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Objectives To develop a structured reporting (SR) template for whole-body CT examinations of polytrauma patients, based on the consensus of a panel of emergency radiology experts from the Italian Society of Medical and Interventional Radiology. Methods A multi-round Delphi method was used to quantify inter-panelist agreement for all SR sections. Internal consistency for each section and quality analysis in terms of average inter-item correlation were evaluated by means of the Cronbach’s alpha (Cα) correlation coefficient. Results The final SR form included 118 items (6 in the “Patient Clinical Data” section, 4 in the “Clinical Evaluation” section, 9 in the “Imaging Protocol” section, and 99 in the “Report” section). The experts’ overall mean score and sum of scores were 4.77 (range 1–5) and 257.56 (range 206–270) in the first Delphi round, and 4.96 (range 4–5) and 208.44 (range 200–210) in the second round, respectively. In the second Delphi round, the experts’ overall mean score was higher than in the first round, and standard deviation was lower (3.11 in the second round vs 19.71 in the first round), reflecting a higher expert agreement in the second round. Moreover, Cα was higher in the second round than in the first round (0.97 vs 0.87). Conclusions Our SR template for whole-body CT examinations of polytrauma patients is based on a strong agreement among panel experts in emergency radiology and could improve communication between radiologists and the trauma team.

Background: preoperative risk assessment of gastrointestinal stromal tumors (GISTS) is required for optimal and personalized treatment planning. Radiomics features are promising tools to predict risk assessment. The purpose of this study is to develop and validate an artificial intelligence classification algorithm, based on CT features, to define GIST’s prognosis as determined by the Miettinen classification. Methods: patients with histological diagnosis of GIST and CT studies were retrospectively enrolled. Eight morphologic and 30 texture CT features were extracted from each tumor and combined to obtain three models (morphologic, texture and combined). Data were analyzed using a machine learning classification (WEKA). For each classification process, sensitivity, specificity, accuracy and area under the curve were evaluated. Inter- and intra-reader agreement were also calculated. Results: 52 patients were evaluated. In the validation population, highest performances were obtained by the combined model (SE 85.7%, SP 90.9%, ACC 88.8%, and AUC 0.954) followed by the morphologic (SE 66.6%, SP 81.8%, ACC 76.4%, and AUC 0.742) and texture (SE 50%, SP 72.7%, ACC 64.7%, and AUC 0.613) models. Reproducibility was high of all manual evaluations. Conclusions: the AI-based radiomics model using a CT feature demonstrates good predictive performance for preoperative risk stratification of GISTs.

Purpose: The Italian Registry of Contrast Material use in Cardiac Computed Tomography (iRCM-CCT) is a multicenter, multivendor, observational study on the use of contrast media (CM) in patients undergoing cardiac computed tomography (CCT). The aim of iRCM-CCT is to assess image quality and safety profile of intravenous CM compounds. Materials and methods: iRCM-CCT enrolled 1842 consecutive patients undergoing CCT (≥50 per site) at 20 cluster sites with the indication of suspected coronary artery disease. Demographic characteristics, CCT, and CM protocols, clinical indications, safety markers, radiation dose reports, qualitative (ie, poor vascular enhancement) and quantitative (ie, HU attenuation values) image parameters were recorded. A centralized coordinating center collected and assessed all image parameters. Results: The cohort included 891 men and 951 women (age: 63±14 y, body mass index: 26±4 kg/m2) studied with ≥64 detector rows computed tomography scanners and different iodinated intravenous CM protocols and compounds (iodixanol, iopamidol, iohexol, iobitridol, iopromide, and iomeprol). The following vascular attenuation was reported: 504±147 HU in the aorta, 451±146 HU in the right coronary artery, 474±146 HU in the left main, 451±146 HU in the left anterior descending artery, and 441±149 HU in the circumflex artery. In 4% of cases the image quality was not satisfactory due to poor enhancement. The following adverse reactions to CM were recorded: 6 (0.3%) extravasations and 17 (0.9%) reactions (11 mild, 4 moderate, 2 severe). Conclusions: In a multicenter registry on CM use during CCT the prevalence of CM-related adverse reactions was very low. The appropriate use of CM is a major determinant of image quality.

Objective To evaluate if an adequate bowel preparation for CT colonography, can be achieved without diet restriction, using a reduced amount of cathartic agent and fecal tagging. To investigate the influence of patients’ characteristics on bowel preparation and the impact on patients’ compliance. Methods In total, 1446 outpatients scheduled for elective CT colonography were prospectively enrolled. All patients had the same bowel preparation based on a reduced amount of cathartic agent (120 g of macrogol in 1.5 l of water) the day before the exam and a fecal tagging agent (60 ml of hyperosmolar oral iodinated agent) the day of the exam. No dietary restrictions were imposed before the exam. The bowel preparation was evaluated using a qualitative and quantitative score. Patients were grouped by age, gender, and presence of diverticula in both scores. Patients’ compliance has been evaluated with a questionnaire after the end of the exam and with a phone-calling interview the day after the exam. Results According to the qualitative score, adequate bowel preparation was achieved in 1349 patients (93.29%) and no statistical differences were observed among the subgroups of patients. Quantitative scores demonstrated that colon distension was significantly better in younger patients and without diverticula. A good patients’ compliance was observed and most patients (96.5%) were willing to repeat it. Conclusions The lack of diet restriction does not affect the quality of CTC preparation and good patient’s compliance could potentially increase the participation rate in CRC screening programs. Key Points • An adequate quality bowel preparation for CT colonography can be achieved without diet restriction, using a reduced amount of cathartic agent (120 g of macrogol in 1.5 l of water) and fecal tagging (60 ml of hyperosmolar oral iodinated agent). • A bowel preparation based on the combination of a reduced amount of cathartic agent and fecal tagging, without diet restriction, allows obtaining good quality in more than 90% of patients. • The bowel preparation scheme proposed reduces the distress and discomfort experienced by the patients improving adherence to CTC.

