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Background: This study aims to evaluate the feasibility, efficacy, and safety of distal transradial access (dRA) for the endovascular management of malfunctioning dialysis fistulas. This study also compares dRA with conventional access techniques, such as proximal radial and transvenous access, focusing on technical success, clinical outcomes, and vascular access site complications (VASCs)., Methods: A retrospective multicenter study was conducted across four hospitals, including 292 patients treated between January 2019 and June 2024. Of these, 57 patients underwent dRA, and 235 received proximal radial or transvenous access. Key outcomes included technical success (successful completion of the procedure), clinical success (restoration of functional dialysis access), and complication rates. Data were collected on procedure times and complication profiles., Results: Technical success was achieved in 96.5% of patients undergoing dRA, compared to 98.3% in those receiving conventional access ( p = 0.388). Clinical success was similar between groups (96.5% vs. 97%, p = 0.835). The overall complication rate was 10.5% for dRA and 8.5% for conventional access ( p = 0.632). Cannulation time was longer for dRA (109.1 vs. 91.9 s, p < 0.001), but total procedure duration was comparable between the groups. No major complications were observed in either cohort, and improved post-procedure access flow rates were recorded in all patients., Conclusions: Distal transradial access is a feasible and effective approach for the endovascular management of malfunctioning dialysis fistulas, with outcomes comparable to conventional access techniques. It provides a safe alternative, particularly for patients with complex fistulas, while maintaining a low complication profile.

Introduction: Acute pancreatitis (AP) is a common disease, and several scores aim to assess its prognosis. Our study aims to automatically recognize mild AP from computed tomography (CT) images in patients with acute abdominal pain but uncertain diagnosis from clinical and serological data through Radiomic model based on formal methods (FMs)., Methods: We retrospectively reviewed the CT scans acquired with Dual Source 256-slice CT scanner (Somatom Definition Flash; Siemens Healthineers, Erlangen, Germany) of 80 patients admitted to the radiology unit of Antonio Cardarelli hospital (Naples) with acute abdominal pain. Patients were divided into 2 groups: 40 underwent showed a healthy pancreatic gland, and 40 affected by four different grades (CTSI 0, 1, 2, 3) of mild pancreatitis at CT without clear clinical presentation or biochemical findings. Segmentation was manually performed. Radiologists identified 6 patients with a high expression of diseases (CTSI 3) to formulate a formal property (Rule) to detect AP in the testing set automatically. Once the rule was formulated, and Model Checker classified 70 patients into "healthy" or "unhealthy"., Results: The model achieved: accuracy 81%, precision 78% and recall 81%. Combining FMs results with radiologists agreement, and applying the mode in clinical practice, the global accuracy would have been 100%., Conclusions: Our model was reliable to automatically detect mild AP at primary diagnosis even in uncertain presentation and it will be tested prospectively in clinical practice., (© 2024. The Author(s).)

Adhesive Capsulitis of the shoulder is a painful pathology limiting shoulder movements, commonly known as "Frozen Shoulder". Since this pathology limits movement, it is important to make an early diagnosis. Diagnosing capsulitis relies on clinical assessment, although diagnostic imaging, such as Magnetic Resonance Imaging, can provide predictive or supportive information for specific characteristic signs. However, its diagnosis is not so simple nor so immediate, indeed it remains a difficult topic for many general radiologists and expert musculoskeletal radiologists. This study aims to investigate whether it is possible to use disease signs within a medical image to automatically diagnose Adhesive Capsulitis. To this purpose, we propose an automatic Model Checking-based approach to quickly diagnose the Adhesive Capsulitis taking as input the radiomic feature values from the medical images. Furthermore, we compare the performance achieved by our method with diagnostic results obtained by professional radiologists with different levels of experience. To the best of our knowledge, this is the first method for the automatic diagnosis of Adhesive Capsulitis of the Shoulder., (© 2024. The Author(s).)

Purpose: To assess the efficacy of machine learning and radiomics analysis by computed tomography (CT) in presurgical setting, to predict RAS mutational status in colorectal liver metastases., Methods: Patient selection in a retrospective study was carried out from January 2018 to May 2021 considering the following inclusion criteria: patients subjected to surgical resection for liver metastases; proven pathological liver metastases; patients subjected to enhanced CT examination in the presurgical setting with a good quality of images; and RAS assessment as standard reference. A total of 851 radiomics features were extracted using the PyRadiomics Python package from the Slicer 3D image computing platform after slice-by-slice segmentation on CT portal phase by two expert radiologists of each individual liver metastasis performed first independently by the individual reader and then in consensus. Balancing technique was performed, and inter- and intraclass correlation coefficients were calculated to assess the between-observer and within-observer reproducibility of features. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers were considered. Moreover, features selection was performed before and after a normalized procedure using two different methods (3-sigma and z-score)., Results: Seventy-seven liver metastases in 28 patients with a mean age of 60 years (range 40-80 years) were analyzed. The best predictors, at univariate analysis for both normalized procedures, were original&#95;shape&#95;Maximum2DDiameter and wavelet&#95;HLL&#95;glcm&#95;InverseVariance that reached an accuracy of 80%, an AUC ≥ 0.75, a sensitivity ≥ 80% and a specificity ≥ 70% (p value <  < 0.01). However, a multivariate analysis significantly increased the accuracy in RAS prediction when a linear regression model (LRM) was used. The best performance was obtained using a LRM combining linearly 12 robust features after a z-score normalization procedure: AUC of 0.953, accuracy 98%, sensitivity 96%, specificity of 100%, PPV 100% and NPV 96% (p value <  < 0.01). No statistically significant increase was obtained considering the tested machine learning both without normalization and with normalization methods., Conclusions: Normalized approach in CT radiomics analysis allows to predict RAS mutational status in colorectal liver metastases patients., (© 2024. Italian Society of Medical Radiology.)

Background : Acute liver injury occurs most frequently due to trauma, but it can also occur because of sepsis or drug-induced injury. This review aims to analyze artificial intelligence (AI)'s ability to detect and quantify liver injured areas in adults and pediatric patients. Methods : A literature analysis was performed on the PubMed Dataset. We selected original articles published from 2018 to 2023 and cohorts with ≥10 adults or pediatric patients. Results : Six studies counting 564 patients were collected, including 170 (30%) children and 394 adults. Four (66%) articles reported AI application after liver trauma, one (17%) after sepsis, and one (17%) due to chemotherapy. In five (83%) studies, Computed Tomography was performed, while in one (17%), FAST-UltraSound was performed. The studies reported a high diagnostic performance; in particular, three studies reported a specificity rate > 80%. Conclusions : Radiomics models seem reliable and applicable to clinical practice in patients affected by acute liver injury. Further studies are required to achieve larger validation cohorts.

