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BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has caused extreme challenges for the healthcare system. Medical masks have been proven to effectively block disease transmission. Radiotherapeutic departments are at unique risk for disease exposure with the repeated daily treatment schedule. A protocol of mask wearing during daily treatment was established, and the effect of wearing medical masks on dosimetry during proton beam therapy (PBT) was validated.MethodsA department protocol of medical mask wearing was initiated after the COVID-19 pandemic. Medical masks that were made under standardized specification and regulation were obtained for analyses. The physical and dosimetric characteristics of these medical masks were measured by different proton energies using commercialized measurement tools.ResultsPatients and staff were able to adopt the protocol on a weekly basis, and no adverse events were reported. The average physical thickness of a single piece of medical mask was 0.5 mm with a water equivalent thickness (WET) of 0.1 mm.ConclusionOur study revealed that mask wearing for patients undergoing daily radiotherapy is feasible and can provide basic protection for patients and staff. The impact of mask wearing on dosimetry was only 0.1 mm in WET, which has no impact on clinical PBT treatment. A medical mask-wearing policy can be applied safely without dosimetric concerns and should be considered as a standard practice for PBT centers during the COVID-19 pandemic.

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has caused extreme challenges for the healthcare system. Medical masks have been proven to effectively block disease transmission. Radiotherapeutic departments are at unique risk for disease exposure with the repeated daily treatment schedule. A protocol of mask wearing during daily treatment was established, and the effect of wearing medical masks on dosimetry during proton beam therapy (PBT) was validated.MethodsA department protocol of medical mask wearing was initiated after the COVID-19 pandemic. Medical masks that were made under standardized specification and regulation were obtained for analyses. The physical and dosimetric characteristics of these medical masks were measured by different proton energies using commercialized measurement tools.ResultsPatients and staff were able to adopt the protocol on a weekly basis, and no adverse events were reported. The average physical thickness of a single piece of medical mask was 0.5 mm with a water equivalent thickness (WET) of 0.1 mm.ConclusionOur study revealed that mask wearing for patients undergoing daily radiotherapy is feasible and can provide basic protection for patients and staff. The impact of mask wearing on dosimetry was only 0.1 mm in WET, which has no impact on clinical PBT treatment. A medical mask-wearing policy can be applied safely without dosimetric concerns and should be considered as a standard practice for PBT centers during the COVID-19 pandemic.

Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications. Consensus guidelines recommend a set of more than 40 OARs in the head and neck (H&N) region, however, due to the predictable prohibitive labor-cost of this task, most institutions choose a substantially simplified protocol by delineating a smaller subset of OARs and neglecting the dose distributions associated with other OARs. In this work we propose a novel, automated and highly effective stratified OAR segmentation (SOARS) system using deep learning to precisely delineate a comprehensive set of 42 H&N OARs. SOARS stratifies 42 OARs into anchor, mid-level, and small & hard subcategories, with specifically derived neural network architectures for each category by neural architecture search (NAS) principles. We built SOARS models using 176 training patients in an internal institution and independently evaluated on 1327 external patients across six different institutions. It consistently outperformed other state-of-the-art methods by at least 3-5% in Dice score for each institutional evaluation (up to 36% relative error reduction in other metrics). More importantly, extensive multi-user studies evidently demonstrated that 98% of the SOARS predictions need only very minor or no revisions for direct clinical acceptance (saving 90% radiation oncologists workload), and their segmentation and dosimetric accuracy are within or smaller than the inter-user variation. These findings confirmed the strong clinical applicability of SOARS for the OAR delineation process in H&N cancer radiotherapy workflows, with improved efficiency, comprehensiveness, and quality.