Fitz-Hugh-Curtis Syndrome is a rare disorder manifesting as a complication associated with Pelvic Inflammatory Disease. The initial presentation generally consists of concomitant right upper quadrant and lower abdominal pain. This syndrome is characterized by inflammation of the peritoneum with the involvement of hepatic capsule and the tissues surrounding the liver. Intrahepatic involvement is rare and not yet well investigated. An accurate interpretation of Computed Tomography and Magnetic Resonance Imaging findings is missing in the literature. This report presents a case of Fitz-Hugh-Curtis Syndrome in which Computed Tomography and Magnetic Resonance Imaging showed a region of heterogeneously decreased enhancement and abnormal signal intensity within the liver mimicking a lesion.

Objective To evaluate the accuracy and reproducibility of hiatal surface area (HSA) measurement on dedicated multidetector computed tomography (MDCT) acquisition, in patients, previously subjected to laparoscopic sleeve gastrectomy (LSG), and affected by gastroesophageal reflux disease (GERD). Intraoperative HSA measurement was considered the reference standard. Methods Fifty-two candidates for laparoscopic hiatal hernia repair were prospectively included in the study. MDCT images were acquired during swallowing of oral iodinated contrast media and during strain. Measurements were performed by nine readers divided into three groups according to their experience. Results were compared with intraoperative measurements by means of Spearman correlation coefficient. Reproducibility was evaluated with intra- and interreader agreement by means of weighted Cohen’s kappa and intraclass correlation coefficient (ICC). Results Significant differences between MDCT and intraoperative HSA measurements were observed for swallowing imaging for less experienced readers (p = 0.037, 0.025, 0.028 and 0.019). No other statistically significant differences were observed (p > 0.05). The correlation between HSA measured intraoperatively and on MDCT was higher for strain imaging compared to swallowing (r = 0.94—0.92 vs 0.94—0.89). The overall reproducibility of MDCT HSA measurement was excellent (ICC of 0.95; 95% CI 0,8993 to 0,9840) independently of reader’s experience Conclusion HSA can be accurately measured on MDCT images. This method is reproducible and minimally influenced by reader experience. The preoperative measurement of HSA has potential advantages for surgeons in terms of correct approach to hiatal defects in obese patient.

To determine whether the quality of a low-volume reduced bowel preparation (LV-RBP) for CT Colonography (CTC) is noninferior to full-volume reduced bowel preparation (FV-RBP) regimen. In this randomized controlled trial, consecutive participants referred for CTC were randomly assigned to receive LV-RBP (52.5 g of PMF104 in 500 mL of water) or FV-RBP (105 g of PMF104 in 1000 mL of water). Images were independently reviewed by five blinded readers who rated the quality of bowel preparation from 0 (best score) to 3 (worst score). The primary outcome was the noninferiority of LV-RBP to FV-RBP in the proportion of colonic segments scored 0 for cleansing quality, with noninferiority margin of 10%. Volume of residual fluids, colonic distension, lesions and polyps detection rates and patient tolerability were secondary outcomes. From March 2019 to January 2020, 110 participants (mean age 65 years ± 14 [standard deviation]; 74 women) were allocated to LV-RBP (n = 55) or FV-RBP (n = 55) arms. There were 92% segment scored 0 in colon cleansing quality in LV-RBP and 94% in FV-RBP for prone scans, and 94% vs 92% for supine scans. Risk difference was – 2.1 (95% CI −5.9 to 1.7) and 1.5 (95% CI −2.4 to 5.4) for prone and supine positions, respectively. Residual fluids and colonic distension were also noninferior in LV-RBP. LV-RBP was associated with a lower number of evacuations during preparation (7 ± 5 vs 10 ± 6, p = 0.002). The LV-RBP for CTC demonstrated noninferior quality of colon cleansing with improved gastrointestinal tolerability compared to FV-RBP regimen.

Background: To evaluate the diagnostic performance of a Machine Learning (ML) algorithm based on Texture Analysis (TA) parameters in the prediction of Pathological Complete Response (pCR) to Neoadjuvant Chemoradiotherapy (nChRT) in Locally Advanced Rectal Cancer (LARC) patients. Methods: LARC patients were prospectively enrolled to undergo pre- and post-nChRT 3T MRI for initial loco-regional staging. TA was performed on axial T2-Weighted Images (T2-WI) to extract specific parameters, including skewness, kurtosis, entropy, and mean of positive pixels. For the assessment of TA parameter diagnostic performance, all patients underwent complete surgical resection, which served as a reference standard. ROC curve analysis was carried out to determine the discriminatory accuracy of each quantitative TA parameter to predict pCR. A ML-based decisional tree was implemented combining all TA parameters in order to improve diagnostic accuracy. Results: Forty patients were considered for final study population. Entropy, kurtosis and MPP showed statistically significant differences before and after nChRT in patients with pCR; in particular, when patients with Pathological Partial Response (pPR) and/or Pathological Non-Response (pNR) were considered, entropy and skewness showed significant differences before and after nChRT (all p < 0.05). In terms of absolute value changes, pre- and post-nChRT entropy, and kurtosis showed significant differences (0.31 ± 0.35, in pCR, −0.02 ± 1.28 in pPR/pNR, (p = 0.04); 1.87 ± 2.19, in pCR, −0.06 ± 3.78 in pPR/pNR (p = 0.0005); 107.91 ± 274.40, in pCR, −28.33 ± 202.91 in pPR/pNR, (p = 0.004), respectively). According to ROC curve analysis, pre-treatment kurtosis with an optimal cut-off value of ≤3.29 was defined as the best discriminative parameter, resulting in a sensitivity and specificity in predicting pCR of 81.5% and 61.5%, respectively. Conclusions: TA parameters extracted from T2-WI MRI images could play a key role as imaging biomarkers in the prediction of response to nChRT in LARC patients. ML algorithms can be used to efficiently combine all TA parameters in order to improve diagnostic accuracy.

