The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review

Vial, Alanna; Stirling, David; Field, Matthew; Ros, Montserrat; Ritz, Christian; Carolan, Martin G; Holloway, Lois; Miller, Andrew

doi:10.21037/tcr.2018.05.02

Cited by 167 publications

(92 citation statements)

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“…4 Deep learning is increasingly being applied to radiomics, or the detection of clinically relevant features in imaging data beyond what can be perceived by the human eye. 5 Both radiomics and deep learning are most commonly found in oncology-oriented image analysis. Their combination appears to promise greater accuracy in diagnosis than the previous generation of automated tools for image analysis, known as computer-aided detection or CAD.…”

Section: Machine Learning -Neural Network and Deep Learningmentioning

confidence: 99%

The potential for artificial intelligence in healthcare

Davenport

Kalakota

2019

Future Healthcare Journal

2,219

978

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The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.

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Section: Machine Learning -Neural Network and Deep Learningmentioning

confidence: 99%

The potential for artificial intelligence in healthcare

Davenport

Kalakota

2019

Future Healthcare Journal

2,219

978

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“…Deep Learning has recently shown great potential for computer-assisted diagnosis [38,40] and for prediction of response to therapy [41] in patients with lung cancer. A potential drawback of Deep Learning, however, is that the resulting features are not as easy to interpret as the hand-designed ones, nor readily linkable to clinically relevant image findings [42]. The paradox, then, is that, even if the results are good, we don't know why; as a consequence, it is hard to investigate the methods for possible sources of bias and/or mistakes (for a discussion on the perils of excessively complex algorithms, see also ([43], Ch.…”

Section: Deep Learningmentioning

confidence: 99%

PET/CT Radiomics in Lung Cancer: An Overview

et al. 2020

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Quantitative extraction of imaging features from medical scans ('radiomics') has attracted a lot of research attention in the last few years. The literature has consistently emphasized the potential use of radiomics for computer-assisted diagnosis, as well as for predicting survival and response to treatment. Radiomics is appealing in that it enables full-field analysis of the lesion, provides nearly real-time results, and is non-invasive. Still, a lot of studies suffer from a series of drawbacks such as lack of standardization and repeatability. Such limitations, along with the unmet demand for large enough image datasets for training the algorithms, are major hurdles that still limit the application of radiomics on a large scale. In this paper, we review the current developments, potential applications, limitations, and perspectives of PET/CT radiomics with specific focus on the management of patients with lung cancer.

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“…As much as these highthroughput data acquisition approaches challenge the data-to-discovery process, they drive the development of new sophisticated computational methods for data analysis and interpretation. In particular, the synergy of cancer research and machine learning has led to groundbreaking discoveries in diagnosis, prognosis and treatment planning for cancer * Contributed equally VARIATIONAL AUTOENCODERS FOR CANCER DATA INTEGRATION patients (Levine et al, 2019;Vial et al, 2018). Typically, such machine learning methods are developed to address particular complexities inherent in individual data types, separately.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have addressed and highlighted the importance of data integration at different scales (Žitnik et al, 2019;López de Maturana et al, 2019;Karczewski and Snyder, 2018;Huang et al, 2017;Gomez-Cabrero et al, 2014). In the context of analysing cancer data, it has been shown that such integrative approaches yield improved performance for accurate diagnosis, survival analysis and treatment planning (Vial et al, 2018;Gevaert et al, 2006;Thomas et al, 2014;Kristensen et al, 2014;Shen et al, 2009). In particular, Wang et al (2014) show that, for the case of 5 different cancer profiles, integrating mRNA expression, DNA methylation and miRNA data leads to more accurate survival profiles than each of the individual types of data alone.…”

Section: Introductionmentioning

confidence: 99%

Variational autoencoders for cancer data integration: design principles and computational practice

Simidjievski

Bodnar

Tariq

et al. 2019

Preprint

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International initiatives such as the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) are collecting multiple data sets at different genome-scales with the aim to identify novel cancer bio-markers and predict patient survival. To analyse such data, several machine learning, bioinformatics and statistical methods have been applied, among them neural networks such as autoencoders. Although these models provide a good statistical learning framework to analyse multi-omic and/or clinical data, there is a distinct lack of work on how to integrate diverse patient data and identify the optimal design best suited to the available data.In this paper, we investigate several autoencoder architectures that integrate a variety of cancer patient data types (e.g., multi-omics and clinical data). We perform extensive analyses of these approaches and provide a clear methodological and computational framework for designing systems that enable clinicians to investigate cancer traits and translate the results into clinical applications. We demonstrate how these networks can be designed, built and, in particular, applied to tasks of integrative analyses of heterogeneous breast cancer data. The results show that these approaches yield relevant data representations that, in turn, lead to accurate and stable diagnosis.

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The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review

Cited by 167 publications

References 0 publications

The potential for artificial intelligence in healthcare

The potential for artificial intelligence in healthcare

PET/CT Radiomics in Lung Cancer: An Overview

Variational autoencoders for cancer data integration: design principles and computational practice

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