The burden of radiation exposure in congenital heart disease: the Italian cohort profile and bioresource collection in HARMONIC project.

BACKGROUND: The European-funded Health Effects of Cardiac Fluoroscopy and Modern Radiotherapy in Pediatrics (HARMONIC) project aims to improve knowledge on the effects of medical exposure to ionizing radiation (IR) received during childhood. One of its objectives is to build a consolidated European cohort of pediatric patients who have undergone cardiac catheterization (Cath) procedures, with the goal of enhancing the assessment of long-term radiation-associated cancer risk. The purpose of our study is to provide a detailed description of the Italian cohort contributing to the HARMONIC project, including an analysis of cumulative IR exposure, reduction trend over the years and an overview of the prospective collection of biological samples for research in this vulnerable population. METHODS: In a single-center retrospective cohort study, a total of 584 patients (323 males) with a median age of 6 (2-13) years, referred at the Pediatric Cardiology in Niguarda Hospital from January 2015 to October 2023, were included. Biological specimens from a subset of 60 patients were prospectively collected for biobanking at baseline, immediately post-procedure and after 12 months. RESULTS: Two hundred fifty-nine (44%) patients were under 1 year old at their first procedure. The median KAP/weight was 0.09 Gy·cm2/kg (IQR: 0.03-0.20), and the median fluoroscopy time was 8.10 min (IQR: 4.00-16.25). KAP/weight ratio showed a positive correlation with the fluoroscopy time (Spearman's rho = 0.679, p < 0.001). Significant dose reduction was observed either after implementation of an upgraded technology system and a radiation training among staff. The Italian cohort includes 1858 different types of specimens for Harmonic biobank, including blood, plasma, serum, clot, cell pellet/lymphocytes, saliva. CONCLUSIONS: In the Italian Harmonic cohort, radiation dose in cardiac catheterization varies by age and procedure type. An institution's radiological protection strategy has contributed to a reduction in radiation dose over time. Biological samples provide a valuable resource for future research, offering an opportunity to identify potential early biomarkers for health surveillance and personalized risk assessment.

Quantitative Computed Tomography and Response to Pronation in COVID-19 ARDS.

BACKGROUND: The use of prone position (PP) has been widespread during the COVID-19 pandemic. Whereas it has demonstrated benefits, including improved oxygenation and lung aeration, the factors influencing the response in terms of gas exchange to PP remain unclear. In particular, the association between baseline quantitative computed tomography (CT) scan results and gas exchange response to PP in invasively ventilated subjects with COVID-19 ARDS is unknown. The present study aimed to compare baseline quantitative CT results between subjects responding to PP in terms of oxygenation or CO2 clearance and those who did not. METHODS: This was a single-center, retrospective observational study including critically ill, invasively ventilated subjects with COVID-19-related ARDS admitted to the ICUs of Niguarda Hospital between March 2020-November 2021. Blood gas samples were collected before and after PP. Subjects in whom the PaO2 /FIO2 increase was ≥ 20 mm Hg after PP were defined as oxygen responders. CO2 responders were defined when the ventilatory ratio (VR) decreased during PP. Automated quantitative CT analyses were performed to obtain tissue mass and density of the lungs. RESULTS: One hundred twenty-five subjects were enrolled, of which 116 (93%) were O2 responders and 51 (41%) CO2 responders. No difference in quantitative CT characteristics and oxygen were observed between responders and non-responders (tissue mass 1,532 ± 396 g vs 1,654 ± 304 g, P = .28; density -544 ± 109 HU vs -562 ± 58 HU P = .42). Similar findings were observed when dividing the population according to CO2 response (tissue mass 1,551 ± 412 g vs 1,534 ± 377 g, P = .89; density -545 ± 123 HU vs -546 ± 94 HU, P = .99). CONCLUSIONS: Most subjects with COVID-19-related ARDS improved their oxygenation at the first pronation cycle. The study suggests that baseline quantitative CT scan data were not associated with the response to PP in oxygenation or CO2 in mechanically ventilated subjects with COVID-19-related ARDS.

