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Late gadolinium enhancement (LGE) is the most relevant tool of cardiac magnetic resonance for tissue characterization, and it plays a pivotal role for diagnostic and prognostic assessment of cardiomyopathies. The pattern of presentation of LGE allows differential diagnosis between ischaemic and non-ischaemic heart disease with high diagnostic accuracy, and among different cardiomyopathies, specific presentation of LGE may help to make a diagnosis. Late gadolinium enhancement may be caused by conditions that significantly increase the interstitial space or, less frequently, that slow down Gd exit, like myocardial fibrosis. In chronic myocardial infarction, hypertrophic cardiomyopathies (HCM), dilated cardiomyopathy, Fabry disease, and other conditions, LGE is a marker of myocardial fibrosis, but also in patients with acute myocarditis where LGE may be also explained by the increase of interstitial space caused by interstitial oedema or by tissue infiltration of inflammatory cells. In cardiac amyloidosis, LGE represents myocardial fibrosis but the interstitial overload of amyloid proteins should also be considered as a potential cause of LGE. The identification of the pattern of presentation of LGE is also very important. In the ischaemic pattern, LGE always involves the subendocardial layer with more or less transmural extent, it is confluent, and every single scar should be located in the territory of one coronary artery. In the non-ischaemic pattern, LGE does not fulfil the previous criteria, being midwall, subepicardial, or mixed, not necessarily confluent or confined to a territory of one coronary artery. For cardiomyopathies, the exact pattern of non-ischaemic LGE is important. Quantitative analysis of LGE is required in some specific conditions as in HCM. Magnetic resonance imaging with LGE technique should be performed in every patient with suspect of cardiomyopathy. The lack of standardization of pulse sequence and mostly of quantification methods is the main limitation of LGE technique.

The application of radiomics for non-oncologic diseases is currently emerging. Despite its relative infancy state, the evidence highlights the potential of radiomics approaches to serve as neuroimaging biomarkers in the field of the neurodegenerative brain. This systematic review presents the last progress and potential application of radiomics in the field of neurodegenerative nuclear imaging applied to positron-emission tomography (PET) and single-photon emission computed tomography (SPECT) by focusing mainly on the two most common neurodegenerative disorders, Alzheimer's (AD) and Parkinson's disease (PD). A comprehensive review of the current literature was performed using the PubMed and Web of Science databases up to November 2022. The final collection of eighteen relevant publications was grouped as AD-related and PD-related. The main efforts in the field of AD dealt with radiomics-based early diagnosis of preclinical AD and the prediction of MCI to AD conversion, meanwhile, in the setting of PD, the radiomics techniques have been used in the attempt to improve the assessment of PD diagnosis, the differential diagnosis between PD and other parkinsonism, severity assessment, and outcome prediction. Although limited evidence with relatively small cohort studies, it seems that radiomics-based analysis using nuclear medicine tools, mainly [18F]Fluorodeoxyglucose (FDG) and β-amyloid (Aβ) PET, and dopamine transporter (DAT) SPECT, can be used for computer-aided diagnoses in AD-continuum and parkinsonian disorders. Combining nuclear radiomics analysis with clinical factors and introducing a multimodality approach can significantly improve classification and prediction efficiency in neurodegenerative disorders.

Tailoring treatment in patients with Hodgkin lymphoma (HL) is paramount to maximize outcomes while avoiding unnecessary toxicity. We aimed to compare the performance of SUVmax reduction (ΔSUVmax%) versus Deauville score (DS) in assessing chemotherapy response in pediatric HL patients undergoing 18F-FDG PET-CT. Fifty-two patients with biopsy-proven HL (age 8-16 years) were enrolled at baseline, interim (after the 2nd or 3rd chemotherapy round), and post-therapy (upon completion of first-line chemotherapy) 18F-FDG PET-CT. Interim and post-therapy DS and ΔSUVmax% were compared as response predictors. Patients were classified as responders and non-responders based on 24-month clinical follow-up. Interim DS showed a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy of 100%, 80.4%, 100%, 40% and 82.7%, respectively in predicting response. Post-therapy DS showed a sensitivity, specificity, PPV, NPV and accuracy of 66.7%, 97.8%, 95.7%, 80% and 94.2%. Interim ΔSUVmax% showed a sensitivity, specificity, PPV, NPV and accuracy of 83.3%, 82.6%, 97.4%, 38.5% and 82.7%, with a 56.3% cutoff. Post-therapy ΔSUVmax% showed a sensitivity, specificity, PPV, NPV and accuracy of 83.3%, 84.8%, 97.5%, 41.7% and 84.6%, with a 76.8% cutoff. Sensitivity, specificity (p

