Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models

Kickingereder, Philipp; Burth, Sina; Wick, Antje; Goetz, Michael; Eidel, Oliver; Schlemmer, Heinz Peter; Maier‐Hein, Klaus H.; Wick, Wolfgang; Bendszus, Martin; Radbruch, Alexander; Bonekamp, David

doi:10.1148/radiol.2016160845

Cited by 341 publications

(261 citation statements)

References 42 publications

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“…These findings are consistent with the recently reported results in 38 , where the combination of clinical and radiomic features were found to improve prediction of GBM survival, as compared to radiomic features alone.…”

Section: Resultssupporting

confidence: 93%

Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings

et al. 2016

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Objective Despite 90% Glioblastoma (GBM) recurrences occurring in the peritumoral brain zone (PBZ), its contribution in patient survival is poorly understood. The current study leverages computerized texture (i.e. radiomic) analysis to evaluate the efficacy of PBZ features from preoperative MRI in predicting long (>18-months) versus short-term (<7-months) survival in GBM. Methods 65 patient exams (29 short-term, 36 long-term) with Gadolinium-contrast T1w, FLAIR, T2w sequences from the Cancer Imaging Archive were employed. An expert manually segmented each study as: enhancing lesion, PBZ, and tumour necrosis. 402 radiomic features (capturing co-occurrence, gray-level dependence, directional gradients) was obtained for each region. Evaluation was performed using 3-fold cross validation, such that a subset of studies was used to select the most predictive features, and the remaining subset was used to evaluate their efficacy in predicting survival. Results A subset of 10 radiomic “peritumoral” MRI features, suggestive of intensity heterogeneity and textural patterns, was found to be predictive of survival (p = 1.47 × 10−5), as compared to features from enhancing tumour, necrotic regions, and known clinical factors. Conclusion Our preliminary analysis suggests that radiomic features from the PBZ on routine pre-operative MRI may be predictive of long-, versus short-term survival in GBM.

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Section: Resultssupporting

confidence: 93%

Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings

et al. 2016

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“…Although these studies are important, these approaches are generally expensive and require considerable development before clinical use. Some investigators have used radiomic features to distinguish radiation necrosis from tumor progression [10; 12; 32–34], but those studies used radiomic features obtained at a single time point instead of using changes in radiomic features over time to model or classify features. Because radiomic features seem to keep changing over time, determining the best time to extract features for modeling is difficult, and models built on these features may not be sufficiently robust to account for variations over time.…”

Section: Discussionmentioning

confidence: 99%

A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images

et al. 2017

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Objectives To develop a model using radiomic features extracted from MR images to distinguish radiation necrosis from tumor progression in brain metastases after Gamma knife radiosurgery. Methods We retrospectively identified 87 patients with pathologically confirmed necrosis (24 lesions) or progression (73 lesions), and calculated 285 radiomic features from 4 MR sequences (T1, T1 post-contrast, T2, and fluid-attenuated inversion recovery) obtained at 2 follow-up time points per lesion per patient. Reproducibility of each feature between the two time points was calculated within each group to identify a subset of features with distinct reproducible values between two groups. Changes in radiomic features from one time point to the next (delta radiomics) were used to build a model to classify necrosis and progression lesions. Results A combination of 5 radiomic features from both T1 post-contrast and T2 MR images were found to be useful in distinguishing necrosis from progression lesions. Delta radiomic features with a RUSBoost ensemble classifier had an overall predictive accuracy of 73.2% and an area under the curve value of 0.73 in leave-one-out cross-validation. Conclusions Delta radiomic features extracted from MR images have potential for distinguishing radiation necrosis from tumor progression after radiosurgery for brain metastases.

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“…ML holds also a great potential for dealing with "big data" generated by hybrid imaging methods [280]. For example, ML has been successfully applied in hybrid imaging to predict survival [283], treatment outcome [284], and tumor grading [285]. Furthermore, unsupervised ML allows the identification of breast cancer subtypes [286].…”

Section: Image-derived Prediction Modelsmentioning

confidence: 99%

Hybrid Imaging: Instrumentation and Data Processing

et al. 2018

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State-of-the-art patient management frequently requires the use of non-invasive imaging methods to assess the anatomy, function or molecular-biological conditions of patients or study subjects. Such imaging methods can be singular, providing either anatomical or molecular information, or they can be combined, thus, providing "anato-metabolic" information. Hybrid imaging denotes image acquisitions on systems that physically combine complementary imaging modalities for an improved diagnostic accuracy and confidence as well as for increased patient comfort. The physical combination of formerly independent imaging modalities was driven by leading innovators in the field of clinical research and benefited from technological advances that permitted the operation of PET and MR in close physical proximity, for example. This review covers milestones of the development of various hybrid imaging systems for use in clinical practice and small-animal research. Special attention is given to technological advances that helped the adoption of hybrid imaging, as well as to introducing methodological concepts that benefit from the availability of complementary anatomical and biological information, such as new types of image reconstruction and data correction schemes. The ultimate goal of hybrid imaging is to provide useful, complementary and quantitative information during patient work-up. Hybrid imaging also opens the door to multi-parametric assessment of diseases, which will help us better understand the causes of various diseases that currently contribute to a large fraction of healthcare costs.

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Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models

Cited by 341 publications

References 42 publications

Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings

Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings

A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images

Hybrid Imaging: Instrumentation and Data Processing

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