Morphological MRI-based features provide pretreatment survival prediction in glioblastoma

Pérez-Beteta, Julián; Molina-García, David; Martínez-González, Alicia; Henares-Molina, Araceli; Amo-Salas, Mariano; Luque, Belén; Arregui, Elena; Calvo, Manuel; Borrás, José M.; Martino, Juan; Velásquez, Carlos; Meléndez-Asensio, Bárbara; Lope, Andreu; Moreno, Raquel; Barcia, Juan A.; Asenjo, Beatriz; Benavides, Manuel; Herruzo, Ismael; Lara, Pedro C.; Cabrera, R.; Albers, David; Navarro, Miguel; Pérez-Romasanta, Luis A.; Revert, Antonio; Arana, Estanislao; Pérez-Garcı́a, Vı́ctor M.

doi:10.1007/s00330-018-5758-7

Cited by 23 publications

(29 citation statements)

References 31 publications

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“…11), where we can observe how the differences are higher for smaller δ s values. Thus, both previous evidences [30,32,33] and the ones obtained in this work point out in the same direction that larger rim widths are indicative of more aggressive tumors.…”

Section: 'In Silico' Simulations Show That Tumor Rim Width Correlatessupporting

confidence: 87%

“…In this paper we wanted to study the connection between the rim width of tumor areas in glioblastoma as observed in postcontrast pretreatment T1-weighted images and tumor aggressiveness. This connection has been observed in survival analyses in studies using large patient datasets [30,32,33]. The idea was based on intuitions based on a simple GBM growth mathematical model [35].…”

Section: Discussionmentioning

confidence: 83%

“…In that study, thinner widths were found to be associated with better patients prognosis [30]. Then, a larger study based on 311 patients with unifocal tumors confirmed the results and provided multivariate survival predictors based on the biomarker and other morphological and clinical data [32]. Indeed the multivariate, mathematical-model-based approach provided substantially better prognosis indicators than 'blind' machine learning approaches [27].…”

mentioning

confidence: 82%

“…large rim widths are associated with worse survival [30,32,33] using Kaplan-Meir and other statistical methods. To compare the computational estimates of the rim width with the tumor aggressiveness, we used the tumor growth speed calculated previously in the simulations, obtaining P < 0.001 and r = 0.6482.…”

Section: 'In Silico' Simulations Show That Tumor Rim Width Correlatesmentioning

confidence: 99%

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Tumor width on T1-weighted MRI images of glioblastoma as a prognostic biomarker: a mathematical model

Pérez-Beteta

Belmonte-Beitia

Pérez-Garcı́a

2020

Math. Model. Nat. Phenom.

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We construct a minimal macroscopic model of glioblastoma growth including necrosis to explain the recently observed correlation between MRI-observed features and tumor growth speed. A theoretical study of the modified model was carried out. In particular, we obtained an expression for the minimal wave speed of the traveling wave solutions. We also solved numerically the model using a set of realistic parameter values and used these numerical solutions to compare the model dynamics against patient's imaging and clinical data. The mathematical model provides theoretical support to the observation that tumors with broad contrast enhancing areas as observed in T1-weighted pretreatment postcontrast magnetic resonance images have worse survival than those with thinner areas.Mathematics Subject Classification. 92B05, 92B99, 92C50, 92C55.

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Section: 'In Silico' Simulations Show That Tumor Rim Width Correlatessupporting

confidence: 87%

Section: Discussionmentioning

confidence: 83%

mentioning

confidence: 82%

Section: 'In Silico' Simulations Show That Tumor Rim Width Correlatesmentioning

confidence: 99%

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Tumor width on T1-weighted MRI images of glioblastoma as a prognostic biomarker: a mathematical model

Pérez-Beteta

Belmonte-Beitia

Pérez-Garcı́a

2020

Math. Model. Nat. Phenom.

