Radiomics: Extracting more information from medical images using advanced feature analysis

By Philippe Lambin, Emmanuel Rios-Velazquez, R. Leijenaar, S. Carvalho, R. V. van Stiphout, P. Granton, C. Zegers, Robert James Gillies, R. Boellard, Andre L. A. J. Dekker and 1 more. European Journal of Cancer, 2012

5,387 citations recorded by the source corpus.

Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics – the high-throughput extraction of large amounts of image features from radiographic images – addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory.

Subfields: Artificial Intelligence, Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9971ff92d9d09b4736228bdbd7726e2e19b9aa2d

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Citations in the corpus

Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries (2017, positive): “As our methodology permits learning from features of all kinds, we intend to include variables that could potentially have higher predictive value, such as radiomics and genomics features, to learn more sophisticated models in a distributed manner (22, 23).”
Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology (2018, background): “For image analysis, ML algorithms can identify subtle patterns beyond the threshold of human detection, with the potential to extract valuable new information [14].”
Radiogenomics and Radiotherapy Response Modeling (2017, background): “, 2014b) and radiomics (Lambin et al., 2012; Aerts et al., 2014; El Naqa, 2014b) have emerged as”
Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures (2017, background): “survival, local relapse); this field of research is now commonly called radiomics.(6)”
Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images (2015, positive): “Our work demonstrates that PCa diagnosis can be improved by combining data-augmented classification together with more of the latent information in standard MRIs (the so-called “radiomics hypothesis”) (27, 28) compared with using ADC mean or T2 signal intensities alone, thereby reducing the potentia”
Machine Learning methods for Quantitative Radiomic Biomarkers (2015, neutral): “1038/srep13087 wrapper methods and (3) embedded methods.”
Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification (2017, background): “In particular, the increasing quantity of CT image assays has created a unique avenue for data-driven analysis to capture underlying cancer characteristics at a macroscopic level, allowing identi ﬁ cation of prognostic imaging biomarkers [3].”
Artificial intelligence and machine learning for medical imaging: A technology review. (2021, neutral): “An example of this strategy is the field of radiomics [104,105], where “ -omics-like ” features are extracted from radiological images in order to predict some indicator of interest like a disease grade or a patient ’ s survival.”
Characterization of PET/CT images using texture analysis: the past, the present… any future? (2016, background): “large number of features from images (including TA metrics, shape descriptors and other quantitative metrics) has become popular under the denomination radiomics [20, 21].”
Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors and Machine-learning Approaches (2017, background): “vision to extract many quantitative features from radiologic images.(20) The extracted quantitative features are typically”