OBJECTIVES This study aimed to evaluate the diagnostic performance of the maximum intensity projection (MIP) reformations of breast computed tomography (B-CT) images as a stand-alone method for the detection and characterization of breast imaging findings. MATERIALS AND METHODS A total of 160 women undergoing B-CT between August 2018 and December 2020 were retrospectively included; 80 patients with known breast imaging findings were matched with 80 patients without imaging findings according to age and amount of fibroglandular tissue (FGT). A total of 71 benign and 9 malignant lesions were included. Images were evaluated using 15-mm MIP in 3 planes by 2 radiologists with experience in B-CT. The presence of lesions and FGT were evaluated, using the BI-RADS classification. Interreader agreement and descriptive statistics were calculated. RESULTS The interreader agreement of the 2 readers for finding a lesion (benign or malignant) was 0.86 and for rating according to BI-RADS classification was 0.82. One of 9 cancers (11.1%) was missed by both readers due to dense breast tissue. BI-RADS 1 was correctly applied to 73 of 80 patients (91.3%) by reader 1 and to 74 of 80 patients (92.5%) by reader 2 without recognizable lesions. BI-RADS 2 or higher with a lesion in at least one of the breasts was correctly applied in 69 of 80 patients (86.3%) by both readers. For finding a malignant lesion, sensitivity was 88.9% (95% confidence interval [CI], 51.75%-99.72%) for both readers, and specificity was 99.3% (95% CI, 96.4%-100%) for reader 1 and 100% (95% CI, 97.20%-100.00%) for reader 2. CONCLUSIONS Evaluation of B-CT images using the MIP reformations may help to reduce the reading time with high diagnostic performance and confidence.

Background: We sought to evaluate the diagnostic accuracy of post-mortem cardiac magnetic resonance (PMCMR) of explanted hearts to detect the cardiac causes of sudden death. Methods: PMCMR was performed in formalin-fixed explanted hearts of 115 cases of sudden death. Histological sampling of myocardium was performed using two different approaches: (1) guideline-based sampling; (2) guideline-based plus PMCMR-driven sampling. Results: Forensic diagnosis of cardiac cause of death was ascertained in 72 (63%) patients. When the guideline-driven histological sampling was used, the PMCMR interpretation matched with final forensic diagnosis in 93 out of 115 cases (81%) with sensitivity of 88% (79–95%), specificity of 65% (47–80%), PPV of 84% (78–90%), NPV of 73% (58–84%), accuracy of 81% (72–88%), and AUC of 0.77 (0.68–0.84). When a PMCMR-driven approach was added to the guideline-based one, the matching increased to 102 (89%) cases with a PMCMR sensitivity of 89% (80–94%), a specificity of 86% (67–96%), PPV of 95% (89–98%), NPV of 73% (59–83%), accuracy of 89% (81–93%), and AUC of 0.88 (0.80–0.93). Conclusions: PMCMR has high accuracy to identify the cardiac cause of sudden death and may be considered a valid auxilium for forensic diagnosis. PMCMR could improve histological diagnosis in conditions with focal myocardial involvement or demonstrating signs of myocardial ischemia.