Liver steatosis is the most common chronic liver disease and affects 10-24% of the general population. As the grade of disease can range from fat infiltration to steatohepatitis and cirrhosis, an early diagnosis is needed to set the most appropriate therapy. Innovative noninvasive radiological techniques have been developed through MRI and US. MRI-PDFF is the reference standard, but it is not so widely diffused due to its cost. For this reason, ultrasound tools have been validated to study liver parenchyma. The qualitative assessment of the brightness of liver parenchyma has now been supported by quantitative values of attenuation and scattering to make the analysis objective and reproducible. We aim to demonstrate the reliability of quantitative ultrasound in assessing liver fat and to confirm the inter-operator reliability in different respiratory phases. We enrolled 45 patients examined during normal breathing at rest, peak inspiration, peak expiration, and semi-sitting position. The highest inter-operator agreement in both attenuation and scattering parameters was achieved at peak inspiration and peak expiration, followed by semi-sitting position. In conclusion, this technology also allows to monitor uncompliant patients, as it grants high reliability and reproducibility in different body position and respiratory phases., (© 2024. The Author(s).)

Purpose: To assess the efficacy of radiomics features, obtained by magnetic resonance imaging (MRI) with hepatospecific contrast agent, in pre-surgical setting, to predict RAS mutational status in liver metastases., Methods: Patients with MRI in pre-surgical setting were enrolled in a retrospective study. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. The features were extracted considering the agreement with the Imaging Biomarker Standardization Initiative (IBSI). Balancing was performed through synthesis of samples for the underrepresented classes using the self-adaptive synthetic oversampling (SASYNO) approach. Inter- and intraclass correlation coefficients (ICC) were calculated to assess the between-observer and within-observer reproducibility of all radiomics characteristics. For continuous variables, nonparametric Wilcoxon-Mann-Whitney test was utilized. Benjamini and Hochberg's false discovery rate (FDR) adjustment for multiple testing was used. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. Moreover, features selection were performed before and after a normalized procedure using two different methods (3-sigma and z-score). McNemar test was used to assess differences statistically significant between dichotomic tables. All statistical procedures were done using MATLAB R2021b Statistics and Machine Toolbox (MathWorks, Natick, MA, USA)., Results: Seven normalized radiomics features, extracted from arterial phase, 11 normalized radiomics features, from portal phase, 12 normalized radiomics features from hepatobiliary phase and 12 normalized features from T2-W SPACE sequence were robust predictors of RAS mutational status. The multivariate analysis increased significantly the accuracy in RAS prediction when a LRM was used, combining 12 robust normalized features extracted by VIBE hepatobiliary phase reaching an accuracy of 99%, a sensitivity 97%, a specificity of 100%, a PPV of 100% and a NPV of 98%. No statistically significant increase was obtained, considering the tested classifiers DT, KNN and SVM, both without normalization and with normalization methods., Conclusions: Normalized approach in MRI radiomics analysis allows to predict RAS mutational status., (© 2024. Italian Society of Medical Radiology.)

Background: Small renal masses (SRMs) are defined as contrast-enhanced renal lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a renal cell carcinomas (RCCs). Currently, 50-61% of all renal tumors are found incidentally. Methods : The characteristics of the lesion influence the choice of the type of management, which include several methods SRM of management, including nephrectomy, partial nephrectomy, ablation, observation, and also stereotactic body radiotherapy. Typical imaging methods available for differentiating benign from malignant renal lesions include ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI). Results: Although ultrasound is the first imaging technique used to detect small renal lesions, it has several limitations. CT is the main and most widely used imaging technique for SRM characterization. The main advantages of MRI compared to CT are the better contrast resolution and tissue characterization, the use of functional imaging sequences, the possibility of performing the examination in patients allergic to iodine-containing contrast medium, and the absence of exposure to ionizing radiation. For a correct evaluation during imaging follow-up, it is necessary to use a reliable method for the assessment of renal lesions, represented by the Bosniak classification system. This classification was initially developed based on contrast-enhanced CT imaging findings, and the 2019 revision proposed the inclusion of MRI features; however, the latest classification has not yet received widespread validation. Conclusions : The use of radiomics in the evaluation of renal masses is an emerging and increasingly central field with several applications such as characterizing renal masses, distinguishing RCC subtypes, monitoring response to targeted therapeutic agents, and prognosis in a metastatic context.

Background: Surgical resection is still considered the optimal treatment for colorectal liver metastasis (CRLM). Although laparoscopic and robotic surgery demonstrated their reliability especially in referral centers, the comparison between perioperative outcomes of robotic liver resection (RLR) and open (OLR) liver resection are still debated when performed in referral centers for robotic surgery, not dedicated to HPB. Our study aimed to verify the efficacy and safety of perioperative outcomes after RLR and OLR for CRLM in an HUB&Spoke learning program (H&S) between a high volume center for liver surgery and high volume center for robotic surgery., Methods: We analyzed prospective databases of Pineta Grande Hospital (Castel Volturno) and Robotic Surgical Units (Foligno-Spoleto and Arezzo) from 2011 to 2021. A 1:1 propensity score matching (PSM) was performed according to baseline characteristics of patients, solitary/multiple CRLM, anterolateral/posterosuperior location., Results: 383 patients accepted to be part of the study (268 ORL and 115 RLR). After PSM, 45 patients from each group were included. Conversion rate was 8.89 %. RLR group had a significantly lower blood loss (226 vs. 321 ml; p=0.0001 ), and fewer major complications (13.33 % vs. 17.78 %; p=0.7722 ). R0 resection was obtained in 100% of OLR ( vs. 95.55%, p =0.4944 . Hospital stay was 8.8 days in RLR ( vs. 15; p=0.0001 ). Conclusion: H&S represents a safe and effective program to train general surgeons also in Hepatobiliary surgery providing R0 resection rate, blood loss volume and morbidity rate superimposable to referral centers. Furthermore, H&S allow a reduction of health mobility with consequent money saving for patients and institutions., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Purpose: We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases., Methods: Patients with MRI in a pre-surgical setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. Balancing was performed and inter- and intraclass correlation coefficients were calculated to assess the between observer and within observer reproducibility of all radiomics extracted features. A Wilcoxon-Mann-Whitney nonparametric test and receiver operating characteristics (ROC) analysis were carried out. Balancing and feature selection procedures were performed. Linear and non-logistic regression models (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered., Results: The internal training set included 49 patients and 119 liver metastases. The validation cohort consisted of a total of 28 single lesion patients. The best single predictor to classify tumor budding was original&#95;glcm&#95;Idn obtained in the T1-W VIBE sequence arterial phase with an accuracy of 84%; wavelet&#95;LLH&#95;firstorder&#95;10Percentile was obtained in the T1-W VIBE sequence portal phase with an accuracy of 92%; wavelet&#95;HHL&#95;glcm&#95;MaximumProbability was obtained in the T1-W VIBE sequence hepatobiliary excretion phase with an accuracy of 88%; and wavelet&#95;LLH&#95;glcm&#95;Imc1 was obtained in T2-W SPACE sequences with an accuracy of 88%. Considering the linear regression analysis, a statistically significant increase in accuracy to 96% was obtained using a linear weighted combination of 13 radiomic features extracted from the T1-W VIBE sequence arterial phase. Moreover, the best classifier was a KNN trained with the 13 radiomic features extracted from the arterial phase of the T1-W VIBE sequence, obtaining an accuracy of 95% and an AUC of 0.96. The validation set reached an accuracy of 94%, a sensitivity of 86% and a specificity of 95%., Conclusions: Machine learning and radiomics analysis are promising tools in predicting tumor budding. Considering the linear regression analysis, there was a statistically significant increase in accuracy to 96% using a weighted linear combination of 13 radiomics features extracted from the arterial phase compared to a single radiomics feature.

