Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.

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作者:

Thagaard JBroeckx GPage DBJahangir CAVerbandt SKos ZGupta RKhiroya RAbduljabbar KAcosta Haab GAcs BAkturk GAlmeida JSAlvarado-Cabrero IAmgad MAzmoudeh-Ardalan FBadve SBaharun NBBalslev EBellolio ERBheemaraju VBlenman KRBotinelly Mendonça Fujimoto LBouchmaa NBurgues OChardas AChon U Cheang MCiompi FCooper LACoosemans ACorredor GDahl ABDantas Portela FLDeman FDemaria SDoré Hansen JDudgeon SNEbstrup TElghazawy MFernandez-Martín CFox SBGallagher WMGiltnane JMGnjatic SGonzalez-Ericsson PIGrigoriadis AHalama NHanna MGHarbhajanka AHart SNHartman JHauberg SHewitt SHida AIHorlings HMHusain ZHytopoulos EIrshad SJanssen EAKahila MKataoka TRKawaguchi KKharidehal DKhramtsov AIKiraz UKirtani PKodach LLKorski KKovács ALaenkholm AVLang-Schwarz CLarsimont DLennerz JKLerousseau MLi XLy AMadabhushi AMaley SKManur Narasimhamurthy VMarks DKMcDonald ESMehrotra RMichiels SMinhas FUAAMittal SMoore DAMushtaq SNighat HPapathomas TPenault-Llorca FPerera RDPinard CJPinto-Cardenas JCPruneri GPusztai LRahman ARajpoot NMRapoport BLRau TTReis-Filho JSRibeiro JMRimm DRoslind AVincent-Salomon ASalto-Tellez MSaltz JSayed SScott ESiziopikou KPSotiriou CStenzinger ASughayer MASur DFineberg SSymmans FTanaka STaxter TTejpar STeuwen JThompson EATramm TTran WTvan der Laak Jvan Diest PJVerghese GEViale GVieth MWahab NWalter TWaumans YWen HYYang WYuan YZin RMAdams SBartlett JLoibl SDenkert CSavas PLoi SSalgado RSpecht Stovgaard E

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摘要:

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.

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DOI:

10.1002/path.6155

被引量:

6

年份:

1970

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