Background: Immune checkpoint inhibitors (ICIs) represent the standard therapy for malignant melanoma. However, a number of patients do not respond to ICIs and biomarker development remains challenging. Methods: This single cohort observational study investigates the association between mRNA levels of programmed cell death-1 (PD-1) ligand 1 (PD-L1), interferon-gamma (IFN-γ), and transforming growth factor-β (TGF-β) in circulating extracellular vesicles (EVs) in 30 patients with metastatic melanoma treated with first line anti-PD-1 antibodies. Blood samples were collected at baseline and RNA extracted from EVs. The RNA levels of PD-L1, IFN-γ, and TGF-β were analysed by digital droplet PCR (ddPCR). A biomarker-radiomic analysis was performed in a subset of patients.Results: Patients with high TGF-β expression (cut-off fractional abundance [FA] >0.19) at baseline had longer median progression-free survival (8.4 vs 1.8 months; p=0.006) and overall survival (17.9 vs 2.63 months; p=0.0009). Moreover, radiomic analysis demonstrated that patients with high TGF-β expression at baseline had smaller lesions (2.41 ± 3.27 ml vs 42.79 ± 101.08 ml, pConclusions: These results provide evidence that high TGF-β expression in extracellular vesicles at baseline is associated with a better response to immunotherapy. Further investigation on a larger patient population is needed to validate the predictive power of this potential biomarker of response to ICIs.

Simple Summary Breast cancer is a leading cause of morbidity and mortality worldwide. The metastatic disease is largely responsible for cancer patient deaths, and its treatment implies usually different therapies. In this context, the prediction of response to treatment or depiction of treatment-resistant phenotypes is essential in clinical practice, especially in the new era of precision medicine. In this study, we used several combinations of feature selection methods and machine-learning classifiers to construct predictive models of the metabolic response to the treatment of metastatic breast cancer lesions. These models were based on clinical variables and radiomic features extracted from 2-deoxy-2-[F-18]fluoro-D-glucose positron emission tomography/computed tomography ([F-18]F-FDG PET/CT) images, obtained prior to the treatment. Our main goal was to know if this prediction was feasible and to identify those combinations with better predictive performance. We found that several combinations were successful to predict the metabolic response to treatment, of which the least absolute shrinkage and selection operator (Lasso) + support vector machines (SVM) had the best mean performance in terms of area under the curve, in both training and validation cohorts. Model performances depended largely on the selected combinations. Background: This study aimed to identify optimal combinations between feature selection methods and machine-learning classifiers for predicting the metabolic response of individual metastatic breast cancer lesions, based on clinical variables and radiomic features extracted from pretreatment [F-18]F-FDG PET/CT images. Methods: A total of 48 patients with confirmed metastatic breast cancer, who received different treatments, were included. All patients had an [F-18]F-FDG PET/CT scan before and after the treatment. From 228 metastatic lesions identified, 127 were categorized as responders (complete or partial metabolic response) and 101 as non-responders (stable or progressive metabolic response), by using the percentage changes in SULpeak (peak standardized uptake values normalized for body lean body mass). The lesion pool was divided into training (n = 182) and testing cohorts (n = 46); for each lesion, 101 image features from both PET and CT were extracted (202 features per lesion). These features, along with clinical and pathological information, allowed the prediction model's construction by using seven popular feature selection methods in cross-combination with another seven machine-learning (ML) classifiers. The performance of the different models was investigated with the receiver-operating characteristic curve (ROC) analysis, using the area under the curve (AUC) and accuracy (ACC) metrics. Results: The combinations, least absolute shrinkage and selection operator (Lasso) + support vector machines (SVM), or random forest (RF) had the highest AUC in the cross-validation, with 0.93 +/- 0.06 and 0.92 +/- 0.03, respectively, whereas Lasso + neural network (NN) or SVM, and mutual information (MI) + RF, had the higher AUC and ACC in the validation cohort, with 0.90/0.72, 0.86/0.76, and 87/85, respectively. On average, the models with Lasso and models with SVM had the best mean performance for both AUC and ACC in both training and validation cohorts. Conclusions: Image features obtained from a pretreatment [F-18]F-FDG PET/CT along with clinical vaiables could predict the metabolic response of metastatic breast cancer lesions, by their incorporation into predictive models, whose performance depends on the selected combination between feature selection and ML classifier methods.