Pleural mesothelioma (PM) is an aggressive disease that has a strong causal relationship with asbestos exposure and represents a major challenge from both a diagnostic and therapeutic viewpoint. Despite recent improvements in patient care, PM typically carries a poor outcome, especially in advanced stages. Therefore, a timely and effective diagnosis taking advantage of currently available imaging techniques is essential to perform an accurate staging and dictate the most appropriate treatment strategy. Our aim is to provide a brief, but exhaustive and up-to-date overview of the role of multimodal medical imaging in the management of PM., (© 2023. The Author(s).)

Background and Objective: In recent years, magnetic resonance imaging (MRI) has shown excellent results in the study of the prostate gland. MRI has indeed shown to be advantageous in the prostate cancer (PCa) detection, as in guiding targeting biopsy, improving its diagnostic yield. Although current acquisition protocols provide for multiparametric acquisition, recent evidence has shown that biparametric protocols can be non-inferior in PCa detection. Diffusion-weighted imaging (DWI) sequence, in particular, plays a key role, particularly in the peripheral zone which accounts for the larger part of the prostate. High b-values are generally recommended, although with the possibility of obtaining non-Gaussian diffusion effects, which requires a more sophisticated model for the analysis, namely through the diffusion kurtosis imaging (DKI). Purpose of this narrative review was to analyze the current applications and clinical evidence regarding the use of DKI with a main focus on PCa detection, also in comparison with DWI., Methods: This narrative review synthesized the findings of literature retrieved from main researches, narrative and systematic reviews, and meta-analyses obtained from PubMed., Key Content and Findings: DKI analyses the non-Gaussian water diffusivity and describe the effect of signal intensity decay related to high b-value through two main metrics (D app and K app ). Differently from DWI-apparent diffusion coefficient (DWI-ADC) which reflects only water restriction outside of cells, DKI metrics are supposed to represent also the direct interaction of water molecules with cell membranes and intracellular compounds. This review describes current evidence on ADC and DKI metrics in clinical imaging, and finally collect the results derived from the main articles focused on DWI and DKI models in detecting PCa., Conclusions: DKI advantages, compared to conventional ADC analysis, still remain controversial. Wider application and greater technical knowledge of DKI, however, may help in proving its intrinsic validity in the field of oncology and therefore in the study of clinically significant PCa. Finally, a deep understanding of DKI is important for radiologists to better understand what K app and D app mean in the context of different cancer and how these metrics may vary specifically in PCa imaging., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://gs.amegroups.com/article/view/10.21037/gs-23-53/coif). R.F. is an employee at Medical Oncology Division, Igea SpA. The other authors have no conflicts of interest to declare., (2023 Gland Surgery. All rights reserved.)

Background: Pancreatic ductal adenocarcinoma (PDAC) is the seventh leading cause of cancer-related deaths worldwide. Surgical resection is the main driver to improving survival in resectable tumors, while neoadjuvant treatment based on chemotherapy (and radiotherapy) is the best option-treatment for a non-primally resectable disease. CT-based imaging has a central role in detecting, staging, and managing PDAC. As several authors have proposed radiomics for risk stratification in patients undergoing surgery for PADC, in this narrative review, we have explored the actual fields of interest of radiomics tools in PDAC built on pre-surgical imaging and clinical variables, to obtain more objective and reliable predictors., Methods: The PubMed database was searched for papers published in the English language no earlier than January 2018., Results: We found 301 studies, and 11 satisfied our research criteria. Of those included, four were on resectability status prediction, three on preoperative pancreatic fistula (POPF) prediction, and four on survival prediction. Most of the studies were retrospective., Conclusions: It is possible to conclude that many performing models have been developed to get predictive information in pre-surgical evaluation. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.

Objective: The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated., Methods: The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed., Results: The best predictor to identify tumor growth was the Wavelet&#95;HLH&#95;glcm&#95;MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet&#95;HLH&#95;ngtdm&#95;Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet&#95;LLH&#95;glcm&#95;Imc1 with an accuracy of 88% and to identify mucinous type was wavelet&#95;LLH&#95;glcm&#95;JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence., Conclusions: The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy., (© 2023. Italian Society of Medical Radiology.)

Background: Artificial Intelligence (AI)-based analysis represents an evolving medical field. In the last few decades, several studies have reported the diagnostic efficiency of AI applied to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) to early detect liver metastases (LM), mainly from colorectal cancer. Despite the increase in information and the development of different procedures in several radiological fields, an accurate method of predicting LM has not yet been found. This review aims to compare the diagnostic efficiency of different AI methods in the literature according to accuracy, sensibility, precision, and recall to identify early LM., Methods: A narrative review of the literature was conducted on PubMed. A total of 336 studies were screened., Results: We selected 17 studies from 2012 to 2022. In total, 14,475 patients were included, and more than 95% were affected by colorectal cancer. The most frequently used imaging tool to early detect LM was found to be CT (58%), while MRI was used in three cases. Four different AI analyses were used: deep learning, radiomics, machine learning, and fuzzy systems in seven (41.18%), five (29.41%), four (23.53%), and one (5.88%) cases, respectively. Four studies achieved an accuracy of more than 90% after MRI and CT scan acquisition, while just two reported a recall rate ≥90% (one method using MRI and CT and one CT)., Conclusions: Routinely acquired radiological images could be used for AI-based analysis to early detect LM. Simultaneous use of radiomics and machine learning analysis applied to MRI or CT images should be an effective method considering the better results achieved in the clinical scenario.

To investigate the in vivo ablation characteristics of a microwave ablation antenna in the livers of humans with tumors, a retrospective analysis of the ablation zones was conducted after applying Emprint microwave ablation systems for treatment. Percutaneous microwave ablations performed between January 2022 and September 2022 were included in this study. Subsequently, immediate post-ablation echography images were subjected to retrospective evaluation to state the long ablated diameter, short ablated diameter, and volume. The calculated ablation lengths and volume indices were then compared between in vivo and ex vivo results obtained from laboratory experiments conducted on porcine liver. The ex vivo data showed a good correlation between energy delivered and both increasing ablated dimensions (both p < 0.001) and volume ( p < 0.001). The in vivo data showed a good correlation for dimensions ( p = 0.037 and p = 0.019) and a worse correlation for volume ( p = 0.142). When comparing ex vivo and in vivo data for higher energies, the ablated volumes grew much more rapidly in ex vivo cases compared to in vivo ones. Finally, a set of correlations to scale ex vivo results with in vivo ones is presented. This phenomenon was likely due to the absence of perfusion, which acts as a cooling system.

Uterine cervical and endometrial cancers are two major gynecological malignancies, affecting women's health worldwide. Magnetic resonance imaging (MRI) is appropriate for evaluating malignant disease, thanks to the excellent soft tissue contrast and multiplanar imaging ability. Recently, functional MR techniques, namely diffusion-weighted imaging (DWI) and dynamic contrast-enhanced imaging (DCE), have proved to be a precious support not only in cancer diagnosis but also in disease staging, in the therapy planning, in monitoring response to treatment and during long-term recurrence surveillance. In the field of gynecologic oncology, the European Society of Urogenital Radiology (ESUR) recommends DWI and dynamic contrast-enhanced imaging (DCE-MRI) for local staging of endometrial and cervical cancer, but the potential application of functional imaging in all different aspects of patient management seems very promising. The aim of this article is to summarize the existing literature, providing a comprehensive update on the role of functional MRI in endometrial and cervical cancer.