Radiological imaging is currently employed as the most effective technique for screening, diagnosis, and follow up of patients with breast cancer (BC), the most common type of tumor in women worldwide. However, the introduction of the omics sciences such as metabolomics, proteomics, and molecular genomics, have optimized the therapeutic path for patients and implementing novel information parallel to the mutational asset targetable by specific clinical treatments. Parallel to the “omics” clusters, radiological imaging has been gradually employed to generate a specific omics cluster termed “radiomics”. Radiomics is a novel advanced approach to imaging, extracting quantitative, and ideally, reproducible data from radiological images using sophisticated mathematical analysis, including disease-specific patterns, that could not be detected by the human eye. Along with radiomics, radiogenomics, defined as the integration of “radiology” and “genomics”, is an emerging field exploring the relationship between specific features extracted from radiological images and genetic or molecular traits of a particular disease to construct adequate predictive models. Accordingly, radiological characteristics of the tissue are supposed to mimic a defined genotype and phenotype and to better explore the heterogeneity and the dynamic evolution of the tumor over the time. Despite such improvements, we are still far from achieving approved and standardized protocols in clinical practice. Nevertheless, what can we learn by this emerging multidisciplinary clinical approach? This minireview provides a focused overview on the significance of radiomics integrated by RNA sequencing in BC. We will also discuss advances and future challenges of such radiomics-based approach.

The use of artificial intelligence (AI) and radiomics in the healthcare setting to advance disease diagnosis and management and facilitate the creation of new therapeutics is gaining popularity. Given the vast amount of data collected during cancer therapy, there is significant concern in leveraging the algorithms and technologies available with the underlying goal of improving oncologic care. Radiologists will attain better precision and effectiveness with the advent of AI technology, making machine-assisted medical services a valuable and important option for future oncologic medical care. As a result, it is critical to figure out which specific radiology activities are best positioned to gain from AI and radiomics models and methods of oncologic imaging, while also considering the algorithms' capabilities and constraints. Our purpose is to overview the current evidence and future prospects of AI and radiomics algorithms used in oncologic imaging efforts with an emphasis on the three most frequent cancers worldwide, i.e., lung cancer, breast cancer and colorectal cancer. We discuss how AI and radiomics could be used to detect and characterize cancers and assess therapy response.

Objective The aim of this study was to perform a meta-analysis assessing the diagnostic yield of computed tomography (CT) for the identification of coronavirus disease 2019 (COVID-19) using repeated reverse transcriptase polymerase chain reaction testing or confirmed true-negative state as reference standard. Methods In May 2020, we interrogated the MEDLINE, Embase, and CENTRAL databases. Pooled sensitivity, specificity, and diagnostic odds ratios of CT for COVID-19 identification were computed. Cumulative positive predictive value (PPV) and negative predictive value, stratified by disease prevalence, were calculated. Results Ten articles were included (1332 patients). Pooled sensitivity, specificity, and summary diagnostic odds ratio of CT were 82% [95% confidence interval (CI), 79%-84%], 68% (95% CI, 65%-71%), and 18 (95% CI, 9.8-32.8). The PPV and negative predictive value were 54% (95% CI, 30%-77%) and 94% (95% CI, 88%-99%) at a COVID-19 prevalence lower than 40%, and 80% (95% CI, 62%-91%) and 77% (95% CI, 68%-85%) at a prevalence higher than 40%. Conclusion CT yields higher specificity and PPV, albeit lower sensitivity, than previously reported for the identification of COVID-19.

Purpose. To evaluate signal intensity (SI) differences between 3.0 T and 1.5 T on T2-weighted (T2w), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) in rectal cancer pre-, during, and postneoadjuvant chemoradiotherapy (CRT). Materials and Methods. 22 patients with locally advanced rectal cancer were prospectively enrolled. All patients underwent T2w, DWI, and ADC pre-, during, and post-CRT on both 3.0 T MRI and 1.5 T MRI. A radiologist drew regions of interest (ROIs) of the tumor and obturator internus muscle on the selected slice to evaluate SI and relative SI (rSI). Additionally, a subanalysis evaluating the SI before and after-CRT (∆SI pre-post) in complete responder patients (CR) and nonresponder patients (NR) on T2w, DWI, and ADC was performed. Results. Significant differences were observed for T2w and DWI on 3.0 T MRI compared to 1.5 T MRI pre-, during, and post-CRT (all P < 0.001 ), whereas no significant differences were reported for ADC among all controls (all P > 0.05 ). rSI showed no significant differences in all the examinations for all sequences (all P > 0.05 ). ∆SI showed significant differences between 3.0 T and 1.5 T MRI for DWI-∆SI in CR and NR ( 188.39 ± 166.90 vs. 30.45 ± 21.73 and 169.70 ± 121.87 vs. 22.00 ± 31.29 , respectively, all P 0.02) and ADC-∆SI for CR ( − 0.58 ± 0.27 vs. − 0.21 ± 0.24 P value 0.02), while no significant differences were observed for ADC-∆SI in NR and both CR and NR for T2w-∆SI. Conclusion. T2w-SI and DWI-SI showed significant differences for 3.0 T compared to 1.5 T in all three controls, while ADCSI showed no significant differences in all three controls on both field strengths. rSI was comparable for 3.0 T and 1.5 T MRI in rectal cancer patients; therefore, rectal cancer patients can be assessed both at 3.0 T MRI and 1.5 T MRI. However, a significant DWI-∆SI and ADC-∆SI on 3.0 T in CR might be interpreted as a better visual assessment in discriminating response to therapy compared to 1.5 T. Further investigations should be performed to confirm future possible clinical application.