The authors wish to make the following corrections to this paper [...]

Purpose: To assess the ability of apparent diffusion coefficient (ADC) measurements in predicting the histological grade of endometrial cancer. A secondary goal was to assess the agreement between MRI and surgical staging as an accurate measurement. Methods: Patients with endometrial cancers diagnosed between 2018–2020 and having received both MRI and surgical staging were retrospectively enrolled. Patients were characterized according to histology, tumor size, FIGO stage (MRI and surgical stage), and functional MRI parameters (DCE and DWI/ADC). Statistical analysis was performed to determine if an association could be identified between ADC variables and histology grade. Secondarily, we assessed the degree of agreement between the MRI and surgical stages according to the FIGO classification. Results: The cohort included 45 women with endometrial cancer. Quantitative analysis of ADC variables did not find a statistically significant association with histological tumor grades. DCE showed higher sensitivity than DWI/ADC in the assessment of myometrial invasion (85.00% versus 65.00%) with the same specificity (80.00%). A good agreement between MRI and histopathology for the FIGO stage was found (kappa of 0.72, p < 0.01). Differences in staging between MRI and surgery were detected in eight cases, which could not be justified by the interval between MRI and surgery. Conclusions. ADC values were not useful for predicting endometrial cancer grade, despite the good agreement between MRI interpretation and histopathology of endometrial cancer staging at our center.

This study aimed to identify optimal combinations between feature selection methods and machine-learning classifiers for predicting the metabolic response of individual metastatic breast cancer lesions, based on clinical variables and radiomic features extracted from pretreatment [A total of 48 patients with confirmed metastatic breast cancer, who received different treatments, were included. All patients had an [The combinations, least absolute shrinkage and selection operator (Lasso) + support vector machines (SVM), or random forest (RF) had the highest AUC in the cross-validation, with 0.93 ± 0.06 and 0.92 ± 0.03, respectively, whereas Lasso + neural network (NN) or SVM, and mutual information (MI) + RF, had the higher AUC and ACC in the validation cohort, with 0.90/0.72, 0.86/0.76, and 87/85, respectively. On average, the models with Lasso and models with SVM had the best mean performance for both AUC and ACC in both training and validation cohorts.Image features obtained from a pretreatment [

BACKGROUND Artificial intelligence in radiology has the potential to assist with the diagnosis, prognostication and therapeutic response prediction of various cancers. A few studies have reported that texture analysis can be helpful in predicting the response to chemotherapy for colorectal liver metastases, however, the results have varied. Necrotic metastases were not clearly excluded in these studies and in most studies the full range of texture analysis features were not evaluated. This study was designed to determine if the computed tomography (CT) texture analysis results of non-necrotic colorectal liver metastases differ from previous reports. A larger range of texture features were also evaluated to identify potential new biomarkers. AIM To identify potential new imaging biomarkers with CT texture analysis which can predict the response to first-line cytotoxic chemotherapy in non-necrotic colorectal liver metastases (CRLMs). METHODS Patients who presented with CRLMs from 2012 to 2020 were retrospectively selected on the institutional radiology information system of our private radiology practice. The inclusion criteria were non-necrotic CRLMs with a minimum size of 10 mm (diagnosed on archived 1.25 mm portal venous phase CT scans) which were treated with standard first-line cytotoxic chemotherapy (FOLFOX, FOLFIRI, FOLFOXIRI, CAPE-OX, CAPE-IRI or capecitabine). The final study cohort consisted of 29 patients. The treatment response of the CRLMs was classified according to the RECIST 1.1 criteria. By means of CT texture analysis, various first and second order texture features were extracted from a single non-necrotic target CRLM in each responding and non-responding patient. Associations between features and response to chemotherapy were assessed by logistic regression models. The prognostic accuracy of selected features was evaluated by using the area under the curve. RESULTS There were 15 responders (partial response) and 14 non-responders (7 stable and 7 with progressive disease). The responders presented with a higher number of CRLMs (P = 0.05). In univariable analysis, eight texture features of the responding CRLMs were associated with treatment response, but due to strong correlations among some of the features, only two features, namely minimum histogram gradient intensity and long run low grey level emphasis, were included in the multiple analysis. The area under the receiver operating characteristic curve of the multiple model was 0.80 (95%CI: 0.64 to 0.96), with a sensitivity of 0.73 (95%CI: 0.48 to 0.89) and a specificity of 0.79 (95%CI: 0.52 to 0.92). CONCLUSION Eight first and second order texture features, but particularly minimum histogram gradient intensity and long run low grey level emphasis are significantly correlated with treatment response in non-necrotic CRLMs.