Liver imaging encompasses a broad spectrum of diseases in different clinical backgrounds. The available literature is vast and reported data often lacks standardization. Because of all these issues, the differential diagnosis and the characterization of liver lesions can be challenging for the beginner. The aim of this narrative review is to provide the basics for an algorithm approach to liver lesions on cross-sectional imaging. First, some tips for the optimization of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) protocols will be provided. Liver Imaging Reporting and Data System (LI-RADS, version 2018) working group is proposing the adoption of their standardized lexicon beyond the original target population of LI-RADS (i.e., liver cirrhosis). Thus, the main imaging findings will be defined following the LI-RADS lexicon. Since the contrast study is the most important for lesion characterization, this narrative review separates the lesions into avascular, hypovascular, and hypervascular, with a focus on chronic liver disease (CLD) and hepatocellular carcinoma (HCC).

Introduction: Treatment strategies for primary aldosteronism (PA) include unilateral adrenalectomy and medical treatment with mineralocorticoid receptor (MR) antagonists. Whether these two different treatment strategies are comparable in mitigating the detrimental effect of PA on outcomes is still debated., Objectives: The primary aim of this systematic review is to identify, appraise and synthesise existing literature comparing clinical outcomes after treatment in patients with PA., Methods and Analysis: A systematic and comprehensive search will be performed using PubMed, Web of Science and EMBASE, for studies published until December 2022. Observational and interventional studies will be eligible for inclusion. The quality of observational studies will be assessed using the Newcastle-Ottawa Scale, while interventional studies will be assessed using the Cochrane Effective Practice Organization of Care tool. The collected evidence will be narratively synthesised. We will perform meta-analysis to pool estimates from studies considered to be homogeneous. Reporting of the systematic review and meta-analysis will be in accordance with the Meta-analysis of Observational Studies in Epidemiology Preferred Reporting Items for Systematic reviews and Meta-Analysis guidelines., Ethics and Dissemination: As this study is based solely on the published literature, no ethics approval is required. This review will aim to provide some estimates on outcomes, including survival, rates of clinical and biochemical control, cardiovascular and cerebrovascular events, as well as data on quality of life and renal function, in patients with PA treated surgically or with MR antagonists. The study findings will be presented at scientific meetings and will be published in an international peer-reviewed scientific journal., Prospero Registration Number: CRD42022362506., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: The aim of this multicenter study was to evaluate the prevalence and features of dual left anterior descending artery (LAD) subtypes using coronary CT angiography (CCTA)., Methods: A retrospective multicenter analysis of 2083 CCTA from December 2020 to November 2022 was conducted to search for the presence and morphological features of dual LAD. The two classifications used were the updated classification of Spindola-Franco and the Jariwala classification. Statistical tests were conducted to evaluate the prevalence of dual LADs among sexes and its association with angina in patients without significant coronary stenoses and/or associated cardiac anomalies., Results: Dual LAD was observed in 124 (5.96%) patients analyzed. According to the Spindola-Franco revisited classification, type I dual LAD was the most common (71/124, 57.26%). According to the Jariwala classification, all cases were group I. In the general population, there was a higher prevalence of dual LAD among females (7.3% females vs. 5.1% males; p value: 0.04). No statistically significant difference was found in the prevalence of angina in the dual LAD population compared to the no dual LAD population (2.1% vs. 1.5%; p value: 0.10)., Conclusions: The acknowledgment and reporting of LAD duplication is helpful for an optimal management of coronary patients with this condition. Dual LAD was more frequent in the female population, mainly not related with angina. Myocardial bridge was more frequent in the dual LAD population than in the no dual LAD population.

This review has the purpose of illustrating schematically and comprehensively the key concepts for the beginner who approaches chest radiology for the first time. The approach to thoracic imaging may be challenging for the beginner due to the wide spectrum of diseases, their overlap, and the complexity of radiological findings. The first step consists of the proper assessment of the basic imaging findings. This review is divided into three main districts (mediastinum, pleura, focal and diffuse diseases of the lung parenchyma): the main findings will be discussed in a clinical scenario. Radiological tips and tricks, and relative clinical background, will be provided to orient the beginner toward the differential diagnoses of the main thoracic diseases.

Background: This paper offers an assessment of radiomics tools in the evaluation of intrahepatic cholangiocarcinoma., Methods: The PubMed database was searched for papers published in the English language no earlier than October 2022., Results: We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, and prediction of staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic tools developed through machine learning, deep learning, and neural network for the recurrence and prediction of biological characteristics. The majority of the studies were retrospective., Conclusions: It is possible to conclude that many performing models have been developed to make differential diagnosis easier for radiologists to predict recurrence and genomic patterns. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.

In modern clinical practice, there is an increasing dependence on imaging techniques in several settings, and especially during emergencies. Consequently, there has been an increase in the frequency of imaging examinations and thus also an increased risk of radiation exposure. In this context, a critical phase is a woman's pregnancy management that requires a proper diagnostic assessment to reduce radiation risk to the fetus and mother. The risk is greatest during the first phases of pregnancy at the time of organogenesis. Therefore, the principles of radiation protection should guide the multidisciplinary team. Although diagnostic tools that do not employ ionizing radiation, such as ultrasound (US) and magnetic resonance imaging (MRI) should be preferred, in several settings as polytrauma, computed tomography (CT) nonetheless remains the examination to perform, beyond the fetus risk. In addition, protocol optimization, using dose-limiting protocols and avoiding multiple acquisitions, is a critical point that makes it possible to reduce risks. The purpose of this review is to provide a critical evaluation of emergency conditions, e.g., abdominal pain and trauma, considering the different diagnostic tools that should be used as study protocols in order to control the dose to the pregnant woman and fetus.

Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver tumor, with a median survival of only 13 months. Surgical resection remains the only curative therapy; however, at first detection, only one-third of patients are at an early enough stage for this approach to be effective, thus rendering early diagnosis as an efficient approach to improving survival. Therefore, the identification of higher-risk patients, whose risk is correlated with genetic and pre-cancerous conditions, and the employment of non-invasive-screening modalities would be appropriate. For several at-risk patients, such as those suffering from primary sclerosing cholangitis or fibropolycystic liver disease, the use of periodic (6-12 months) imaging of the liver by ultrasound (US), magnetic Resonance Imaging (MRI)/cholangiopancreatography (MRCP), or computed tomography (CT) in association with serum CA19-9 measurement has been proposed. For liver cirrhosis patients, it has been proposed that at-risk iCCA patients are monitored in a similar fashion to at-risk HCC patients. The possibility of using Artificial Intelligence models to evaluate higher-risk patients could favor the diagnosis of these entities, although more data are needed to support the practical utility of these applications in the field of screening. For these reasons, it would be appropriate to develop screening programs in the research protocols setting. In fact, the success of these programs reauires patient compliance and multidisciplinary cooperation.