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“…THis is despite the fact that the structure of a tumor invasion front has been investigated for a rather long time, because it is believed that determining the front's properties would shed light on the mechanisms behind its growth [25,26], and would enable one to predict its behavior [27,28]. In particular, the surface regularity of brain tumors was recently proposed as a strong predictive indication of a patient's chance of survival [29,30]. Specifically, a rougher, possibly self-affine, surface of tumors was associated with shorter survival time.…”

Section: Introductionmentioning

confidence: 99%

Effect of heterogeneity and spatial correlations on the structure of a tumor invasion front in cellular environments

2019

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Analysis of invasion front has been widely used to decipher biological properties, as well as the growth dynamics of the corresponding populations. Likewise, the invasion front of tumors has been investigated, from which insights into the biological mechanisms of tumor growth have been gained. We develop a model to study how tumors' invasion front depends on the relevant properties of a cellular environment. To do so, we develop a model based on a nonlinear reaction-diffusion equation, the Fisher-Kolmogorov-Petrovsky-Piskunov (FKPP) equation, to model tumor growth. Our study aims to understand how heterogeneity in the cellular environment's stiffness, as well as spatial correlations in its morphology, the existence of both of which has been demonstrated by experiments, affects the properties of tumor invasion front. It is demonstrated that three important factors affect the properties of the front, namely, the spatial distribution of the local diffusion coefficients, the spatial correlations between them, and the ratio of the cells' duplication rate and their average diffusion coefficient. Analyzing the scaling properties of tumor invasion front computed by solving the governing equation, we show that, contrary to several previous claims, the invasion front of tumors and cancerous cell colonies cannot be described by the well-known models of kinetic growth, such as the Kardar-Parisi-Zhang equation. 1 arXiv :1909.11410v1 [cond-mat.stat-mech]

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The Nomogram of MRI‐based Radiomics with Complementary Visual Features by Machine Learning Improves Stratification of Glioblastoma Patients: A Multicenter Study

et al. 2021

Magnetic Resonance Imaging

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Background: Glioblastomas (GBMs) represent both the most common and the most highly malignant primary brain tumors. The subjective visual imaging features from MRI make it challenging to predict the overall survival (OS) of GBM. Radiomics can quantify image features objectively as an emerging technique. A pragmatic and objective method in the clinic to assess OS is strongly in need. Purpose: To construct a radiomics nomogram to stratify GBM patients into long-vs. short-term survival. Study Type: Retrospective. Population: One-hundred and fifty-eight GBM patients from Brain Tumor Segmentation Challenge 2018 (BRATS2018) were for model construction and 32 GBM patients from the local hospital for external validation. Field Strength/Sequence: 1.5 T and 3.0 T MRI Scanners, T 1 WI, T 2 WI, T 2 FLAIR, and contrast-enhanced T 1 WI sequences Assessment: All patients were divided into long-term or short-term based on a survival of greater or fewer than 12 months. All BRATS2018 subjects were divided into training and test sets, and images were assessed for ependymal and pia mater involvement (EPI) and multifocality by three experienced neuroradiologists. All tumor tissues from multiparametric MRI were fully automatically segmented into three subregions to calculate the radiomic features. Based on the training set, the most powerful radiomic features were selected to constitute radiomic signature. Statistical Tests: Receiver operating characteristic (ROC) curve, sensitivity, specificity, and the Hosmer-Lemeshow test. Results: The nomogram had a survival prediction accuracy of 0.878 and 0.875, a specificity of 0.875 and 0.583, and a sensitivity of 0.704 and 0.833, respectively, in the training and test set. The ROC curve showed the accuracy of the nomogram, radiomic signature, age, and EPI for external validation set were 0.858, 0.826, 0.664, and 0.66 in the validate set, respectively. Data Conclusion: Radiomics nomogram integrated with radiomic signature, EPI, and age was found to be robust for the stratification of GBM patients into long-vs. short-term survival. Level of Evidence: 3 Technical Efficacy Stage: 2

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Morphological MRI-based features provide pretreatment survival prediction in glioblastoma

Cited by 23 publications

References 31 publications

Tumor width on T1-weighted MRI images of glioblastoma as a prognostic biomarker: a mathematical model

Tumor width on T1-weighted MRI images of glioblastoma as a prognostic biomarker: a mathematical model

Effect of heterogeneity and spatial correlations on the structure of a tumor invasion front in cellular environments

The Nomogram of MRI‐based Radiomics with Complementary Visual Features by Machine Learning Improves Stratification of Glioblastoma Patients: A Multicenter Study

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