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Objective: To evaluate radiation dose and image quality of a double-low CCTA protocol reconstructed utilizing high-strength deep learning image reconstructions (DLIR-H) compared to standard adaptive statistical iterative reconstruction (ASiR-V) protocol in non-obese patients., Materials and Methods: From June to October 2022, consecutive patients, undergoing clinically indicated CCTA, with BMI < 30 kg/m 2 were prospectively included and randomly assigned into three groups: group A (100 kVp, ASiR-V 50%, iodine delivery rate [IDR] = 1.8 g/s), group B (80 kVp, DLIR-H, IDR = 1.4 g/s), and group C (80 kVp, DLIR-H, IDR = 1.2 g/s). High-concentration contrast medium was administered. Image quality analysis was evaluated by two radiologists. Radiation and contrast dose, and objective and subjective image quality were compared across the three groups., Results: The final population consisted of 255 patients (64 ± 10 years, 161 men), 85 per group. Group B yielded 42% radiation dose reduction (2.36 ± 0.9 mSv) compared to group A (4.07 ± 1.2 mSv; p < 0.001) and achieved a higher signal-to-noise ratio (30.5 ± 11.5), contrast-to-noise-ratio (27.8 ± 11), and subjective image quality (Likert scale score: 4, interquartile range: 3-4) compared to group A and group C (all p ≤ 0.001). Contrast medium dose in group C (44.8 ± 4.4 mL) was lower than group A (57.7 ± 6.2 mL) and B (50.4 ± 4.3 mL), all the comparisons were statistically different (all p < 0.001)., Conclusion: DLIR-H combined with 80-kVp CCTA with an IDR 1.4 significantly reduces radiation and contrast medium exposure while improving image quality compared to conventional 100-kVp with 1.8 IDR protocol in non-obese patients., Clinical Relevance Statement: Low radiation and low contrast medium dose coronary CT angiography protocol is feasible with high-strength deep learning reconstruction and high-concentration contrast medium without compromising image quality., Key Points: Minimizing the radiation and contrast medium dose while maintaining CT image quality is highly desirable. High-strength deep learning iterative reconstruction protocol yielded 42% radiation dose reduction compared to conventional protocol. "Double-low" coronary CTA is feasible with high-strength deep learning reconstruction without compromising image quality in non-obese patients., (© 2024. The Author(s).)

A key attribute of some long noncoding RNAs (lncRNAs) is their ability to regulate expression of neighbouring genes in cis. However, such 'cis-lncRNAs' are presently defined using ad hoc criteria that, we show, are prone to false-positive predictions. The resulting lack of cis-lncRNA catalogues hinders our understanding of their extent, characteristics and mechanisms. Here, we introduce TransCistor, a framework for defining and identifying cis-lncRNAs based on enrichment of targets amongst proximal genes. TransCistor's simple and conservative statistical models are compatible with functionally defined target gene maps generated by existing and future technologies. Using transcriptome-wide perturbation experiments for 268 human and 134 mouse lncRNAs, we provide the first large-scale survey of cis-lncRNAs. Known cis-lncRNAs are correctly identified, including XIST, LINC00240 and UMLILO, and predictions are consistent across analysis methods, perturbation types and independent experiments. We detect cis-activity in a minority of lncRNAs, primarily involving activators over repressors. Cis-lncRNAs are detected by both RNA interference and antisense oligonucleotide perturbations. Mechanistically, cis-lncRNA transcripts are observed to physically associate with their target genes and are weakly enriched with enhancer elements. In summary, TransCistor establishes a quantitative foundation for cis-lncRNAs, opening a path to elucidating their molecular mechanisms and biological significance., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Radiomics is a new emerging field that includes extraction of metrics and quantification of so-called radiomic features from medical images. The growing importance of radiomics applied to oncology in improving diagnosis, cancer staging and grading, and improved personalized treatment, has been well established; yet, this new analysis technique has still few applications in cardiovascular imaging. Several studies have shown promising results describing how radiomics principles could improve the diagnostic accuracy of coronary computed tomography angiography (CCTA) and magnetic resonance imaging (MRI) in diagnosis, risk stratification, and follow-up of patients with coronary heart disease (CAD), ischemic heart disease (IHD), hypertrophic cardiomyopathy (HCM), hypertensive heart disease (HHD), and many other cardiovascular diseases. Such quantitative approach could be useful to overcome the main limitations of CCTA and MRI in the evaluation of cardiovascular diseases, such as readers' subjectiveness and lack of repeatability. Moreover, this new discipline could potentially overcome some technical problems, namely the need of contrast administration or invasive examinations. Despite such advantages, radiomics is still not applied in clinical routine, due to lack of standardized parameters acquisition, inconsistent radiomic methods, lack of external validation, and different knowledge and experience among the readers. The purpose of this manuscript is to provide a recent update on the status of radiomics clinical applications in cardiovascular imaging., (© 2023. The Author(s).)

