Radiomics in neuro-oncology: Basics, workflow, and applications

Lohmann, Philipp; Galldiks, Norbert; Köcher, Martin; Heinzel, Alexander; Filß, Christian; Stegmayr, Carina; Mottaghy, Felix M.; Fink, Gereon R.; Shah, N. Jon; Langen, Karl‐Josef

doi:10.1016/j.ymeth.2020.06.003

Cited by 104 publications

(83 citation statements)

References 68 publications

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“…The growing availability of highperformance computing allows the extraction of quantitative imaging features from medical images that are usually beyond human perception. Especially, radiomics allows the extraction of quantitative features from standard-of-care neuroimages from CT, MRI, or PET, and may provide additional, potentially relevant diagnostic information for decision-making [67]. Since these features' computation is possible on already acquired neuroimages during routine follow-up, this information can be provided at a low cost.…”

Section: Potential Of Radiomicsmentioning

confidence: 99%

Imaging of Response to Radiosurgery and Immunotherapy in Brain Metastases: Quo Vadis?

Galldiks

Werner

Lohmann

et al. 2021

Curr Treat Options Neurol

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Purpose of Review This review presents an overview of how advanced imaging techniques may help to overcome shortcomings of anatomical MRI for response assessment in patients with brain metastases who are undergoing stereotactic radiosurgery, immunotherapy, or combinations thereof. Recent Findings Study results suggest that parameters derived from amino acid PET, diffusion- and perfusion-weighted MRI, MR spectroscopy, and newer MRI methods are particularly helpful for the evaluation of the response to radiosurgery or checkpoint inhibitor immunotherapy and provide valuable information for the differentiation of radiotherapy-induced changes such as radiation necrosis from brain metastases. The evaluation of these imaging modalities is also of great interest in the light of emerging high-throughput analysis methods such as radiomics, which allow the acquisition of additional data at a low cost. Summary Preliminary results are promising and should be further evaluated. Shortcomings are different levels of PET and MRI standardization, the number of patients enrolled in studies, and the monocentric and retrospective character of most studies.

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Section: Potential Of Radiomicsmentioning

confidence: 99%

Imaging of Response to Radiosurgery and Immunotherapy in Brain Metastases: Quo Vadis?

Galldiks

Werner

Lohmann

et al. 2021

Curr Treat Options Neurol

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“…Notable examples for wrapper methods include forward feature selection, backward feature elimination, exhaustive feature selection (greedy algorithm), or bidirectional search. 16 , 76 …”

Section: Overview Of Radiomic and Radiogenomics Pipelinementioning

confidence: 99%

“…A range of classifiers including random forest, support vector machines (SVMs), and generalized linear models have been used for diagnosis and treatment response evaluation applications. 6 , 12 , 76 For instance, previous studies have used SVM classifier to predict the histopathological grade (LGGs vs GBMs) of a given primary brain tumor using MRI scans. 76–78 Research groups have employed least absolute shrinkage and selection operator (LASSO) logistic regression and SVM models 79 , 80 to differentiate pseudo-progression from early tumor progression in GBM patients.…”

Section: Overview Of Radiomic and Radiogenomics Pipelinementioning

confidence: 99%

“… 6 , 12 , 76 For instance, previous studies have used SVM classifier to predict the histopathological grade (LGGs vs GBMs) of a given primary brain tumor using MRI scans. 76–78 Research groups have employed least absolute shrinkage and selection operator (LASSO) logistic regression and SVM models 79 , 80 to differentiate pseudo-progression from early tumor progression in GBM patients. Prasanna et al 7 have shown that radiomic features extracted from the peri-tumoral edema on multi-parametric MRI were able to distinguish short-term survivors (OS <7 months) and long-term survivors (OS >18 months) in 65 GBMs studies.…”

Section: Overview Of Radiomic and Radiogenomics Pipelinementioning

confidence: 99%

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Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges

