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Design of the SynthRAD2023 Grand Challengehttps://synthrad2023.grand-challenge.org/ following the structured challenge submission provided at https://www.biomedical-challenges.org/. The challenge will be part of MICCAI 2023. Versions v0.1design submitted on 4-11-2022 to MICCAI; v0.2 revised on 02-2023 as part of the rebuttal; v0.3automatically updated by MICCAI: https://doi.org/10.5281/zenodo.7835406; v1.0textually updated on 03-2023 according to the approved submitted changes by MICCAI (Zenodo:10.5281/zenodo.7746020); v1.1 - latesttextual revision + update timeline figure, added abstract and reference to MICCAI automatically generated Zenodo; Abstract The impact of medical imaging on oncological patients' diagnosis and therapy, has grown significantly over the last decades. Especially in radiotherapy (RT), imaging plays a crucial role in the entire workflow, from treatment simulation to patient positioning and monitoring. Traditionally, 3D computed tomography (CT) is considered the primary imaging modality in RT, providing accurate and high-resolution patient geometry and enabling direct electron density conversion needed for dose calculations and plan optimization [Chernak et al., 1975]. X-ray-based imaging has been widely adopted for patient positioning and monitoring before, during, and after dose delivery. Recently, 3D cone-beam computed tomography (CBCT) - often integrated with the dose delivery machine - has been widely adopted, playing a vital role in traditional and more advanced image-guided adaptive radiation therapy (IGART) workflows in both photon and proton therapy. A key challenge in using CBCT is that several artifacts, such as shading, streaking, and cupping, affect image reconstruction. As a result, CBCT quality is insufficient to perform accurate dose calculations or replanning. Consequently, patients must be referred to a rescan CT when anatomical differences are noted between daily images and the planning CT [Ramella et al., 2017]. As an alternative, image synthesis has been proposed to improve the quality of CBCT to the CT level, producing the so-called “synthetic CT” (sCT) [Kida et al., 2018]. Additionally, CBCT-based sCT allows online adaptive CBCT-based RT workflows to improve the quality of IGART provided to the patients. In parallel, over the last decades, magnetic resonance imaging (MRI) has also proved its added value for tumor and organs-at-risk delineation thanks to its superb soft-tissue contrast [Schmidt et al., 2015]. MRI can be acquired to match patient positioning to the planned one and monitor changes before, during, or after the dose delivery [Lagendijk et al., 2004]. To benefit from the complementary advantages offered by different imaging modalities, MRI is generally registered to CT. Such a workflow requires obtaining both CT and MRI, increasing workload, and introducing additional radiation to the patient. Recently, MRI-only based RT has been proposed to simplify and speed up the workflow, decreasing patients' exposure to ionizing radiation. This is particularly relevant for repeated simulations or fragile populations like children. MRI-only RT may reduce treatment costs and workload and eliminate residual registration errors using both imaging modalities. Additionally, MRI-only techniques can benefit MRI-guided RT [Edmund and Nyholm, 2017]. The main obstacle in introducing MRI-only RT is the lack of tissue attenuation information required for accurate dose calculations. Many methods have been proposed to convert MR to CT-equivalent images, yielding sCTs suitable for treatment planning and dose calculation. Artificial intelligence algorithms such as machine learning or deep learning have become the best-performing methods for deriving sCT from MRI or CBCT. With many algorithms available, all tested on different datasets, it is unclear which algorithms are better than others. Unfortunately, no public datasets or challenges have been designed to benchmark and compare different approaches. A recent review of deep learning-based sCT generation also advocated for public challenges to provide data and evaluation metrics for such open comparison [Spadea & Maspero et al., 2021]. We now designed a challenge to provide the first platform offering public datasets and evaluation metrics to benchmark and compare the latest algorithms in sCT generation. For this purpose, two tasks were defined: 1) MRI-to-sCT generation to facilitate MRI-only RT and 2) CBCT-to-sCT generation to facilitate IGART and online adaptive RT. A multi-center dataset of matched input (CBCT or MRI) and target (CT) image pairs with heterogeneous acquisition protocols will be divided into balanced training, validation, and test sets. We will share the inputs and targets from the training set to design and evaluate