Objective: The aim of this study is to describe the technique and to report early results of thoraco-abdominal biopsies in the Interventional Magnetic Resonance Imaging Suite (IMRIS)., Materials and Methods: We prospectively evaluated patients with indications for MRI-guided biopsy between January 2021 and May 2022. Exclusion criteria were indication for US-/CT-guided biopsy, contraindication to percutaneous biopsy, inability to lie flat for at least 30 min, claustrophobic, severe obesity, or non-MRI compatible devices. Biopsies were performed by 3 interventional radiologists, with at least 8 years of experience in oncological interventional radiology. Epidemiological, clinical, procedural, and histopathological data were retrospectively collected., Results: From an initial population of 117 patients, 57 patients (32 male, mean age 64 ± 8 y) were finally enrolled. All 57 patients suspected thoraco-abdominal malignant lesions finally underwent MRI-guided percutaneous biopsy. The mean duration of the entire procedure was 37 min (range 28-65 min); the mean duration of the total needle-in-patient time was 10 min (range 6-19 min). Technical and clinical success were obtained for all the biopsies performed. Malignancy was demonstrated in 47/57 (82%) cases and benignancy in the remaining 10/57 (18%) cases. No major complications were detected after the biopsies; two minor compliances (severe pain) occurred and were managed conservatively., Conclusion: Our initial experience demonstrated the technical feasibility and the accuracy of MRI-guided biopsies of thoraco-abdominal masses. The reported data associated with the best comfort for the patient and for the operator make the use of MRI a valid alternative to other methods, especially in lesions that are difficult to approach via US or CT., Clinical Relevance Statement: Interventional MRI is one of the most important innovations available for interventional radiologists. This method will broaden the diagnostic and therapeutic possibilities, allowing treatment of lesions up to now not approachable percutaneously. For this, it is necessary to start publishing the data of the few groups that are developing the method., Key Points: • To evaluate the use of MRI as a guide for percutaneous biopsies of various districts. • Our preliminary experience confirms experience demonstrated the technical feasibility and the accuracy of MRI-guided biopsies of thoraco-abdominal masses. • Interventional MRI can become the reference method for percutaneous biopsies in particular for lesions with difficult percutaneous approach., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Long noncoding RNAs (lncRNAs) are linked to cancer via pathogenic changes in their expression levels. Yet, it remains unclear whether lncRNAs can also impact tumour cell fitness via function-altering somatic "driver" mutations. To search for such driver-lncRNAs, we here perform a genome-wide analysis of fitness-altering single nucleotide variants (SNVs) across a cohort of 2583 primary and 3527 metastatic tumours. The resulting 54 mutated and positively-selected lncRNAs are significantly enriched for previously-reported cancer genes and a range of clinical and genomic features. A number of these lncRNAs promote tumour cell proliferation when overexpressed in in vitro models. Our results also highlight a dense SNV hotspot in the widely-studied NEAT1 oncogene. To directly evaluate the functional significance of NEAT1 SNVs, we use in cellulo mutagenesis to introduce tumour-like mutations in the gene and observe a significant and reproducible increase in cell fitness, both in vitro and in a mouse model. Mechanistic studies reveal that SNVs remodel the NEAT1 ribonucleoprotein and boost subnuclear paraspeckles. In summary, this work demonstrates the utility of driver analysis for mapping cancer-promoting lncRNAs, and provides experimental evidence that somatic mutations can act through lncRNAs to enhance pathological cancer cell fitness., (© 2023. The Author(s).)

Purpose: To perform a comprehensive intraindividual objective and subjective image quality evaluation of coronary CT angiography (CCTA) reconstructed with deep learning image reconstruction (DLIR) and to assess correlation with routinely applied hybrid iterative reconstruction algorithm (ASiR-V)., Material and Methods: Fifty-one patients (29 males) undergoing clinically indicated CCTA from April to December 2021 were prospectively enrolled. Fourteen datasets were reconstructed for each patient: three DLIR strength levels (DLIR&#95;L, DLIR&#95;M, and DLIR&#95;H), ASiR-V from 10% to 100% in 10%-increment, and filtered back-projection (FBP). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) determined objective image quality. Subjective image quality was assessed with a 4-point Likert scale. Concordance between reconstruction algorithms was assessed by Pearson correlation coefficient., Results: DLIR algorithm did not impact vascular attenuation (P ≥ 0.374). DLIR&#95;H showed the lowest noise, comparable with ASiR-V 100% (P = 1) and significantly lower than other reconstructions (P ≤ 0.021). DLIR&#95;H achieved the highest objective quality, with SNR and CNR comparable to ASiR-V 100% (P = 0.139 and 0.075, respectively). DLIR&#95;M obtained comparable objective image quality with ASiR-V 80% and 90% (P ≥ 0.281), while achieved the highest subjective image quality (4, IQR: 4-4; P ≤ 0.001). DLIR and ASiR-V datasets returned a very strong correlation in the assessment of CAD (r = 0.874, P = 0.001)., Conclusion: DLIR&#95;M significantly improves CCTA image quality and has very strong correlation with routinely applied ASiR-V 50% dataset in the diagnosis of CAD., (© 2023. The Author(s).)

