Radiomic MRI Phenotyping of Glioblastoma: Improving Survival                     Prediction

Bae, Soong June; Choi, Yoon Seong; Ahn, Sung Soo; Chang, Jong Hee; Kang, Seok‐Gu; Kim, Eui Hyun; Kim, Se Hoon; Lee, Seung Koo

doi:10.1148/radiol.2018180200

Cited by 179 publications

(144 citation statements)

References 35 publications

(50 reference statements)

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“…The promising evidence of MRI‐based radiomics, confirms that GBM radiomics can non‐invasively and cost‐effectively depict GBM tumor heterogeneity, and predict patients’ outcomes . However, the critical issue for adoption of radiomics features in the routine clinical oncology workflow as validating biomarkers is their robustness and reproducibility.…”

Section: Discussionmentioning

confidence: 92%

“…Currently, many attentions of the GBM mMRI‐based radiomics studies were drowned to prognosis and prediction model, while they have not used a pre‐specific image preprocessing pipeline. For an instant, in a mMRI radiomics study co‐registration, resampling (1mm 3 ), and histogram intensity normalization, in other work registration, skull stripping, bias field correction, and intensity normalization, and in Ref. [] skull stripping, registration, bias field correction and histogram matching, were implemented in mMRI preprocessing steps.…”

Section: Introductionmentioning

confidence: 99%

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Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma

Moradmand

Aghamiri

Ghaderi

2019

J Applied Clin Med Phys

110

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To investigate the effect of image preprocessing, in respect to intensity inhomogeneity correction and noise filtering, on the robustness and reproducibility of the radiomics features extracted from the Glioblastoma (GBM) tumor in multimodal MR images (mMRI).In this study, for each patient 1461 radiomics features were extracted from GBM subregions (i.e., edema, necrosis, enhancement, and tumor) of mMRI (i.e., FLAIR, T1, T1C, and T2) volumes for five preprocessing combinations (in total 116 880 radiomics features).The robustness and reproducibility of the radiomics features were assessed under four comparisons: (a) Baseline versus modified bias field; (b) Baseline versus modified bias field followed by noise filtering; (c) Baseline versus modified noise, and (d) Baseline versus modified noise followed bias field correction. The concordance correlation coefficient (CCC), dynamic range (DR), and interclass correlation coefficient (ICC) were used as metrics. Shape features and subsequently, local binary pattern (LBP) filtered images were highly stable and reproducible against bias field correction and noise filtering in all measurements. In all MRI modalities, necrosis regions (NC: n ̅~4 49/1461, 30%) had the highest number of highly robust features, with CCC and DR >= 0.9, in comparison with edema (ED: n ̅~2 96/1461, 20%), enhanced (EN: n ̅~2 81/1461, 19%) and active-tumor regions (TM: n ̅~2 54/1461, 17%). The necrosis regions (NC: n~449/1461, 30%) had a higher number of highly robust features (CCC and DR >= 0.9) than edema (ED: n~296/1461, 20%), enhanced (EN: n~281/1461, 19%) and active-tumor (TM: n~254/1461, 17%) regions across all modalities. Furthermore, our results identified that the percentage of high reproducible features with ICC >= 0.9 after bias field correction (23.2%), and bias field correction followed by noise filtering (22.4%) were higher in contrast with noise smoothing and also noise smoothing follow by bias correction. These preliminary findings imply that preprocessing sequences can also have a significant impact on the robustness and reproducibility of mMRI-based radiomics features and identification of generalizable and consistent preprocessing algorithms is a pivotal step before imposing radiomics biomarkers into the clinic for GBM patients.

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Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma

Moradmand

Aghamiri

Ghaderi

2019

J Applied Clin Med Phys

110

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“…In recent years, quantitative computational imaging features ("radiomics") have been shown to add prognostic value above clinical and molecular factors in GBM patients . Interestingly, a combined clinical and radiomic model achieved similar prognostic performances with C‐indices of 0.70 and 0.65 for OS and PFS, respectively.…”

Section: Discussionmentioning

confidence: 99%

Combining multimodal imaging and treatment features improves machine learning‐based prognostic assessment in patients with glioblastoma multiforme