Insulinoma is an insulin-producing pancreatic neuroendocrine tumor that can be malignant in about 10% of cases. Locoregional invasion, lymph node metastases, or remote metastases are the main criteria of malignant insulinoma. Its incidence in patients with pre-existing diabetes mellitus (DM) is exceptionally rare. In this report, we describe a 66-year-old man with long-standing type 2 DM who presented with recurrent episodes of diaphoresis due to severe hypoglycemia despite the withdrawal of insulin therapy, hypercalcitoninemia, and biochemical and radiological findings suggestive of metastatic malignant insulinoma. Unfortunately, after few days of diazoxide treatment, edema, hypotension, oliguria, and water retention were observed, patient’s clinical status deteriorated rapidly, and he died in our department from acute renal failure.

International audience; Cholangiocarcinoma (CCA) is a malignant tumour arising from the biliary system. In Europe, this tumour frequently presents as a sporadic cancer in patients without defined risk factors and is usually diagnosed at advanced stages with a consequent poor prognosis. Therefore, the identification of biomarkers represents an utmost need for patients with CCA. Numerous studies proposed a wide spectrum of biomarkers at tissue and molecular levels. With the present paper, a multidisciplinary group of experts within the European Network for the Study of Cholangiocarcinoma discusses the clinical role of tissue biomarkers and provides a selection based on their current relevance and potential applications in the framework of CCA. Recent advances are proposed by dividing biomarkers based on their potential role in diagnosis, prognosis and therapy response. Limitations of current biomarkers are also identified, together with specific promising areas (ie, artificial intelligence, patient-derived organoids, targeted therapy) where research should be focused to develop future biomarkers.

The aim of this study was to develop and validate a decision support model using data mining algorithms, based on morphologic features derived from MRI images, to discriminate between complete responders (CR) and non-complete responders (NCR) patients after neoadjuvant chemoradiotherapy (CRT), in a population of patients with locally advanced rectal cancer (LARC).Two populations were retrospectively enrolled: group A (65 patients) was used to train a data mining decision tree algorithm whereas group B (30 patients) was used to validate it. All patients underwent surgery; according to the histology evaluation, patients were divided in CR and NCR. Staging and restaging MRI examinations were retrospectively analysed and seven parameters were considered for data mining classification. Five different classification methods were tested and evaluated in terms of sensitivity, specificity, accuracy and AUC in order to identify the classification model able to achieve the best performance. The best classification algorithm was subsequently applied to group B for validation: sensitivity, specificity, positive and negative predictive value, accuracy and ROC curve were calculated. Inter and intra-reader agreement were calculated.Four features were selected for the development of the classification algorithm: MRI tumor regression grade (MR-TRG), staging volume (SV), tumor volume reduction rate (TVRR) and signal intensity reduction rate (SIRR). The decision tree J48 showed the highest efficiency: when applied to group B, all the CR and 18/21 NCR were correctly classified (sensitivity 85.71%, specificity 100%, PPV 100%, NPV 94.2%, accuracy 95.7%, AUC 0.833). Both inter- and intra-reader evaluation showed good agreement (κ 0.6).The proposed decision support model may help in distinguishing between CR and NCR patients with LARC after CRT.

Background Structured reporting (SR) in radiology is becoming increasingly necessary and has been recognized recently by major scientific societies. This study aims to build structured CT-based reports in colon cancer during the staging phase in order to improve communication between the radiologist, members of multidisciplinary teams and patients. Materials and methods A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A modified Delphi process was used to develop the SR and to assess a level of agreement for all report sections. Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation. Results The final SR version was built by including n = 18 items in the “Patient Clinical Data” section, n = 7 items in the “Clinical Evaluation” section, n = 9 items in the “Imaging Protocol” section and n = 29 items in the “Report” section. Overall, 63 items were included in the final version of the SR. Both in the first and second round, all sections received a higher than good rating: a mean value of 4.6 and range 3.6–4.9 in the first round; a mean value of 5.0 and range 4.9–5 in the second round. In the first round, Cronbach’s alpha (Cα) correlation coefficient was a questionable 0.61. In the first round, the overall mean score of the experts and the sum of scores for the structured report were 4.6 (range 1–5) and 1111 (mean value 74.07, STD 4.85), respectively. In the second round, Cronbach’s alpha (Cα) correlation coefficient was an acceptable 0.70. In the second round, the overall mean score of the experts and the sum of score for structured report were 4.9 (range 4–5) and 1108 (mean value 79.14, STD 1.83), respectively. The overall mean score obtained by the experts in the second round was higher than the overall mean score of the first round, with a lower standard deviation value to underline greater agreement among the experts for the structured report reached in this round. Conclusions A wide implementation of SR is of critical importance in order to offer referring physicians and patients optimum quality of service and to provide researchers with the best quality data in the context of big data exploitation of available clinical data. Implementation is a complex procedure, requiring mature technology to successfully address the multiple challenges of user-friendliness, organization and interoperability.