Abstract: Image reconstruction processing in computed tomography (CT) has evolved tremendously since its creation, succeeding at optimizing radiation dose while maintaining adequate image quality. Computed tomography vendors have developed and implemented various technical advances, such as automatic noise reduction filters, automatic exposure control, and refined imaging reconstruction algorithms.Focusing on imaging reconstruction, filtered back-projection has represented the standard reconstruction algorithm for over 3 decades, obtaining adequate image quality at standard radiation dose exposures. To overcome filtered back-projection reconstruction flaws in low-dose CT data sets, advanced iterative reconstruction algorithms consisting of either backward projection or both backward and forward projections have been developed, with the goal to enable low-dose CT acquisitions with high image quality. Iterative reconstruction techniques play a key role in routine workflow implementation (eg, screening protocols, vascular and pediatric applications), in quantitative CT imaging applications, and in dose exposure limitation in oncologic patients.Therefore, this review aims to provide an overview of the technical principles and the main clinical application of iterative reconstruction algorithms, focusing on the strengths and weaknesses, in addition to integrating future perspectives in the new era of artificial intelligence., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Diffuse liver diseases are highly prevalent conditions around the world, including pathological liver changes that occur when hepatocytes are damaged and liver function declines, often leading to a chronic condition. In the last years, Magnetic Resonance Imaging (MRI) is reaching an important role in the study of diffuse liver diseases moving from qualitative to quantitative assessment of liver parenchyma. In fact, this can allow noninvasive accurate and standardized assessment of diffuse liver diseases and can represent a concrete alternative to biopsy which represents the current reference standard. MRI approach already tested for other pathologies include diffusion-weighted imaging (DWI) and radiomics, able to quantify different aspects of diffuse liver disease. New emerging MRI quantitative methods include MR elastography (MRE) for the quantification of the hepatic stiffness in cirrhotic patients, dedicated gradient multiecho sequences for the assessment of hepatic fat storage, and iron overload. Thus, the aim of this review is to give an overview of the technical principles and clinical application of new quantitative MRI techniques for the evaluation of diffuse liver disease., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2022 Marta Zerunian et al.)

Purpose: The purpose of this study was to compare image quality and coronary interpretability of triple-rule-out systolic and diastolic protocols in patients with acute chest pain., Materials and Methods: From March 2016 to October 2017 the authors prospectively enrolled patients with undifferentiated acute chest pain, who were at low to intermediate cardiovascular risk. Those with heart rate >75 bpm underwent a systolic prospectively triggered acquisition (systolic triggering [ST]), and in those with ≤75 bpm, end-diastolic triggering (DT) was instead performed. Examinations were evaluated for coronary artery disease, aortic dissection, and pulmonary embolism. Image quality was assessed using a Likert scale. Coronary arteries interpretability was evaluated both on a per-vessel and a per segment basis. The occurrence of major adverse cardiovascular events was investigated., Results: The final study population was 189 patients. Fifty-two patients (27.5%) underwent systolic acquisition and 137 (72.5%) underwent diastolic acquisition. No significant differences in overall image quality were observed between DT and ST groups (median score 5 [interquartile ranges 4 to 5] vs. 4 [interquartile ranges 4 to 5], P =0.074). Although both DT and ST protocols showed low percentages of noninterpretable coronary arteries on a per-vessel (1.5% and 6.7%, respectively) and per-segment analysis (1% and 4.7%, respectively), these percentages resulted significantly higher for ST groups ( P <0.001). Obstructive coronary stenosis was observed in 18 patients. Only one case of pulmonary embolism was diagnosed and no cases of aortic dissection were found in our population. No death or major adverse cardiovascular events were observed during follow-up among the 2 groups., Conclusions: Results showed that triple-rule-out computed tomography angiography is a reliable technique in patients with acute chest pain and that an ST acquisition protocol could be considered an alternative acquisition protocol in patients with higher heart rate, reaching a good image quality., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Adrenal lesions are frequently incidentally diagnosed during investigations for other clinical conditions. Despite being usually benign, nonfunctioning, and silent, they can occasionally cause discomfort or be responsible for various clinical conditions due to hormonal dysregulation; therefore, their characterization is of paramount importance for establishing the best therapeutic strategy. Imaging techniques such as ultrasound, computed tomography, magnetic resonance, and PET-TC, providing anatomical and functional information, play a central role in the diagnostic workup, allowing clinicians and surgeons to choose the optimal lesion management. This review aims at providing an overview of the most encountered adrenal lesions, both benign and malignant, including describing their imaging characteristics.

Long noncoding RNAs (lncRNAs) are widely dysregulated in cancer, yet their functional roles in cancer hallmarks remain unclear. We employ pooled CRISPR deletion to perturb 831 lncRNAs detected in KRAS-mutant non-small cell lung cancer (NSCLC) and measure their contribution to proliferation, chemoresistance, and migration across two cell backgrounds. Integrative analysis of these data outperforms conventional "dropout" screens in identifying cancer genes while prioritizing disease-relevant lncRNAs with pleiotropic and background-independent roles. Altogether, 80 high-confidence oncogenic lncRNAs are active in NSCLC, which tend to be amplified and overexpressed in tumors. A follow-up antisense oligonucleotide (ASO) screen shortlisted two candidates, Cancer Hallmarks in Lung LncRNA 1 ( CHiLL1 ) and GCAWKR , whose knockdown consistently suppressed cancer hallmarks in two- and three-dimension tumor models. Molecular phenotyping reveals that CHiLL1 and GCAWKR control cellular-level phenotypes via distinct transcriptional networks. This work reveals a multi-dimensional functional lncRNA landscape underlying NSCLC that contains potential therapeutic vulnerabilities., Competing Interests: T.P., R.E., and R.J. have filed a patent application (21202363.4) relating to the therapeutic targeting of lncRNAs identified here. Applicant: University of Bern. R.J. is a paid consultant for NextRNA (USA)., (© 2022 The Author(s).)

