The Potential of Radiomic-Based Phenotyping in Precision Medicine

Aerts, Hugo J.W.L.

doi:10.1001/jamaoncol.2016.2631

Cited by 533 publications

(398 citation statements)

References 78 publications

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“…More recently, MR fingerprinting (MRF) 80 was introduced to advance the role of quantitative MRI by using pseudorandomized acquisition parameters. However, large-scale, high-quality benchmark datasets, including complete clinical labels, standard radiomic features, and molecular profiles are not widely available for the purpose of data sharing, experimental evaluation, and reproducibility of radiomics towards precision medicine 81, 82 .…”

Section: Research Opportunities and Challengesmentioning

confidence: 99%

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

Zhou¹,

Scott

Chaudhury

et al. 2017

AJNR Am J Neuroradiol

327

213

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Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challenges remain for methodological developments to optimize feature extraction and provide rapid information flow in clinical settings. Equally important, to be clinically useful, predictive radiomic properties must be clearly linked to meaningful biological characteristics as well as qualitative imaging properties familiar to radiologist. Here we use a cross-disciplinary approach to highlight studies in radiomics. We review brain tumor radiological studies (e.g., imaging interpretation) through computational models (e.g., computer vision and machine learning) that provide novel clinical insights. In particular, we outline current quantitative image feature extraction and prediction strategies with different level of available clinical classes for supporting clinical decision making. We further discuss machine learning challenges and data opportunities to advance radiomic studies.

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Section: Research Opportunities and Challengesmentioning

confidence: 99%

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

Zhou¹,

Scott

Chaudhury

et al. 2017

AJNR Am J Neuroradiol

327

213

View full text Add to dashboard Cite

show abstract

“…Change of the status quo may be met with antagonism, as evidenced by the resistance to transition from diagnostically useful genomics/proteomics platforms to analogously validated imaging methodologies. Also, considering the range of multiplexed-imaging methods currently in development, there is a striking lack of robust standardised methodology to permit data comparison across centres and enable exploitation for machine learning and other big data approaches for segmentation, registration, detection and heterogeneity determination 10 .…”

Section: Multiplexing For the Classification Of Cancer Phenotypesmentioning

confidence: 99%

“…In essence, this constitutes a way of achieving 'activatable' high LET radiotherapy. In a landmark study, BNCT using liposomal delivery of 10 B significantly reduced tumour growth in a preclinical EMT6 model of mammary adenocarcinoma 242 . As in EBT, beam positioning is a critical element in BNCT.…”

Section: Imaging and Radiotherapymentioning

confidence: 99%

Multiplexed imaging for diagnosis and therapy

et al. 2017

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Complex molecular and metabolic phenotypes depict cancers as a constellation of different diseases with common themes. Precision imaging of such phenotypes requires flexible and tuneable modalities capable of identifying phenotypic fingerprints by using a restricted number of parameters whilst ensuring sensitivity to dynamic biological regulation. Common phenotypes can be detected by in vivo imaging technologies, and effectively define the emerging standards for disease classification and patient stratification in radiology. However, for the imaging data to accurately represent a complex fingerprint, the individual imaging parameters need to be measured and analysed in relation to their wider spatial and molecular context. In this respect, targeted palettes of molecular imaging probes facilitate the detection of heterogeneity in oncogene-driven alterations and their response to treatment, and lead to the expansion of rational-design elements for the combination of imaging experiments. In this Review, we evaluate criteria for conducting multiplexed imaging, and discuss its opportunities for improving patient diagnosis and the monitoring of therapy.

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“…These methods excel at identifying complex patterns in images and in using this information to guide clinical decisions (1)(2)(3). AI encompasses quantitative image analysis, also known as radiomics (4)(5)(6)(7)(8)(9), which involves either the application of predefined engineered algorithms (that often rely on input from expert radiologists) or the use of deep learning technologies that can automatically learn feature representations from example data (4). Consequently, AI is expected to play a key role in automating clinical tasks that presently can only be done by human experts (1,2).…”

Section: -4 ó2017 Aacrmentioning

confidence: 99%

Data Science in Radiology: A Path Forward

Aerts

2018

Clinical Cancer Research

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Artificial intelligence (AI), especially deep learning, has the potential to fundamentally alter clinical radiology. AI algorithms, which excel in quantifying complex patterns in data, have shown remarkable progress in applications ranging from self-driving cars to speech recognition. The AI application within radiology, known as radiomics, can provide detailed quantifications of the radiographic characteristics of underlying tissues.

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The Potential of Radiomic-Based Phenotyping in Precision Medicine

Cited by 533 publications

References 78 publications

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

Multiplexed imaging for diagnosis and therapy

Data Science in Radiology: A Path Forward

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