The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Radiology, Journal Year: 2020, Volume and Issue: 295(2), P. 328 - 338

Published: March 10, 2020

The image biomarker standardisation initiative (IBSI) is an independent international collaboration which works towards standardising the extraction of biomarkers from acquired imaging for purpose high-throughput quantitative analysis (radiomics). Lack reproducibility and validation studies considered to be a major challenge field. Part this lies in scantiness consensus-based guidelines definitions process translating into biomarkers. IBSI therefore seeks provide nomenclature definitions, benchmark data sets, values verify processing calculations, as well reporting guidelines, analysis.

Language: Английский

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A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study

The Lancet Oncology, Journal Year: 2018, Volume and Issue: 19(9), P. 1180 - 1191

Published: Aug. 14, 2018

Language: Английский

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The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges

Theranostics, Journal Year: 2019, Volume and Issue: 9(5), P. 1303 - 1322

Published: Jan. 1, 2019

Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring temporal spatial characteristics of tumor.Progress computational methods, especially artificial intelligence medical image process analysis, has converted these images into quantitative minable data associated with clinical events oncology management.This concept was first described as radiomics 2012.Since then, computer scientists, radiologists, oncologists have gravitated towards this new tool exploited advanced methodologies to mine information behind images.On basis a great quantity radiographic novel technologies, researchers developed validated radiomic models that may improve accuracy diagnoses therapy response assessments.Here, we review recent methodological developments radiomics, including acquisition, segmentation, feature extraction, modelling, well rapidly developing deep learning technology.Moreover, outline main applications diagnosis, treatment planning evaluations field aim personalized medicine.Finally, discuss challenges scope applicability methods.

Language: Английский

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Preparing Medical Imaging Data for Machine Learning

Radiology, Journal Year: 2020, Volume and Issue: 295(1), P. 4 - 15

Published: Feb. 18, 2020

Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of imaging life cycle from image creation diagnosis outcome prediction. chief obstacles development clinical implementation AI algorithms availability sufficiently large, curated, representative training data that includes expert labeling (eg, annotations). Current supervised methods require a curation process for optimally train, validate, test algorithms. Currently, most research groups industry have limited access based on small sample sizes geographic areas. In addition, preparation is costly time-intensive process, results which with utility poor generalization. this article, authors describe fundamental steps preparing algorithm development, explain current limitations curation, explore new approaches address problem availability. © RSNA, 2020

Language: Английский

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AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy

Medical Physics, Journal Year: 2018, Volume and Issue: 46(2), P. 576 - 589

Published: Nov. 27, 2018

Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN as input generating masks of all interest one shot. AnatomyNet is built upon the popular 3D U-net architecture, but extends it three important ways: 1) a new encoding scheme to allow auto-segmentation on instead local patches or subsets slices, 2) incorporating squeeze-and-excitation residual blocks layers for better feature representation, 3) loss function combining Dice scores focal facilitate training neural model. These features are designed address two main challenges deep-learning-based segmentation: a) segmenting small anatomies (i.e., optic chiasm nerves) occupying only few b) with inconsistent data annotations missing ground truth some anatomical structures. Results: We collected 261 train used MICCAI Head Neck Auto Segmentation Challenge 2015 benchmark dataset evaluate performance AnatomyNet. The objective segment nine anatomies: brain stem, chiasm, mandible, nerve left, right, parotid gland submandibular right. Compared previous state-of-the-art results competition, increases similarity coefficient by 3.3% average. takes about 0.12 seconds fully image dimension 178 x 302 225, significantly faster than methods. In addition, model able process delineate pass, requiring little pre- post-processing. https://github.com/wentaozhu/AnatomyNet-for-anatomical-segmentation.git.