Background: to assess the performance and speed of two commercially available advanced cardiac software packages in the automated identification of coronary vessels as an aiding tool for inexperienced readers. Methods: Hundred and sixty patients undergoing coronary CT angiography (CCTA) were prospectively enrolled from February until September 2021 and randomized in two groups, each one composed by 80 patients. Patients in group 1 were scanned on Revolution EVO CT Scanner (GE Healthcare), while patients in group 2 had the CCTA performed on Brilliance iCT (Philips Healthcare); each examination was evaluated on the respective vendor proprietary advanced cardiac software (software 1 and 2, respectively). Two inexperienced readers in cardiac imaging verified the software performance in the automated identification of the three major coronary vessels: (RCA, LCx, and LAD) and in the number of identified coronary segments. Time of analysis was also recorded. Results: software 1 correctly and automatically nominated 202/240 (84.2%) of the three main coronary vessels, while software 2 correctly identified 191/240 (79.6%) (p = 0.191). Software 1 achieved greater performances in recognizing the LCx (81.2% versus 67.5%; p = 0.048), while no differences have been reported in detecting the RCA (p = 0.679), and the LAD (p = 0.618). On a per-segment analysis, software 1 outperformed software 2, automatically detecting 942/1062 (88.7%) coronary segments, while software 2 detected 797/1078 (73.9%) (p < 0.001). Average reconstruction and detection time was of 13.8 s for software 1 and 21.9 s for software 2 (p < 0.001). Conclusions: automated cardiac software packages are a reliable and time-saving tool for inexperienced reader. Software 1 outperforms software 2 and might therefore better assist inexperienced CCTA readers in automated identification of the three main vessels and coronaries segments, with a consistent time saving of the reading session.

Purpose: Lung severity score (LSS) and quantitative chest CT (QCCT) analysis could have a relevant impact to stratify patients affected by COVID-19 pneumonia at the hospital admission. The study aims to assess LSS and QCCT performances in severity stratification of COVID-19 patients., Materials and Methods: From April 19, 2020, until May 3, 2020, patients with chest CT suggestive for interstitial pneumonia and tested positive for COVID-19 were retrospectively enrolled and stratified for hospital admission as Group 1, 2 and 3 (home isolation, low intensive care and intensive care, respectively). For LSS, lungs were divided in 20 regions and visually assessed by two radiologists who scored for each region from non-lung involvement as 0, < 50% assigned as 1 and > 50% as 2. QCCT was performed with a dedicated software that extracts pulmonary involvement expressed in liters and percentage. LSS and QCCT were analyzed with ROC curve analysis to predict the performance of both methods. P values < 0.05 were considered statistically significant., Results: Final population enrolled included 136 patients (87 males, mean age 66 ± 16), 19 patients in Group 1, 86 in Group 2 and 31 in Group 3. Significant differences for LSS were observed in almost all comparisons, especially in Group 1 vs 3 (AUC 0.850, P < 0,0001) and Group 1 + 2 vs 3 (AUC 0.783, P < 0,0001). QCCT showed significant results in almost all comparisons, especially between Group 1 vs 3 (AUC 0.869, P < 0,0001). LSS and QCCT comparison between Group 1 and Group 2 did not show significant differences., Conclusions: LSS and QCCT could represent promising tools to stratify COVID-19 patient severity at the admission., (© 2022. The Author(s).)

Purpose: To evaluate the potential role of texture-based radiomics analysis in differentiating Coronavirus Disease-19 (COVID-19) pneumonia from pneumonia of other etiology on Chest CT., Materials and Methods: One hundred and twenty consecutive patients admitted to Emergency Department, from March 8, 2020, to April 25, 2020, with suspicious of COVID-19 that underwent Chest CT, were retrospectively analyzed. All patients presented CT findings indicative for interstitial pneumonia. Sixty patients with positive COVID-19 real-time reverse transcription polymerase chain reaction (RT-PCR) and 60 patients with negative COVID-19 RT-PCR were enrolled. CT texture analysis (CTTA) was manually performed using dedicated software by two radiologists in consensus and textural features on filtered and unfiltered images were extracted as follows: mean intensity, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. Nonparametric Mann-Whitney test assessed CTTA ability to differentiate positive from negative COVID-19 patients. Diagnostic criteria were obtained from receiver operating characteristic (ROC) curves., Results: Unfiltered CTTA showed lower values of mean intensity, MPP, and kurtosis in COVID-19 positive patients compared to negative patients (p = 0.041, 0.004, and 0.002, respectively). On filtered images, fine and medium texture scales were significant differentiators; fine texture scale being most significant where COVID-19 positive patients had lower SD (p = 0.004) and MPP (p = 0.004) compared to COVID-19 negative patients. A combination of the significant texture features could identify the patients with positive COVID-19 from negative COVID-19 with a sensitivity of 60% and specificity of 80% (p = 0.001)., Conclusions: Preliminary evaluation suggests potential role of CTTA in distinguishing COVID-19 pneumonia from other interstitial pneumonia on Chest CT., (© 2021. The Author(s).)