Blockchain usage in healthcare, in radiology, in particular, is at its very early infancy. Only a few research applications have been tested, however, blockchain technology is widely known outside healthcare and widely adopted, especially in Finance, since 2009 at least. Learning by history, radiology is a potential ideal scenario to apply this technology. Blockchain could have the potential to increase radiological data value in both clinical and research settings for the patient digital record, radiological reports, privacy control, quantitative image analysis, cybersecurity, radiomics and artificial intelligence.Up-to-date experiences using blockchain in radiology are still limited, but radiologists should be aware of the emergence of this technology and follow its next developments. We present here the potentials of some applications of blockchain in radiology.

Imaging is fundamental in the management of colorectal cancer (CRC). Contrast-enhanced computed tomography (CECT) and contrast-enhanced magnetic resonance (CEMR) are the imaging modalities recommended to stage and restage CRC. The aim of this review is to summarize the most relevant studies published on the role of contrast-enhanced imaging for the management of CRC. There are published data demonstrating the accuracy of CECT and CEMR for local staging of both colon and rectal cancer, as well as for the evaluation of distant metastases. Moreover, different aspects of these methods, in particular perfusion techniques, are emerging as prognostic biomarkers. The optimization of contrast media injection protocols is essential to ensure reproducibility and high image quality. Contrast-enhanced imaging is mandatory in the management of CRC both for the evaluation of the primary tumor and of distant metastases.

To compare the effect of contrast medium iodine concentration on contrast enhancement, heart rate, and injection pressure when injected at a constant iodine delivery rate in coronary CT angiography (CTA). One thousand twenty-four patients scheduled for coronary CTA were prospectively randomized to receive one of four contrast media: iopromide 300 mg I/ml, iohexol 350 mg I/ml, iopromide 370 mg I/ml, or iomeprol 400 mg I/ml. Contrast media were delivered at an equivalent iodine delivery rate of 2.0 g I/s. Intracoronary attenuation was measured and compared (per vessel and per segment). Heart rate before and after contrast media injection was documented. Injection pressure was recorded (n = 403) during contrast medium injection and compared between groups. Intracoronary attenuation values were similar for the different contrast groups. The mean attenuation over all segments ranged between 384 HU for 350 mg I/ml and 395 HU for 400 mg I/ml (p = 0.079). Dose-length product (p = 0.8424), signal-to-noise ratio (all p > 0.05), time to peak (p = 0.324), and changes in heart rate (p = 0.974) were comparable between groups. The peak pressures differed: 197.4 psi for 300 mg I/ml (viscosity 4.6 mPa s), 229.8 psi for 350 mg I/ml (10.4 mPa s), 216.1 psi for 370 mg I/ml (9.5 mPa s), and 243.7 psi for 400 mg I/ml (12.6 mPa s) (p

The purpose of this study was to assess the impact of adaptive statistical iterative reconstruction (ASiR) on half-dose coronary artery calcium scoring (CACS) acquisition protocol.Between September 2016 and October 2017, 89 patients (54 male patients, mean age 64.6±10.7 y) with a clinically indicated coronary computed tomography angiography were prospectively enrolled. On a 64-row computed tomography scanner, patients underwent a standard CACS protocol (120 kVp, 170 mAs) reconstructed by filtered-back projection, and a half-dose CACS protocol (120 kVp, 85 mAs) reconstructed by ASiR at different percentages, from 10% to 100%, in 10% increments. CACS determinants (Agatston score, number of plaques, volume, and mass), signal-to-noise ratio, contrast-to-noise ratio, and radiation dose of both protocols were calculated. Patient risk categories based on CACS were determined for each protocol, and analysis of risk reclassification of half-dose protocol was performed. Depending on their body mass index (BMI), patients were divided into nonobese (BMI30 kg/m) and obese (BMI≥30 kg/m) groups to investigate the influence of BMI on CACS determinants and risk reclassification.Half-dose protocol reconstructed with ASiR 70% showed no significant differences in any CACS determinant compared with the standard protocol for both nonobese and obese patients (all P≥0.070 and ≥0.066, respectively) and reclassified 1 (1.7%) and 6 (20.0%) patients, respectively, with excellent (κ=0.91) and good (κ=0.74) agreement with standard protocol, respectively. ASiR 70% also resulted in a higher signal-to-noise ratio (1.88±0.78) and contrast-to-noise ratio (7.10±2.73) compared with standard protocol (all P≤0.001). Half-dose protocol provided 52% less radiation dose than standard acquisition (0.31±0.06 vs. 0.64±0.10 mSv; P0.001).ASIR 70% coupled with reduction of tube current by 50% allowed for significant dose reduction and no detrimental effects on image quality, with minimal patient reclassification in nonobese patients. In obese patients, excessive noise may lead to a clinically significant reclassification rate.

The fracture avulsion of the greater tuberosity (GT) represents 2% of all humerus fractures, but the true incidence is likely to be higher, being challenging the initial diagnosis on radiograph. The fracture avulsion of the GT could have different treatments: nondisplaced or minimally displaced fractures are treated conservatively, whereas for displaced or comminuted fractures surgical treatment is preferred. The most important finding of this study is the employment of an all-arthroscopic transosseous augmented technique for the treatment of a displaced humeral GT fracture avulsion. This technique shows all the advantages of the transosseous fixation and arthroscopic approach., Technique Video Video 1 The video shows a novel transosseous arthroscopic technique for the treatment of fracture avulsion of the greater tuberosity.