algorithms generating sCT. In the first evaluation round, called validation, input images will be shared. The participating team can upload their outputs to validate their performance and see how they rank on the leaderboard on image similarity metrics. Finally, independent test cases will be shared to generate sCT for our final evaluation. Brain and pelvic datasets from three Dutch centers (UMC Utrecht, UMC Groningen, and Radboud Nijmegen) have been collected, providing more than 500 patients for MRI-to-CT (Task 1) and 500 patients for CBCT-to-CT (Task 2) undergoing radiotherapy treatments in the corresponding departments. The challenge runs onhttps://synthrad2023.grand-challenge.org/, and the pre-processing and evaluation code used to rank the submissions based on image and dose evaluations will be shared. Challenge participants may choose to participate only in task 1 (MRI-to-sCT), only in task 2 (CBCT-to-sCT), or in both, for both anatomical regions. In the initial training/validation phases, training and validation data will be made available to submit to an open leaderboard to enable the development of algorithms. The test input MRI/CBCT and ground truth CT will not be shared with the participants to avoid optimistic biases. We envision that this challenge will enable a fair and open assessment of different approaches. References Chernak E.S., Rodriguez-Antunez A., Jelden G.L., Dhaliwal R.S., Lavik P.S. The use of computed tomography for radiation therapy treatment planning. Radiology. 1975 Dec;117(3):613-4. https://doi.org/10.1148/117.3.613 Ramella, S., Fiore, M., Silipigni, S., Zappa, M. C., Jaus, M., Alberti, A. M., ... & D’Angelillo, R. M. (2017). Local control and toxicity of adaptive radiotherapy using weekly CT imaging: results from the LARTIA trial in stage III NSCLC. Journal of Thoracic Oncology, 12(7), 1122-1130. https://doi.org/10.1016/j.jtho.2017.03.025 Kida, S., Nakamoto, T., Nakano, M., Nawa, K., Haga, A., Kotoku, J. I., ... & Nakagawa, K. (2018). Cone beam computed tomography image quality improvement using a deep convolutional neural network. Cureus, 10(4). https://doi.org/10.7759%2Fcureus.2548 Schmidt M. A., Payne G. S. Radiotherapy planning using MRI. Phys Med Biol. 2015;60:R323 Lagendijk, J. J., Raaymakers, B. W., Van den Berg, C. A., Moerland, M. A., Philippens, M. E., & Van Vulpen, M. (2014). MR guidance in radiotherapy. Physics in Medicine & Biology, 59(21), R349. https://doi.org/10.1088/0031-9155/59/21/r349 Edmund, J. M., & Nyholm, T. (2017). A review of substitute CT generation for MRI-only radiation therapy. Radiation Oncology, 12(1), 1-15. https://doi.org/10.1186/s13014-016-0747-y Spadea, M. F. & Maspero, M., Zaffino, P., & Seco, J. (2021). Deep learning-based synthetic-CT generation in radiotherapy and PET: A review. Medical Physics, 48(11), 6537–6566. https://doi.org/10.1002/mp.15150 Schwarz, C. G., Kremers, W. K., Wiste, H. J., Gunter, J. L., Vemuri, P., Spychalla, A. J., ... & Alzheimer's Disease Neuroimaging Initiative. (2021). Changing the face of neuroimaging research: Comparing a new MRI de-facing technique with popular alternatives. NeuroImage, 231, 117845. https://doi.org/10.1016/j.neuroimage.2021.117845 Low, D.A., Harms, W.B., Mutic, S., and Purdy, J.A. (1998), A technique for the quantitative evaluation of dose distributions. Med. Phys., 25: 656-661. https://doi.org/10.1118/1.598248 Hall, W. A., Paulson, E., Davis, B. J., Spratt, D. E., Morgan, T. M., Dearnaley, D., ... & Lawton, C. A. (2021). NRG oncology updated international consensus atlas on pelvic lymph node volumes for intact and postoperative prostate cancer. International Journal of Radiation Oncology* Biology* Physics, 109(1), 174-185.https://doi.org/10.1016/j.ijrobp.2020.08.034 Lambrecht, M., Eekers, D. B., Alapetite, C., Burnet, N. G., Calugaru, V., Coremans, I. E., ... & Troost, E. G. (2018). Radiation dose constraints for organs at risk in neuro-oncology; the European Particle Therapy Network consensus. Radiotherapy and Oncology, 128(1), 26-36 https://doi.org/10.1016/j.radonc.2018.05.001 Ezzell, G. A., Burmeister, J. W., Dogan, N., LoSasso, T. J., Mechalakos, J. G., Mihailidis, D., ... & Xiao, Y. (2009). IMRT commissioning: multiple institution planning and dosimetry comparisons, a report from AAPM Task Group 119. Medical physics, 36(11), 5359-5373. https://doi.org/10.1118/1.3238104 Drzymala, R. E., Mohan, R., Brewster, L., Chu, J., Goitein, M., Harms, W., & Urie, M. (1991). Dose-volume histograms. International Journal of Radiation Oncology* Biology* Physics, 21(1), 71-78. https://doi.org/10.1016/0360-3016(91)90168-4 International Commission on Radiation Units and Measurements, ICRU Report 83, Prescribing, recording, andreporting intensity-modulated photon-beam therapy (IMRT)(ICRU Report 83), Bethesda, MD (2010) https://www.fnkv.cz/soubory/216/icru-83.pdf

