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@ARTICLE{Thagaard:278730,
      author       = {J. Thagaard and G. Broeckx and D. B. Page and C. A.
                      Jahangir and S. Verbandt and Z. Kos and R. Gupta and R.
                      Khiroya and K. Abduljabbar and G. Acosta Haab and B. Acs and
                      G. Akturk and J. S. Almeida and I. Alvarado-Cabrero and M.
                      Amgad and F. Azmoudeh-Ardalan and S. Badve and N. B. Baharun
                      and E. Balslev and E. R. Bellolio and V. Bheemaraju and K.
                      R. Blenman and L. Botinelly Mendonça Fujimoto and N.
                      Bouchmaa and O. Burgues and A. Chardas and M. Chon U Cheang
                      and F. Ciompi and L. A. Cooper and A. Coosemans and G.
                      Corredor and A. B. Dahl and F. L. Dantas Portela and F.
                      Deman and S. Demaria and J. Doré Hansen and S. N. Dudgeon
                      and T. Ebstrup and M. Elghazawy and C. Fernandez-Martín and
                      S. B. Fox and W. M. Gallagher and J. M. Giltnane and S.
                      Gnjatic and P. I. Gonzalez-Ericsson and A. Grigoriadis and
                      N. Halama$^*$ and M. G. Hanna and A. Harbhajanka and S. N.
                      Hart and J. Hartman and S. Hauberg and S. Hewitt and A. I.
                      Hida and H. M. Horlings and Z. Husain and E. Hytopoulos and
                      S. Irshad and E. A. Janssen and M. Kahila and T. R. Kataoka
                      and K. Kawaguchi and D. Kharidehal and A. I. Khramtsov and
                      U. Kiraz and P. Kirtani and L. L. Kodach and K. Korski and
                      A. Kovács and A.-V. Laenkholm and C. Lang-Schwarz and D.
                      Larsimont and J. K. Lennerz and M. Lerousseau and X. Li and
                      A. Ly and A. Madabhushi and S. K. Maley and V. Manur
                      Narasimhamurthy and D. K. Marks and E. S. McDonald and R.
                      Mehrotra and S. Michiels and F. U. A. A. Minhas and S.
                      Mittal and D. A. Moore and S. Mushtaq and H. Nighat and T.
                      Papathomas and F. Penault-Llorca and R. D. Perera and C. J.
                      Pinard and J. C. Pinto-Cardenas and G. Pruneri and L.
                      Pusztai and A. Rahman and N. M. Rajpoot and B. L. Rapoport
                      and T. T. Rau and J. S. Reis-Filho and J. M. Ribeiro and D.
                      Rimm and A. Roslind and A. Vincent-Salomon and M.
                      Salto-Tellez and J. Saltz and S. Sayed and E. Scott and K.
                      P. Siziopikou and C. Sotiriou and A. Stenzinger and M. A.
                      Sughayer and D. Sur and S. Fineberg and F. Symmans and S.
                      Tanaka and T. Taxter and S. Tejpar and J. Teuwen and E. A.
                      Thompson and T. Tramm and W. T. Tran and J. van der Laak and
                      P. J. van Diest and G. E. Verghese and G. Viale and M. Vieth
                      and N. Wahab and T. Walter and Y. Waumans and H. Y. Wen and
                      W. Yang and Y. Yuan and R. M. Zin and S. Adams and J.
                      Bartlett and S. Loibl and C. Denkert and P. Savas and S. Loi
                      and R. Salgado and E. Specht Stovgaard},
      title        = {{P}itfalls in machine learning-based assessment of
                      tumor-infiltrating lymphocytes in breast cancer: a report of
                      the international immuno-oncology biomarker working group.},
      journal      = {The journal of pathology},
      volume       = {260},
      number       = {5},
      issn         = {0368-3494},
      address      = {Bognor Regis [u.a.]},
      publisher    = {Wiley},
      reportid     = {DKFZ-2023-01701},
      pages        = {498-513},
      year         = {2023},
      note         = {2023 Aug;260(5):498-513},
      abstract     = {The clinical significance of the tumor-immune interaction
                      in breast cancer is now established, and tumor-infiltrating
                      lymphocytes (TILs) have emerged as predictive and prognostic
                      biomarkers for patients with triple-negative (estrogen
                      receptor, progesterone receptor, and HER2-negative) breast
                      cancer and HER2-positive breast cancer. How computational
                      assessments of TILs might complement manual TIL assessment
                      in trial and daily practices is currently debated. Recent
                      efforts to use machine learning (ML) to automatically
                      evaluate TILs have shown promising results. We review
                      state-of-the-art approaches and identify pitfalls and
                      challenges of automated TIL evaluation by studying the root
                      cause of ML discordances in comparison to manual TIL
                      quantification. We categorize our findings into four main
                      topics: (1) technical slide issues, (2) ML and image
                      analysis aspects, (3) data challenges, and (4) 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
                      for TIL 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 triple-negative breast cancer.
                      © 2023 The Authors. The Journal of Pathology published by
                      John Wiley $\&$ Sons Ltd on behalf of The Pathological
                      Society of Great Britain and Ireland.},
      subtyp        = {Review Article},
      keywords     = {deep learning (Other) / digital pathology (Other) /
                      guidelines (Other) / image analysis (Other) / machine
                      learning (Other) / pitfalls (Other) / prognostic biomarker
                      (Other) / triple-negative breast cancer (Other) /
                      tumor-infiltrating lymphocytes (Other)},
      cin          = {D240},
      ddc          = {610},
      cid          = {I:(DE-He78)D240-20160331},
      pnm          = {314 - Immunologie und Krebs (POF4-314)},
      pid          = {G:(DE-HGF)POF4-314},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37608772},
      doi          = {10.1002/path.6155},
      url          = {https://inrepo02.dkfz.de/record/278730},
}