PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer

Negahbani, Farzin; Sabzi, Rasool; Jahromi, Bita Pakniyat; Firouzabadi, Dena; Movahedi, Fateme; Kohandel-Shirazi, Mahsa; Majidi, Shayan; Dehghanian, Amirreza

doi:10.1038/s41598-021-86912-w

Cited by 43 publications

(28 citation statements)

References 40 publications

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“…(17,35,36). Numerous applications such as quantification of receptor status (37), estimation of Ki-67 index (38), or implementation into Ziehl-Neelsen staining (39) As a data-driven model, an unbiased and comprehensive training dataset is always preferred in an ideal condition. Our current model can be improved from the following aspects.…”

Section: Discussionmentioning

confidence: 99%

“…( 17 , 35 , 36 ). Numerous applications such as quantification of receptor status ( 37 ), estimation of Ki-67 index ( 38 ), or implementation into Ziehl-Neelsen staining ( 39 ) and Masson's Trichrome ( 40 ) are potentially feasible in cardiovascular studies as well. Besides generic image classification and segmentation, DNN has demonstrated its viability to synthesize pseudo H&E images from Raman spectroscopy and other multi-modality non-linear imaging techniques, augmenting non-invasive and in vivo diagnosis ( 41 ).…”

Section: Discussionmentioning

confidence: 99%

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Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury

Jiao

Yuan

Sodimu

et al. 2022

Front. Cardiovasc. Med.

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Deep neural networks have become the mainstream approach for analyzing and interpreting histology images. In this study, we established and validated an interpretable DNN model to assess endomyocardial biopsy (EMB) data of patients with myocardial injury. Deep learning models were used to extract features and classify EMB histopathological images of heart failure cases diagnosed with either ischemic cardiomyopathy or idiopathic dilated cardiomyopathy and non-failing cases (organ donors without a history of heart failure). We utilized the gradient-weighted class activation mapping (Grad-CAM) technique to emphasize injured regions, providing an entry point to assess the dominant morphology in the process of a comprehensive evaluation. To visualize clustered regions of interest (ROI), we utilized uniform manifold approximation and projection (UMAP) embedding for dimension reduction. We further implemented a multi-model ensemble mechanism to improve the quantitative metric (area under the receiver operating characteristic curve, AUC) to 0.985 and 0.992 on ROI-level and case-level, respectively, outperforming the achievement of 0.971 ± 0.017 and 0.981 ± 0.020 based on the sub-models. Collectively, this new methodology provides a robust and interpretive framework to explore local histopathological patterns, facilitating the automatic and high-throughput quantification of cardiac EMB analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury

Jiao

Yuan

Sodimu

et al. 2022

Front. Cardiovasc. Med.

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“…More recently, although alternative approaches are actively pursued [51][52][53][54], well consolidated methodologies derived by CNNs are still being used [55]. In particular, two families of algorithms deserve a mention for the rather large popularity gained in the last few years, both stemming from the original R-CNN model [56].…”

Section: Lymphocyte Detection and Density Mapsmentioning

confidence: 99%

Quantification of the Immune Content in Neuroblastoma: Deep Learning and Topological Data Analysis in Digital Pathology

Bussola

Papa

Castellano

et al. 2021

IJMS

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We introduce here a novel machine learning (ML) framework to address the issue of the quantitative assessment of the immune content in neuroblastoma (NB) specimens. First, the EUNet, a U-Net with an EfficientNet encoder, is trained to detect lymphocytes on tissue digital slides stained with the CD3 T-cell marker. The training set consists of 3782 images extracted from an original collection of 54 whole slide images (WSIs), manually annotated for a total of 73,751 lymphocytes. Resampling strategies, data augmentation, and transfer learning approaches are adopted to warrant reproducibility and to reduce the risk of overfitting and selection bias. Topological data analysis (TDA) is then used to define activation maps from different layers of the neural network at different stages of the training process, described by persistence diagrams (PD) and Betti curves. TDA is further integrated with the uniform manifold approximation and projection (UMAP) dimensionality reduction and the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm for clustering, by the deep features, the relevant subgroups and structures, across different levels of the neural network. Finally, the recent TwoNN approach is leveraged to study the variation of the intrinsic dimensionality of the U-Net model. As the main task, the proposed pipeline is employed to evaluate the density of lymphocytes over the whole tissue area of the WSIs. The model achieves good results with mean absolute error 3.1 on test set, showing significant agreement between densities estimated by our EUNet model and by trained pathologists, thus indicating the potentialities of a promising new strategy in the quantification of the immune content in NB specimens. Moreover, the UMAP algorithm unveiled interesting patterns compatible with pathological characteristics, also highlighting novel insights into the dynamics of the intrinsic dataset dimensionality at different stages of the training process. All the experiments were run on the Microsoft Azure cloud platform.

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“…Garcia et al (20) proposed a deep learning model to count TILs in IHC images of gastric cancer tissue samples by using a model trained with 70x70 square pixel patches extracted from biopsy micrographs scanned at 40x magnification and labeled by pathologists. PathoNet, developed by Negahbani et al (21), implements a deep learning model based on the U-Net architecture (22) for detection and classification of Ki-67 and TILs in breast cancer cases.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer

et al. 2022

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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 abundance and spatial distribution of TILs in 23 different types of cancer. We trained three state-of-the-art, popular convolutional neural network (CNN) architectures (namely VGG16, Inception-V4, and ResNet-34) with a large volume of training data, which combined manual annotations from pathologists (strong annotations) and computer-generated labels from our previously reported first-generation TIL model for 13 cancer types (model-generated annotations). Specifically, this training dataset contains TIL positive and negative patches from cancers in additional organ sites and curated data to help improve algorithmic performance by decreasing known false positives and false negatives. Our new TIL workflow also incorporates automated thresholding to convert model predictions into binary classifications to generate TIL maps. The new TIL models all achieve better performance with improvements of up to 13% in accuracy and 15% in F-score. We report these new TIL models and a curated dataset of TIL maps, referred to as TIL-Maps-23, for 7983 WSIs spanning 23 types of cancer with complex and diverse visual appearances, which will be publicly available along with the code to evaluate performance.Code Available at:https://github.com/ShahiraAbousamra/til_classification.

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PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer

Cited by 43 publications

References 40 publications

Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury

Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury

Quantification of the Immune Content in Neuroblastoma: Deep Learning and Topological Data Analysis in Digital Pathology

Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer

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