Liver resection is still the most effective treatment of primary liver malignancies, including hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), and of metastatic disease, such as colorectal liver metastases. The type of liver resection (anatomic versus non anatomic resection) depends on different features, mainly on the type of malignancy (primary liver neoplasm versus metastatic lesion), size of tumor, its relation with blood and biliary vessels, and the volume of future liver remnant (FLT). Imaging plays a critical role in postoperative assessment, offering the possibility to recognize normal postoperative findings and potential complications. Ultrasonography (US) is the first-line diagnostic tool to use in post-surgical phase. However, computed tomography (CT), due to its comprehensive assessment, allows for a more accurate evaluation and more normal findings than the possible postoperative complications. Magnetic resonance imaging (MRI) with cholangiopancreatography (MRCP) and/or hepatospecific contrast agents remains the best tool for bile duct injuries diagnosis and for ischemic cholangitis evaluation. Consequently, radiologists should be familiar with the surgical approaches for a better comprehension of normal postoperative findings and of postoperative complications.

Pancreatic cancer (PC) is one of the deadliest cancers, and it is responsible for a number of deaths almost equal to its incidence. The high mortality rate is correlated with several explanations; the main one is the late disease stage at which the majority of patients are diagnosed. Since surgical resection has been recognised as the only curative treatment, a PC diagnosis at the initial stage is believed the main tool to improve survival. Therefore, patient stratification according to familial and genetic risk and the creation of screening protocol by using minimally invasive diagnostic tools would be appropriate. Pancreatic cystic neoplasms (PCNs) are subsets of lesions which deserve special management to avoid overtreatment. The current PC screening programs are based on the annual employment of magnetic resonance imaging with cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS). For patients unfit for MRI, computed tomography (CT) could be proposed, although CT results in lower detection rates, compared to MRI, for small lesions. The actual major limit is the incapacity to detect and characterize the pancreatic intraepithelial neoplasia (PanIN) by EUS and MR/MRCP. The possibility of utilizing artificial intelligence models to evaluate higher-risk patients could favour the diagnosis of these entities, although more data are needed to support the real utility of these applications in the field of screening. For these motives, it would be appropriate to realize screening programs in research settings.

Bile duct tumor thrombus (BDTT) is an uncommon finding in hepatocellular carcinoma (HCC), potentially mimicking cholangiocarcinoma (CCA). Recent studies have suggested that HCC with BDTT could represent a prognostic factor. We report the case of a 47-year-old male patient admitted to the University Hospital of Bari with abdominal pain. Blood tests revealed the presence of an untreated hepatitis B virus infection (HBV), with normal liver function and without jaundice. Abdominal ultrasonography revealed a cirrhotic liver with a segmental dilatation of the third bile duct segment, confirmed by a CT scan and liver MRI, which also identified a heterologous mass. No other focal hepatic lesions were identified. A percutaneous ultrasound-guided needle biopsy was then performed, detecting a moderately differentiated HCC. Finally, the patient underwent a third hepatic segmentectomy, and the histopathological analysis confirmed the endobiliary localization of HCC. Subsequently, the patient experienced a nodular recurrence in the fourth hepatic segment, which was treated with ultrasound-guided percutaneous radiofrequency ablation (RFA). This case shows that HCC with BDTT can mimic different types of tumors. It also indicates the value of an early multidisciplinary patient assessment to obtain an accurate diagnosis of HCC with BDTT, which may have prognostic value that has not been recognized until now.

Soft tissue sarcomas (STSs) are rare, heterogeneous, and very often asymptomatic diseases. Their diagnosis is fundamental, as is the identification of the degree of malignancy, which may be high, medium, or low. The Italian Medical Oncology Association and European Society of Medical Oncology (ESMO) guidelines recommend magnetic resonance imaging (MRI) because the clinical examination is typically ineffective. The diagnosis of these rare diseases with artificial intelligence (AI) techniques presents reduced datasets and therefore less robust methods. However, the combination of AI techniques with radiomics may be a new angle in diagnosing rare diseases such as STSs. Results obtained are promising within the literature, not only for the performance but also for the explicability of the data. In fact, one can make tumor classification, site localization, and prediction of the risk of developing metastasis. Thanks to the synergy between computer scientists and radiologists, linking numerical features to radiological evidence with excellent performance could be a new step forward for the diagnosis of rare diseases., Competing Interests: The authors declare that they have no conflicts of interest., (© The Author(s) 2023.)

Introduction: Colorectal cancer (CRC) is the third most common malignant neoplastic disease in the world. Approximately 25-35% of patients affected by CRC will develop liver metastasis, and a percentage of 15-25% occurred in synchronous liver metastases (SCRLM) at the moment of CRC diagnosis or previously. Our aim is to investigate through an extensive literature review the effectiveness and safety of simultaneous SCRLM and CRC in open, laparoscopic and robotic surgery analyzing pre-, intra- and post-operative surgical outcomes and 1-, 3- and 5- years overall survival and disease-free survival., Evidence Acquisition: A literature review was carried out on PubMed (Medline) and Cochrane libraries until 16 th of April 2022., Evidence Synthesis: Forty-one articles were included and subjected to a qualitative and quantitative analysis. A total of 3038 patients were included; 1730 out of 3038 (56.94%) patients who underwent a simultaneous resection for SCRLM were identified. The mean age was 61.10±9.95 and a 1170 of males and 840 of females emerges. The mean blood loss was 422.23±238.31 mL, the mean operative time was 368.94±88.47 min. The mean Length of Stay was 14.21±6.06 days; 126 (12.79%) patients were Clavien-Dindo grade ≥III complications., Conclusions: Minimally invasive surgery (MIS) for simultaneous SCRLM and CRC resections shows peri-operative advantages over open surgery. Furthermore, considering postoperative benefits and long-term outcome, MIS could be the choice treatment in these patients.

Cardiovascular diseases (CVDs) are among the most common causes of access to the Emergency Department and among the leading causes of death worldwide. Accurate diagnostic algorithms are mandatory to ensure a rapid life-saving treatment. However, non-specific clinical presentation and unnecessary referrals to other subspecialties may lead to misinterpretation of the diagnosis and delays. In recent years, the development of imaging technologies has allowed Computed Tomography (CT) to play a prominent role in the concepts of CVD rule-in and rule-out. An optimization strategy for CT protocols is needed to reduce variability and improve image quality. A correct diagnostic suspicion is crucial, as different districts (i.e., heart, aorta and pulmonary circulation) may require different investigation techniques. Additionally, the CVD pre-test probability assessment is highly correlated with CT accuracy. The purpose of this narrative review is to analyze the current role of CT in the approach to the CVDs in the ED, and to analyze the main strategies of CT optimization.