Background The long-term post-acute pulmonary sequelae of COVID-19 remain unknown. Purpose To evaluate lung injury in patients affected by COVID-19 pneumonia at the 6-month follow-up CT examination compared with the baseline chest CT examination. Materials and Methods From March 19, 2020, to May 24, 2020, patients with moderate to severe COVID-19 pneumonia who had undergone baseline chest CT were prospectively enrolled at their 6-month follow-up. The CT qualitative findings, semiquantitative Lung Severity Score (LSS), and the well-aerated lung volume at quantitative chest CT (QCCT) analysis were analyzed. The performance of the baseline LSS and QCCT findings for predicting fibrosis-like changes (reticular pattern and/or honeycombing) at the 6-month follow-up chest CT examination was tested by using receiver operating characteristic curves. Univariable and multivariable logistic regression analyses were used to test clinical and radiologic features that were predictive of fibrosis-like changes. The multivariable analysis was performed with clinical parameters alone (clinical model), radiologic parameters alone (radiologic model), and the combination of clinical and radiologic parameters (combined model). Results One hundred eighteen patients who had undergone baseline chest CT and agreed to undergo follow-up chest CT at 6 months were included in the study (62 women; mean age, 65 years ± 12 [standard deviation]). At follow-up chest CT, 85 of 118 (72%) patients showed fibrosis-like changes and 49 of 118 (42%) showed ground-glass opacities. The baseline LSS (>14) and QCCT findings (≤3.75 L and ≤80%) showed excellent performance for predicting fibrosis-like changes at follow-up chest CT. In the multivariable analysis, the areas under the curve were 0.89 (95% CI: 0.77, 0.96) for the clinical model, 0.81 (95% CI: 0.68, 0.9) for the radiologic model, and 0.92 (95% CI: 0.81, 0.98) for the combined model. Conclusion At 6-month follow-up chest CT, 72% of patients showed late sequelae, in particular fibrosis-like changes. The baseline Lung Severity Score and the well-aerated lung volume at quantitative chest CT (QCCT) analysis showed excellent performance for predicting fibrosis-like changes at the 6-month chest CT (area under the curve, >0.88). Male sex, cough, lymphocytosis, and the well-aerated lung volume at QCCT analysis were significant predictors of fibrosis-like changes at 6 months, demonstrating an inverse correlation (area under the curve, 0.92). © RSNA, 2021 See also the editorial by Wells and Devaraj in this issue.

Purpose: [ 18 F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET/CT) has a central role in the lung nodules' characterization even if, with SUV < 2.5, percutaneous CT-guided Lung Biopsy (CTLB) is needed to assess nodule nature. In that scenario, CT Texture Analysis (CTTA) could be a non-invasive imaging biomarker. Our purpose is to test CTTA ability in differentiating malignant from benign nodules., Method: Patients that underwent FDG PET/CT followed by CTLB between January 2013 and December 2018 were retrospectively enrolled. Were included patients with lung nodule SUV < 2.5 and histological diagnosis., Exclusion Criteria: nodules SUV > 2.5, patients who refused CTLB or received oncological treatment before CTLB, indeterminate pathology report, CT motion artifacts. Two radiologists in consensus performed CTTA, drawing a volumetric Region of Interest of nodule with a dedicated first order TA software with and without spatial scaling filters, on preliminary CT performed for CTLB. Statistics included a comparison between malignant and benign neoplasms distribution (2-tailed T-test or Mann-Whitney test according to normal/non-normal data distribution), P-values < 0.05 were considered statistically significant. CTTA accuracy was tested with Receiver Operating Characteristics (ROC) curve., Results: Form an initial population of 1178, 46 patients encountered inclusion criteria. Pathologist reported 27/46 (59%) malignant and 19/46 (41%) benign nodules. In malignant lesions CTTA showed lower Kurtosis' and higher Skewness' values (all P ≤ 0.0013 and all filtered TA P < 0.024, respectively). ROC curve showed significant Area Under the Curve for Kurtosis and Skewness (0.654 and 0.642, P < 0.001) at medium filtration., Conclusions: CTTA is a promising radiological tool to characterize benign and malignant lung nodules, even in those cases without an altered glucose metabolism., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Coronavirus disease 2019 (COVID-19) is a respiratory syndrome caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) first described in Wuhan, Hubei Province, China in the last months of 2019 and then declared as a pandemic. Typical symptoms are represented by fever, cough, dyspnea and fatigue, but SARS-CoV-2 infection can also cause gastrointestinal symptoms (vomiting, diarrhoea, abdominal pain, loss of appetite) or be totally asymptomatic. As reported in literature, many patients with COVID-19 pneumonia had a secondary abdominal involvement (bowel, pancreas, gallbladder, spleen, liver, kidneys), confirmed by laboratory tests and also by radiological features. Usually the diagnosis of COVID-19 is suspected and then confirmed by real-time reverse-transcription-polymerase chain reaction (RT-PCR), after the examination of the lung bases of patients, admitted to the emergency department with abdominal symptoms and signs, who underwent abdominal-CT. The aim of this review is to describe the typical and atypical abdominal imaging findings in patients with SARS-CoV-2 infection reported since now in literature., (© 2021 The Authors. Published by the British Institute of Radiology.)