[ANMCO Position paper: Ionizing radiation exposure and radioprotection in the cath-lab]. / Position paper ANMCO: Radioesposizione e radioprotezione nei laboratori di cardiologia interventistica.

In the last decades, because of the improvements in the percutaneous treatment of coronary heart disease, valvular heart disease, congenital heart defects, and the increasing number of cardiac resynchronization therapy and cardioverter-defibrillator implantations, the interventional cardiologists' radio-exposure has importantly risen, causing concerns for ionizing radiation-associated diseases such as cancer and neurodegenerative disorders. Consequently, the radiation exposure issue importantly affects operators' safety. However, our knowledge of this field is poor and most operators are unaware to be at risk, especially because of the absence of effective preventive measures. The aim of this ANMCO position paper is to improve the awareness of operators and identify new ways of reducing operator ionizing radiation dose and minimizing the risk.

Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches.

BACKGROUND: To develop a pipeline for automatic extraction of quantitative metrics and radiomic features from lung computed tomography (CT) and develop artificial intelligence (AI) models supporting differential diagnosis between coronavirus disease 2019 (COVID-19) and other viral pneumonia (non-COVID-19). METHODS: Chest CT of 1,031 patients (811 for model building; 220 as independent validation set (IVS) with positive swab for severe acute respiratory syndrome coronavirus-2 (647 COVID-19) or other respiratory viruses (384 non-COVID-19) were segmented automatically. A Gaussian model, based on the HU histogram distribution describing well-aerated and ill portions, was optimised to calculate quantitative metrics (QM, n = 20) in both lungs (2L) and four geometrical subdivisions (GS) (upper front, lower front, upper dorsal, lower dorsal; n = 80). Radiomic features (RF) of first (RF1, n = 18) and second (RF2, n = 120) order were extracted from 2L using PyRadiomics tool. Extracted metrics were used to develop four multilayer-perceptron classifiers, built with different combinations of QM and RF: Model1 (RF1-2L); Model2 (QM-2L, QM-GS); Model3 (RF1-2L, RF2-2L); Model4 (RF1-2L, QM-2L, GS-2L, RF2-2L). RESULTS: The classifiers showed accuracy from 0.71 to 0.80 and area under the receiving operating characteristic curve (AUC) from 0.77 to 0.87 in differentiating COVID-19 versus non-COVID-19 pneumonia. Best results were associated with Model3 (AUC 0.867 ± 0.008) and Model4 (AUC 0.870 ± 0.011. For the IVS, the AUC values were 0.834 ± 0.008 for Model3 and 0.828 ± 0.011 for Model4. CONCLUSIONS: Four AI-based models for classifying patients as COVID-19 or non-COVID-19 viral pneumonia showed good diagnostic performances that could support clinical decisions.

Diagnostic Performance in Differentiating COVID-19 from Other Viral Pneumonias on CT Imaging: Multi-Reader Analysis Compared with an Artificial Intelligence-Based Model.

Growing evidence suggests that artificial intelligence tools could help radiologists in differentiating COVID-19 pneumonia from other types of viral (non-COVID-19) pneumonia. To test this hypothesis, an R-AI classifier capable of discriminating between COVID-19 and non-COVID-19 pneumonia was developed using CT chest scans of 1031 patients with positive swab for SARS-CoV-2 (n = 647) and other respiratory viruses (n = 384). The model was trained with 811 CT scans, while 220 CT scans (n = 151 COVID-19; n = 69 non-COVID-19) were used for independent validation. Four readers were enrolled to blindly evaluate the validation dataset using the CO-RADS score. A pandemic-like high suspicion scenario (CO-RADS 3 considered as COVID-19) and a low suspicion scenario (CO-RADS 3 considered as non-COVID-19) were simulated. Inter-reader agreement and performance metrics were calculated for human readers and R-AI classifier. The readers showed good agreement in assigning CO-RADS score (Gwet's AC2 = 0.71, p < 0.001). Considering human performance, accuracy = 78% and accuracy = 74% were obtained in the high and low suspicion scenarios, respectively, while the AI classifier achieved accuracy = 79% in distinguishing COVID-19 from non-COVID-19 pneumonia on the independent validation dataset. The R-AI classifier performance was equivalent or superior to human readers in all comparisons. Therefore, a R-AI classifier may support human readers in the difficult task of distinguishing COVID-19 from other types of viral pneumonia on CT imaging.

