Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer

Han, Ling; Gupta, Rajarsi; Hou, Le; Abousamra, Shahira; Fassler, Danielle; Torre-Healy, Luke A.; Moffitt, Richard A.; Kurç, Tahsin; Samaras, Dimitris; Batiste, Rebecca; Zhao, Tianhao; Rao, Arvind; Dyke, Alison L. Van; Sharma, Ashish; Bremer, Erich; Almeida, Jonas S.; Saltz, Joel

doi:10.1016/j.ajpath.2020.03.012

Cited by 84 publications

(54 citation statements)

References 53 publications

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“…An added advantage of computational methods is the potential to additionally address the spatial distribution of TILs and thus provide insights into the biologic and clinical significance of distribution patterns and TILs-tumour interactions. The significance of spatial distribution and hot spots has recently been examined in BC using artificial intelligence algorithms [27][28][29] and data suggest that spatial distribution may provide added prognostic information [27].…”

Section: Discussionmentioning

confidence: 99%

Intra-Tumour Heterogeneity Is One of the Main Sources of Inter-Observer Variation in Scoring Stromal Tumour Infiltrating Lymphocytes in Triple Negative Breast Cancer

Kilmartin

O’Loughlin

Andreu

et al. 2021

Cancers

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Stromal tumour infiltrating lymphocytes (sTILs) are a strong prognostic marker in triple negative breast cancer (TNBC). Consistency scoring sTILs is good and was excellent when an internet-based scoring aid developed by the TIL-WG was used to score cases in a reproducibility study. This study aimed to evaluate the reproducibility of sTILs assessment using this scoring aid in cases from routine practice and to explore the potential of the tool to overcome variability in scoring. Twenty-three breast pathologists scored sTILs in digitized slides of 49 TNBC biopsies using the scoring aid. Subsequently, fields of view (FOV) from each case were selected by one pathologist and scored by the group using the tool. Inter-observer agreement was good for absolute sTILs (ICC 0.634, 95% CI 0.539–0.735, p < 0.001) but was poor to fair using binary cutpoints. sTILs heterogeneity was the main contributor to disagreement. When pathologists scored the same FOV from each case, inter-observer agreement was excellent for absolute sTILs (ICC 0.798, 95% CI 0.727–0.864, p < 0.001) and good for the 20% (ICC 0.657, 95% CI 0.561–0.756, p < 0.001) and 40% (ICC 0.644, 95% CI 0.546–0.745, p < 0.001) cutpoints. However, there was a wide range of scores for many cases. Reproducibility scoring sTILs is good when the scoring aid is used. Heterogeneity is the main contributor to variance and will need to be overcome for analytic validity to be achieved.

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Section: Discussionmentioning

confidence: 99%

Intra-Tumour Heterogeneity Is One of the Main Sources of Inter-Observer Variation in Scoring Stromal Tumour Infiltrating Lymphocytes in Triple Negative Breast Cancer

Kilmartin

O’Loughlin

Andreu

et al. 2021

Cancers

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“…Overall, the ResNet34 was the best performer in both cancer detection task and lymphocyte detection task, even surpassing the Saltz study's accuracy in the case of breast cancer. Using their ResNet34 model, Le et al (2020) showed that their estimated TIL infiltration was a significant survival predictor.…”

Section: Region Of Interest-level Analysis Methodsmentioning

confidence: 99%

Deep Learning of Histopathology Images at the Single Cell Level

Lee

Lockhart

Xie

et al. 2021

Front. Artif. Intell.

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The tumor immune microenvironment (TIME) encompasses many heterogeneous cell types that engage in extensive crosstalk among the cancer, immune, and stromal components. The spatial organization of these different cell types in TIME could be used as biomarkers for predicting drug responses, prognosis and metastasis. Recently, deep learning approaches have been widely used for digital histopathology images for cancer diagnoses and prognoses. Furthermore, some recent approaches have attempted to integrate spatial and molecular omics data to better characterize the TIME. In this review we focus on machine learning-based digital histopathology image analysis methods for characterizing tumor ecosystem. In this review, we will consider three different scales of histopathological analyses that machine learning can operate within: whole slide image (WSI)-level, region of interest (ROI)-level, and cell-level. We will systematically review the various machine learning methods in these three scales with a focus on cell-level analysis. We will provide a perspective of workflow on generating cell-level training data sets using immunohistochemistry markers to “weakly-label” the cell types. We will describe some common steps in the workflow of preparing the data, as well as some limitations of this approach. Finally, we will discuss future opportunities of integrating molecular omics data with digital histopathology images for characterizing tumor ecosystem.

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“…Most studies of computational TILs have employed patch- or object detection-based approaches [ 26 , 27 , 28 , 29 ] with manual region outlining as part of the pipeline [ 30 ]. Some of these also used multiplexed immunofluorescence (mIF) [ 31 ] or immunohistochemistry (IHC) [ 32 , 33 ] to classify cells as lymphocytes.…”

Section: Introductionmentioning

confidence: 99%

Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers

Thagaard

Stovgaard

Vognsen

et al. 2021

Cancers

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Triple-negative breast cancer (TNBC) is an aggressive and difficult-to-treat cancer type that represents approximately 15% of all breast cancers. Recently, stromal tumor-infiltrating lymphocytes (sTIL) resurfaced as a strong prognostic biomarker for overall survival (OS) for TNBC patients. Manual assessment has innate limitations that hinder clinical adoption, and the International Immuno-Oncology Biomarker Working Group (TIL-WG) has therefore envisioned that computational assessment of sTIL could overcome these limitations and recommended that any algorithm should follow the manual guidelines where appropriate. However, no existing studies capture all the concepts of the guideline or have shown the same prognostic evidence as manual assessment. In this study, we present a fully automated digital image analysis pipeline and demonstrate that our hematoxylin and eosin (H&E)-based pipeline can provide a quantitative and interpretable score that correlates with the manual pathologist-derived sTIL status, and importantly, can stratify a retrospective cohort into two significant distinct prognostic groups. We found our score to be prognostic for OS (HR: 0.81 CI: 0.72–0.92 p = 0.001) independent of age, tumor size, nodal status, and tumor type in statistical modeling. While prior studies have followed fragments of the TIL-WG guideline, our approach is the first to follow all complex aspects, where appropriate, supporting the TIL-WG vision of computational assessment of sTIL in the future clinical setting.

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Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer

Cited by 84 publications

References 53 publications

Intra-Tumour Heterogeneity Is One of the Main Sources of Inter-Observer Variation in Scoring Stromal Tumour Infiltrating Lymphocytes in Triple Negative Breast Cancer

Intra-Tumour Heterogeneity Is One of the Main Sources of Inter-Observer Variation in Scoring Stromal Tumour Infiltrating Lymphocytes in Triple Negative Breast Cancer

Deep Learning of Histopathology Images at the Single Cell Level

Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers

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