Purpose: To assess the efficacy of radiomics features obtained by T2-weighted sequences to predict clinical outcomes following liver resection in colorectal liver metastases patients., Methods: This retrospective analysis was approved by the local Ethical Committee board and radiological databases were interrogated, from January 2018 to May 2021, to select patients with liver metastases with pathological proof and MRI study in pre-surgical setting. The cohort of patients included a training set and an external validation set. The internal training set included 51 patients with 61 years of median age and 121 liver metastases. The validation cohort consisted a total of 30 patients with single lesion with 60 years of median age. For each volume of interest, 851 radiomics features were extracted as median values using PyRadiomics. Nonparametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbours (KNN), artificial neural network (NNET) and decision tree (DT)) were considered., Results: The best predictor to discriminate expansive versus infiltrative front of tumour growth was obtained by wavelet&#95;LHL&#95;gldm&#95;DependenceNonUniformityNormalized with an accuracy of 82%; to discriminate high grade versus low grade or absent was the wavelet&#95;LLH&#95;glcm&#95;Imc1 with accuracy of 88%; to differentiate the mucinous type of tumour was the wavelet&#95;LLH&#95;glcm&#95;JointEntropy with accuracy of 92% while to identify tumour recurrence was the wavelet&#95;LLL&#95;glcm&#95;Correlation with accuracy of 85%. Linear regression model increased the performance obtained with respect to the univariate analysis exclusively in the discrimination of expansive versus infiltrative front of tumour growth reaching an accuracy of 90%, a sensitivity of 95% and a specificity of 80%. Considering significant texture metrics tested with pattern recognition approaches, the best performance was reached by the KNN in the discrimination of the tumour budding considering the four textural predictors obtaining an accuracy of 93%, a sensitivity of 81% and a specificity of 97%., Conclusions: Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach., (© 2022. Italian Society of Medical Radiology.)

Patients presenting to the emergency with thoracic symptoms could have a wide variety of causes, even if the traumatic and vascular causes are excluded. Therefore, the diagnosis is often a challenge for emergency physicians. Anamnesis, physical examination and laboratory testing need to be integrated with imaging to get a rapid diagnosis and to distinguish among the potential causes. This review discusses the role of diagnostic imaging studies in the emergency setting in patients with non-traumatic non-cardiovascular thoracic symptoms. The use of chest x-ray, bedside lung Ultrasound and Computed Tomography in the diagnosis and care of these patients have been reviewed as well as the common findings on imaging.

To assess Radiomics and Machine Learning Analysis in Liver Colon and Rectal Cancer Metastases (CRLM) Growth Pattern, we evaluated, retrospectively, a training set of 51 patients with 121 liver metastases and an external validation set of 30 patients with a single lesion. All patients were subjected to MRI studies in pre-surgical setting. For each segmented volume of interest (VOI), 851 radiomics features were extracted using PyRadiomics package. Nonparametric test, univariate, linear regression analysis and patter recognition approaches were performed. The best results to discriminate expansive versus infiltrative front of tumor growth with the highest accuracy and AUC at univariate analysis were obtained by the wavelet&#95;LHH&#95;glrlm&#95;ShortRunLowGray Level Emphasis from portal phase of contrast study. With regard to linear regression model, this increased the performance obtained respect to the univariate analysis for each sequence except that for EOB-phase sequence. The best results were obtained by a linear regression model of 15 significant features extracted by the T2-W SPACE sequence. Furthermore, using pattern recognition approaches, the diagnostic performance to discriminate the expansive versus infiltrative front of tumor growth increased again and the best classifier was a weighted KNN trained with the 9 significant metrics extracted from the portal phase of contrast study, with an accuracy of 92% on training set and of 91% on validation set. In the present study, we have demonstrated as Radiomics and Machine Learning Analysis, based on EOB-MRI study, allow to identify several biomarkers that permit to recognise the different Growth Patterns in CRLM.

Background: Metabolic and bariatric surgery (BS) are considered life-changing and life-saving treatments for obese patients. The Italian Society of Obesity Surgery (SICOB) requires at least 25 operations per year to achieve the standard of care in the field. Despite the increasing need to treat obese patients, some small southern regions of Italy, such as Molise, do not have enough experience in bariatric procedures to be allowed to perform them. Therefore, our aim was to run a Hub and Spoke Program with a referral center in BS to treat obese patients and provide a proper learning curve in BS in Molise., Methods: In 2020, the "A. Cardarelli Hospital" in Campobasso, Molise, started a formal "Learning Model of Hub and Spoke Collaboration" with the Hub center "Ospedale Del Mare", Naples. A multidisciplinary approach was achieved. Patients were supervised and operated under the supervision and tutoring of the referral center. We retrospectively reviewed our prospectively collected database from February 2020 to August 2021 in order to analyze the safety and effectiveness of our learning program., Results: In total, 13 (3 men and 10 women) patients underwent BS with the mean age of 47.08 years and a presurgery BMI of 41.79. Seven (53.84%) patients were the American Society of Anesthesiologist (ASA) II, and 6 (46.16%) patients were ASA III. Twelve (92.31%) procedures were laparoscopic sleeve gastrectomies, 1 (7.69%) patient underwent endoscopic BioEnterics Intragastric Balloon (BIB) placement. One (8.33%) sleeve gastrectomy was associated to gastric band removal. Mean surgical time was 110.14 ± 23.54 min. The mean length of stay was 4.07 ± 2.40 days. No Clavien-Dindo ≥ III and mortality were reported. The follow-up program showed a mean decrease of 11.82 in terms of body mass index (BMI) value. The last 5 procedures were performed by the whole equips from "A. Cardarelli" under external tutoring without any impact on complication rate., Conclusion: The setup of a proper Hub and Spoke Program may allow to perform BS to provide the standard of care. This approach may reduce health costs and related patient migration., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Buondonno, Avella, Cappuccio, Scacchi, Vaschetti, Di Marzo, Maida, Luciani, Amato, Brunese, Esposito, Selvaggi, Guerra and Rocca.)

In the last decades, nanotechnology has been used in a wide range of biomedical applications, both diagnostic and therapeutic. In this scenario, imaging techniques represent a fundamental tool to obtain information about the properties of nanoconstructs and their interactions with the biological environment in preclinical and clinical settings. This paper reviews the state of the art of the application of magnetic resonance imaging in the field of nanomedicine, as well as the use of nanoparticles as diagnostic and therapeutic tools, especially in cancer, including the characteristics that hinder the use of nanoparticles in clinical practice.

Purpose: To assess the role of ultrasound (US) in detecting and characterizing ductal carcinoma in situ (DCIS) of the breast and to investigate the correlation between ultrasonographic and biological features of DCIS., Methods: In total, 171 patients (mean age 44; range 39-62) with 178 lesions were retrospectively evaluated by two independent radiologists searching for US mass or non-mass lesions. Immunohistochemistry analysis was performed to determine estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression. The US detection rate and pattern distribution among the lesion types were evaluated. The χ 2 test was used to evaluate the correlation between the US findings and the biological factors. Statistical significance was indicated by p values < 0.05. Inter-observer agreement was calculated by Kohen's k test., Results: US detected 35% (63/178) of all lesions. Fifty-two (83%) lesions were classified as mass lesions, and 11 (17%) as non-mass lesions (p < 0.0001). Among the mass lesions, the most common shape was irregular (79%; p < 0.0001), with 45 (87%) lesions having indistinct margins. Hypoechogenicity was the most common echo pattern (49 cases, 94%; p < 0.0001). Microcalcifications were found in 23 cases (37%; p = 0.004) and were associated with mass lesions in 15 cases (65%) and with non-mass lesions in 8 cases (35%) (p = 0.21). An almost perfect inter-observer agreement (k = 0.87) was obtained between the two radiologists. A significant ER expression was found in mass lesions (83%; p < 0.0001), with no significant PR (p = 0.89) or HER2 expression (p = 0.81). Among the lesions with microcalcifications, only 7 out of 23 cases (30%) were positive for HER2 (p = 0.09)., Conclusion: DCIS represents a heterogeneous pathological process with variable US appearance (mass-like, non-mass-like, or occult). The most common US finding is represented by mass-type, hypoechogenic lesions with indistinct margins. A significant ER expression exists among mass-type lesions, while microcalcifications seem not to be associated with HER2 expression., (© 2021. Società Italiana di Ultrasonologia in Medicina e Biologia (SIUMB).)