During the coronavirus disease 19 (COVID-19) pandemic, extracorporeal membrane oxygenation (ECMO) has been proposed as a possible therapy for COVID-19 patients with acute respiratory distress syndrome. This pictorial review is intended to provide radiologists with up-to-date information regarding different types of ECMO devices, correct placement of ECMO cannulae, and imaging features of potential complications and disease evolution in COVID-19 patients treated with ECMO, which is essential for a correct interpretation of diagnostic imaging, so as to guide proper patient management.

Tailoring treatment in patients with Hodgkin lymphoma (HL) is paramount to maximize outcomes while avoiding unnecessary toxicity. We aimed to compare the performance of SUVmax reduction (ΔSUVmax%) and the PET ratio (rPET) versus the Deauville score (DS) for assessing the chemotherapy response in pediatric HL patients undergoing 18F-FDG PET-CT. Fifty-two patients with biopsy-proven HL (aged 8–16 years) were enrolled at baseline, interim (after the second or third chemotherapy round) and post-therapy (on completion of first-line chemotherapy). Interim and post-therapy DS, ΔSUVmax% and rPET were compared as response predictors. Patients were classified as responders or non-responders based on a 24-month clinical follow-up. Interim DS showed a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy of 100%, 80.4%, 100%, 40% and 82.7%, respectively, in predicting the therapy response. Post-therapy DS showed a sensitivity, specificity, PPV, NPV and accuracy of 66.7%, 97.8%, 95.7%, 80% and 94.2%, repsectively. Interim ΔSUVmax% showed a sensitivity, specificity, PPV, NPV and accuracy of 83.3%, 82.6%, 97.4%, 38.5% and 82.7%, respectively, with a 56.3% cutoff. Post-therapy ΔSUVmax% showed a sensitivity, specificity, PPV, NPV and accuracy of 83.3%, 84.8%, 97.5%, 41.7% and 84.6%, respectively, with a 76.8% cutoff. Compared to ΔSUVmax%, DS showed a significantly higher sensitivity, specificity (p < 0.05) and NPV (p < 0.01). The sensitivity, specificity, PPV, NPV and accuracy of rPET in predicting the therapy response at 24 months were 76.1%, 100%, 100%, 35.3% and 78.8%, respectively, with a cut-off of 1.31. DS and rPET showed comparable predictive performance (p > 0.58). In conclusion, DS is an easier method with better performance than ΔSUVmax% and rPET in predicting the chemotherapy response in pediatric HL patients.

Magnetic resonance imaging (MRI) has been shown to be an accurate imaging technique for the preoperative assessment of local staging of endometrial cancer and for evaluating the depth of myometrial invasion.This was a single-center retrospective study performed on patients with histopathologically proven endometrial carcinoma who underwent an MRI examination of the pelvis between October 2017 and May 2020.In the present analysis, mean apparent diffusion coefficient (ADC) values for each histologic grade were 0.72 ± 0.13 × 10Preoperative noninvasive radiological assessment for tumor volume, TV/ UV or tumor volume/uterine volume is important surrogate markers for preoperative prognostication of endometrial carcinoma.

