Browsing by Author "Viale, G."

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    Recommendations for standardized pathological characterization of residual disease for neoadjuvant clinical trials of breast cancer by the BIG-NABCG collaboration
    (Oxford University Press, 2015-07) Bossuyt, V.; Provenzano, E.; Symmans, W. F.; Boughey, J. C.; Coles, C.; Curigliano, G.; Dixon, J. M.; Esserman, L. J.; Fastner, G.; Kuehn, T.; Peintinger, F.; von Minckwitz, G.; White, J.; Yang, W.; Badve, Sunil; Denkert, C.; MacGrogan, G.; Penault-Llorca, F.; Viale, G.; Cameron, D.; Breast International Group-North American Breast Cancer Group (BIG-NABCG) collaboration; Department of Pathology and Laboratory Medicine, IU School of Medicine
    Neoadjuvant systemic therapy (NAST) provides the unique opportunity to assess response to treatment after months rather than years of follow-up. However, significant variability exists in methods of pathologic assessment of response to NAST, and thus its interpretation for subsequent clinical decisions. Our international multidisciplinary working group was convened by the Breast International Group-North American Breast Cancer Group (BIG-NABCG) collaboration and tasked to recommend practical methods for standardized evaluation of the post-NAST surgical breast cancer specimen for clinical trials that promote accurate and reliable designation of pathologic complete response (pCR) and meaningful characterization of residual disease. Recommendations include multidisciplinary communication; clinical marking of the tumor site (clips); and radiologic, photographic, or pictorial imaging of the sliced specimen, to map the tissue sections and reconcile macroscopic and microscopic findings. The information required to define pCR (ypT0/is ypN0 or ypT0 yp N0), residual ypT and ypN stage using the current AJCC/UICC system, and the Residual Cancer Burden system were recommended for quantification of residual disease in clinical trials.
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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.