The aim of this study was to assess the efficacy of radiomics features obtained by EOB-MRI phase in order to predict clinical outcomes following liver resection in Colorectal Liver Metastases Patients, and evaluate recurrence, mutational status, pathological characteristic (mucinous) and surgical resection margin. This retrospective analysis was approved by the local Ethical Committee board of National Cancer of Naples, IRCCS "Fondazione Pascale". Radiological databases were interrogated from January 2018 to May 2021 in order to select patients with liver metastases with pathological proof and EOB-MRI study in pre-surgical setting. The cohort of patients included a training set (51 patients with 61 years of median age and 121 liver metastases) and an external validation set (30 patients with single lesion with 60 years of median age). For each segmented volume of interest by 2 expert radiologists, 851 radiomics features were extracted as median values using PyRadiomics. non-parametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. The best predictor to discriminate expansive versus infiltrative front of tumor growth was HLH&#95;glcm&#95;MaximumProbability extraxted on VIBE&#95;FA30 with an accuracy of 84%, a sensitivity of 83%, and a specificity of 82%. The best predictor to discriminate tumor budding was Inverse Variance obtained by the original GLCM matrix extraxted on VIBE&#95;FA30 with an accuracy of 89%, a sensitivity of 96% and a specificity of 65%. The best predictor to differentiate the mucinous type of tumor was the HHL&#95;glszm&#95;ZoneVariance extraxted on VIBE&#95;FA30 with an accuracy of 85%, a sensitivity of 46% and a specificity of 95%. The best predictor to identify tumor recurrence was the LHL&#95;glcm&#95;Correlation extraxted on VIBE&#95;FA30 with an accuracy of 86%, a sensitivity of 52% and a specificity of 97%. The best linear regression model was obtained in the identification of the tumor growth front considering the height textural significant metrics by VIBE&#95;FA10 (an accuracy of 89%; sensitivity of 93% and a specificity of 82%). Considering significant texture metrics tested with pattern recognition approaches, the best performance for each outcome was reached by a KNN in the identification of recurrence with the 3 textural significant features extracted by VIBE&#95;FA10 (AUC of 91%, an accuracy of 93%; sensitivity of 99% and a specificity of 77%). Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

Background: Frailty has been associated with increased mortality among hepatobiliary pancreatic (HBP) cancer patients. Nevertheless, estimates of frailty prevalence in HBP cancers and the precise average effect regarding mortality remains uncertain. The present systematic review and meta-analysis aimed to quantify: (1) the prevalence of frailty in patients with liver and pancreatic cancers and (2) the impact of frailty on mortality in patients affected by liver and pancreatic cancers., Methods: MEDLINE/PubMed database search was conducted from inception until 1 November 2021, the pooled prevalence and relative risk (RR) estimate were calculated., Results: A total of 34,276 patients were identified and the weighted prevalence of frailty was 39%; (95% [C.I.] 23-56; I2 = 99.9%, p < 0.0001). Frailty was significantly associated with increased mortality RR 1.98 (95% [C.I.] 1.49-2.63; I2 = 75.9%, p = 0.006)., Conclusions: Frailty prevalence is common among HBP cancer patients and exerts a significant negative impact on survival. These findings are characterized by significant heterogeneity and caution is warranted on their interpretation. However, stratification of patients with HBP cancer by frailty status may provide prognostic information and may inform priorities for decision-making strategy.

Abdominal acute pain is a manifestation of heterogeneous medical conditions, with difficult clinical-laboratory assessment. Multi-detector CT (MDCT) is the gold standard imaging technique for evaluating adult patients with acute abdominal pain. Due to its fast execution and the high spatial resolution, CT is fundamental in the diagnostic and therapeutic work-up of patients with time-dependent pathology that could require surgical treatment, reducing mortality and morbidity. However, the radiological risk connected to the ionizing radiation use should not be underestimated, especially in young patients. The aim of this study is to identify optimized CT protocols to apply in the management of non-traumatic acute abdomen. In particular, this review is focused on the main emergency settings: acute pancreatitis, small bowel obstruction, acute appendicitis and acute diverticulitis. This survey would not be complete without mentioning Dual-Energy CT (DECT) technique, one of the last frontiers in CT, achieving encouraging results also in acute abdominal conditions.

Introduction and Objectives: The Prostate Imaging Reporting and Data System (PI-RADS) version 2 emerged as standard in prostate magnetic resonance imaging examination. The Pi-RADS scores are assigned by radiologists and indicate the likelihood of a clinically significant cancer. The aim of this paper is to propose a methodology to automatically mark a magnetic resonance imaging with its related PI-RADS., Materials and Methods: We collected a dataset from two different institutions composed by DWI ADC MRI for 91 patients marked by expert radiologists with different PI-RADS score. A formal model is generated starting from a prostate magnetic resonance imaging, and a set of properties related to the different PI-RADS scores are formulated with the help of expert radiologists and pathologists., Results: Our methodology relies on the adoption of formal methods and radiomic features, and in the experimental analysis, we obtain a specificity and sensitivity equal to 1., Q Conclusions: The proposed methodology is able to assign the PI-RADS score by analyzing prostate magnetic resonance imaging with a very high accuracy., (© 2021. Italian Society of Medical Radiology.)

Background: Liver metastases are a leading cause of cancer-associated deaths in patients affected by colorectal cancer (CRC). The multidisciplinary strategy to treat CRC is more effective when the radiological diagnosis is accurate and early. Despite the evolving technologies in radiological accuracy, the radiological diagnosis of Colorectal Cancer Liver Metastases (CRCLM) is still a key point. The aim of our study was to define a new patient representation different by Artificial Intelligence models, using Formal Methods (FMs), to help clinicians to predict the presence of liver metastasis when still undetectable using the standard protocols., Methods: We retrospectively reviewed from 2013 to 2020 the CT scan of nine patients affected by CRC who would develop liver lesions within 4 months and 8 years. Seven patients developed liver metastases after primary staging before any liver surgery, and two patients were enrolled after R0 liver resection. Twenty-one patients were enrolled as the case control group (CCG). Regions of Interest (ROIs) were identified through manual segmentation on the medical images including only liver parenchyma and eventual benign lesions, avoiding major vessels and biliary ducts. Our predictive model was built based on formally verified radiomic features., Results: The precision of our methods is 100%, scheduling patients as positive only if they will be affected by CRCLM, showing a 93.3% overall accuracy. Recall was 77.8%., Conclusion: FMs can provide an effective early detection of CRCLM before clinical diagnosis only through non-invasive radiomic features even in very heterogeneous and small clinical samples.

