intratumoral ADC values in predicting the tumor type.

The paper aim was to present a case of bilateral, advanced, orbital lymphoma diagnosed in a middle aged man who was admitted in a clinical condition which almost defied reality. The entire orbito-facial region was replaced by massive ulcero-necrotic masses which completely distorted the normal anatomy, giving an alien-like resemblance of an otherwise ordinary man. The patient was submitted to several imaging examinations (head and whole body computer tomography, head MRI, laterocervical ultrasound and elastography) and surgical biopsy. The final diagnosis was stage IVB diffuse large B-cell lymphoma (DLBCL). Currently the patient is undergoing chemotherapy with astonishing response (clinically visible tumoral shrinkage). The differential diagnosis of orbital masses may be extensive, starting from inflammatory conditions, such as cellulitis, pseudotumor, sarcoidosis and finishing with metastases from lung, renal or breast cancers. However, considering the substantial tumor volume in this case and imaging aspects, lymphomatous origin was the first diagnostic verified and ultimately confirmed. The peculiarities of the case do not reside in the final diagnosis, for DLBCL is the most common form of non-Hodgkin lymphoma in middle aged men, but its debut or spread to orbits is rare, usually unilateral and diagnosed in less advanced stage.

Angiomatosis or diffuse hemangioma is a very rare benign vascular tumor, consisting of blood and lymphatic channels growing diffusely in the breast parenchyma. We report a case of diffuse breast angiomatosis in a 34-year-old woman with pubertal anisomastia. Ultrasound raised the suspicion of vascular tumor, by showing large cystic spaces separated by septae with moderate blood flow, similar to those found in cystic lymphangioma. We discuss the imaging (mammography, ultrasound, and MRI) and pathologic findings, with a brief review of the literature.

Aim: Prostate biopsies are usually done with transrectal ultrasound (TRUS) in B-mode (B TRUS) but multiparametric MRI (mpMRI) is the gold imaging standard for the visualization of clinically significant prostate cancer (PCa), since a lowPCa detection rate is reported for B TRUS. The aim of this study was to assess the visibility of MRI lesions on B TRUS and to determine which factors may influence the visibility on B TRUS.Material and methods: 142 men with 148 lesions reported on mpMRI underwent a B TRUS/mpMRI fusion targeted biopsy of the prostate and were included in this retrospective study. During the biopsy, images were obtained and stored in the institution’s PACS. These images were reviewed by two radiologists to determine, whether an mpMRI lesion was or was not visible on B TRUS.Results: Overall 92 from 148 mpMRI lesions (62.2%) were visible on B TRUS. The location of the lesion in the prostate, the PIRADS classification of the lesions and the size of the lesion had no significant influence on the visibility on B TRUS. Only the prostate volume had a significant influence on visibility: in smaller prostates significantly more lesions were visible on B TRUS than in large glands (p+0.041; 45.1 ml vs 54 ml).Conclusion: The use of newer high-end ultrasound units as well as experience gained from fusion biopsies enables us to see 62.2 % of all suspicious mpMRI lesions on B TRUS. B TRUS images merit a thorough examination during a conventional biopsy setting.

Objectives The purpose of this study was to compare the value of real-time sonoelastography with T2-weighted endorectal magnetic resonance imaging (MRI) for prostate cancer detection. Methods Thirty-three patients with an elevated prostate-specific antigen level were investigated with real-time sonoelastography and T2-weighted endorectal MRI for prostate cancer diagnosis before systematic prostate biopsy. Real-time sonoelastography was performed to assess prostate tissue elasticity, and hard areas were considered suspicious for prostate cancer. Low–signal intensity nodules on T2-weighted endorectal MRI were considered suspicious for prostate cancer. Imaging findings were assigned to 6 areas of the peripheral zone (sextants), and their cancer detection rates were compared. Results Overall, prostate cancer was detected in 13 of 33 patients (39.4%). Both real-time sonoelastography and T2-weighted endorectal MRI detected 11 cancer-positive patients (84.6%). Real-time sonoelastography showed 27 suspicious lesions in 198 sextants, and 15 (55.6%) were cancer positive. T2-weighted endorectal MRI showed 31 suspicious lesions in 198 sextants, and 13 (40.6%) were cancer positive. These findings resulted in sensitivity rates and negative predictive values per patient of 84.6% and 86.7%, respectively, for sonoelastography and 84.6% and 83.3% for MRI. The per-sextant analysis showed sensitivity rates and negative predictive values of 57.7% and 93.6% for sonoelastography and 50.0% and 92.2% for MRI. Conclusions Real-time sonoelastography showed comparable results as T2-weighted endorectal MRI for prostate cancer detection.

