Pre-clinic study of radiopharmaceutical for Covid-19 inactivation: Dose distribution with Monte Carlo Simulation.

Monte Carlo simulation method and Nuclear Medicine MIRD method were used to evaluate the effect of radiopharmaceuticals on Covid-19 disease. The mean absorbed organ dose in the target organ and gamma radiation emitter attenuation properties such as linear attenuation coefficients, energy absorption build-up factors (EABF), exposure build-up factors (EBF), and relative dose distributions (RDD) were examined. The results showed that radiopharmaceuticals containing gamma radiation emitters which are densely ionizing charged particles induced membrane damage and produced protein damage.

Mapping of UV-C dose and SARS-CoV-2 viral inactivation across N95 respirators during decontamination.

During public health crises like the COVID-19 pandemic, ultraviolet-C (UV-C) decontamination of N95 respirators for emergency reuse has been implemented to mitigate shortages. Pathogen photoinactivation efficacy depends critically on UV-C dose, which is distance- and angle-dependent and thus varies substantially across N95 surfaces within a decontamination system. Due to nonuniform and system-dependent UV-C dose distributions, characterizing UV-C dose and resulting pathogen inactivation with sufficient spatial resolution on-N95 is key to designing and validating UV-C decontamination protocols. However, robust quantification of UV-C dose across N95 facepieces presents challenges, as few UV-C measurement tools have sufficient (1) small, flexible form factor, and (2) angular response. To address this gap, we combine optical modeling and quantitative photochromic indicator (PCI) dosimetry with viral inactivation assays to generate high-resolution maps of "on-N95" UV-C dose and concomitant SARS-CoV-2 viral inactivation across N95 facepieces within a commercial decontamination chamber. Using modeling to rapidly identify on-N95 locations of interest, in-situ measurements report a 17.4 ± 5.0-fold dose difference across N95 facepieces in the chamber, yielding 2.9 ± 0.2-log variation in SARS-CoV-2 inactivation. UV-C dose at several on-N95 locations was lower than the lowest-dose locations on the chamber floor, highlighting the importance of on-N95 dose validation. Overall, we integrate optical simulation with in-situ PCI dosimetry to relate UV-C dose and viral inactivation at specific on-N95 locations, establishing a versatile approach to characterize UV-C photoinactivation of pathogens contaminating complex substrates such as N95s.

Radiomics-based machine learning differentiates "ground-glass" opacities due to COVID-19 from acute non-COVID-19 lung disease.

Ground-glass opacities (GGOs) are a non-specific high-resolution computed tomography (HRCT) finding tipically observed in early Coronavirus disesase 19 (COVID-19) pneumonia. However, GGOs are also seen in other acute lung diseases, thus making challenging the differential diagnosis. To this aim, we investigated the performance of a radiomics-based machine learning method to discriminate GGOs due to COVID-19 from those due to other acute lung diseases. Two sets of patients were included: a first set of 28 patients (COVID) diagnosed with COVID-19 infection confirmed by real-time polymerase chain reaction (RT-PCR) between March and April 2020 having (a) baseline HRCT at hospital admission and (b) predominant GGOs pattern on HRCT; a second set of 30 patients (nCOVID) showing (a) predominant GGOs pattern on HRCT performed between August 2019 and April 2020 and (b) availability of final diagnosis. Two readers independently segmented GGOs on HRCTs using a semi-automated approach, and radiomics features were extracted using a standard open source software (PyRadiomics). Partial least square (PLS) regression was used as the multivariate machine-learning algorithm. A leave-one-out nested cross-validation was implemented. PLS ß-weights of radiomics features, including the 5% features with the largest ß-weights in magnitude (top 5%), were obtained. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. The Youden's test assessed sensitivity and specificity of the classification. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The predictive model delivered an AUC of 0.868 (Youden's index = 0.68, sensitivity = 93%, specificity 75%, p = 4.2 × 10-7). Of the seven features included in the top 5% features, five were texture-related. A radiomics-based machine learning signature showed the potential to accurately differentiate GGOs due to COVID-19 pneumonia from those due to other acute lung diseases. Most of the discriminant radiomics features were texture-related. This approach may assist clinician to adopt the appropriate management early, while improving the triage of patients.

Quantitative UV-C dose validation with photochromic indicators for informed N95 emergency decontamination.

With COVID-19 N95 shortages, frontline medical personnel are forced to reuse this disposable-but sophisticated-multilayer respirator. Widely used to decontaminate nonporous surfaces, UV-C light has demonstrated germicidal efficacy on porous, non-planar N95 respirators when all surfaces receive ≥1.0 J/cm2 dose. Of utmost importance across disciplines, translation of empirical evidence to implementation relies upon UV-C measurements frequently confounded by radiometer complexities. To enable rigorous on-respirator measurements, we introduce a photochromic indicator dose quantification technique for: (1) UV-C treatment design and (2) in-process UV-C dose validation. While addressing outstanding indicator limitations of qualitative readout and insufficient dynamic range, our methodology establishes that color-changing dosimetry can achieve the necessary accuracy (>90%), uncertainty (<10%), and UV-C specificity (>95%) required for UV-C dose measurements. In a measurement infeasible with radiometers, we observe a striking ~20× dose variation over N95s within one decontamination system. Furthermore, we adapt consumer electronics for accessible quantitative readout and use optical attenuators to extend indicator dynamic range >10× to quantify doses relevant for N95 decontamination. By transforming photochromic indicators into quantitative dosimeters, we illuminate critical considerations for both photochromic indicators themselves and UV-C decontamination processes.

