Radiomics in radiooncology – Challenging the medical physicist

Peeken, Jan C.; Bernhofer, Michael; Wiestler, Benedikt; Goldberg, Tatyana; Cremers, Daniel; Rost, Burkhard; Wilkens, Jan J.; Combs, Stephanie E.; Nüsslin, Fridtjof

doi:10.1016/j.ejmp.2018.03.012

Cited by 78 publications

(62 citation statements)

References 89 publications

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“…They can also be used to reduce the gadolinium dose in contrast-enhanced brain MRI by an order of magnitude [88] without significant reduction in image quality. Deep learning is applied in radiotherapy [89], in PET-MRI attenuation correction [90,91], in radiomics [92,93] (see [94] for a review of radiomics related to radiooncology and medical physics), and for theranostics in neurosurgical imaging, combining confocal laser endomicroscopy with deep learning models for automatic detection of intraoperative CLE images on-the-fly [95].…”

Section: Deep Learning Medical Imaging and Mrimentioning

confidence: 99%

An overview of deep learning in medical imaging focusing on MRI

Lundervold

2019

Zeitschrift für Medizinische Physik

1,430

883

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What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI.Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging. Artificial neural networksArtificial neural networks (ANNs) is one of the most famous machine learning models, introduced already in the 1950s, and actively studied since [41, Chapter 1.2]. 11 Roughly, a neural network consists of a number of connected computational units, called neurons, arranged in layers. There's an input layer where data enters the network, followed by one or more hidden layers transforming the data as it flows through, before ending at an output layer that produces the neural network's predictions. The network is trained to output useful predictions by identifying patterns in a set of labeled training data, fed through the network while the outputs are compared with the actual labels by an objective function. During training the network's parametersthe strength of each neuron-is tuned until the patterns identified by the network result in good 10 https://www.deeplearningbook.org/ 11 The loose connection between artificial neural networks and neural networks in the brain is often mentioned, but quite over-blown considering the complexity of biological neural networks. However, there is some interesting recent work connecting neuroscience and artificial neural networks, indicating an increase in the cross-fertilization between the two fields [43,44,45].3 predictions for the training data. Once the patterns are learned, the network can be used to make predictions on new, unseen data, i.e. generalize to new data.It h...

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Section: Deep Learning Medical Imaging and Mrimentioning

confidence: 99%

An overview of deep learning in medical imaging focusing on MRI

Lundervold

2019

Zeitschrift für Medizinische Physik

1,430

883

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“…Radiomic analysis consists of the high-throughput mining of quantitative features from standard-of-care medical imaging 1 and is becoming a powerful tool for treatment planning, outcome prediction, and personalized therapy. [2][3][4] Radiomic models are often built using features obtained from baseline or follow-up computed tomography (CT) or positron emission tomography (PET) images. 1,[4][5][6] Recently, there has been an increased interest in performing radiomic studies using MR images.…”

Section: Introductionmentioning

confidence: 99%

Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI‐radiomic features

Tixier

Young

et al. 2019

Medical Physics

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Purpose The use of radiomic features as biomarkers of treatment response and outcome or as correlates to genomic variations requires that the computed features are robust and reproducible. Segmentation, a crucial step in radiomic analysis, is a major source of variability in the computed radiomic features. Therefore, we studied the impact of tumor segmentation variability on the robustness of MRI radiomic features. Method Fluid‐attenuated inversion recovery (FLAIR) and contrast‐enhanced T1‐weighted (T1WICE) MRI of 90 patients diagnosed with glioblastoma were segmented using a semiautomatic algorithm and an interactive segmentation with two different raters. We analyzed the robustness of 108 radiomic features from five categories (intensity histogram, gray‐level co‐occurrence matrix, gray‐level size‐zone matrix (GLSZM), edge maps, and shape) using intra‐class correlation coefficient (ICC) and Bland and Altman analysis. Results Our results show that both segmentation methods are reliable with ICC ≥ 0.96 and standard deviation (SD) of mean differences between the two raters (SDdiffs) ≤ 30%. Features computed from the histogram and co‐occurrence matrices were found to be the most robust (ICC ≥ 0.8 and SDdiffs ≤ 30% for most features in these groups). Features from GLSZM were shown to have mixed robustness. Edge, shape, and GLSZM features were the most impacted by the choice of segmentation method with the interactive method resulting in more robust features than the semiautomatic method. Finally, features computed from T1WICE and FLAIR images were found to have similar robustness when computed with the interactive segmentation method. Conclusion Semiautomatic and interactive segmentation methods using two raters are both reliable. The interactive method produced more robust features than the semiautomatic method. We also found that the robustness of radiomic features varied by categories. Therefore, this study could help motivate segmentation methods and feature selection in MRI radiomic studies.

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“…Interestingly, the term "radiogenomics" initially belonged to the realm of radiation oncology where it reflected the prediction of radiotherapy-induced toxicity based on the genetic profile of the tumor [7]. In recent years, radiogenomics has come to carry the connotation of linking radiomic features to even broader biological parameters beyond genomics such as proteomics and metabolomics [8]. While this is most likely the ultimate destiny of the field, it has yet to become the standard definition.…”

Section: Introductionmentioning

confidence: 99%

Radiogenomics: bridging imaging and genomics

et al. 2019

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From diagnostics to prognosis to response prediction, new applications for radiomics are rapidly being developed. One of the fastest evolving branches involves linking imaging phenotypes to the tumor genetic profile, a field commonly referred to as "radiogenomics." In this review, a general outline of radiogenomic literature concerning prominent mutations across different tumor sites will be provided. The field of radiogenomics originates from image processing techniques developed decades ago; however, many technical and clinical challenges still need to be addressed. Nevertheless, increasingly accurate and robust radiogenomic models are being presented and the future appears to be bright.

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Radiomics in radiooncology – Challenging the medical physicist

Cited by 78 publications

References 89 publications

An overview of deep learning in medical imaging focusing on MRI

An overview of deep learning in medical imaging focusing on MRI

Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI‐radiomic features

Radiogenomics: bridging imaging and genomics

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