Validation of a dose tracking software for skin dose map calculation in interventional radiology.

PURPOSE: Validate the skin dose software within the radiation dose index monitoring system NEXO[DOSE]® (Bracco Injeneering S.A., Lausanne, Switzerland). It provides the skin dose distribution in interventional radiology (IR) procedures. METHODS: To determine the skin dose distribution and the Peak Skin Dose (PSD) in IR procedures, the software uses exposure and geometrical parameters taken from the radiation dose structured report and additional information specific to each angiographic system. To test the accuracy of the software, GafChromic® XR-RV3 films, wrapped under a cylindrical PMMA phantom, were irradiated with different setups. Calculations and films results are compared in terms of absolute dose and geometric accuracy, using two angiographic systems (Philips Integris Allura FD20, Siemens AXIOM-ArtisZeego). RESULTS: Calculated and film measured PSD values agree with an average difference of 7% ± 5%. The discrepancies in dose evaluation increase up to 33% in lower dose regions, because the algorithm does not consider the out-of-field scatter contribution of the neighboring fields, which is more significant in these areas. Regarding the geometric accuracy, the differences between the simulated dose spatial distributions and the measured ones are<3 mm (4%) in simple tests and 5 mm (5%) in setups closer to clinical practice. Moreover, similar results are obtained for the two studied angiographic system vendors. CONCLUSIONS: NEXO[DOSE]® provides an accurate skin dose distribution and PSD estimate. It will allow faster and more accurate monitoring of patient follow-up in the future.

Patient organ and effective dose estimation in CT: comparison of four software applications.

BACKGROUND: Radiation dose in computed tomography (CT) has become a topic of high interest due to the increasing numbers of CT examinations performed worldwide. Hence, dose tracking and organ dose calculation software are increasingly used. We evaluated the organ dose variability associated with the use of different software applications or calculation methods. METHODS: We tested four commercial software applications on CT protocols actually in use in our hospital: CT-Expo, NCICT, NCICTX, and Virtual Dose. We compared dose coefficients, estimated organ doses and effective doses obtained by the four software applications by varying exposure parameters. Our results were also compared with estimates reported by the software authors. RESULTS: All four software applications showed dependence on tube voltage and volume CT dose index, while only CT-Expo was also dependent on other exposure parameters, in particular scanner model and pitch caused a variability till 50%. We found a disagreement between our results and those reported by the software authors (up to 600%), mainly due to a different extent of examined body regions. The relative range of the comparison of the four software applications was within 35% for most organs inside the scan region, but increased over the 100% for organs partially irradiated and outside the scan region. For effective doses, this variability was less evident (ranging from 9 to 36%). CONCLUSIONS: The two main sources of organ dose variability were the software application used and the scan region set. Dose estimate must be related to the process used for its calculation.

Patient dose in angiographic interventional procedures: A multicentre study in Italy.

PURPOSE: The Council Directive 2013/59/EURATOM considers interventional radiology to be a special practice involving high doses of radiation and requiring strict monitoring to ensure the best quality assurance programs. This work reports the early experience of managing dose data from patients undergoing angiography in a multicentre study. MATERIALS AND METHODS: The study was based on a survey of about 15,200 sample procedures performed in 21 Italian hospitals centres involved on a voluntary basis. The survey concerned the collection of data related to different interventional radiology procedures: interventional cardiology, radiology, neuroradiology, vascular surgery, urology, endoscopy and pain therapy from a C-Arm and fixed units. The analysis included 11 types of procedures and for each procedure, air-kerma, kerma-area product and fluoroscopy time were collected. RESULTS: The duration and dose values of fluoroscopic exposure for each procedure is strongly dependent on individual clinical circumstances including the complexity of the procedure; the observed distribution of patient doses was very wide, even for a specified protocol. The median values of the parameters were compared with the diagnostic reference levels (DRL) proposed for some procedures in Italy (ISTISAN) or internationally. This work proposes local DRL values for three procedures. CONCLUSION: This first data collection serves to take stock of the situation on patient's dosimetry in several sectors and is the starting point for obtaining and updating DRL recalling that these levels are dependent on experience and technology available.