Dosimetric and radiation cancer risk evaluation of high resolution thorax CT during COVID-19 outbreak.

PURPOSE: The aim of this work was to evaluate the dosimetric impact of high-resolution thorax CT during COVID-19 outbreak in the University Hospital of Parma. In two months we have performed a huge number of thorax CT scans collecting effective and equivalent organ doses and evaluating also the lifetime attributable risk (LAR) of lung and other major cancers. MATERIALS AND METHOD: From February 24th to April 28th, 3224 high-resolution thorax CT were acquired. For all patients we have examined the volumetric computed tomography dose index (CTDIvol), the dose length product (DLP), the size-specific dose estimate (SSDE) and effective dose (E103) using a dose tracking software (Radimetrics Bayer HealthCare). From the equivalent dose to organs for each patient, LAR for lung and major cancers were estimated following the method proposed in BEIR VII which considers age and sex differences. RESULTS: Study population included 3224 patients, 1843 male and 1381 female, with an average age of 67 years. The average CTDIvol, SSDE and DLP, and E103 were 6.8 mGy, 8.7 mGy, 239 mGy·cm and 4.4 mSv respectively. The average LAR of all solid cancers was 2.1 cases per 10,000 patients, while the average LAR of leukemia was 0.2 cases per 10,000 patients. For both male and female the organ with a major cancer risk was lung. CONCLUSIONS: Despite the impressive increment in thoracic CT examinations due to COVID-19 outbreak, the high resolution low dose protocol used in our hospital guaranteed low doses and very low risk estimation in terms of LAR.

SmartPDT®: Smartphone enabled real-time dosimetry via satellite observation for daylight photodynamic therapy.

BACKGROUND: Actinic keratosis (AK) affects one quarter of over 60  year olds in Europe with the risk of transforming into invasive squamous cell carcinoma. Daylight photodynamic therapy (dPDT) is an effective and patient preferred treatment that uses sunlight to clear AK. Currently, there is no standardised method for measuring the light received during treatment. METHODS: SmartPDT® is a smartphone-based application and web-portal, developed by siHealth Ltd, enabling remote delivery of dPDT. It uses satellite imagery and computational algorithms to provide real-time determination of exposure to PpIX-effective solar radiation ("light dose"). The application also provides forecast of expected radiant exposures for 24- and 48-hs prior to the treatment period. Validation of the real-time and forecasted radiant exposure algorithms was performed against direct ground-based measurement under all weather conditions in Chilton, UK. RESULTS: Agreement between direct ground measurements and satellite-determined radiant exposure for 2-h treatment was excellent at -0.1 % ± 5.1 % (mean ±â€¯standard deviation). There was also excellent agreement between weather forecasted radiant exposure and ground measurement, 1.8 % ± 17.7 % at 24-hs and 1.6 % ± 25.2 % at 48-hs. Relative Root Mean Square of the Error (RMSEr) demonstrated that agreement improved as time to treatment reduced (RMSEr = 22.5 % (48 -hs), 11.2 % (24-hs), 5.2 % (real-time)). CONCLUSION: Agreement between satellite-determined, weather-forecasted and ground-measured radiant exposure was better than any existing published literature for dPDT. The SmartPDT® application and web-portal has excellent potential to assist with remote delivery of dPDT, an important factor in reducing risk in an elderly patient population during the Covid-19 pandemic.

Development and execution of a pandemic preparedness plan: Therapeutic medical physics and radiation dosimetry during the COVID-19 crisis.

The SARS-CoV-2 coronavirus pandemic has spread around the world including the United States. New York State has been hardest hit by the virus with over 380 000 citizens with confirmed COVID-19, the illness associated with the SARS-CoV-2 virus. At our institution, the medical physics and dosimetry group developed a pandemic preparedness plan to ensure continued operation of our service. Actions taken included launching remote access to clinical systems for all dosimetrists and physicists, establishing lines of communication among staff members, and altering coverage schedules to limit on-site presence and decrease risk of infection. The preparedness plan was activated March 23, 2020, and data were collected on treatment planning and chart checking efficiency for 6 weeks. External beam patient load decreased by 25% during the COVID-19 crisis, and special procedures were almost entirely eliminated excepting urgent stereotactic radiosurgery or brachytherapy. Efficiency of treatment planning and chart checking was slightly better than a comparable 6-week interval in 2019. This is most likely due to decreased patient load: Fewer plans to generate and more physicists available for checking without special procedure coverage. Physicists and dosimetrists completed a survey about their experience during the crisis and responded positively about the preparedness plan and their altered work arrangements, though technical problems and connectivity issues made the transition to remote work difficult. Overall, the medical physics and dosimetry group successfully maintained high-quality, efficient care while minimizing risk to the staff by minimizing on-site presence. Currently, the number of COVID-19 cases in our area is decreasing, but the preparedness plan has demonstrated efficacy, and we will be ready to activate the plan should COVID-19 return or an unknown virus manifest in the future.