Radiomics is a process that allows the extraction and analysis of quantitative data from medical images. It is an evolving field of research with many potential applications in medical imaging. The purpose of this review is to offer a deep look into radiomics, from the basis, deeply discussed from a technical point of view, through the main applications, to the challenges that have to be addressed to translate this process in clinical practice. A detailed description of the main techniques used in the various steps of radiomics workflow, which includes image acquisition, reconstruction, pre-processing, segmentation, features extraction and analysis, is here proposed, as well as an overview of the main promising results achieved in various applications, focusing on the limitations and possible solutions for clinical implementation. Only an in-depth and comprehensive description of current methods and applications can suggest the potential power of radiomics in fostering precision medicine and thus the care of patients, especially in cancer detection, diagnosis, prognosis and treatment evaluation.

Multiparametric magnetic resonance imaging (MRI) of the prostate has become a vital imaging tool in daily radiological practice for the stratification of the risk of prostate cancer. There has been a recent update to the Prostate Imaging-Reporting and Data System (PIRADS). The updated changes in PI-RADS, which is version 2.1, have been described with information pertaining to the recommended imaging protocols, the techniques on how to perform prostate MRI and a simplified approach to interpreting and reporting MRI of the prostate. Explanatory tables, schematic diagrams and key representative images have been used to provide the reader with a useful approach to interpreting and then stratifying lesions in the four anatomical zones of the prostate gland. The intention of this article is to address challenges of interpretation and reporting of prostate lesions in daily practice.&nbsp

Purpose: To evaluate technical outcome, diagnostic yield and safety of computed tomographic fluoroscopy-guided percutaneous core needle biopsies in patients with mediastinal masses. Methods: Overall, 155 CT fluoroscopy-guided mediastinal core needle biopsies, performed from March 2010 to June 2020 were included. Size of lesion, size of needle, access path, number of success, number of biopsies per session, diagnostic yield, patient’s position, effective dose, rate of complications, tumor localization, size of tumor and histopathological diagnosis were considered. Post-interventional CT was performed, and patients observed for any complications. Complications were classified per the Society of Interventional Radiology (SIR). Results: 148 patients (age, 54.7 ± 18.2) underwent 155 CT-fluoroscopy-guided percutaneous biopsies with tumors in the anterior (114; 73.5%), middle (17; 11%) and posterior (24; 15.5%) mediastinum, of which 152 (98%) were technically successful. For placement of the biopsy needle, in 82 (52.9%) procedures a parasternal trajectory was chosen, in 36 (23.3%) a paravertebral access, in 20 (12.9%) through the lateral intercostal space and in 17 (11%) the thoracic anterior midline, respectively. A total of 136 (89.5%) of the biopsies were considered adequate for a specific histopathologic analysis. Total DLP (dose-length product) was 575.7 ± 488.8 mGy*cm. Mean lesion size was 6.0 ± 3.3 cm. Neoplastic pathology was diagnosed in 115 (75.7%) biopsies and 35 (23%) biopsy samples showed no evidence of malignancy. Minor complications were observed in 18 (11.6%) procedures and major pneumothorax requiring drainage insertion in 3 interventions (1.9%). Conclusion: CT fluoroscopy-guided percutaneous core needle biopsy of mediastinal masses is an effective and safe procedure for the initial assessment of patients with mediastinal tumors.

Purpose: To assess agreement between 123I-metaiodobenzylguanidine (MIBG) and Whole Body Magnetic Resonance Imaging with diffusion-weighted whole-body imaging with background body signal suppression (WB MRI-DWIBS) in High and Low, Intermediate risk neuroblastoma, a retrospective review was performed on MIBG and DWIBS paired scans acquired at diagnosis, response-to-therapy steps, after-surgery, off therapy.Methods: 80 paired MIBG and DWIBS scans were acquired for 31 patients between June 2009 and June 2019 within 30 days, without intercurrent therapy. SIOPEN Semi-quantitative scoring systems for NB with 12 body sections was applied at whole body MIBG and WB MRI-DWIBS acquired to evaluate the disease extent. We evaluated specificity, sensitivity, overall accuracy, positive predictive value (PPV) and negative predictive value (NPV) of WB MRI-DWIBS respect MIBG scintigraphy considered as gold standard, and vice versa.Results: DWIBS and MIBG images were concordant in 890 out of the 960 analyzed segments, with high agreement between the two techniques (kendal =0.85 PConclusions: DWIBS and MIBG images showed very high concordance. WB MRI may represent an alternative in weak-avid MIBG tumors and for follow up assessment. An integrated imaging model is proposed for HR, Low and Intermediate Risk protocols.

