Browsing by Author "Adams, S."

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    Dominantly Inherited Alzheimer Network Trials Unit (DIAN-TU): Trial Satisfaction and Attitudes towards Future Clinical Trials
    (Springer, 2024) Liu, H.; Li, J.; Ziegemeier, E.; Adams, S.; McDade, E.; Clifford, D. B.; Cao, Y.; Wang, G.; Li, Y.; Mills, S. L.; Santacruz, A. M.; Belyew, S.; Grill, J. D.; Snider, B. J.; Mummery, C. J.; Surti, G.; Hannequin, D.; Wallon, D.; Berman, S. B.; Jimenez-Velazquez, I. Z.; Roberson, E. D.; van Dyck, C. H.; Honig, L. S.; Sanchez-Valle, R.; Brooks, W. S.; Gauthier, S.; Galasko, D.; Masters, C. L.; Brosch, J.; Hsiung, G. Y. R.; Jayadev, S.; Formaglio, M.; Masellis, M.; Clarnette, R.; Pariente, J.; Dubois, B.; Pasquier, F.; Bateman, R. J.; Llibre-Guerra, J. J.; DIAN-TU Study Team; Neurology, School of Medicine
    Background: Clinical trial satisfaction is increasingly important for future trial designs and is associated with treatment adherence and willingness to enroll in future research studies or to recommend trial participation. In this post-trial survey, we examined participant satisfaction and attitudes toward future clinical trials in the Dominantly Inherited Alzheimer Network Trials Unit (DIAN-TU). Methods: We developed an anonymous, participant satisfaction survey tailored to participants enrolled in the DIAN-TU-001 double-blind clinical trial of solanezumab or gantenerumab and requested that all study sites share the survey with their trial participants. A total of 194 participants enrolled in the trial at 24 study sites. We utilized regression analysis to explore the link between participants' clinical trial experiences, their satisfaction, and their willingness to participate in upcoming trials. Results: Survey responses were received over a sixteen-month window during 2020-2021 from 58 participants representing 15 study sites. Notably, 96.5% of the survey respondents expressed high levels of satisfaction with the trial, 91.4% would recommend trial participation, and 96.5% were willing to enroll again. Age, gender, and education did not influence satisfaction levels. Participants reported enhanced medical care (70.7%) and pride in contributing to the DIAN-TU trial (84.5%). Satisfaction with personnel and procedures was high (98.3%). Respondents had a mean age of 48.7 years, with most being from North America and Western Europe, matching the trial's demographic distribution. Participants' decisions to learn their genetic status increased during the trial, and most participants endorsed considering future trial participation regardless of the DIAN-TU-001 trial outcome. Conclusion: Results suggest that DIAN-TU-001 participants who responded to the survey exhibited high motivation to participate in research, overall satisfaction with the clinical trial, and willingness to participate in research in the future, despite a long trial duration of 4-7 years with detailed annual clinical, cognitive, PET, MRI, and lumbar puncture assessments. Implementation of features that alleviate barriers and challenges to trial participation is like to have a high impact on trial satisfaction and reduce participant burden.
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    Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning
    (Elsevier, 2018-10) Klauschen, F.; Müller, K.-R.; Binder, A.; Bockmayr, M.; Hägele, M.; Seegerer, P.; Wienert, S.; Pruneri, G.; de Maria, S.; Badve, Sunil; Michiels, S.; Nielsen, T. O.; Adams, S.; Savas, P.; Symmans, F.; Willis, S.; Gruosso, T.; Park, M.; Haibe-Kains, B.; Gallas, B.; Thompson, A. M.; Cree, I.; Sotiriou, C.; Hewitt, S. M.; Rimm, D.; Viale, G.; Loi, S.; Loibl, S.; Salgado, R.; Denkert, C.; Pathology and Laboratory Medicine, School of Medicine
    The extent of tumor-infiltrating lymphocytes (TILs), along with immunomodulatory ligands, tumor-mutational burden and other biomarkers, has been demonstrated to be a marker of response to immune-checkpoint therapy in several cancers. Pathologists have therefore started to devise standardized visual approaches to quantify TILs for therapy prediction. However, despite successful standardization efforts visual TIL estimation is slow, with limited precision and lacks the ability to evaluate more complex properties such as TIL distribution patterns. Therefore, computational image analysis approaches are needed to provide standardized and efficient TIL quantification. Here, we discuss different automated TIL scoring approaches ranging from classical image segmentation, where cell boundaries are identified and the resulting objects classified according to shape properties, to machine learning-based approaches that directly classify cells without segmentation but rely on large amounts of training data. In contrast to conventional machine learning (ML) approaches that are often criticized for their "black-box" characteristics, we also discuss explainable machine learning. Such approaches render ML results interpretable and explain the computational decision-making process through high-resolution heatmaps that highlight TILs and cancer cells and therefore allow for quantification and plausibility checks in biomedical research and diagnostics.