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

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

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  2. Association of germline variation with the survival of women with BRCA1/2 pathogenic variants and breast cancer

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  3. The Prosigna 50-gene profile and responsiveness to adjuvant anthracycline-based chemotherapy in high-risk breast cancer patients

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  4. The FANCM:p.Arg658* truncating variant is associated with risk of triple-negative breast cancer

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  5. The evidence that motivated an independent review of breast cancer

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  1. Evaluation of analytical accuracy of HER2 status in patients with breast cancer: Comparison of HER2 GPA with HER2 IHC and HER2 FISH

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  2. Prognostic value of histopathological DCIS features in a large-scale international interrater reliability study

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  3. Synchronous bilateral breast cancer: a nationwide study on histopathology and etiology

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

    Research output: Contribution to journalJournal articleResearchpeer-review

  • International Immuno-Oncology Biomarker Working Group
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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.

Original languageEnglish
JournalCurrent Medical Literature. Breast Cancer
Volume6
Issue number1
Pages (from-to)16
ISSN2374-4677
DOIs
Publication statusPublished - 2020

ID: 62371544