Peeken

Goldberg²,

Pyka

et al. 2018

Cancer Medicine

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Background For Glioblastoma (GBM), various prognostic nomograms have been proposed. This study aims to evaluate machine learning models to predict patients' overall survival (OS) and progression‐free survival (PFS) on the basis of clinical, pathological, semantic MRI‐based, and FET‐PET/CT‐derived information. Finally, the value of adding treatment features was evaluated. Methods One hundred and eighty‐nine patients were retrospectively analyzed. We assessed clinical, pathological, and treatment information. The VASARI set of semantic imaging features was determined on MRIs. Metabolic information was retained from preoperative FET‐PET/CT images. We generated multiple random survival forest prediction models on a patient training set and performed internal validation. Single feature class models were created including "clinical," "pathological," "MRI‐based," and "FET‐PET/CT‐based" models, as well as combinations. Treatment features were combined with all other features. Results Of all single feature class models, the MRI‐based model had the highest prediction performance on the validation set for OS (C‐index: 0.61 [95% confidence interval: 0.51‐0.72]) and PFS (C‐index: 0.61 [0.50‐0.72]). The combination of all features did increase performance above all single feature class models up to C‐indices of 0.70 (0.59‐0.84) and 0.68 (0.57‐0.78) for OS and PFS, respectively. Adding treatment information further increased prognostic performance up to C‐indices of 0.73 (0.62‐0.84) and 0.71 (0.60‐0.81) on the validation set for OS and PFS, respectively, allowing significant stratification of patient groups for OS. Conclusions MRI‐based features were the most relevant feature class for prognostic assessment. Combining clinical, pathological, and imaging information increased predictive power for OS and PFS. A further increase was achieved by adding treatment features.

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“…On a related note, the addition of radiomic features extracted from MRI also showed improved prediction of survival compared with those of conventional radiologic (relative cerebral perfusion, apparent diffusion coefficient) measures [17]. Recent studies revealed that the combination of MRI radiomics features with clinical and genetic features improved prognosis on glioma patients when evaluated against the models trained on clinical and genetic features alone [18,[20][21][22]. Our investigation builds on the described previous work on digital pathology images by implementing comprehensive quantitative analysis of pathology images in terms of morphological, texture, statistical, signal strength, and clinical features from a large cohort of glioma patients.…”

Section: Discussionmentioning

confidence: 99%

Glioma Grading via Analysis of Digital Pathology Images Using Machine Learning

Rathore

Niazi

Iftikhar

et al. 2020

Cancers

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Cancer pathology reflects disease progression (or regression) and associated molecular characteristics, and provides rich phenotypic information that is predictive of cancer grade and has potential implications in treatment planning and prognosis. According to the remarkable performance of computational approaches in the digital pathology domain, we hypothesized that machine learning can help to distinguish low-grade gliomas (LGG) from high-grade gliomas (HGG) by exploiting the rich phenotypic information that reflects the microvascular proliferation level, mitotic activity, presence of necrosis, and nuclear atypia present in digital pathology images. A set of 735 whole-slide digital pathology images of glioma patients (median age: 49.65 years, male: 427, female: 308, median survival: 761.26 days) were obtained from TCGA. Sub-images that contained a viable tumor area, showing sufficient histologic characteristics, and that did not have any staining artifact were extracted. Several clinical measures and imaging features, including conventional (intensity, morphology) and advanced textures features (gray-level co-occurrence matrix and gray-level run-length matrix), extracted from the sub-images were further used for training the support vector machine model with linear configuration. We sought to evaluate the combined effect of conventional imaging, clinical, and texture features by assessing the predictive value of each feature type and their combinations through a predictive classifier. The texture features were successfully validated on the glioma patients in 10-fold cross-validation (accuracy = 75.12%, AUC = 0.652). The addition of texture features to clinical and conventional imaging features improved grade prediction compared to the models trained on clinical and conventional imaging features alone (p = 0.045 and p = 0.032 for conventional imaging features and texture features, respectively). The integration of imaging, texture, and clinical features yielded a significant improvement in accuracy, supporting the synergistic value of these features in the predictive model. The findings suggest that the texture features, when combined with conventional imaging and clinical markers, may provide an objective, accurate, and integrated prediction of glioma grades. The proposed digital pathology imaging-based marker may help to (i) stratify patients into clinical trials, (ii) select patients for targeted therapies, and (iii) personalize treatment planning on an individual person basis.

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Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction

Cited by 179 publications

References 35 publications

Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma

Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma

Combining multimodal imaging and treatment features improves machine learning‐based prognostic assessment in patients with glioblastoma multiforme

Glioma Grading via Analysis of Digital Pathology Images Using Machine Learning

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