Design of the SynthRAD2023 Grand Challengehttps://synthrad2023.grand-challenge.org/ following the structured challenge submission provided at https://www.biomedical-challenges.org/. The challenge will be part of MICCAI 2023. This design has been first submited on 4-11-2022, revised on 02-2023 and textually updated on 03-2023 according to the approved submitted changes by MICCAI. &nbsp

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks (DNNs), many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy, including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy.

Background: Medical advances prolong life and treat illness but many patients have chronically debilitating conditions that prevent them from making end-of-life (EOL) decisions for themselves. These situations are difficult to navigate for both patient and physician. This study investigates physicians’ feelings and approach toward EOL care, physician-assisted suicide (PAS), and euthanasia. Methods: An anonymous, self-administered online survey was distributed through the New Jersey Medical School servers and American College of Surgeons forums. The survey presented clinical EOL vignettes and subjective questions regarding PAS and euthanasia. Results: We obtained 142 responses from attending physicians. Respondents were typically male (61%), married (85%), identified as Christian (54%), had more than 20 years of experience (55%), and worked at a university hospital (57%). Religious beliefs and years of work experience seemed to be significant contributors in EOL decision making, whereas gender and medical specialty were not significantly influential. Conclusion: Factors such as years of work experience and religious belief may influence medical professionals’ opinions about PAS and euthanasia and their subsequent actions regarding EOL care. In many cases, the boundaries are blurred and require further study before concrete conclusions can be made.

OBJECTIVE To determine whether a low aspartate aminotransferase (AST) to alanine aminotransferase (ALT) ratio (AST/ALT ratio) is associated with insulin resistance among those without liver dysfunction. METHODS In this cross-sectional study of the National Health and Nutrition Examination Survey (NHANES) 2011-2016, we included 2747 (1434 male and 1313 nonpregnant female) adults ≥20 years without evidence of liver dysfunction (ALT

Background and Aim The literature is lacking on associations of endoscopic retrograde cholangiopancreatography (ERCP) related outcomes in rheumatoid arthritis (RA) patients. The aim of this study is to evaluate the effects of RA on clinical outcomes and hospital resource utilization in patients undergoing ERCP. Methods The National Inpatient Sample database was used to identify hospitalized patients who had underwent an ERCP study from 2012 to 2014 using International Classification of Diseases‐Ninth Edition (ICD‐9) codes. Primary outcomes were mortality, hospital charges, and length of stay. Secondary outcomes were ERCP‐related complications. Chi‐squared tests for categorical data and independent t‐test for continuous data were utilized. Multivariate analysis was performed to assess the primary outcomes. Results There was 83 890 ERCP procedures performed, of which 970 patients had RA. In patients with RA, 74.2% were female, and the average age was 65.7 years. RA primary outcomes of mortality rate and hospital cost were lower and statistically significant. There was no statistically significant difference in secondary outcomes except for lower cholecystectomy rates in RA patients. Conclusion With a high inflammatory state, it was hypothesized that RA would be associated with worse outcomes after ERCP. Yet, the primary outcomes of mortality and hospital cost were found to be lower than controls, with no difference in secondary outcomes. We posit that immunosuppressants used to treat RA provides a protective effect to overall complications with ERCP., As the literature is lacking on the associations of endoscopic retrograde cholangiopancreatography‐related outcomes in rheumatoid arthritis patients, we evaluated the effects of rheumatoid arthritis on clinical outcomes and hospital resource utilization in patients undergoing endoscopic retrograde cholangiopancreatography and discovered a lower hospital cost and mortality rate in rheumatoid patients.

Graphical abstract, Radiomics is referred to as quantitative imaging of biomarkers used for clinical outcome prognosis or tumor characterization. In order to bridge radiomics and its clinical application, we aimed to build an integrated solution of radiomics extraction with an open-source Picture Archiving and Communication System (PACS). The integrated SQLite4Radiomics software was tested in three different imaging modalities and its performance was benchmarked in lung cancer open datasets RIDER and MMD with median extraction time of 10.7 (percentiles 25–75: 8.9–18.7) seconds per ROI in three different configurations.

