Dataset Information

Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks.

ABSTRACT: Recent years have seen a growing awareness of the role the immune system plays in successful cancer treatment, especially in novel therapies like immunotherapy. The characterization of the immunological composition of tumors and their micro-environment is thus becoming a necessity. In this paper we introduce a deep learning-based immune cell detection and quantification method, which is based on supervised learning, i.e., the input data for training comprises labeled images. Our approach objectively deals with staining variation and staining artifacts in immunohistochemically stained lung cancer tissue and is as precise as humans. This is evidenced by the low cell count difference to humans of 0.033 cells on average. This method, which is based on convolutional neural networks, has the potential to provide a new quantitative basis for research on immunotherapy.

SUBMITTER: Aprupe L

PROVIDER: S-EPMC6462181 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks.

Aprupe Lilija L Litjens Geert G Brinker Titus J TJ van der Laak Jeroen J Grabe Niels N

PeerJ 20190410

Recent years have seen a growing awareness of the role the immune system plays in successful cancer treatment, especially in novel therapies like immunotherapy. The characterization of the immunological composition of tumors and their micro-environment is thus becoming a necessity. In this paper we introduce a deep learning-based immune cell detection and quantification method, which is based on supervised learning, i.e., the input data for training comprises labeled images. Our approach objecti ...[more]

PMID: 30993030

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Project description:BackgroundCentral to protein biology is the understanding of how structural elements give rise to observed function. The surfeit of protein structural data enables development of computational methods to systematically derive rules governing structural-functional relationships. However, performance of these methods depends critically on the choice of protein structural representation. Most current methods rely on features that are manually selected based on knowledge about protein structures. These are often general-purpose but not optimized for the specific application of interest. In this paper, we present a general framework that applies 3D convolutional neural network (3DCNN) technology to structure-based protein analysis. The framework automatically extracts task-specific features from the raw atom distribution, driven by supervised labels. As a pilot study, we use our network to analyze local protein microenvironments surrounding the 20 amino acids, and predict the amino acids most compatible with environments within a protein structure. To further validate the power of our method, we construct two amino acid substitution matrices from the prediction statistics and use them to predict effects of mutations in T4 lysozyme structures.ResultsOur deep 3DCNN achieves a two-fold increase in prediction accuracy compared to models that employ conventional hand-engineered features and successfully recapitulates known information about similar and different microenvironments. Models built from our predictions and substitution matrices achieve an 85% accuracy predicting outcomes of the T4 lysozyme mutation variants. Our substitution matrices contain rich information relevant to mutation analysis compared to well-established substitution matrices. Finally, we present a visualization method to inspect the individual contributions of each atom to the classification decisions.ConclusionsEnd-to-end trained deep learning networks consistently outperform methods using hand-engineered features, suggesting that the 3DCNN framework is well suited for analysis of protein microenvironments and may be useful for other protein structural analyses.

Project description:BACKGROUND:Ultrashort echo time (UTE) proton MRI has gained popularity for assessing lung structure and function in pulmonary imaging; however, the development of rapid biomarker extraction and regional quantification has lagged behind due to labor-intensive lung segmentation. PURPOSE:To evaluate a deep learning (DL) approach for automated lung segmentation to extract image-based biomarkers from functional lung imaging using 3D radial UTE oxygen-enhanced (OE) MRI. STUDY TYPE:Retrospective study aimed to evaluate a technical development. POPULATION:Forty-five human subjects, including 16 healthy volunteers, 5 asthma, and 24 patients with cystic fibrosis. FIELD STRENGTH/SEQUENCE:1.5T MRI, 3D radial UTE (TE = 0.08 msec) sequence. ASSESSMENT:Two 3D radial UTE volumes were acquired sequentially under normoxic (21% O2 ) and hyperoxic (100% O2 ) conditions. Automated segmentation of the lungs using 2D convolutional encoder-decoder based DL method, and the subsequent functional quantification via adaptive K-means were compared with the results obtained from the reference method, supervised region growing. STATISTICAL TESTS:Relative to the reference method, the performance of DL on volumetric quantification was assessed using Dice coefficient with 95% confidence interval (CI) for accuracy, two-sided Wilcoxon signed-rank test for computation time, and Bland-Altman analysis on the functional measure derived from the OE images. RESULTS:The DL method produced strong agreement with supervised region growing for the right (Dice: 0.97; 95% CI = [0.96, 0.97]; P < 0.001) and left lungs (Dice: 0.96; 95% CI = [0.96, 0.97]; P < 0.001). The DL method averaged 46 seconds to generate the automatic segmentations in contrast to 1.93 hours using the reference method (P < 0.001). Bland-Altman analysis showed nonsignificant intermethod differences of volumetric (P ? 0.12) and functional measurements (P ? 0.34) in the left and right lungs. DATA CONCLUSION:DL provides rapid, automated, and robust lung segmentation for quantification of regional lung function using UTE proton MRI. LEVEL OF EVIDENCE:2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1169-1181.

Dataset Information

Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks.

Publications

Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks.

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