Iterative reconstructions (IR) might alter radiomic features extraction. We aim to evaluate the influence of Adaptive Statistical Iterative Reconstruction-V (ASIR-V) on CT radiomic features. Patients who underwent unenhanced abdominal CT (Revolution Evo, GE Healthcare, USA) were retrospectively enrolled. Raw data of filtered-back projection (FBP) were reconstructed with 10 levels of ASIR-V (10-100%). CT texture analysis (CTTA) of liver, kidney, spleen and paravertebral muscle for all datasets was performed. Six radiomic features (mean intensity, standard deviation (SD), entropy, mean of positive pixel (MPP), skewness, kurtosis) were extracted and compared between FBP and all ASIR-V levels, with and without altering the spatial scale filter (SSF). CTTA of all organs revealed significant differences between FBP and all ASIR-V reconstructions for mean intensity, SD, entropy and MPP (all p < 0.0001), while no significant differences were observed for skewness and kurtosis between FBP and all ASIR-V reconstructions (all p > 0.05). A per-filter analysis was also performed comparing FBP with all ASIR-V reconstructions for all six SSF separately (SSF0-SSF6). Results showed significant differences between FBP and all ASIR-V reconstruction levels for mean intensity, SD, and MPP (all filters p < 0.0315). Skewness and kurtosis showed no differences for all comparisons performed (all p > 0.05). The application of incremental ASIR-V levels affects CTTA across various filters. Skewness and kurtosis are not affected by IR and may be reliable quantitative parameters for radiomic analysis.

Radiomics has the potential to play a pivotal role in oncological translational imaging, particularly in cancer detection, prognosis prediction and response to therapy evaluation. To date, several studies established Radiomics as a useful tool in oncologic imaging, able to support clinicians in practicing evidence-based medicine, uniquely tailored to each patient and tumor. Mineable data, extracted from medical images could be combined with clinical and survival parameters to develop models useful for the clinicians in cancer patients' assessment. As such, adding Radiomics to traditional subjective imaging may provide a quantitative and extensive cancer evaluation reflecting histologic architecture. In this Part II, we present an overview of radiomic applications in thoracic, genito-urinary, breast, neurological, hematologic and musculoskeletal oncologic applications.

(1) Introduction and Aim: The aim of this study is to investigate the prognostic value, in terms of response and survival, of CT-based radiomics features for patients with HCC undergoing drug-eluting beads transarterial chemoembolization (DEB-TACE). (2) Materials and Methods: Pre-treatment CT examinations of 50 patients with HCC, treated with DEB-TACE were manually segmented to obtain the tumor volumetric region of interest, extracting radiomics features with TexRAD. Response to therapy evaluation was performed basing on post-procedural CT examination compared to pre-procedural CT, using modified RECIST criteria for HCC. The prognostic value of texture analysis was evaluated, investigating the correlation between radiomics features, response to therapy and overall survival. Three models based on texture and clinical variables and a combination of them were finally built; (3) Results: Entropy, skewness, MPP and kurtosis showed a significant correlation with complete response (CR) to TACE (all p < 0.001). A predictive model to identify patients with a high and low probability of CR was evaluated with an ROC curve, with an AUC of 0.733 ( p < 0.001). The three models built for survival prediction yielded an HR of 2.19 (95% CI: 2.03-2.35) using texture features, of 1.7 (95% CI: 1.54-1.9) using clinical data and of 4.61 (95% CI: 4.24-5.01) combining both radiomics and clinical data (all p < 0.0001). (4) Conclusion: Texture analysis based on pre-treatment CT examination is associated with response to therapy and survival in patients with HCC undergoing DEB-TACE, especially if combined with clinical data.

Radiomics has been playing a pivotal role in oncological translational imaging, particularly in cancer diagnosis, prediction prognosis, and therapy response assessment. Recently, promising results were achieved in management of cancer patients by extracting mineable high-dimensional data from medical images, supporting clinicians in decision-making process in the new era of target therapy and personalized medicine. Radiomics could provide quantitative data, extracted from medical images, that could reflect microenvironmental tumor heterogeneity, which might be a useful information for treatment tailoring. Thus, it could be helpful to overcome the main limitations of traditional tumor biopsy, often affected by bias in tumor sampling, lack of repeatability and possible procedure complications. This quantitative approach has been widely investigated as a non-invasive and an objective imaging biomarker in cancer patients; however, it is not applied as a clinical routine due to several limitations related to lack of standardization and validation of images acquisition protocols, features segmentation, extraction, processing, and data analysis. This field is in continuous evolution in each type of cancer, and results support the idea that in the future Radiomics might be a reliable application in oncologic imaging. The first part of this review aimed to describe some radiomic technical principles and clinical applications to gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy.