Background: Surgical resection is the only possible choice of treatment in several pancreatic disorders that included periampullar neoplasms. The development of a postoperative pancreatic fistula (POPF) is the main complication. Despite three different surgical strategies that have been proposed-pancreatojejunostomy (PJ), pancreatogastrostomy (PG), and pancreatic duct occlusion (DO)-none of them has been clearly validated to be superior. The aim of this study was to analyse the postoperative outcomes after DO. Methods: We retrospectively reviewed 56 consecutive patients who underwent Whipple's procedure from January 2007 to December 2014 in a tertiary Hepatobiliary Surgery and Liver Transplant Unit. After pancreatic resection in open surgery, we performed DO of the Wirsung duct with Cyanoacrylate glue independently from the stump characteristics. The mean follow-up was 24.5 months. Results: In total, 29 (60.4%) were men and 19 were (39.6%) women with a mean age of 62.79 (SD ± 10.02) years. Surgical indications were in 95% of cases malignant diseases. The incidence of POPF after DO was 31 (64.5%): 10 (20.8%) patients had a Grade A fistula, 18 (37.5%) Grade B fistula, and 3 (6.2%) Grade C fistula. No statistical differences were demonstrated in the development of POPF according to pancreatic duct diameter groups ( p = 0.2145). Nevertheless, the POPF rate was significantly higher in the soft pancreatic group ( p = 0.0164). The mean operative time was 358.12 min (SD ± 77.03, range: 221-480 min). Hospital stay was significantly longer in patients who developed POPF ( p < 0.001). According to the Clavien-Dindo (CD) classification, seven of 48 (14.58%) patients were classified as CD III-IV. At the last follow-up, 27 of the 31 (87%) patients were alive. Conclusions: Duct occlusion could be proposed as a safe alternative to pancreatic anastomosis especially in low-/medium-volume centers in selected cases at higher risk of clinically relevant POPF., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Giuliani, Avella, Segreto, Izzo, Buondonno, Coluzzi, Cappuccio, Brunese, Vaschetti, Scacchi, Guerra, Amato, Calise and Rocca.)

Magnetic resonance imaging (MRI) is a non-invasive imaging technique (non-ionizing radiation) with superior soft tissue contrasts and potential morphological and functional applications. However, long examination and interpretation times, as well as higher costs, still represent barriers to MRI use in clinical routine. Abbreviated MRI protocols have emerged as an alternative to standard MRI protocols. Abbreviated MRI protocols eliminate redundant sequences that negatively affect cost, acquisition time, patient comfort. However, the diagnostic information is generally not compromised. Abbreviated MRI protocols have already been utilized for hepatocellular carcinoma, for prostate cancer detection, and for nonalcoholic fatty liver disease screening.

The increase in oncology knowledge and the possibility of creating personalized medicine by selecting a more suitable therapy related to tumor subtypes, as well as the patient's management with cancer within a multidisciplinary team has improved the clinical outcomes. Early detection of cancer through screening-based imaging is probably the major contributor to a reduction in mortality for certain cancers. Nowadays, imaging can also characterize several lesions and predict their histopathological features and can predict tumor behaviour and prognosis. CT is the main diagnostic tool in oncologic imaging and is widely used for the tumors detection, staging, and follow-up. Moreover, since CT accounts for 49-66% of overall patient radiation exposure, the constant reduction, optimization, dose inter- and intraindividual consistency are major goals in radiological field. In the recent years, numerous dose reduction techniques have been established and created voltage modulation keeping a satisfactory image quality. The introduction of CT dual-layer detector technology enabled the acquisition of spectral data without additional CT x-ray tube or additional acquisitions. In addition, since MRI does not expose the body to radiation, it has become a mainstay of non-invasive diagnostic radiology modality since the 1980s.

This article provides an overview of diagnostic evaluation and ablation treatment assessment in Hepatocellular Carcinoma (HCC). Only studies, in the English language from January 2010 to January 202, evaluating the diagnostic tools and assessment of ablative therapies in HCC patients were included. We found 173 clinical studies that satisfied the inclusion criteria.HCC may be noninvasively diagnosed by imaging findings. Multiphase contrast-enhanced imaging is necessary to assess HCC. Intravenous extracellular contrast agents are used for CT, while the agents used for MRI may be extracellular or hepatobiliary. Both gadoxetate disodium and gadobenate dimeglumine may be used in hepatobiliary phase imaging. For treatment-naive patients undergoing CT, unenhanced imaging is optional; however, it is required in the post treatment setting for CT and all MRI studies. Late arterial phase is strongly preferred over early arterial phase. The choice of modality (CT, US/CEUS or MRI) and MRI contrast agent (extracelllar or hepatobiliary) depends on patient, institutional, and regional factors. MRI allows to link morfological and functional data in the HCC evaluation. Also, Radiomics is an emerging field in the assessment of HCC patients.Postablation imaging is necessary to assess the treatment results, to monitor evolution of the ablated tissue over time, and to evaluate for complications. Post- thermal treatments, imaging should be performed at regularly scheduled intervals to assess treatment response and to evaluate for new lesions and potential complications., (© 2021. The Author(s).)

Aim: Prostate cancer represents the most common cancer afflicting men. It may be asymptomatic at the early stage. In this paper, we propose a methodology aimed to detect the prostate cancer grade by computing non-invasive shape-based radiomic features directly from magnetic resonance images., Materials and Methods: We use a freely available dataset composed by coronal magnetic resonance images belonging to 112 patients. We represent magnetic resonance slices in terms of formal model, and we exploit model checking to check whether a set of properties (formulated with the support of pathologists and radiologists) is verified on the formal model. Each property is related to a different cancer grade with the aim to cover all the cancer grade groups., Results: An average specificity equal to 0.97 and an average sensitivity equal to 1 have been obtained with our methodology., Conclusion: The experimental analysis demonstrates the effectiveness of radiomics and formal verification for Gleason grade group detection from magnetic resonance.

Background and Objectives: The role of physical activity (PA) in elderly patients admitted to surgical units for mild acute diverticulitis in the development of disability has not been clarified so far. Our aim is to demonstrate the relationship between physical activity and better post-discharge outcomes on disability in elderly population affected by diverticular disease. Materials and Methods: We retrospectively reviewed data of 56 patients (32 Males-24 females) collected from October 2018 and March 2020 at Cardarelli Hospital in Campobasso. We included patients older than 65 yrs admitted for acute bleeding and acute diverticulitis stage ≤II, characterized by a good independence status, without cognitive impairment and low risk of immobilization, as evaluated by activity of daily living (ADL) and the instrumental activity of daily living (IADL) and Exton-Smith Scale. "Physical Activity Scale for the Elderly" (PASE) Score evaluated PA prior to admission and at first check up visit. Results: 30.4% of patients presented a good PA, 46.4% showed moderate PA and 23.2% a low PA score. A progressive reduction in ADL and IADL score was associated with lower physical activity ( p value = 0.0038 and 0.0017). We consider cognitive performance reduction with a cut off of loss of more than 5 points in Short Port of ADL and IADL and a loss of more than 15 points on Exton-Smith Scale, ( p -value 0.017 and 0.010). In the logistic regression analysis, which evaluated the independent role of PASE in disability development, statistical significance was not reached, showing an Odds Ratio of 0.51 95% CI 0.25-1.03 p value 0.062. Discussion: Reduced physical activity in everyday life in elderly is associated with increased post-hospitalization disability regarding independence, cognitive performance and immobilization. Conclusions: Poor physical performance diagnosis may allow to perform a standardized multidimensional protocol to improve PA to reduce disability incidence.