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Purpose: National Cancer Institute Molecular Analysis for Therapy Choice (NCI-MATCH) is a precision medicine basket trial designed to test the effectiveness of treating cancers based on specific genetic changes in patients' tumors, regardless of cancer type. Multiple subprotocols have each tested different targeted therapies matched to specific genetic aberrations. Most subprotocols exhibited low rates of tumor shrinkage as evaluated across all tumor types enrolled. We hypothesized that these results may arise because these precision cancer therapies have tumor type-specific efficacy, as is common among other cancer therapies., Experimental Design: To test the hypothesis that certain tumor types are more sensitive to specific therapies than other tumor types, we applied permutation testing to tumor volume change and progression-free survival data from 10 published NCI-MATCH subprotocols (together n = 435 patients). FDR was controlled by the Benjamini-Hochberg procedure., Results: Six of ten subprotocols exhibited statistically significant evidence of tumor-specific drug sensitivity, four of which were previously considered negative based on response rate across all tumors. This signal-finding analysis highlights potential uses of FGFR tyrosine kinase inhibition in urothelial carcinomas with actionable FGFR aberrations and MEK inhibition in lung cancers with BRAF non-V600E mutations. In addition, it identifies low-grade serious ovarian carcinoma with BRAF v600E mutation as especially sensitive to BRAF and MEK co-inhibition (dabrafenib plus trametinib), a treatment that received accelerated FDA approval for advanced solid tumors with BRAF v600E mutation., Conclusions: These findings support the value of basket trials because even when precision medicines do not have tumor-agnostic activity, basket trials can identify tumor-specific activity for future study., (©2023 American Association for Cancer Research.)

Background: NCI-MATCH is a precision medicine basket trial designed to test the effectiveness of treating cancers based on specific genetic changes in patients' tumors, regardless of cancer type. Multiple subprotocols have each tested different targeted therapies matched to specific genetic aberrations. Most subprotocols exhibited low rates of tumor shrinkage as evaluated across all tumor types enrolled. We hypothesized that these results may arise because these precision cancer therapies have tumor type-specific efficacy, as is common among other cancer therapies., Methods: To test the hypothesis that certain tumor types are more sensitive to specific therapies than other tumor types, we applied permutation testing to tumor volume change and progression-free survival data from ten published NCI-MATCH subprotocols (together n=435 patients). False discovery rate was controlled by the Benjamini-Hochberg procedure., Results: Six of ten subprotocols exhibited statistically significant evidence of tumor-specific drug sensitivity, four of which were previously considered negative based on response rate across all tumors. This signal-finding analysis highlights potential uses of FGFR tyrosine kinase inhibition in urothelial carcinomas with actionable FGFR aberrations, MEK inhibition in lung cancers with BRAF non- V600E mutations, and MEK inhibition in cholangiocarcinomas with NRAS mutations., Conclusions: These findings support the value of basket trials because even when precision medicines do not have tumor-agnostic activity, basket trials can identify tumor-specific activity for future study.

Identifying the presence of intravenous contrast material on CT scans is an important component of data curation for medical imaging-based artificial intelligence model development and deployment. Use of intravenous contrast material is often poorly documented in imaging metadata, necessitating impractical manual annotation by clinician experts. Authors developed a convolutional neural network (CNN)-based deep learning platform to identify intravenous contrast enhancement on CT scans. For model development and validation, authors used six independent datasets of head and neck (HN) and chest CT scans, totaling 133 480 axial two-dimensional sections from 1979 scans, which were manually annotated by clinical experts. Five CNN models were trained first on HN scans for contrast enhancement detection. Model performances were evaluated at the patient level on a holdout set and external test set. Models were then fine-tuned on chest CT data and externally validated. This study found that Digital Imaging and Communications in Medicine metadata tags for intravenous contrast material were missing or erroneous for 1496 scans (75.6%). An EfficientNetB4-based model showed the best performance, with areas under the curve (AUCs) of 0.996 and 1.0 in HN holdout ( n = 216) and external ( n = 595) sets, respectively, and AUCs of 1.0 and 0.980 in the chest holdout ( n = 53) and external ( n = 402) sets, respectively. This automated, scan-to-prediction platform is highly accurate at CT contrast enhancement detection and may be helpful for artificial intelligence model development and clinical application. Keywords: CT, Head and Neck, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN), Machine Learning Algorithms, Contrast Material Supplemental material is available for this article. © RSNA, 2022., Competing Interests: Disclosures of conflicts of interest: Z.Y. No relevant relationships. J.M.Q. No relevant relationships. A.H. Consultant for Altis Labs; shareholder in Altis Labs. R.Z. No relevant relationships. D.P. No relevant relationships. J.L. No relevant relationships. Z.Z. No relevant relationships. R.H.M. Contract/grant from ViewRay; consulting for ViewRay and AstraZeneca; payment for expert testimony from U.S. District Attorney's Office of New York. H.J.W.L.A. No relevant relationships. B.H.K. RSNA Research Scholar Award NIH K08DE030216., (© 2022 by the Radiological Society of North America, Inc.)