Analysis of a multicentre cloud-based CT dosimetric database: preliminary results.

BACKGROUND: To manage and analyse dosimetric data provided by computed tomography (CT) scanners from four Italian hospitals. METHODS: A radiation dose index monitoring (RDIM) software was used to collect anonymised exams stored in a cloud server. Since hospitals use different names for the same procedure, digital imaging and communications in medicine (DICOM) tags more appropriate to describe exams were selected and associated to study common names (SCNs) from a radiology playbook according to scan region and use of contrast media. Retrospective analysis was carried out to describe population and to evaluate dosimetric indexes and inaccuracies associated with SCNs. RESULTS: More than 400 procedures were clustered into 95 SCNs, but 78% of exams on adults were described with only 10 SCNs. Median values of dose-length product (DLP) and volumetric CT dose index (CTDIvol) for three analysed SCNs were in agreement with those previously published. The percentage of inaccuracies does not heavily affect the dosimetric analysis on the whole cloud, since variations in median values reached at most 8%. CONCLUSIONS: Implementation of a cloud-based RDIM software and related issues were described, showing the strength of the chosen playbook-based clustering and its usefulness for homogeneous data analysis. This approach may allow for optimisation actions, accurate assessment of the risk associated with radiation exposure, comparison of different facilities, and, last but not least, collection of information for the implementation of the 2013/59 Euratom Directive.

Endoscopic ultrasound-guided fine-needle aspiration for suspected malignancies adjacent to the gastrointestinal tract.

AIM: To investigate the impact of endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) in association with a multidisciplinary team evaluation for the detection of gastrointestinal malignancies. METHODS: A cohort of 1019 patients with suspected malignant lesions adjacent to the gastrointestinal tract received EUS-FNA after a standardized multidisciplinary team evaluation (MTE) and were divided into 4 groups according to their specific malignant risk score (MRS). Patients with a MRS of 0 (without detectable risk of malignancy) received only EUS without FNA. For patients with a MRS score ranging from 1 (low risk) - through 2 (intermediate risk) - to 3 (high risk), EUS-FNA cytology of the lesion was planned for a different time and was prioritized for those patients at higher risk for cancer. The accuracy, efficiency and quality assessment for the early detection of patients with potentially curable malignant lesions were evaluated for the whole cohort and in the different classes of MRSs. The time to definitive cytological diagnosis (TDCD), accuracy, sensitivity, specificity, positive and negative predictive values, and the rate of inconclusive tests were calculated for all patients and for each MRS group. RESULTS: A total of 1019 patients with suspected malignant lesions were evaluated by EUS-FNA. In 515 patients of 616 with true malignant lesions the tumor was diagnosed by EUS-FNA; 421 patients with resectable lesions received early surgical treatment, and 94 patients received chemo-radiotherapy. The overall diagnostic accuracy for the 1019 lesions in which a final diagnosis was obtained by EUS-FNA was 0.95. When patients were stratified by MTE into 4 classes of MRSs, a higher rate of patients in the group with higher cancer risk (MRS-3) received early treatment and EUS-FNA showed the highest level of accuracy (1.0). TDCD was also shorter in the MRS-3 group. The number of patients who received surgical treatment or chemo-radiotherapy was significantly higher in the MRS-3 patient group (36.3% in MRS-3, 10.7% in MRS-2, and 3.5% in MRS-1). CONCLUSION: EUS-FNA can effectively detect a curable malignant lesions at an earlier time and at a higher rate in patients with a higher cancer risk that were evaluated using MTE.