Introduction: COVID-19 pneumonia is characterized by ground-glass opacities (GGOs) and consolidations on Chest CT, although these CT features cannot be considered specific, at least on a qualitative analysis. The aim is to evaluate if Quantitative Chest CT could provide reliable information in discriminating COVID-19 from non-COVID-19 patients., Materials and Methods: From March 31, 2020 until April 18, 2020, patients with Chest CT suggestive for interstitial pneumonia were retrospectively enrolled and divided into two groups based on positive/negative COVID-19 RT-PCR results. Patients with pulmonary resection and/or CT motion artifacts were excluded. Quantitative Chest CT analysis was performed with a dedicated software that provides total lung volume, healthy parenchyma, GGOs, consolidations and fibrotic alterations, expressed both in liters and percentage. Two radiologists in consensus revised software analysis and adjusted areas of lung impairment in case of non-adequate segmentation. Data obtained were compared between COVID-19 and non-COVID-19 patients and p < 0.05 were considered statistically significant. Performance of statistically significant parameters was tested by ROC curve analysis., Results: Final population enrolled included 190 patients: 136 COVID-19 patients (87 male, 49 female, mean age 66 ± 16) and 54 non-COVID-19 patients (25 male, 29 female, mean age 63 ± 15). Lung quantification in liters showed significant differences between COVID-19 and non-COVID-19 patients for GGOs (0.55 ± 0.26L vs 0.43 ± 0.23L, p = 0.0005) and fibrotic alterations (0.05 ± 0.03 L vs 0.04 ± 0.03 L, p < 0.0001). ROC analysis of GGOs and fibrotic alterations showed an area under the curve of 0.661 (cutoff 0.39 L, 68% sensitivity and 59% specificity, p < 0.001) and 0.698 (cutoff 0.02 L, 86% sensitivity and 44% specificity, p < 0.001), respectively., Conclusions: Quantification of GGOs and fibrotic alterations on Chest CT could be able to identify patients with COVID-19.

In December 2019 a novel coronavirus, named severe acute respiratory syndrome coronavirus 2 was identified and the disease associated was named coronavirus disease 2019 (COVID-19). Fever, cough, myalgia, fatigue associated to dyspnea represent most common clinical symptoms of the disease. The reference standard for diagnosis of severe acute respiratory syndrome coronavirus 2 infection is real time reverse-transcription polymerase chain reaction test applied on respiratory tract specimens. Despite of lower specificity, chest computed tomography (CT), as reported in manifold scientific studies, showed high sensitivity, therefore it may help in the early detection, management and follow-up of COVID-19 pneumonia. Patients affected by COVID-19 pneumonia usually showed on chest CT some typical features, such as: Bilateral ground glass opacities characterized by multilobe involvement with posterior and peripheral distribution; parenchymal consolidations with or without air bronchogram; interlobular septal thickening; crazy paving pattern, represented by interlobular and intralobular septal thickening surrounded by ground-glass opacities; subsegmental pulmonary vessels enlargement (> 3 mm). Halo sign, reversed halo sign, cavitation and pleural or pericardial effusion represent some of atypical findings of COVID-19 pneumonia. On the other hand lymphadenopathy's and bronchiectasis' frequency is unclear, indeed conflicting data emerged in literature. Radiologists play a key role in recognition of high suspicious findings of COVID-19 on chest CT, both typical and atypical ones. Thus, the aim of this review is to illustrate typical and atypical CT findings of COVID-19., Competing Interests: Conflict-of-interest statement: There is no conflict of interest associated with any of the senior author or other coauthors contributed their efforts in this manuscript., (©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.)

Background The standard for diagnosis of severe acute respiratory syndrome coronavirus 2 is a reverse transcription polymerase chain reaction (RT-PCR) test, but chest CT may play a complimentary role in the early detection of Coronavirus Disease 2019 (COVID-19) pneumonia. Purpose To investigate CT features of patients with COVID-19 in Rome, Italy, and to compare the accuracy of CT with that of RT-PCR. Materials and Methods In this prospective study from March 4, 2020, until March 19, 2020, consecutive patients suspected of having COVID-19 infection and respiratory symptoms were enrolled. Exclusion criteria were contrast material-enhanced chest CT performed for vascular indications, patients who refused chest CT or hospitalization, and severe CT motion artifact. All patients underwent RT-PCR and chest CT. Diagnostic performance of CT was calculated using RT-PCR as the reference standard. Chest CT features were calculated in a subgroup of patients with positive RT-PCR and CT findings. CT features of hospitalized patients and patients in home isolation were compared using the Pearson χ 2 test. Results The study population included 158 consecutive participants (83 male, 75 female; mean age, 57 years ± 17 [standard deviation]). Of the 158 participants, fever was observed in 97 (61%), cough was observed in 88 (56%), dyspnea was observed in 52 (33%), lymphocytopenia was observed in 95 (60%), increased C-reactive protein level was observed in 139 (88%), and elevated lactate dehydrogenase level was observed in 128 (81%). Sensitivity, specificity, and accuracy of CT were 97% (95% confidence interval [CI]: 88%, 99%) (60 of 62), 56% (95% CI: 45%, 66%) (54 of 96), and 72% (95% CI: 64%, 78%) (114 of 158), respectively. In the subgroup of 58 participants with positive RT-PCR and CT findings, ground-glass opacities were present in all 58 (100%), both multilobe and posterior involvement were present in 54 (93%), bilateral pneumonia was present in 53 (91%), and subsegmental vessel enlargement (>3 mm) was present in 52 (89%). Conclusion The typical pattern of COVID-19 pneumonia in Rome, Italy, was peripheral ground-glass opacities with multilobe and posterior involvement, bilateral distribution, and subsegmental vessel enlargement (>3 mm). Chest CT had high sensitivity (97%) but lower specificity (56%). © RSNA, 2020.