The rapid spread of COVID-19 and disruption of normal supply chains resulted in severe shortages of personal protective equipment (PPE), particularly devices with few suppliers such as powered air-purifying respirators (PAPRs). A scarcity of information describing design and performance criteria represents a substantial barrier to new approaches to address these shortages. We sought to apply open-source product development to PAPRs to enable alternative sources of supply and further innovation. We describe the design, prototyping, validation, and user testing of locally manufactured, modular, PAPR components, including filter cartridges and blower units, developed by the Greater Boston Pandemic Fabrication Team (PanFab). Two designs, one with a fully custom-made filter and blower unit housing, and the other with commercially available variants (the "Custom" and "Commercial" designs respectively) were developed. Engineering performance of the prototypes was measured and safety validated using NIOSH-equivalent tests on apparatus available under pandemic conditions, at university laboratories. Feedback on designs was obtained from four individuals, including two clinicians working in an ambulatory clinical setting and two research technical staff for whom PAPR use is a standard part of occupational PPE. Respondents rated the PanFab Custom PAPR a 4 to 5 on a 5 Likert-scale 1) as compared to current PPE options, 2) for the sense of security with use in a clinical setting, and 3) for comfort. The three other versions of the designs (with a commercial blower unit, filter, or both) performed favorably, with survey responses consisting of scores ranging from 3-5. Engineering testing and clinical feedback demonstrate that the PanFab designs represents favorable alternative PAPRs in terms of user comfort, mobility, and sense of security. A nonrestrictive license promotes innovation in respiratory protection for current and future medical emergencies.

Background: During the current COVID-19 pandemic, supply chains for Personal Protective Equipment (PPE) have been severely disrupted and many products, particularly surgical N95 filtering facepiece respirators (FFRs; "masks") are in short supply. As a consequence, an Emergency Use Authorization (EUA) from the FDA has allowed importation of N95-type masks manufactured to international standards; these include KN95 masks from China and FFP2 masks from the European Union., Methods: We conducted a survey of mask in the inventory of major academic medical centers in Boston, MA to determine provenance and manufacturer. We then assembled a simple apparatus for performing a necessary (but not sufficient) test of filtration performance and tested masks from the inventory; an accompanying website shows how to build and use the testing apparatus., Results: Our survey showed that, seven months after the start of the COVID-19 pandemic, over 100 different makes and models of N95-type masks are in the inventory of local hospitals as opposed to 2-5 models under normal circumstances. A substantial number of unfamiliar masks are from unknown manufacturers. Many did not perform to accepted standards and are likely to be counterfeit. Due to the absence of publicly available information on mask suppliers in the FDA EUA and confusing or inconsistent labeling of KN95 masks, it is difficult to distinguish legitimate and counterfeit products., Conclusions: Many of the FFR masks available for procurement during the COVID-19 pandemic do not provide levels of fit and filtration similar to those of N95 masks and are not acceptable for use in healthcare settings. Based on these results, and in consultation with occupational health officers, we make six recommendations for end users to assist in acquiring legitimate products. In particular, institutions should always assess masks from non-traditional supply chains by checking their markings and manufacturer information against data provided by NIOSH and the latest FDA EUA Appendix A. In the absence of verifiable information on the legitimacy of mask source, institutions should consider measuring mask fit and filtration directly. We also make suggestions for U.S and Chinese regulatory agencies with regard to labeling and public disclosure aimed at increase pandemic resilience.

