Nanostructures Technology, Research, and Applications

Contains reports on seventeen research projects and a list of publications.Joint Services Electronics Program Contract DAAL03-92-C-0001Joint Services Electronics Program Grant DAAH04-95-1-0038Semiconductor Research Corporation Contract 94-MJ-550National Science Foundation Grant ECS 94-07078U.S. Army Research Office Contract DAAL03-92-G-0291Advanced Research Projects Agency/Naval Air Systems Command Contract N00019-92-K-0021National Aeronautics and Space Administration Contract NAS8-36748National Aeronautics and Space Administration Grant NAGW-2003IBM Corporation Contract 1622U.S. Army Research Office Grant DAAH04-94-G-0377U.S. Air Force - Office of Scientific Research Grant F-49-620-92-J-006

Nanostructures, Technology, Research, and Applications

Contains reports on the nanostructures laboratory, eighteen research projects and a list of publications.Joint Services Electronics Program Grant DAAH04-95-1-0038Semiconductor Research Corporation Contract 95-LJ-550National Science Foundation Grant ECS 94-07078U.S. Army Research Office Grant DAAH04-95-1-0564Defense Advanced Research Projects Agency/Naval Air Systems Command Contract N00019-95-K-0131National Aeronautics and Space Administration Contract NAS8-38249National Aeronautics and Space Administration Grant NAGW-2003IBM Corporation Contract 1622U.S. Navy- Office of Naval Research Grant N00014-95-1-1297U.S. Army Research Office Grant DAAH04-94-G-0377U.S. Air Force - Office of Scientific Research Grant F-49-620-92-J-0064U.S. Air Force - Office of Scientific Research Grant F-49-620-95-1-031

Optics and Quantum Electronics

Contains table of contents for Section 3 and reports on twenty research projects.Charles S. Draper Laboratories Contract DL-H-467138Joint Services Electronics Program Contract DAAL03-92-C-0001Joint Services Electronics Program Grant DAAH04-95-1-0038U.S. Air Force - Office of Scientific Research Contract F49620-91-C-0091MIT Lincoln LaboratoryNational Science Foundation Grant ECS 90-12787Fujitsu LaboratoriesNational Center for Integrated PhotonicsHoneywell Technology CenterU.S. Navy - Office of Naval Research (MFEL) Contract N00014-94-1-0717U.S. Navy - Office of Naval Research (MFEL) Grant N00014-91-J-1956National Institutes of Health Grant NIH-5-R01-GM35459-09U.S. Air Force - Office of Scientific Research Grant F49620-93-1-0301MIT Lincoln Laboratory Contract BX-5098Electric Power Research Institute Contract RP3170-25ENEC

Optics and Quantum Electronics

Contains table of contents for Section 3, reports on twenty-one research projects and a list of publications and meeting papers.Joint Services Electronics Program Contract DAAL03-92-C-0001U.S. Air Force - Office of Scientific Research Contract F49620-91-C-0091Charles S. Draper Laboratories Contract DL-H-441692MIT Lincoln LaboratoryNational Science Foundation Grant ECS 90-12787Fujitsu LaboratoriesU.S. Navy - Office of Naval Research Grant N00014-92-J-1302National Center for Integrated Photonic TechnologyNational Science Foundation Grant ECS 85-52701U.S. Navy - Office of Naval Research (MFEL) Grant N00014-91-C-0084U.S. Navy - Office of Naval Research (MFEL) Grant N00014-91-J-1956National Institutes of Health Grant R01-GM35459-08U.S. Air Force - Office of Scientific Research Grant F49620-93-1-0301MIT Lincoln Laboratory Contract BX-5098Electric Power Research Institute Contract RP3170-2

Nanostructures, Technology, Research, and Applications

Contains reports on twenty research projects and a list of publications.Joint Services Electronics Program Grant DAAH04-95-1-0038National Science Foundation Grant ECS-94-07078Semiconductor Research CorporationU.S. Army Research Office Grant DAAH04-95-1-0564Defense Advanced Research Projects Agency/Naval Air Systems Command Contract N00019-95-K-0131National Aeronautics and Space Administration Contract NAS8-38249National Aeronautics and Space Administration Grant NAGW-2003IBM Corporation Contract 1622National Science Foundation Graduate FellowshipU.S. Navy - Office of Naval Research Grant N00014-95-1-1297U.S. Army Research Office Contract DAAH04-94-G-0377U.S. Air Force - Office of Scientific Research Grant F49620-92-J-0064U.S. Air Force - Office of Scientific Research Grant F49620-95-1-0311National Science Foundation Contract DMR 94-0034U.S. Air Force - Office of Scientific Research Contract F49620-96-0126Harvard-Smithsonian Astrophysical Observatory Contract SV630304National Aeronautics and Space Administration Grant NAG5-5105Los Alamos National Laboratory Contract E57800017-9

Nanostructures Technology, Research, and Applications

Contains reports on twenty-four research projects and a list of publications.Joint Services Electronics Program Grant DAAHO4-95-1-0038Defense Advanced Research Projects Agency/Semiconductor Research Corporation SA1645-25508PGU.S. Army Research Office Grant DAAHO4-95-1-0564Defense Advanced Research Projects Agency/U.S. Navy - Naval Air Systems Command Contract N00019-95-K-0131Suss Advanced Lithography P. O. 51668National Aeronautics and Space Administration Contract NAS8-38249National Aeronautics and Space Administration Grant NAGW-2003Defense Advanced Research Projects Agency/U.S. Army Research Office Grant DAAHO4-951-05643M CorporationDefense Advanced Research Projects Agency/U.S. Navy - Office of Naval Research Contract N66001-97-1-8909National Science Foundation Graduate FellowshipU.S. Army Research Office Contract DAAHO4-94-G-0377National Science Foundation Contract DMR-940034National Science Foundation Grant DMR 94-00334Defense Advanced Research Projects Agency/U.S. Air Force - Office of Scientific Research Contract F49620-96-1-0126Harvard-Smithsonian Astrophysical Observatory Contract SV630304National Aeronautics and Space Administration Grant NAG5-5105Los Alamos National Laboratory Contract E57800017-9GSouthwest Research Institute Contract 83832MIT Lincoln Laboratory Advanced Concepts ProgramMIT Lincoln Laboratory Contract BX-655

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

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

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

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