Introduction A more structured role of radiographers is advisable to speed up the management of patients with suspected COVID-19. The purpose of our study was to evaluate the diagnostic performance of radiographers in the detection of COVID-19 pneumonia on chest CT using CO-RADS descriptors. Methods CT images of patients who underwent RT-PCR and chest CT due to COVID-19 suspicion between March and July 2020 were analysed retrospectively. Six readers, including two radiologists, two highly experienced radiographers and two less experienced radiographers, independently scored each CT using the CO-RADS lexicon. ROC curves were used to investigate diagnostic accuracy, and Fleiss’κ statistics to evaluate inter-rater agreement. Results 714 patients (419 men; 295 women; mean age: 64 years ±19SD) were evaluated. CO-RADS> 3 was identified as optimal diagnostic threshold. Highly experienced radiographers achieved an average sensitivity of 58.7% (95%CI: 52.5–64.7), an average specificity of 81.8% (95%CI: 77.9–85.2), and a mean AUC of 0.72 (95%CI: 0.68–0.75). Among less experienced radiographers, an average sensitivity of 56.3% (95%CI: 50.1–62.2) and an average specificity of 81.5% (95%CI: 77.6–84.9) were observed, with a mean AUC of 0.71 (95%CI: 0.68–0.74). Consultant radiologists achieved an average sensitivity of 60.0% (95%CI: 53.7–65.8), an average specificity of 81.7% (95%CI: 77.8–85.1), and a mean AUC of 0.73 (95%CI: 0.70–0.77). Conclusion Radiographers can adequately recognise the classic appearances of COVID-19 on CT, as described by the CO-RADS assessment scheme, in a way comparable to expert radiologists. Implications for practice Radiographers, as the first healthcare professionals to evaluate CT images in patients with suspected SARS-CoV-2 infection, could diagnose COVID-19 pneumonia by means of a categorical reporting scheme at CT in a reliable way, hence playing a primary role in the early management of these patients.

Background: Structured reporting (SR) in radiology has been recognized recently by major scientific societies. This study aims to build structured computed tomography (CT) and magnetic resonance (MR)-based reports in pancreatic adenocarcinoma during the staging phase in order to improve communication between the radiologist and members of multidisciplinary teams. Materials and Methods: A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A modified Delphi process was used to develop the CT-SR and MRI-SR, assessing a level of agreement for all report sections. Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation. Results: The final CT-SR version was built by including n = 16 items in the “Patient Clinical Data” section, n = 11 items in the “Clinical Evaluation” section, n = 7 items in the “Imaging Protocol” section, and n = 18 items in the “Report” section. Overall, 52 items were included in the final version of the CT-SR. The final MRI-SR version was built by including n = 16 items in the “Patient Clinical Data” section, n = 11 items in the “Clinical Evaluation” section, n = 8 items in the “Imaging Protocol” section, and n = 14 items in the “Report” section. Overall, 49 items were included in the final version of the MRI-SR. In the first round for CT-SR, all sections received more than a good rating. The overall mean score of the experts was 4.85. The Cα correlation coefficient was 0.85. In the second round, the overall mean score of the experts was 4.87, and the Cα correlation coefficient was 0.94. In the first round, for MRI-SR, all sections received more than a good rating. The overall mean score of the experts was 4.73. The Cα correlation coefficient was 0.82. In the second round, the overall mean score of the experts was 4.91, and the Cα correlation coefficient was 0.93. Conclusions: The CT-SR and MRI-SR are based on a multi-round consensus-building Delphi exercise derived from the multidisciplinary agreement of expert radiologists in order to obtain more appropriate communication tools for referring physicians.

Cholangiocarcinoma (CCA) is the second most common liver malignancy and the incidence and mortality rates of this disease are worldwide increasing. This paper deals with the problem of Intrahepatic Cholangiocarcinoma (IH-CCA) classification using Computed Tomography (CT) images. Precisely, a radiomics-based approach is proposed by exploiting abdominal volumetric CT data in order to differentiate large bile duct from small bile duct IH-CCA. The developed method relies on the investigation of intrinsic discriminative properties of CT scans according to feature selection methods. The effectiveness of the proposed method is proved by enrolling in the study a total of 26 patients, including 16 patients with large bile duct and 10 with small bile duct pathological disease, respectively. The conducted tests have shown that our approach is a baseline to provide an efficient classification process with a low computational cost in order to facilitate clinical decision-making procedures.

PURPOSE: To assess the potential role of computed tomography (CT) texture analysis (CTTA) in identifying vulnerable patients with carotid artery atherosclerosis. METHODS: In this case-control pilot study, 12 patients with carotid atherosclerosis and a subsequent history of transient ischemic attack or stroke were age and sex matched with 12 control cases with asymptomatic carotid atherosclerosis (follow-up time 103.58 ± 9.2 months). CTTA was performed using a commercially available research software package (TexRAD) by an operator blinded to clinical data. CTTA comprised a filtration-histogram technique to extract features at different scales corresponding to spatial scale filter (fine = 2 mm, medium = 3 mm, coarse = 4 mm), followed by quantification using histogram-based statistical parameters: mean, kurtosis, skewness, entropy, standard deviation, and mean value of positive pixels. A single axial slice was selected to best represent the largest cross-section of the carotid bifurcation or the greatest degree of stenosis, in presence of an atherosclerotic plaque, on each side. RESULTS: CTTA revealed a statistically significant difference in skewness between symptomatic and asymptomatic patients at the medium (0.22 ± 0.35 vs - 0.18 ± 0.39, p < 0.001) and coarse (0.23 ± 0.22 vs 0.03 ± 0.29, p = 0.003) texture scales. At the fine-texture scale, skewness (0.20 ± 0.59 vs - 0.18 ± 0.58, p = 0.009) and standard deviation (366.11 ± 117.19 vs 300.37 ± 82.51, p = 0.03) were significant before correction. CONCLUSION: Our pilot study highlights the potential of CTTA to identify vulnerable patients in stroke and TIA. CT texture may have the potential to act as a novel risk stratification tool in patients with carotid atherosclerosis.