Background: In response to supply shortages during the COVID-19 pandemic, N95 filtering facepiece respirators (FFRs or "masks"), which are typically single-use devices in healthcare settings, are routinely being used for prolonged periods and in some cases decontaminated under "reuse" and "extended use" policies. However, the reusability of N95 masks is often limited by degradation or breakage of elastic head bands and issues with mask fit after repeated use. The purpose of this study was to develop a frame for N95 masks, using readily available materials and 3D printing, which could replace defective or broken bands and improve fit., Results: An iterative design process yielded a mask frame consisting of two 3D-printed side pieces, malleable wire links that users press against their face, and cut lengths of elastic material that go around the head to hold the frame and mask in place. Volunteers (n= 41; average BMI= 25.5), of whom 31 were women, underwent qualitative fit with and without mask frames and one or more of four different brands of FFRs conforming to US N95 or Chinese KN95 standards. Masks passed qualitative fit testing in the absence of a frame at rates varying from 48 - 92% (depending on mask model and tester). For individuals for whom a mask passed testing, 75-100% (average = 86%) also passed testing with a frame holding the mask in place. Among users for whom a mask failed in initial fit testing, 41% passed using a frame. Success varied with mask model and across individuals., Conclusions: The use of mask frames can prolong the lifespan of N95 and KN95 masks by serving as a substitute for broken or defective bands without adversely affecting fit. Frames also have the potential to improve fit for some individuals who cannot fit existing masks. Frames therefore represent a simple and inexpensive way of extending the life and utility of PPE in short supply. For clinicians and institutions interested in mask frames, designs and specifications are provided without restriction for use or modification. To ensure adequate performance in clinical settings, qualitative fit testing with user-specific masks and frames is required., Competing Interests: Competing interests A Mostaghimi is a consultant or has received honoraria from Pfizer, 3Derm, and hims and has equity in Lucid Dermatology and hims. He is an associate editor for JAMA Dermatology. Mostaghimi declares that none of these relationships are directly or indirectly related to the content of this manuscript.PK Sorger is a member of the SAB or Board of Directors of Applied Biomath, Glencoe Software and RareCyte Inc and has equity in these companies. In the last five years the Sorger lab has received research funding from Novartis and Merck. Sorger declares that none of these relationships are directly or indirectly related to the content of this manuscript.NR LeBoeuf is a consultant for or has received honoraria from the following companies: Seattle Genetics, Sanofi and Bayer.

Objective: The COVID-19 pandemic has led to widespread shortages of personal protective equipment (PPE) for healthcare workers, including filtering facepiece respirators (FFRs) such as N95 masks. These masks are normally intended for single use, but their sterilization and subsequent reuse could substantially mitigate a world-wide shortage., Design: Quality assurance., Setting: A sealed environment chamber installed in the animal facility of an academic medical center., Interventions: One to five sterilization cycles using ionized hydrogen peroxide (iHP), generated by SteraMist equipment (TOMI; Frederick, MD)., Main Outcome Measures: Personal protective equipment, including five N95 mask models from three manufacturers, were evaluated for efficacy of sterilization following iHP treatment (measured with bacterial spores in standard biological indicator assemblies). Additionally, N95 masks were assessed for their ability to efficiently filter particles down to 0.3um and for their ability to form an airtight seal using a quantitative fit test. Filtration efficiency was measured using ambient particulate matter at a university lab and an aerosolized NaCl challenge at a National Institute for Occupational Safety and Health (NIOSH) pre-certification laboratory., Results: The data demonstrate that N95 masks sterilized using SteraMist iHP technology retain function up to five cycles, the maximum number tested to date. Some but not all PPE could also be sterilized using an iHP environmental chamber, but pre-treatment with a handheld iHP generator was required for semi-enclosed surfaces such as respirator hoses., Conclusions: A typical iHP environment chamber with a volume of ~80 m3 can treat ~7000 masks per day, as well as other items of PPE, making this an effective approach for a busy medical center.

Due to supply chain disruption, the COVID-19 pandemic has caused severe shortages in personal protective equipment (PPE) for health care professionals. Local fabrication based on 3D printing is one way to address this challenge, particularly in the case of simple products such as protective face shields. As a consequence, many public domain designs for face shields have become available. No clear path exists, however, for introducing a locally fabricated and unapproved product into a clinical setting. In a US health care setting, face shields are regulated by the Food and Drug Administration (FDA); similar policies exist in other countries. We describe a research protocol under which rapid iteration on an existing design, coupled with clinical feedback and real-world testing in an emergency department, allowed a face shield to be adopted by the incident command team at a major academic medical center. We describe our design and testing process and provide an overview of regulatory considerations associated with fabrication and testing of face shields and related products. All designs, materials used, testing protocols, and survey results are reported in full to facilitate the execution of similar face shield efforts in other clinical settings. Our work serves as a case study for development of a robust local response to pandemics and other health care emergencies, with implications for healthcare professionals, hospital administrators, regulatory agencies and concerned citizens.