Background Elastography is an addition to grey-scale ultrasonic examination that has gained substantial traction within the last decade. Strain ratio (SR) has been incorporated as a semiquantitative measure within strain elastography, thus a potential imaging biomarker. The World Federation for Ultrasound in Medicine and Biology (WFUMB) published guidelines in 2015 for breast elastography. These guidelines acknowledge the marked variance in SR cut-off values used in differentiating benign from malignant lesions. The objective of this review was to include more recent evidence and seek to determine the optimal strain ratio cut off value for differentiating between benign and malignant breast lesions. Methods Comprehensive search of MEDLINE and Web of Science electronic databases with additional searches via Google Scholar and handsearching set from January 2000 to May 2020 was carried out. For retrieved studies, screening for eligibility, data extraction and analysis was done as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy (PRISMA-DTA) Statement guidelines of 2018. Quality and risk of bias assessment of the studies were performed using the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Results A total of 424 articles, 412 from electronic database and 12 additional searches were retrieved and 65 studies were included in the narrative synthesis and subgroup analysis. The overall threshold effect indicated significant heterogeneity among the studies with Spearman correlation coefficient of Logit (TPR) vs Logit (FPR) at − 0.301, p-value = 0.015. A subgroup under machine model consisting seven studies with 783 patients and 844 lesions showed a favourable threshold, Spearman’s correlation coefficient,0.786 (p = 0.036). Conclusion From our review, currently the optimal breast SR cut-off point or value remains unresolved despite the WFUMB guidelines of 2015. Machine model as a possible contributor to cut-off value determination was suggested from this review which can be subjected to more industry and multi-center research determination.

Prostate cancer detection with magnetic resonance imaging is based on a standardized MRI-protocol according to the PI-RADS guidelines including morphologic imaging, diffusion weighted imaging, and perfusion. To facilitate data acquisition and analysis the contrast-enhanced perfusion is often omitted resulting in a biparametric prostate MRI protocol. The intention of this review is to analyze the current value of biparametric prostate MRI in combination with methods of machine-learning and deep learning in the detection, grading, and characterization of prostate cancer; if available a direct comparison with human radiologist performance was performed. PubMed was systematically queried and 29 appropriate studies were identified and retrieved. The data show that detection of clinically significant prostate cancer and differentiation of prostate cancer from non-cancerous tissue using machine-learning and deep learning is feasible with promising results. Some techniques of machine-learning and deep-learning currently seem to be equally good as human radiologists in terms of classification of single lesion according to the PIRADS score.