Background: Structured reporting (SR) in oncologic imaging is becoming necessary and has recently been recognized by major scientific societies. The aim of this study was to build MRI-based structured reports for rectal cancer (RC) staging and restaging in order to provide clinicians all critical tumor information. Materials and Methods: A panel of radiologist experts in abdominal imaging, called the members of the Italian Society of Medical and Interventional Radiology, was established. The modified Delphi process was used to build the SR and to assess the level of agreement in all sections. The Cronbach’s alpha (Cα) correlation coefficient was used to assess the internal consistency of each section and to measure the quality analysis according to the average inter-item correlation. The intraclass correlation coefficient (ICC) was also evaluated. Results: After the second Delphi round of the SR RC staging, the panelists’ single scores and sum of scores were 3.8 (range 2–4) and 169, and the SR RC restaging panelists’ single scores and sum of scores were 3.7 (range 2–4) and 148, respectively. The Cα correlation coefficient was 0.79 for SR staging and 0.81 for SR restaging. The ICCs for the SR RC staging and restaging were 0.78 (p &lt, 0.01) and 0.82 (p &lt, 0.01), respectively. The final SR version was built and included 53 items for RC staging and 50 items for RC restaging. Conclusions: The final version of the structured reports of MRI-based RC staging and restaging should be a helpful and promising tool for clinicians in managing cancer patients properly. Structured reports collect all Patient Clinical Data, Clinical Evaluations and relevant key findings of Rectal Cancer, both in staging and restaging, and can facilitate clinical decision-making.

Structured reporting (SR) in radiology is becoming increasingly necessary and has been recognized recently by major scientific societies. This study aims to build structured CT-based reports for lymphoma patients during the staging phase to improve communication between radiologists, members of multidisciplinary teams, and patients. A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology (SIRM), was established. A modified Delphi process was used to develop the SR and to assess a level of agreement for all report sections. The Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation. The final SR version was divided into four sections: (a) Patient Clinical Data, (b) Clinical Evaluation, (c) Imaging Protocol, and (d) Report, including n = 13 items in the “Patient Clinical Data” section, n = 8 items in the “Clinical Evaluation” section, n = 9 items in the “Imaging Protocol” section, and n = 32 items in the “Report” section. Overall, 62 items were included in the final version of the SR. A dedicated section of significant images was added as part of the report. In the first Delphi round, all sections received more than a good rating (≥3). The overall mean score of the experts and the sum of score for structured report were 4.4 (range 1–5) and 1524 (mean value of 101.6 and standard deviation of 11.8). The Cα correlation coefficient was 0.89 in the first round. In the second Delphi round, all sections received more than an excellent rating (≥4). The overall mean score of the experts and the sum of scores for structured report were 4.9 (range 3–5) and 1694 (mean value of 112.9 and standard deviation of 4.0). The Cα correlation coefficient was 0.87 in this round. The highest overall means value, highest sum of scores of the panelists, and smallest standard deviation values of the evaluations in this round reflect the increase of the internal consistency and agreement among experts in the second round compared to first round. The accurate statement of imaging data given to referring physicians is critical for patient care, the information contained affects both the decision-making process and the subsequent treatment. The radiology report is the most important source of clinical imaging information. It conveys critical information about the patient’s health and the radiologist’s interpretation of medical findings. It also communicates information to the referring physicians and records this information for future clinical and research use. The present SR was generated based on a multi-round consensus-building Delphi exercise and uses standardized terminology and structures, in order to adhere to diagnostic/therapeutic recommendations and facilitate enrolment in clinical trials, to reduce any ambiguity that may arise from non-conventional language, and to enable better communication between radiologists and clinicians.

Objective To conduct a multireader validation study to evaluate the interobserver variability and the diagnostic accuracy for the lung involvement by COVID-19 of COVID-19 Reporting and Data System (CO-RADS) score. Methods This retrospective study included consecutive symptomatic patients who underwent chest CT and reverse transcriptase-polymerase chain reaction (RT-PCR) from March 2020 to May 2020 for suspected COVID-19. Twelve readers with different levels of expertise independently scored each CT using the CO-RADS scheme for detecting pulmonary involvement by COVID-19. Receiver operating characteristic (ROC) curves were computed to investigate diagnostic yield. Fleiss’ kappa statistics was used to evaluate interreader agreement. Results A total of 572 patients (mean age, 63 ± 20 [standard deviation]; 329 men; 142 patients with COVID-19 and 430 patients without COVID-19) were evaluated. There was a moderate agreement for CO-RADS rating among all readers (Fleiss’ K = 0.43 [95% CI 0.42–0.44]) with a substantial agreement for CO-RADS 1 category (Fleiss’ K = 0.61 [95% CI 0.60–0.62]) and moderate agreement for CO-RADS 5 category (Fleiss’ K = 0.60 [95% CI 0.58–0.61]). ROC analysis showed the CO-RADS score ≥ 4 as the optimal threshold, with a cumulative area under the curve of 0.72 (95% CI 66–78%), sensitivity 61% (95% CI 52–69%), and specificity 81% (95% CI 77–84%). Conclusion CO-RADS showed high diagnostic accuracy and moderate interrater agreement across readers with different levels of expertise. Specificity is higher than previously thought and that could lead to reconsider the role of CT in this clinical setting. Key Points • COVID-19 Reporting and Data System (CO-RADS) demonstrated a good diagnostic accuracy for lung involvement by COVID-19 with an average AUC of 0.72 (95% CI 67–75%). • When a threshold of ≥ 4 was used, sensitivity and specificity were 61% (95% CI 52–69%) and 81% (95% CI 76–84%), respectively. • There was an overall moderate agreement for CO-RADS rating across readers with different levels of expertise (Fleiss’ K = 0.43 [95% CI 0.42–0.44]). Electronic supplementary material The online version of this article (10.1007/s00330-020-07273-y) contains supplementary material, which is available to authorized users.

