Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis

Jang, Kevin; Russo, Carlo; Ieva, Antonio Di

doi:10.1007/s00234-020-02403-1

Cited by 46 publications

(20 citation statements)

References 119 publications

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“…For generating high-quality data with segmented and annotated appropriate regions of interest, radiologists need to be integral part of data quantification and curation. 114 …”

Section: Discussionmentioning

confidence: 99%

Radiomics and radiogenomics in gliomas: a contemporary update

et al. 2021

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The natural history and treatment landscape of primary brain tumours are complicated by the varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low-grade lesions), as well as the dilemmas with identification of radiation necrosis, tumour progression, and pseudoprogression on MRI. Radiomics and radiogenomics promise to offer precise diagnosis, predict prognosis, and assess tumour response to modern chemotherapy/immunotherapy and radiation therapy. This is achieved by a triumvirate of morphological, textural, and functional signatures, derived from a high-throughput extraction of quantitative voxel-level MR image metrics. However, the lack of standardisation of acquisition parameters and inconsistent methodology between working groups have made validations unreliable, hence multi-centre studies involving heterogenous study populations are warranted. We elucidate novel radiomic and radiogenomic workflow concepts and state-of-the-art descriptors in sub-visual MR image processing, with relevant literature on applications of such machine learning techniques in glioma management.

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“…For generating high-quality data with segmented and annotated appropriate regions of interest, radiologists need to be integral part of data quantification and curation. 114 …”

Section: Discussionmentioning

confidence: 99%

Radiomics and radiogenomics in gliomas: a contemporary update

et al. 2021

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“…They share an approach to inferring tumor grading, molecular features and/or tumor behavior following treatment in conjunction with tumor imaging and linking this to outcome prediction including survival (46)(47)(48). Broadly, radiomics is an emerging field that involves extraction and quantification of features from medical images (48). The data can then be analyzed using computational analysis and models to identify predictive image biomarkers that characterize tumor behavior.…”

Section: Radiomics and Other Computational Approachesmentioning

confidence: 99%

Survival Prediction in Gliomas: Current State and Novel Approaches

Zhao

Krauze

2021

Gliomas

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Gliomas are neurologically devastating tumors with generally poor outcomes. Traditionally, survival prediction in glioma is studied from clinical features using statistical approaches. With the rapid development of artificial intelligence approaches encompassing machine learning and deep learning, there has been a keen interest among researchers to apply these methods to survival prediction in glioma allowing for integrated processes that encompass pathology, histology, molecular, imaging, and clinical features. This chapter provides an overview of the emerging computational approaches that have the potential to revolutionize survival prediction in glioma. Machine learning and deep learning techniques, including support vector machine, random forest, convolutional neural network, and radiomics, are discussed.

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“…The second is the use of multiparametric approaches, including radiomics, to use existing clinical data to improve prediction and stratification. 65 Incorporation of metabolic information, including glutamine into radiomics analyses, has been limited; however, radiomics may facilitate the use of the large amount of imaging data often untouched in radiologic practice. The final strategy is advancement of emerging molecular-scale methods, including those outlined here.…”

Section: Discussion and Outlookmentioning

confidence: 99%