2022
DOI: 10.3389/fonc.2021.806603
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Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer

Abstract: The role of tumor infiltrating lymphocytes (TILs) as a biomarker to predict disease progression and clinical outcomes has generated tremendous interest in translational cancer research. We present an updated and enhanced deep learning workflow to classify 50x50 um tiled image patches (100x100 pixels at 20x magnification) as TIL positive or negative based on the presence of 2 or more TILs in gigapixel whole slide images (WSIs) from the Cancer Genome Atlas (TCGA). This workflow generates TIL maps to study the ab… Show more

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Cited by 40 publications
(29 citation statements)
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“…The analysis was carried out on the spatial maps of lymphocytic infiltrates generated from the images of hematoxylin and eosin-stained tissue slides from The Cancer Genome Atlas (TCGA) in a previous study [ 12 ]. The TIL maps were obtained by the use of convolutional neural networks on images divided into small patches (50 × 50) and trained for tumor-infiltrating lymphocyte detection [ 12 ].…”
Section: Methodsmentioning
confidence: 99%
See 2 more Smart Citations
“…The analysis was carried out on the spatial maps of lymphocytic infiltrates generated from the images of hematoxylin and eosin-stained tissue slides from The Cancer Genome Atlas (TCGA) in a previous study [ 12 ]. The TIL maps were obtained by the use of convolutional neural networks on images divided into small patches (50 × 50) and trained for tumor-infiltrating lymphocyte detection [ 12 ].…”
Section: Methodsmentioning
confidence: 99%
“…The analysis was carried out on the spatial maps of lymphocytic infiltrates generated from the images of hematoxylin and eosin-stained tissue slides from The Cancer Genome Atlas (TCGA) in a previous study [ 12 ]. The TIL maps were obtained by the use of convolutional neural networks on images divided into small patches (50 × 50) and trained for tumor-infiltrating lymphocyte detection [ 12 ]. In the original paper [ 12 ], the information about TIL was stored using two TIL map scales: (i) binary (if there are TILs on the patch or not); (ii) probability (values from 0.5 to 1; closer to 1 means a higher chance of TILs; values smaller than 0.5 means that there are no TILs on the patch).…”
Section: Methodsmentioning
confidence: 99%
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“…The Stony Brook Biomedical Informatics group has actively contributed to this field of work for many years by characterizing the performance of DL pathology algorithms. Specifically, we have previously developed a DL based method to analyze WSIs to quantify distributions of TILs [23,24]. This method partitions a WSI into a regular mesh of image patches.…”
Section: Introductionmentioning
confidence: 99%
“…The method trains a classification model (based on a VGG16 pre-trained on Ima-geNet) to predict if a given image patch is TIL-positive (i.e., the patch contains 2 or more lymphocytes) or TIL-negative. This classification model has been trained and evaluated with a set of TIL-positive and TIL-negative patches from multiple cancer types with comprehensive analyses carried out to characterize performance of this method [24].…”
Section: Introductionmentioning
confidence: 99%