International audience; This paper relates the post-analysis of the first edition of the HEad and neCK TumOR (HECKTOR) challenge. This challenge was held as a satellite event of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020, and was the first of its kind focusing on lesion segmentation in combined FDG-PET and CT image modalities. The challenge's task is the automatic segmentation of the Gross Tumor Volume (GTV) of Head and Neck (HandN) oropharyngeal primary tumors in FDG-PET/CT images. To this end, the participants were given a training set of 201 cases from four different centers and their methods were tested on a held-out set of 53 cases from a fifth center. The methods were ranked according to the Dice Score Coefficient (DSC) averaged across all test cases. An additional inter-observer agreement study was organized to assess the difficulty of the task from a human perspective. 64 teams registered to the challenge, among which 10 provided a paper detailing their approach. The best method obtained an average DSC of 0.7591, showing a large improvement over our proposed baseline method and the inter-observer agreement, associated with DSCs of 0.6610 and 0.61, respectively. The automatic methods proved to successfully leverage the wealth of metabolic and structural properties of combined PET and CT modalities, significantly outperforming human inter-observer agreement level, semi-automatic thresholding based on PET images as well as other single modality-based methods. This promising performance is one step forward towards large-scale radiomics studies in HandN cancer, obviating the need for error-prone and time-consuming manual delineation of GTVs. (C) 2022 The Authors. Published by Elsevier B.V.

Efforts to make online media accessible to a regional audience have picked up pace in recent years with multilingual captioning and keyboards. However, techniques to extend this access to people with hearing loss are limited. Further, owing to a lack of structure in the education of hearing impaired and regional differences, the issue of standardization of Indian Sign Language (ISL) has been left unaddressed, forcing educators to rely on the local language to support the ISL structure, thereby creating an array of correlations for each object, hindering the language building skills of a student. This paper aims to present a useful technology that can be used to leverage online resources and make them accessible to the hearing-impaired community in their primary mode of communication. Our tool presents an avenue for the early development of language learning and communication skills essential for the education of children with a profound hearing loss. With the proposed technology, we aim to provide a standardized teaching and learning medium to a classroom setting that can utilize and promote ISL. The goals of our proposed system involve reducing the burden of teachers to act as a valuable teaching aid. The system allows for easy translation of any online video and correlation with ISL captioning using a 3D cartoonish avatar aimed to reinforce classroom concepts during the critical period. First, the video gets converted to text via subtitles and speech processing methods. The generated text is understood through NLP algorithms and then mapped to avatar captions which are then rendered to form a cohesive video alongside the original content. We validated our results through a 6-month period and a consequent 2-month study, where we recorded a 37% and 70% increase in performance of students taught using Sign captioned videos against student taught with English captioned videos. We also recorded a 73.08% increase in vocabulary acquisition through signed aided videos.

Background Minoxidil is a widely used over-the-counter topical treatment for hair loss. The response rate for topical minoxidil is relatively low. Minoxidil is a pro-drug, converted to its active form, minoxidil sulfate, by SULT1A1 enzymes located in the scalp. Recently, a novel topical formula that increases the activity of SULT1A1 in hair follicles was reported. Aims To evaluate any benefit of applying the SULT1A1 enzyme booster prior to daily 5% minoxidil treatment. Methods Male androgenic alopecia patients were recruited to a randomized blinded placebo-controlled study. Patients were randomized to receive 5% topical minoxidil plus the novel formula or minoxidil plus a sham adjuvant. Patient's hair growth was monitored using global photography over 60 days. Results Twenty-four males with androgenic alopecia (Norwood scale average 4.4, range 2-6) were randomized and completed the trial: 12 in the active arm and 12 in placebo. 75% of the subjects who used the SULT1A1 adjuvant with their daily minoxidil treatments for 60 days regrew hair versus 33% of those using the placebo adjuvant (p = 0.023). Conclusions In a small cohort of androgenetic alopecia men, adding the SULT1A1 adjuvant to their daily minoxidil treatment regimen improved hair regrowth.

Background and aim We sought to determine the association between alanine aminotransferase (ALT) in the normal range and mortality in the absence of liver dysfunction to better understand ALT’s clinical significance beyond liver injury and inflammation. Methods A cohort of 2,708 male and 3,461 female adults aged 20–75 years without liver dysfunction (ALT Results Relative to males in the lowest quartile (Q1), males in the highest quartile (Q4) had 44% decreased risk of all-cause mortality (aHR [95% CI]: 0.56 [0.42, 0.74]). Females in Q4 had 45% decreased risk of all-cause mortality (aHR [95% CI]: 0.55 [0.40, 0.77]). Males with BMI 2 in Q4 had significantly lower risk of all-cause mortality than Q1; however, this association did not exist in males with BMI ≥25 (BMI Conclusion ALT within the normal range was inversely associated with all-cause mortality in U.S. adults.