A Semiautomated Multilayer Picking Algorithm for Ice-sheet Radar Echograms Applied to Ground-Based Near-Surface Data

Snow accumulation over an ice sheet is the sole mass input, making it a primary measurement for understanding the past, present, and future mass balance. Near-surface frequency-modulated continuous-wave (FMCW) radars image isochronous firn layers recording accumulation histories. The Semiautomated Multilayer Picking Algorithm (SAMPA) was designed and developed to trace annual accumulation layers in polar firn from both airborne and ground-based radars. The SAMPA algorithm is based on the Radon transform (RT) computed by blocks and angular orientations over a radar echogram. For each echogram's block, the RT maps firn segmented-layer features into peaks, which are picked using amplitude and width threshold parameters of peaks. A backward RT is then computed for each corresponding block, mapping the peaks back into picked segmented-layers. The segmented layers are then connected and smoothed to achieve a final layer pick across the echogram. Once input parameters are trained, SAMPA operates autonomously and can process hundreds of kilometers of radar data picking more than 40 layers. SAMPA final pick results and layer numbering still require a cursory manual adjustment to correct noncontinuous picks, which are likely not annual, and to correct for inconsistency in layer numbering. Despite the manual effort to train and check SAMPA results, it is an efficient tool for picking multiple accumulation layers in polar firn, reducing time over manual digitizing efforts. The trackability of good detected layers is greater than 90%

A Second Large Subglacial Impact Crater in Northwest Greenland?

Following the discovery of the Hiawatha impact crater beneath the northwest margin of the Greenland Ice Sheet, we explored satellite and aerogeophysical data in search of additional such craters. Here we report the discovery of a possible second subglacial impact crater that is 36.5 km wide and 183 km southeast of the Hiawatha impact crater. Although buried by 2 km of ice, the structure's rim induces a conspicuously circular surface expression, it possesses a central uplift and it causes a negative gravity anomaly. The existence of two closely-spaced and similarlysized complex craters raises the possibility that they formed during related impact events. However, the second structure's morphology is shallower, its overlying ice is conformal and older, and such an event can be explained by chance. We conclude that the identified structure is very likely an impact crater, but it is unlikely to be a twin of the Hiawatha impact crater

A Possible Second Large Subglacial Impact Crater in Northwest Greenland

Following the discovery of the Hiawatha impact crater beneath the northwest margin of the Greenland Ice Sheet, we explored satellite and aerogeophysical data in search of additional such craters. Here we report the discovery of a possible second subglacial impact crater that is 36.5 km wide and 183 km southeast of the Hiawatha impact crater. Although buried by 2 km of ice, the structure's rim induces a conspicuously circular surface expression, it possesses a central uplift and it causes a negative gravity anomaly. The existence of two closely-spaced and similarlysized complex craters raises the possibility that they formed during related impact events. However, the second structure's morphology is shallower, its overlying ice is conformal and older, and such an event can be explained by chance. We conclude that the identified structure is very likely an impact crater, but it is unlikely to be a twin of the Hiawatha impact crater

Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

Since 2009, the ultra-wideband snow radar on Operation IceBridge (OIB; a NASA airborne mission to survey the polar ice covers) has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Progressive improvements in radar hardware and data processing methodologies have led to improved data quality for subsequent retrieval of snow depth. Existing retrieval algorithms differ in the way the air–snow (a–s) and snow–ice (s–i) interfaces are detected and localized in the radar returns and in how the system limitations are addressed (e.g., noise, resolution). In 2014, the Snow Thickness On Sea Ice Working Group (STOSIWG) was formed and tasked with investigating how radar data quality affects snow depth retrievals and how retrievals from the various algorithms differ. The goal is to understand the limitations of the estimates and to produce a well-documented, long-term record that can be used for understanding broader changes in the Arctic climate system. Here, we assess five retrieval algorithms by comparisons with field measurements from two ground-based campaigns, including the BRomine, Ozone, and Mercury EXperiment (BROMEX) at Barrow, Alaska; a field program by Environment and Climate Change Canada at Eureka, Nunavut; and available climatology and snowfall from ERA-Interim reanalysis. The aim is to examine available algorithms and to use the assessment results to inform the development of future approaches. We present results from these assessments and highlight key considerations for the production of a long-term, calibrated geophysical record of springtime snow thickness over Arctic sea ice

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

Funder: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)Funder: National Center for Research Resources under award number 1 C06 RR12463-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service, the DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558), the DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440), the DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329), the Ohio Third Frontier Technology Validation Fund, the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University.Funder: Susan G Komen Foundation (CCR CCR18547966) and a Young Investigator Grant from the Breast Cancer Alliance.Funder: The Canadian Cancer SocietyFunder: Breast Cancer Research Foundation (BCRF), Grant No. 17-194Abstract: Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring

Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer

Abstract: Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Funder: Breast Cancer Research Foundation (BCRF); doi: https://doi.org/10.13039/100001006Abstract: Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting