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Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival.

ABSTRACT: BACKGROUND:Measurement of volumetric features is challenging in glioblastoma. We investigate whether volumetric features derived from preoperative MRI using a convolutional neural network-assisted segmentation is correlated with survival. METHODS:Preoperative MRI of 120 patients were scored using Visually Accessible Rembrandt Images (VASARI) features. We trained and tested a multilayer, multi-scale convolutional neural network on multimodal brain tumour segmentation challenge (BRATS) data, prior to testing on our dataset. The automated labels were manually edited to generate ground truth segmentations. Network performance for our data and BRATS data was compared. Multivariable Cox regression analysis corrected for multiple testing using the false discovery rate was performed to correlate clinical and imaging variables with overall survival. RESULTS:Median Dice coefficients in our sample were (1) whole tumour 0.94 (IQR, 0.82-0.98) compared to 0.91 (IQR, 0.83-0.94 p?=?0.012), (2) FLAIR region 0.84 (IQR, 0.63-0.95) compared to 0.81 (IQR, 0.69-0.8 p?=?0.170), (3) contrast-enhancing region 0.91 (IQR, 0.74-0.98) compared to 0.83 (IQR, 0.78-0.89 p?=?0.003) and (4) necrosis region were 0.82 (IQR, 0.47-0.97) compared to 0.67 (IQR, 0.42-0.81 p?=?0.005). Contrast-enhancing region/tumour core ratio (HR 4.73 [95% CI, 1.67-13.40], corrected p?=?0.017) and necrotic core/tumour core ratio (HR 8.13 [95% CI, 2.06-32.12], corrected p?=?0.011) were independently associated with overall survival. CONCLUSION:Semi-automated segmentation of glioblastoma using a convolutional neural network trained on independent data is robust when applied to routine clinical data. The segmented volumes have prognostic significance.

SUBMITTER: Wan Y

PROVIDER: S-EPMC7593295 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival.

Wan Yizhou Y Rahmat Roushanak R Price Stephen J SJ

Acta neurochirurgica 20200713 12

<h4>Background</h4>Measurement of volumetric features is challenging in glioblastoma. We investigate whether volumetric features derived from preoperative MRI using a convolutional neural network-assisted segmentation is correlated with survival.<h4>Methods</h4>Preoperative MRI of 120 patients were scored using Visually Accessible Rembrandt Images (VASARI) features. We trained and tested a multilayer, multi-scale convolutional neural network on multimodal brain tumour segmentation challenge (BRA ...[more]

PMID: 32662042

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Project description:PurposeThis study aimed to develop a radiomics signature for the preoperative prognosis prediction of isocitrate dehydrogenase (IDH)-wild-type glioblastoma (GBM) patients and to provide personalized assistance in the clinical decision-making for different patients.Materials and methodsA total of 142 IDH-wild-type GBM patients classified using the new classification criteria of WHO 2021 from two centers were included in the study and randomly divided into a training set and a test set. Firstly, their clinical characteristics were screened using univariate Cox regression. Then, the radiomics features were extracted from the tumor and peritumoral edema areas on their contrast-enhanced T1-weighted image (CE-T1WI), T2-weighted image (T2WI), and T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) magnetic resonance imaging (MRI) images. Subsequently, inter- and intra-class correlation coefficient (ICC) analysis, Spearman's correlation analysis, univariate Cox, and the least absolute shrinkage and selection operator (LASSO) Cox regression were used step by step for feature selection and the construction of a radiomics signature. The combined model was established by integrating the selected clinical factors. Kaplan-Meier analysis was performed for the validation of the discrimination ability of the model, and the C-index was used to evaluate consistency in the prediction. Finally, a Radiomics + Clinical nomogram was generated for personalized prognosis analysis and then validated using the calibration curve.ResultsAnalysis of the clinical characteristics resulted in the screening of four risk factors. The combination of ICC, Spearman's correlation, and univariate and LASSO Cox resulted in the selection of eight radiomics features, which made up the radiomics signature. Both the radiomics and combined models can significantly stratify high- and low-risk patients (p < 0.001 and p < 0.05 for the training and test sets, respectively) and obtained good prediction consistency (C-index = 0.74-0.86). The calibration plots exhibited good agreement in both 1- and 2-year survival between the prediction of the model and the actual observation.ConclusionRadiomics is an independent preoperative non-invasive prognostic tool for patients who were newly classified as having IDH-wild-type GBM. The constructed nomogram, which combined radiomics features with clinical factors, can predict the overall survival (OS) of IDH-wild-type GBM patients and could be a new supplement to treatment guidelines.

Project description:Glioblastoma, the most frequent primary malignant brain neoplasm, is genetically diverse and classified into four transcriptomic subtypes, i. e., classical, mesenchymal, proneural, and neural. Currently, detection of transcriptomic subtype is based on ex vivo analysis of tissue that does not capture the spatial tumor heterogeneity. In view of accumulative evidence of in vivo imaging signatures summarizing molecular features of cancer, this study seeks robust non-invasive radiographic markers of transcriptomic classification of glioblastoma, based solely on routine clinically-acquired imaging sequences. A pre-operative retrospective cohort of 112 pathology-proven de novo glioblastoma patients, having multi-parametric MRI (T1, T1-Gd, T2, T2-FLAIR), collected from the Hospital of the University of Pennsylvania were included. Following tumor segmentation into distinct radiographic sub-regions, diverse imaging features were extracted and support vector machines were employed to multivariately integrate these features and derive an imaging signature of transcriptomic subtype. Extracted features included intensity distributions, volume, morphology, statistics, tumors' anatomical location, and texture descriptors for each tumor sub-region. The derived signature was evaluated against the transcriptomic subtype of surgically-resected tissue specimens, using a 5-fold cross-validation method and a receiver-operating-characteristics analysis. The proposed model was 71% accurate in distinguishing among the four transcriptomic subtypes. The accuracy (sensitivity/specificity) for distinguishing each subtype (classical, mesenchymal, proneural, neural) from the rest was equal to 88.4% (71.4/92.3), 75.9% (83.9/72.8), 82.1% (73.1/84.9), and 75.9% (79.4/74.4), respectively. The findings were also replicated in The Cancer Genomic Atlas glioblastoma dataset. The obtained imaging signature for the classical subtype was dominated by associations with features related to edge sharpness, whereas for the mesenchymal subtype had more pronounced presence of higher T2 and T2-FLAIR signal in edema, and higher volume of enhancing tumor and edema. The proneural and neural subtypes were characterized by the lower T1-Gd signal in enhancing tumor and higher T2-FLAIR signal in edema, respectively. Our results indicate that quantitative multivariate analysis of features extracted from clinically-acquired MRI may provide a radiographic biomarker of the transcriptomic profile of glioblastoma. Importantly our findings can be influential in surgical decision-making, treatment planning, and assessment of inoperable tumors.

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Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival.

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Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival.

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