Yu-Ming Wang,1– 4,* Yang-Wei Hsieh,1,2,* Bing-Shen Huang,1,2,5 Kuo-Chiang Sung,1,2 Kuo-Jung Juan,1,2 Steve P Lee,6 Shen-Hao Lee,1,2,5 Eng-Yen Huang1,2 1Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan; 2Proton and Radiation Therapy Center, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan; 3School of Traditional Chinese, Chang Gung University, Taoyuan, Taiwan; 4Institute of Physiology, National Yang-Ming University, Taipei, Taiwan; 5Department of Radiation Oncology, Linkou Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Taoyuan, Taiwan; 6Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA*These authors contributed equally to this workCorrespondence: Shen-Hao Lee Email r12233@cgmh.org.twEng-Yen HuangDepartment of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, TaiwanEmail hey1200@cgmh.org.twBackground: The coronavirus disease 2019 (COVID-19) pandemic has caused extreme challenges for the healthcare system. Medical masks have been proven to effectively block disease transmission. Radiotherapeutic departments are at unique risk for disease exposure with the repeated daily treatment schedule. A protocol of mask wearing during daily treatment was established, and the effect of wearing medical masks on dosimetry during proton beam therapy (PBT) was validated.Methods: A department protocol of medical mask wearing was initiated after the COVID-19 pandemic. Medical masks that were made under standardized specification and regulation were obtained for analyses. The physical and dosimetric characteristics of these medical masks were measured by different proton energies using commercialized measurement tools.Results: Patients and staff were able to adopt the protocol on a weekly basis, and no adverse events were reported. The average physical thickness of a sin

The identification of early distant metastases (DM) in patients with newly diagnosed, previously untreated nasopharyngeal carcinoma (NPC) plays an important role in selecting the most appropriate treatment approach. Here, we sought to investigate the predictive value of distinct MRI parameters for the detection of early DM.Between November 2010 and June 2011, a total of 51 newly diagnosed NPC patients were included. All of the study participants were followed until December 2014 at a single institution after completion of therapy. DM was defined as early when they were detected on pretreatment FDG-PET scans or within 6 months after initial diagnosis. The following parameters were tested for their ability to predict early DM: pretreatment FDG-PET standardized uptake value (SUV), MRI-derived AJCC tumor staging, tumor volume, and dynamic contrast-enhanced (DCE) values. The DCE-derived ve was defined as the volume fraction of the extravascular, extracellular space.Compared with patients without early DM, patients with early DM had higher SUV, tumor volume, DCE mean (median) ve, ve skewness, ve kurtosis, and the largest mean ve selected among sequential slices (P < 0.05). No differences were identified when early DM were defined only according to the results of pretreatment FDG-PET. Among different quantitative DCE parameters, the mean ve had the highest area under curve (AUC, 0.765). However, the AUCs of SUV, tumor volume, mean ve, ve skewness, ve kurtosis, or the largest mean ve selected among the sequential slices did not differ significantly from one another (P = 0.82).Taken together, our results suggest that DCE-derived ve may be a useful parameter in combination with SUV and tumor volume for predicting early DM. Dynamic contrast-enhanced MRI may be complementary to FDG-PET for selecting the most appropriate treatment approach in NPC patients.

The identification of early distant metastases (DM) in patients with newly diagnosed, previously untreated nasopharyngeal carcinoma (NPC) plays an important role in selecting the most appropriate treatment approach. Here, we sought to investigate the predictive value of distinct MRI parameters for the detection of early DM.Between November 2010 and June 2011, a total of 51 newly diagnosed NPC patients were included. All of the study participants were followed until December 2014 at a single institution after completion of therapy. DM was defined as early when they were detected on pretreatment FDG-PET scans or within 6 months after initial diagnosis. The following parameters were tested for their ability to predict early DM: pretreatment FDG-PET standardized uptake value (SUV), MRI-derived AJCC tumor staging, tumor volume, and dynamic contrast-enhanced (DCE) values. The DCE-derived ve was defined as the volume fraction of the extravascular, extracellular space.Compared with patients without early DM, patients with early DM had higher SUV, tumor volume, DCE mean (median) ve, ve skewness, ve kurtosis, and the largest mean ve selected among sequential slices (P < 0.05). No differences were identified when early DM were defined only according to the results of pretreatment FDG-PET. Among different quantitative DCE parameters, the mean ve had the highest area under curve (AUC, 0.765). However, the AUCs of SUV, tumor volume, mean ve, ve skewness, ve kurtosis, or the largest mean ve selected among the sequential slices did not differ significantly from one another (P = 0.82).Taken together, our results suggest that DCE-derived ve may be a useful parameter in combination with SUV and tumor volume for predicting early DM. Dynamic contrast-enhanced MRI may be complementary to FDG-PET for selecting the most appropriate treatment approach in NPC patients.