Background: In the last few decades many studies have investigated the role of major salivary glands ultrasound (SGUS) in the diagnostic work-up of patients with suspected primary Sjogren’s syndrome (pSS). Recent development of ultra-high-resolution ultrasound systems (UHFUS), with frequencies as high as 70 MHz and capability resolution as fine as 30 μm, has permit new diagnostic applications to a variety of superficial targets including labial salivary glands (LSGs). To date, however, no information are available regarding the use of UHFUS for the study of LSGs in humans. Objectives: To evaluate the feasibility and the diagnostic accuracy of UHFUS of LSGs in patients with suspected SS and to assess the correlations between LSG histopathology, UHFUS and SGUS. Methods: Consecutive patients with clinically suspected pSS were included in this study. All patients underwent a complete diagnostic work-up including conventional SGUS and LSG biopsy. The same expert pathologist calculated for all the samples the focus score (FS), number of foci and evaluated the presence of ectopic germinal centers (GCs). UHFUS of LSG was performed by specialized radiologists scanning first the central compartment of the inferior lip, and then both peripheral compartments. The following parameters were evaluated: distribution of the glands, parenchymal inhomogeneity (score 0-3, from normal to evident), fibrosis and eco color-Doppler vascular pattern and grade of vascular intensity. Results: We included 32 patients with suspected pSS. At the end of the work-up, pSS diagnosis was confirmed in 12/32 (37.5%) cases. No differences between pSS patients and no-SS sicca controls were observed in UHFUS findings related to LSG distribution and eco color-Doppler vascular parameters. By contrast, pSS patients presented statistically significant differences in peripheral UHFUS inhomogeneity (p=0.006), and a higher degree of fibrosis (p=0.01). Considering a score ≥2 in parenchymal inhomogeneity as suggestive for pSS, UHFUS appeared less specific than conventional SGUS (UHFUS Sp=70% vs SGUS Sp=95%) but more sensitive (UHFUS Se=83.3% vs SGUS Se = 58.3%). In addition, when we investigated the relationship between UHFUS, SGUS and LSG histopathology we found that the correlation coefficients between UHFUS inhomogeneity and LSG FS (UHFUS r=0.706** vs SGUS r=0.393*), number of foci (UHFUS r=0.712** vs SGUS r=0.445*), and number of ectopic GCs (UHFUS r=0.525** vs SGUS r=0.141), were significantly higher than those observed with conventional SGUS. Conclusion: the application of UHFUS to the study of LSG in pSS appeared feasible and sensitive. Because of the anatomical details obtained with this technique and its stronger correlation with LSG histopathology, UHFUS might offer unique advantages over the existing major salivary gland imaging modalities in pSS assessment. Disclosure of Interests: Chiara Baldini: None declared, Francesco Ferro: None declared, Veronica Iodice: None declared, Rossana Izzetti: None declared, Valentina Donati: None declared, Silvia Fonzetti: None declared, Teresa Oranges: None declared, Valentina Dini: None declared, Dania Cioni: None declared, Nicoletta Luciano: None declared, Rosa Maria Bruno: None declared, Marta Mosca Paid instructor for: GlaxoSmithKline, Lilly, UCB, Saverio Vitali: None declared, Davide Caramella: None declared

When heated during a radiofrequency ablation (RFA) procedure to ≥40°C, lyso-thermosensitive liposomal doxorubicin (LTLD) produces high drug concentration in the surrounding margins of the ablation zone. The hypothesis that the RFA + LTLD combination can effectively treat hepatocellular carcinoma (HCC) was investigated in the HEAT study: adding LTLD did not improve the efficacy of normal practice RFA. However, among the 285 patients with a solitary lesion who received at least 45-min RFA dwell time, the hazard ratio for overall survival was 0.63 (95% CI: 0.41–0.96; p = 0.04). The OPTIMA study is currently ongoing to test the hypothesis that adding LTLD to a standardized RFA lasting ≥45 min increases survival compared with standardized RFA alone.

The introduction of three-dimensional echography (3D echo) in vascular field is not recent, but it still remains a seldom-used technique because of the costs of ultrasound probe and the need of dedicated laboratories. Therefore, despite significant prognostic implications, the high diagnostic accuracy in plaque definition, and the relative ease of use, 3D echo in vascular field is a niche technique. The purpose of this review is mainly clinical and intends to demonstrate the potential strength of a 3D approach, including technical aspects, in order to present to clinicians and imagers the appealing aspects of a noninvasive and radiation-free methodology with relevant diagnostic and prognostic correlates in the assessment of carotid atherosclerosis. A comprehensive literature search (since 1990s to date) using the PubMed, MEDLINE, and Cochrane libraries databases has been conducted. Articles written in English have been assessed, including reviews, clinical trials, meta-analyses, and interventional/observational studies. Manual cross-referencing was also performed, and relevant references from selected articles were reviewed. The search was limited to studies conducted in humans. Search terms, retrieved also with PubMed Advanced search and AND/OR Boolean operators (mainly in title and abstract), included three-dimensional, echo, stroke/transient ischemic attack, predictors, carotid, imaging, and biomarkers.