The Italian young medical physicist scenario: Results from the young AIFM group survey.

PURPOSE: The young working group of the Italian Association of Medical and Health Physics (AIFM) designed a survey to assess the current situation of the under 35 AIFM members. METHODS: An online survey including 65 questions was designed to gather personal information, educational issues, working and research experience, and to evaluate the AIFM activities. The survey was distributed to the under 35 members between November 2022 and February 2023, through the young AIFM mailing list and social media. RESULTS: 160 answers from 230 affiliates (70%, 31 years median age) were obtained. The results highlighted that 87% of the respondents had a fixed term/permanent employment, mainly in public hospitals (58%). Regarding Medical Physicists (MPs) training, 54% of the students left their region of origin due to the training plan (40%) and the availability of scholarships (25%) in the chosen university. Most of the respondents have no Radiation Protection Expert title, while the remaining 20%, 6%, and 3% are qualified to the first, second, and third level, respectively. Several young MPs (62.2%) were involved in research activities; however, only 28% had teaching experience, mainly within their workplace (20%, safety courses), during AIFM courses (4%), or university lectures (3%). CONCLUSIONS: This survey reported the current situation of the under 35 AIFM members, highlighting the "brain drain" phenomenon from the south to the north of Italy, mainly due to the lack of post-graduate schools, scholarships, and job opportunities. The obtained results will help the future working program of the AIFM.

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.

A single phantom, a single statistical method for low-contrast detectability assessment.

PURPOSE: The assessment of low-contrast-details is a part of the quality control (QC) program in digital radiology. It generally consists of evaluating the threshold contrast (Cth) detectability details for different-sized inserts, appropriately located in dedicated QC test tools. This work aims to propose a simplified method, based on a statistical model approach for threshold contrast estimation, suitable for different modalities in digital radiology. METHODS: A home-madelow-contrast phantom, made of a central aluminium insert with a step-wedge, was assembled and tested. The reliability and robustness of the method were investigated for Mammography, Digital Radiography, Fluoroscopy and Angiography. Imageswere analysed using our dedicated software developed on Matlab®. TheCth is expressed in the same unit (mmAl) for all studied modalities. RESULTS: This method allows the collection of Cthinformation from different modalities and equipment by different vendors, and it could be used to define typical values. Results are summarized in detail. For 0.5 diameter detail, Cthresults are in the range of: 0.018-0.023 mmAl for 2D mammography and 0.26-0.34 mmAl DR images. For angiographic images, for 2.5 mm diameter detail, the Cths median values are 0.55, 0.4, 0.06, 0.12 mmAl for low dose fluoroscopy, coronary fluorography, cerebral and abdominal DSA, respectively. CONCLUSIONS: The statistical method proposed in this study gives a simple approach for Low-Contrast-Details assessment, and the typical values proposed can be implemented in a QA program for digital radiology modalities.

MRI only in a patient with prostate cancer with bilateral metal hip prostheses: case study.

OBJECTIVE: To outline a practical method of performing prostate cancer radiotherapy in patients with bilateral metal hip prostheses with the standard resources available in a modern general hospital. The proposed workflow is based exclusively on magnetic resonance imaging (MRI) to avoid computed tomography (CT) artifacts. CASE DESCRIPTION: This study concerns a 73-year-old man with bilateral hip prostheses with an elevated risk prostate cancer. Magnetic resonance images with assigned electron densities were used for planning purposes, generating a synthetic CT (sCT). Imaging acquisition was performed with an optimized Dixon sequence on a 1.5T MRI scanner. The images were contoured by autosegmentation software, based on an MRI database of 20 patients. The sCT was generated assigning averaged electron densities to each contour. Two volumetric modulated arc therapy plans, a complete arc and a partial one, where the beam entrances through the prostheses were avoided for about 50° on both sides, were compared. The feasibility of matching daily cone beam CT (CBCT) with MRI reference images was also tested by visual evaluations of different radiation oncologists. CONCLUSIONS: The use of magnetic resonance images improved accuracy in targets and organs at risk (OARs) contouring. The complete arc plan was chosen because of 10% lower mean and maximum doses to prostheses with the same planning target volume coverage and OAR sparing. The image quality of the match between performed CBCTs and MRI was considered acceptable. The proposed method seems promising to improve radiotherapy treatments for this complex category of patients.

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.