782 research outputs found
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images
of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL
maps are derived through computational staining using a convolutional neural network trained to
classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and
correlation with overall survival. TIL map structural patterns were grouped using standard
histopathological parameters. These patterns are enriched in particular T cell subpopulations
derived from molecular measures. TIL densities and spatial structure were differentially enriched
among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial
infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic
patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for
the TCGA image archives with insights into the tumor-immune microenvironment
Artificial intelligence-based recurrence prediction outperforms classical histopathological methods in pulmonary adenocarcinoma biopsies
Introduction: Between 10 and 50% of early-stage lung adenocarcinoma patients experience local or distant recurrence. Histological parameters such as a solid or micropapillary growth pattern are well-described risk factors for recurrence. However, not every patient presenting with such a pattern will develop recurrence. Designing a model which can more accurately predict recurrence on small biopsy samples can aid the stratification of patients for surgery, (neo-)adjuvant therapy, and follow-up. Material and Methods: In this study, a statistical model on biopsies fed with histological data from early and advanced-stage lung adenocarcinomas was developed to predict recurrence after surgical resection. Additionally, a convolutional neural network (CNN)-based artificial intelligence (AI) classification model, named AI-based Lung Adenocarcinoma Recurrence Predictor (AILARP), was trained to predict recurrence, with an ImageNet pre-trained EfficientNet that was fine-tuned on lung adenocarcinoma biopsies using transfer learning. Both models were validated using the same biopsy dataset to ensure that an accurate comparison was demonstrated. Results: The statistical model had an accuracy of 0.49 for all patients when using histology data only. The AI classification model yielded a test accuracy of 0.70 and 0.82 and an area under the curve (AUC) of 0.74 and 0.87 on patch-wise and patient-wise hematoxylin and eosin (H&E) stained whole slide images (WSIs), respectively. Conclusion: AI classification outperformed the traditional clinical approach for recurrence prediction on biopsies by a fair margin. The AI classifier may stratify patients according to their recurrence risk, based only on small biopsies. This model warrants validation in a larger lung biopsy cohort.</p
Self-supervised learning in non-small cell lung cancer discovers novel morphological clusters linked to patient outcome and molecular phenotypes
Histopathological images provide the definitive source of cancer diagnosis,
containing information used by pathologists to identify and subclassify
malignant disease, and to guide therapeutic choices. These images contain vast
amounts of information, much of which is currently unavailable to human
interpretation. Supervised deep learning approaches have been powerful for
classification tasks, but they are inherently limited by the cost and quality
of annotations. Therefore, we developed Histomorphological Phenotype Learning,
an unsupervised methodology, which requires no annotations and operates via the
self-discovery of discriminatory image features in small image tiles. Tiles are
grouped into morphologically similar clusters which appear to represent
recurrent modes of tumor growth emerging under natural selection. These
clusters have distinct features which can be identified using orthogonal
methods. Applied to lung cancer tissues, we show that they align closely with
patient outcomes, with histopathologically recognised tumor types and growth
patterns, and with transcriptomic measures of immunophenotype
Towards a Visual-Language Foundation Model for Computational Pathology
The accelerated adoption of digital pathology and advances in deep learning
have enabled the development of powerful models for various pathology tasks
across a diverse array of diseases and patient cohorts. However, model training
is often difficult due to label scarcity in the medical domain and the model's
usage is limited by the specific task and disease for which it is trained.
Additionally, most models in histopathology leverage only image data, a stark
contrast to how humans teach each other and reason about histopathologic
entities. We introduce CONtrastive learning from Captions for Histopathology
(CONCH), a visual-language foundation model developed using diverse sources of
histopathology images, biomedical text, and notably over 1.17 million
image-caption pairs via task-agnostic pretraining. Evaluated on a suite of 13
diverse benchmarks, CONCH can be transferred to a wide range of downstream
tasks involving either or both histopathology images and text, achieving
state-of-the-art performance on histology image classification, segmentation,
captioning, text-to-image and image-to-text retrieval. CONCH represents a
substantial leap over concurrent visual-language pretrained systems for
histopathology, with the potential to directly facilitate a wide array of
machine learning-based workflows requiring minimal or no further supervised
fine-tuning
On the histopathological growth patterns of colorectal liver metastasis:a Study of Histology, Immunology, Genetics, and Prognosis
This thesis aims to validate and establish the histopathological growth patterns of colorectal cancer liver metastasis as a relevant biomarker, and to evaluate immunity and genetics as potential underlying biological mechanisms
HistoPerm: A Permutation-Based View Generation Approach for Improving Histopathologic Feature Representation Learning
Deep learning has been effective for histology image analysis in digital
pathology. However, many current deep learning approaches require large,
strongly- or weakly-labeled images and regions of interest, which can be
time-consuming and resource-intensive to obtain. To address this challenge, we
present HistoPerm, a view generation method for representation learning using
joint embedding architectures that enhances representation learning for
histology images. HistoPerm permutes augmented views of patches extracted from
whole-slide histology images to improve classification performance. We
evaluated the effectiveness of HistoPerm on two histology image datasets for
Celiac disease and Renal Cell Carcinoma, using three widely used joint
embedding architecture-based representation learning methods: BYOL, SimCLR, and
VICReg. Our results show that HistoPerm consistently improves patch- and
slide-level classification performance in terms of accuracy, F1-score, and AUC.
Specifically, for patch-level classification accuracy on the Celiac disease
dataset, HistoPerm boosts BYOL and VICReg by 8% and SimCLR by 3%. On the Renal
Cell Carcinoma dataset, patch-level classification accuracy is increased by 2%
for BYOL and VICReg, and by 1% for SimCLR. In addition, on the Celiac disease
dataset, models with HistoPerm outperform the fully-supervised baseline model
by 6%, 5%, and 2% for BYOL, SimCLR, and VICReg, respectively. For the Renal
Cell Carcinoma dataset, HistoPerm lowers the classification accuracy gap for
the models up to 10% relative to the fully-supervised baseline. These findings
suggest that HistoPerm can be a valuable tool for improving representation
learning of histopathology features when access to labeled data is limited and
can lead to whole-slide classification results that are comparable to or
superior to fully-supervised methods
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment. Tumor-infiltrating lymphocytes (TILs) were identified from standard pathology cancer images by a deep-learning-derived \u201ccomputational stain\u201d developed by Saltz et al. They processed 5,202 digital images from 13 cancer types. Resulting TIL maps were correlated with TCGA molecular data, relating TIL content to survival, tumor subtypes, and immune profiles
Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment
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