Language: Английский

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Machine and deep learning methods for radiomics

Medical Physics, Journal Year: 2020, Volume and Issue: 47(5)

Published: May 1, 2020

Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale extracted imaging information clinical and biological endpoints. The development of methods along with machine learning has enabled the opportunity move data science research towards translation for more personalized cancer treatments. Accumulating evidence indeed demonstrated noninvasive advanced analytics, is, radiomics, can reveal key components tumor phenotype multiple three‐dimensional lesions at time points over beyond course treatment. These developments use CT, PET, US, MR could augment patient stratification prognostication buttressing targeted therapeutic approaches. In recent years, deep architectures have their tremendous potential segmentation, reconstruction, recognition, classification. Many powerful open‐source commercial platforms are currently available embark new areas radiomics. Quantitative research, however, complex statistical principles should be followed realize its full potential. field particular, requires a renewed focus on optimal study design/reporting practices standardization acquisition, feature calculation, rigorous forward. this article, role as major computational vehicle model building radiomics‐based signatures or classifiers, diverse applications, working principles, opportunities, radiomics will reviewed examples drawn primarily from oncology. We also address issues related common applications medical physics, such standardization, extraction, building, validation.

Language: Английский

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From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and Opportunities

IEEE Signal Processing Magazine, Journal Year: 2019, Volume and Issue: 36(4), P. 132 - 160

Published: June 26, 2019

Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping hospitals the availability extensive set images through internal/external communication systems, have resulted a recent surge significant interest "Radiomics". Radiomics is an emerging relatively new research field, which refers to extracting semi-quantitative and/or quantitative features from goal developing predictive prognostic models, expected become critical component for integration image-derived information personalized treatment near future. The conventional workflow typically based on pre-designed (also referred as hand-crafted or engineered features) segmented region interest. Nevertheless, deep caused trends towards learning-based discovery Radiomics). Considering advantages these two approaches, there are also hybrid solutions developed exploit potentials multiple data sources. variety approaches Radiomics, further improvements require comprehensive integrated sketch, this article. This manuscript provides unique interdisciplinary perspective by discussing state-of-the-art context Radiomics.

Language: Английский

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Vulnerabilities of radiomic signature development: The need for safeguards

Radiotherapy and Oncology, Journal Year: 2018, Volume and Issue: 130, P. 2 - 9

Published: Nov. 8, 2018

Refinement of radiomic results and methodologies is required to ensure progression the field. In this work, we establish a set safeguards designed improve support current through detailed analysis signature.A model (MW2018) was fitted externally validated using features extracted from previously reported lung head neck (H&N) cancer datasets gross-tumour-volume contours, as well images with randomly permuted voxel index values; i.e. without meaningful texture. To determine MW2018's added benefit, prognostic accuracy tumour volume alone calculated baseline.MW2018 had an external validation concordance (c-index) 0.64. However, similar performance achieved randomized signal intensities (c-index = 0.64 0.60 for H&N lung, respectively). Tumour c-index correlated strongly three four features. It determined that signature surrogate intensity texture values were not pertinent prognostication.Our experiments reveal vulnerabilities in development processes suggest can be used refine methodologies, productive objective independent

Language: Английский

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Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis

European Journal of Nuclear Medicine and Molecular Imaging, Journal Year: 2019, Volume and Issue: 46(13), P. 2638 - 2655

Published: June 25, 2019

Language: Английский

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Overview of radiomics in breast cancer diagnosis and prognostication

The Breast, Journal Year: 2019, Volume and Issue: 49, P. 74 - 80

Published: Nov. 6, 2019

Diagnosis of early invasive breast cancer relies on radiology and clinical evaluation, supplemented by biopsy confirmation. At least three issues burden this approach: a) suboptimal sensitivity positive predictive power screening diagnostic approaches, respectively; b) invasiveness with discomfort for women undergoing tests; c) long turnaround time recall tests. In the setting, is suboptimal, when a suspicious lesion detected recommended, value modest. Recent technological advances in medical imaging, especially field artificial intelligence applied to image analysis, hold promise addressing challenges detection, assessment treatment response, monitoring disease progression. Radiomics include feature extraction from images; these features are related tumor size, shape, intensity, texture, collectively providing comprehensive characterization, so-called radiomics signature tumor. based hypothesis that extracted quantitative data derives mechanisms occurring at genetic molecular levels. article we focus role potential diagnosis prognostication.

Language: Английский

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