The typical findings on CT in patients affected by novel COVID-19 (coronavirus disease 2019) pneumonia are characterized by ground-glass opacities and/or air space consolidation mainly bilateral and peripherical in distribution, including crazy paving pattern and reversed halo sign. We hereby describe a case of an adolescent male tested positive for COVID-19 with mild respiratory symptoms and presenting with pneumomediastinum as the only CT finding.

This study aims to evaluate the feasibility of a magnetic resonance (MR) automatic method for quantitative assessment of the percentage of fibrosis developed within locally advanced rectal cancers (LARC) after neoadjuvant radiochemotherapy (RCT). A total of 65 patients were enrolled in the study and MR studies were performed on 3.0 Tesla scanner; patients were followed-up for 30 months. The percentage of fibrosis was quantified on T2-weighted images, using automatic K-Means clustering algorithm. According to the percentage of fibrosis, an optimal cut-off point for separating patients into favorable and unfavorable pathologic response groups was identified by ROC analysis and tumor regression grade (MR-TRG) classes were determined and compared to histopathologic TRG. An optimal cut-off point of 81% of fibrosis was identified to differentiate between favorable and unfavorable pathologic response groups resulting in a sensitivity of 78.26% and a specificity of 97.62% for the identification of complete responders (CRs). Interobserver agreement was good (0.85). The agreement between P-TRG and MR-TRG was excellent (0.923). Significant differences in terms of overall survival (OS) and disease free survival (DFS) were found between favorable and unfavorable pathologic response groups. The automatic quantification of fibrosis determined by MR is feasible and reproducible.

To investigate whether diet restriction affects quality of colon cleansing and patient tolerance during reduced bowel preparation for CT colonography (CTC). Asymptomatic and symptomatic patients were enrolled in this pragmatic, single-centre, randomised trial. All patients were randomly assigned (1:1 ratio, blocks of ten) to receive a reduced bowel preparation and faecal tagging with (Diet-Restriction-Group [DR]) or without (No-Diet-Restriction-Group [NDR]) dietary restriction. Five readers performed a blinded subjective image analysis, by means of 4-point Likert-scales from 0 (highest score) to 3 (worst score). Endpoints were the quality of large bowel cleansing and tolerance to the assigned bowel preparation regimen. The trial is registered at ClinicalTrial.gov (URomLSDBAL1). Ninety-five patients were randomly allocated to treatments (48 in NDR-group, 47 in DR-group). Both groups resulted in optimal colon cleansing. The mean residual stool (0.22, 95%CI 0.00-0.44) and fluid burden (0.39, 95%CI 0.25-0.53) scores for patients in DR-group were similar to those in patients in NDR-group (0.25, 95%CI 0.03-0.47 [p = 0.82] and 0.49, 95%CI 0.30-0.67 [p = 0.38], respectively). Tolerance was significantly better in NDR-group. A reduced bowel preparation in association with faecal tagging and without any dietary restriction demonstrated optimal colon cleansing effectiveness for CTC, providing better patient compliance compared with dietary restriction. • Dietary restriction in reduced bowel preparation regimen can be avoided. • The quality of colon cleansing is not affected by dietary restriction. • The quality of faecal tagging is not affected by dietary restriction. • Avoidance of dietary restriction improves patients’ tolerance for CTC.

To prospectively compare two different approaches for estimating the amount of intravenous contrast media (CM) needed for multiphasic MDCT of the liver in obese patients. This single-center, HIPAA-compliant prospective study was approved by our Institutional Review Board. Ninety-six patients (55 men, 41 women), with a total of 42 hypovascular liver lesions, underwent MDCT of the liver. The amount of contrast medium injected was computed according to the patient’s lean body weight which was estimated using either a bioimpedance device (Group A) or the James formula (Group B). The following variables were compared between the two groups: the amount of contrast medium injected (in grams of Iodine, gI), the contrast enhancement index (CEI) and the lesion-to-liver contrast-to-noise ratio. Protocols A and B yielded significant differences in the amount of CM injected (mean values 41.9 ± 4.41 gI in Group A vs. 35.9 ± 5.75 gI in Group B; P = 0.021). The mean CEI value and lesion-to-liver contrast-to-noise ratio measured on the portal phase were significantly higher with protocol A than with protocol B (P

Objective: The aim of this study was to acknowledge errors in patients positioning in CT colonography (CTC) and their effect in radiation exposure. Materials and methods: CTC studies of a total of 199 patients coming from two different referral hospitals were retrospectively reviewed. Two parameters have been considered for the analysis: patient position in relation to gantry isocentre and scan length related to the area of interest. CTDI vol and DLP were extracted for each patient. In order to evaluate the estimated effective total dose and the dose to various organs, we used the CT-EXPO ® software version 2.2. This software provides estimates of effective dose and doses to the other various organs. Results: Average value of the patients’ position is found to be below the isocentre for 48 ± 25mm and 29 ± 27mm in the prone and supine position. It was observed that the increase in CTDI and DLP values for patients in Group 1, due to the inaccurate positioning, was estimated at about 30% and 20% for prone and supine position, respectively, while in Group 2, a decrease in CTDI and DLP values was estimated at about 16% and 18% for prone and supine position, respectively, due to an average position above isocentre. A dose increase ranging from 4 up to 13% was calculated with increasing the over-scanned region below anal orifice. Conclusion: Radiographers and radiologists need to be aware of dose variation and noise effects on vertical positioning and over-scanning. More accurate training need to be achieved even so when examination protocol varies from general practice.