Differential effect of vascularity between long- and short-term survivors with IDH1/2 wild-type glioblastoma

[EN] Introduction: IDH1/2 wt glioblastoma (GB) represents the most lethal tumour of the central nervous system. Tumour vascularity is associated with overall survival (OS), and the clinical relevance of vascular markers, such as rCBV, has already been validated. Nevertheless, molecular and clinical factors may have different influences on the beneficial effect of a favourable vascular signature. Purpose: To evaluate the association between the rCBV and OS of IDH1/2 wt GB patients for long-term survivors (LTSs) and short-term survivors (STSs). Given that initial high rCBV may affect the patient's OS in follow-up stages, we will assess whether a moderate vascularity is beneficial for OS in both groups of patients. Materials and methods: Ninety-nine IDH1/2 wt GB patients were divided into LTSs (OS >= 400 days) and STSs (OS < 400 days). Mann-Whitney and Fisher, uni- and multiparametric Cox, Aalen's additive regression and Kaplan-Meier tests were carried out. Tumour vascularity was represented by the mean rCBV of the high angiogenic tumour (HAT) habitat computed through the haemodynamic tissue signature methodology (available on the ONCOhabitats platform). Results: For LTSs, we found a significant association between a moderate value of rCBV(mean) and higher OS (uni- and multiparametric Cox and Aalen's regression) (p = 0.0140, HR = 1.19; p = 0.0085, HR = 1.22) and significant stratification capability (p = 0.0343). For the STS group, no association between rCBV(mean) and survival was observed. Moreover, no significant differences (p > 0.05) in gender, age, resection status, chemoradiation, or MGMT methylation were observed between LTSs and STSs. Conclusion: We have found different prognostic and stratification effects of the vascular marker for the LTS and STS groups. We propose the use of rCBV(mean) at HAT as a vascular marker clinically relevant for LTSs with IDH1/2 wt GB and maybe as a potential target for randomized clinical trials focused on this group of patients.DPI2016-80054-R (Programa Estatal de Promocion del Talento y su Empleabilidad en I +D+i).; European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 844646; H2020-SC1-BHC-2018-2020 (No. 825750); MTS4up project (National Plan for Scientific and Technical Research and Innovation 2013-2016, No. DPI2016-80054-R); European Union's Horizon 2020 research and innovation programme under Marie Sklodowska-Curie, Grant/Award Number: 844646; Research Council of Norway, Grant/Award Number: 261984; South-Eastern Norway Regional Health Authority, Grant/Award Number: 2017073; European Research Council (ERC) under the European Union's Horizon 2020, Grant/Award Number: 758657Álvarez-Torres, MDM.; Fuster García, E.; Reynes, G.; Juan-Albarracín, J.; Chelebian-Kocharyan, EA.; Oleaga, L.; Pineda, J.... (2021). Differential effect of vascularity between long- and short-term survivors with IDH1/2 wild-type glioblastoma. NMR in Biomedicine. 34(4):1-11. https://doi.org/10.1002/nbm.446211134

Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach

[EN] Purpose: To evaluate the potential of 2D texture features extracted from magnetic resonance (MR) images for differentiating brain metastasis (BM) and glioblastomas (GBM) following a radiomics approach. Methods: This retrospective study included 50 patients with BM and 50 with GBM who underwent T1-weighted MRI between December 2010 and January 2017. Eighty-eight rotation-invariant texture features were computed for each segmented lesion using six texture analysis methods. These features were also extracted from the four images obtained after applying the discrete wavelet transform (88 features x 4 images). Three feature selection methods and five predictive models were evaluated. A 5-fold cross-validation scheme was used to randomly split the study group into training (80 patients) and testing (20 patients), repeating the process ten times. Classification was evaluated computing the average area under the receiver operating characteristic curve. Sensibility, specificity and accuracy were also computed. The whole process was tested quantizing the images with different gray-level values to evaluate their influence in the final results. Results: Highest classification accuracy was obtained using the original images quantized with 128 gray-levels and a feature selection method based on the p-value. The best overall performance was achieved using a support vector machine model with a subset of 32 features (AUC = 0.896 +/- 0.067, sensitivity of 82% and specificity of 80%). Naive Bayes and k-nearest neighbors models showed also valuable results (AUC approximate to 0.8) with a lower number of features (< 13), thus suggesting that these models may be more generalizable when using external validations. Conclusion: The proposed radiomics MRI approach is able to discriminate between GBM and BM with high accuracy employing a set of 2D texture features, thus helping in the diagnosis of brain lesions in a fast and noninvasive way.This work has been partially funded by the Spanish Ministerio de Economia y Competitividad (MINECO, Spain) and FEDER funds [grant number BFU2015-64380-C2-2-R]. David Moratal acknowledges financial support from the Conselleria d'Educacio, Investigacio, Cultura i Esport, Generalitat Valenciana (grants AEST/2017/013, AEST/2018/021, and AEST/2019/037), from the Agencia Valenciana de la Innovacion, Generalitat Valenciana (ref. INNCAD00/19/085), and from the Centro para el Desarrollo Tecnologico Industrial (Programa Eurostars-2, actuacion Interempresas Internacional), Spanish Ministerio de Ciencia, Innovacion y Universidades (ref. CIIP-20192020). Rafael Ortiz-Ramon was supported by grant ACIF/2015/078 from the Conselleria d'Educacio, Investigacio, Cultura i Esport, Generalitat Valenciana (Spain). DocumentOrtiz-Ramón, R.; Ruiz-España, S.; Molla-Olmos, E.; Moratal, D. (2020). Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach. Physica Medica. 76:44-54. https://doi.org/10.1016/j.ejmp.2020.06.016S44547