Beig

Bera

Tiwari

2020

Neuro-Oncology Advances

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Neuro-oncology largely consists of malignancies of the brain and central nervous system including both primary as well as metastatic tumors. Currently, a significant clinical challenge in neuro-oncology is to tailor therapies for patients based on a priori knowledge of their survival outcome or treatment response to conventional or experimental therapies. Radiomics or the quantitative extraction of subvisual data from conventional radiographic imaging has recently emerged as a powerful data-driven approach to offer insights into clinically relevant questions related to diagnosis, prediction, prognosis, as well as assessing treatment response. Furthermore, radiogenomic approaches provide a mechanism to establish statistical correlations of radiomic features with point mutations and next-generation sequencing data to further leverage the potential of routine MRI scans to serve as “virtual biopsy” maps. In this review, we provide an introduction to radiomic and radiogenomic approaches in neuro-oncology, including a brief description of the workflow involving preprocessing, tumor segmentation, and extraction of “hand-crafted” features from the segmented region of interest, as well as identifying radiogenomic associations that could ultimately lead to the development of reliable prognostic and predictive models in neuro-oncology applications. Lastly, we discuss the promise of radiomics and radiogenomic approaches in personalizing treatment decisions in neuro-oncology, as well as the challenges with clinical adoption, which will rely heavily on their demonstrated resilience to nonstandardization in imaging protocols across sites and scanners, as well as in their ability to demonstrate reproducibility across large multi-institutional cohorts.

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“…Among image modalities, CT [ 15 , 16 ] and PET [ 17 , 18 ] were the focus of several radiomics studies, as they are the most widely adopted and standardized imaging modalities in radiotherapy workflows. Even if MRI exhibits a greater variability in acquisition protocols that hinders the collection of large datasets [ 19 ], a growing interest in MRI-based radiomic features in neuro-oncology is observed in the literature [ 20 , 21 , 22 ]. In addition, in conventional X-ray radiotherapy studies [ 23 , 24 , 25 ], the extraction of radiomic features from dose maps (i.e., dosiomics) have been proposed, so that the delivered treatment can be characterized by descriptors of spatial patterns in dose distributions, against the conventional point-wise parameters of dose-volume histograms (DVH).…”

Section: Introductionmentioning

confidence: 99%

Radiomics and Dosiomics for Predicting Local Control after Carbon-Ion Radiotherapy in Skull-Base Chordoma

Buizza

Paganelli

D’Ippolito

et al. 2021

Cancers

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Skull-base chordoma (SBC) can be treated with carbon ion radiotherapy (CIRT) to improve local control (LC). The study aimed to explore the role of multi-parametric radiomic, dosiomic and clinical features as prognostic factors for LC in SBC patients undergoing CIRT. Before CIRT, 57 patients underwent MR and CT imaging, from which tumour contours and dose maps were obtained. MRI and CT-based radiomic, and dosiomic features were selected and fed to two survival models, singularly or by combining them with clinical factors. Adverse LC was given by in-field recurrence or tumour progression. The dataset was split in development and test sets and the models’ performance evaluated using the concordance index (C-index). Patients were then assigned a low- or high-risk score. Survival curves were estimated, and risk groups compared through log-rank tests (after Bonferroni correction α = 0.0083). The best performing models were built on features describing tumour shape and dosiomic heterogeneity (median/interquartile range validation C-index: 0.80/024 and 0.79/0.26), followed by combined (0.73/0.30 and 0.75/0.27) and CT-based models (0.77/0.24 and 0.64/0.28). Dosiomic and combined models could consistently stratify patients in two significantly different groups. Dosiomic and multi-parametric radiomic features showed to be promising prognostic factors for LC in SBC treated with CIRT.

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Radiomics in neuro-oncology: Basics, workflow, and applications

Cited by 104 publications

References 68 publications

Imaging of Response to Radiosurgery and Immunotherapy in Brain Metastases: Quo Vadis?

Imaging of Response to Radiosurgery and Immunotherapy in Brain Metastases: Quo Vadis?

Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges

Radiomics and Dosiomics for Predicting Local Control after Carbon-Ion Radiotherapy in Skull-Base Chordoma

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