Background We tested the feasibility of a simple method for assessment of prostate cancer (PCa) aggressiveness using diffusion-weighted magnetic resonance imaging (MRI) to calculate apparent diffusion coefficient (ADC) ratios between prostate cancer and healthy prostatic tissue. Methods The requirement for institutional review board approval was waived. A set of 20 standardized core transperineal saturation biopsy specimens served as the reference standard for placement of regions of interest on ADC maps in tumorous and normal prostatic tissue of 22 men with PCa (median Gleason score: 7; range, 6–9). A total of 128 positive sectors were included for evaluation. Two diagnostic ratios were computed between tumor ADCs and normal sector ADCs: the ADC peripheral ratio (the ratio between tumor ADC and normal peripheral zone tissue, ADC-PR), and the ADC central ratio (the ratio between tumor ADC and normal central zone tissue, ADC-CR). The performance of the two ratios in detecting high-risk tumor foci (Gleason 8 and 9) was assessed using the area under the receiver operating characteristic curve (AUC). Results Both ADC ratios presented significantly lower values in high-risk tumors (0.48 ± 0.13 for ADC-CR and 0.40 ± 0.09 for ADC-PR) compared with low-risk tumors (0.66 ± 0.17 for ADC-CR and 0.54 ± 0.09 for ADC-PR) (p Conclusions The ADC ratio as an intrapatient-normalized diagnostic tool may be better in detecting high-grade lesions compared with analysis based on tumor ADCs alone, and may reduce the rate of biopsies.

Angiomatosis or diffuse hemangioma is a very rare benign vascular tumor, consisting of blood and lymphatic channels growing diffusely in the breast parenchyma. We report a case of diffuse breast angiomatosis in a 34-year-old woman with pubertal anisomastia. Ultrasound raised the suspicion of vascular tumor, by showing large cystic spaces separated by septae with moderate blood flow, similar to those found in cystic lymphangioma. We discuss the imaging (mammography, ultrasound, and MRI) and pathologic findings, with a brief review of the literature.

Neurological disorders represent major causes of lost years of healthy life and mortality worldwide. Development of their quantitative interdisciplinaryin vivoevaluation is required. Compartment modeling (CM) of brain data acquiredin vivousing magnetic resonance imaging techniques with clinically available contrast agents can be performed to quantitatively assess brain perfusion. Transport of1H spins in water molecules across physiological compartmental brain barriers in three different pools was mathematically modeled and theoretically evaluated in this paper and the corresponding theoretical compartment modeling of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data was analyzed. The pools considered were blood, tissue, and cerebrospinal fluid (CSF). The blood and CSF data were mathematically modeled assuming continuous flow of the1H spins in these pools. Tissue data was modeled using three CMs. Results in this paper show that transport across physiological brain barriers such as the blood to brain barrier, the extracellular space to the intracellular space barrier, or the blood to CSF barrier can be evaluated quantitatively. Statistical evaluations of this quantitative information may be performed to assess tissue perfusion, barriers' integrity, and CSF flowin vivoin the normal or disease-affected brain or to assess response to therapy.

Background & Aims: To assess the role of diffusion weighted imaging sequence (DWI), routinely used in hepatic magnetic resonance imaging (MRI) for the differentiation of focal liver lesions (FLLs) as benign or malignant. Method: 99 FLLs assessed by liver MRI in 80 patients were included in the present study. All lesions were retrospectively analyzed by two experienced radiologists, independent from each other, who were not aware of the previous results obtained by using different imaging techniques. All included FLLs had a final histological diagnosis or a final diagnosis based on consensus reading by two experienced radiologists and follow-up at 6 months. The FLLs signal was qualitatively appreciated on the b-800 sequences and on the apparent diffusion coefficient (ADC) map. The ADC value of each FLL was measured and the ADC ratio between the ADC value of the assessed FLL and that of the surrounding liver parenchyma were calculated. Results: The mean ADC value for benign FLLs as assessed by the two independent readers was 1.78 x 10¯³ and 1.72 x 10¯³, respectively. The mean ADC value for malignant FLLs was 0.92 x 10¯³ for the first reader and 0.95 x 10¯³ for the second reader. The mean ADC ratio for benign FLLs was 1.91 and 1.85 for the two readers and for malignant FLLs was 0.91 and 0.94, respectively. Using an ADC value lower than 1.024 x 10¯³ offers a specificity of 100% and a sensitivity of 62.5% for the diagnosis of malignant FLLs. The ADC value is an indicator which is less prone to interobserver variability (correlation of 0.919→1). The ADC ratio has, as the analysis of the ROC curve shows, the best predictive value for differentiation between benign and malignant FLLs. Analysis of the signal intensity on the DWI b-800 image alone is of no significance in differentiating benign from malignant FLLs (p>0.05). Conclusions: The ADC value and the ADC ratio assessed on liver DWI are useful diagnostic tools in the differential diagnosis of benign vs. malignant FLLs. Quantitative methods such as calculating the ADC value or ADC ratio have better diagnostic value than the qualitative techniques.