Long non-coding RNAs (lncRNAs) represent a huge reservoir of potential cancer targets. Such "onco-lncRNAs" have resisted traditional RNAi methods, but CRISPR-Cas9 genome editing now promises functional screens at high throughput and low cost. The unique biology of lncRNAs demands screening strategies distinct from protein-coding genes. The first such screens have identified hundreds of onco-lncRNAs promoting cell proliferation and drug resistance. Ongoing developments will further improve screen performance and translational relevance. This Review aims to highlight the potential of CRISPR screening technology for discovering new onco-lncRNAs, and to guide molecular oncologists wishing to apply it to their cancer of interest., (Copyright © 2019 Elsevier Inc. All rights reserved.)

The sequence domains underlying long noncoding RNA (lncRNA) activities, including their characteristic nuclear enrichment, remain largely unknown. It has been proposed that these domains can originate from neofunctionalized fragments of transposable elements (TEs), otherwise known as RIDLs (repeat insertion domains of lncRNA), although just a handful have been identified. It is challenging to distinguish functional RIDL instances against a numerous genomic background of neutrally evolving TEs. We here show evidence that a subset of TE types experience evolutionary selection in the context of lncRNA exons. Together these comprise an enrichment group of 5374 TE fragments in 3566 loci. Their host lncRNAs tend to be functionally validated and associated with disease. This RIDL group was used to explore the relationship between TEs and lncRNA subcellular localization. By using global localization data from 10 human cell lines, we uncover a dose-dependent relationship between nuclear/cytoplasmic distribution and evolutionarily conserved L2b, MIRb, and MIRc elements. This is observed in multiple cell types and is unaffected by confounders of transcript length or expression. Experimental validation with engineered transgenes shows that these TEs drive nuclear enrichment in a natural sequence context. Together these data reveal a role for TEs in regulating the subcellular localization of lncRNAs., (© 2019 Carlevaro-Fita et al.; Published by Cold Spring Harbor Laboratory Press.)

CRISPR-Cas9 technology can be used to engineer precise genomic deletions with pairs of single guide RNAs (sgRNAs). This approach has been widely adopted for diverse applications, from disease modelling of individual loci, to parallelized loss-of-function screens of thousands of regulatory elements. However, no solution has been presented for the unique bioinformatic design requirements of CRISPR deletion. We here present CRISPETa, a pipeline for flexible and scalable paired sgRNA design based on an empirical scoring model. Multiple sgRNA pairs are returned for each target, and any number of targets can be analyzed in parallel, making CRISPETa equally useful for focussed or high-throughput studies. Fast run-times are achieved using a pre-computed off-target database. sgRNA pair designs are output in a convenient format for visualisation and oligonucleotide ordering. We present pre-designed, high-coverage library designs for entire classes of protein-coding and non-coding elements in human, mouse, zebrafish, Drosophila melanogaster and Caenorhabditis elegans. In human cells, we reproducibly observe deletion efficiencies of ≥50% for CRISPETa designs targeting an enhancer and exonic fragment of the MALAT1 oncogene. In the latter case, deletion results in production of desired, truncated RNA. CRISPETa will be useful for researchers seeking to harness CRISPR for targeted genomic deletion, in a variety of model organisms, from single-target to high-throughput scales.

Background: Danon disease (DD) is a rare disorder characterized by cardiomyopathy, intellectual disability, and proximal myopathy. It is caused by mutations in the LAMP2 gene on X chromosome. Female patients most often present with late-onset cardiomyopathy and slow disease progression, but early-onset cases with unfavorable prognosis have been reported., Case Report: We describe the clinical, pathological, and molecular features of a novel LAMP2 c.453delT mutation in a female patient with severe hypertrophic cardiomyopathy, Wolff Parkinson White (WPW) syndrome and rapid progression to heart failure, requiring heart transplant. Immunohistochemical analysis of LAMP2 in the explanted heart revealed a mosaic pattern of distribution, with discrete clusters of either stained or unstained cardiac myocytes, the latter being more frequent in the septum. These findings paralleled X chromosome inactivation within the myocardium. Interestingly, multiple foci of microscarring were found on histology in the Left Ventricle (LV) free wall and septum, in a close spatial relationship with remodeling and severe stenosis of intramural coronary arterioles., Conclusions: Our findings suggest that several features may contribute to the early and severe cardiac phenotype in female DD patients. The type of mutation may account for the early disease onset, while both the inhomogeneous distribution of LAMP2 loss and the presence of microvascular remodeling may be determinant in the rapid progression to heart failure., (Copyright © 2016 Elsevier Inc. All rights reserved.)