Robust association between vascular habitats and patient prognosis in glioblastoma: an international retrospective multicenter study

This is the peer reviewed version of the following article: del Mar Álvarez-Torres, M., Juan-Albarracín, J., Fuster-Garcia, E., Bellvís-Bataller, F., Lorente, D., Reynés, G., Font de Mora, J., Aparici-Robles, F., Botella, C., Muñoz-Langa, J., Faubel, R., Asensio-Cuesta, S., García-Ferrando, G.A., Chelebian, E., Auger, C., Pineda, J., Rovira, A., Oleaga, L., Mollà-Olmos, E., Revert, A.J., Tshibanda, L., Crisi, G., Emblem, K.E., Martin, D., Due-Tønnessen, P., Meling, T.R., Filice, S., Sáez, C. and García-Gómez, J.M. (2020), Robust association between vascular habitats and patient prognosis in glioblastoma: An international multicenter study. J Magn Reson Imaging, 51: 1478-1486, which has been published in final form at https://doi.org/10.1002/jmri.26958. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.[EN] Background Glioblastoma (GBM) is the most aggressive primary brain tumor, characterized by a heterogeneous and abnormal vascularity. Subtypes of vascular habitats within the tumor and edema can be distinguished: high angiogenic tumor (HAT), low angiogenic tumor (LAT), infiltrated peripheral edema (IPE), and vasogenic peripheral edema (VPE). Purpose To validate the association between hemodynamic markers from vascular habitats and overall survival (OS) in glioblastoma patients, considering the intercenter variability of acquisition protocols. Study Type Multicenter retrospective study. Population In all, 184 glioblastoma patients from seven European centers participating in the NCT03439332 clinical study. Field Strength/Sequence 1.5T (for 54 patients) or 3.0T (for 130 patients). Pregadolinium and postgadolinium-based contrast agent-enhanced T-1-weighted MRI, T-2- and FLAIR T-2-weighted, and dynamic susceptibility contrast (DSC) T-2* perfusion. Assessment We analyzed preoperative MRIs to establish the association between the maximum relative cerebral blood volume (rCBV(max)) at each habitat with OS. Moreover, the stratification capabilities of the markers to divide patients into "vascular" groups were tested. The variability in the markers between individual centers was also assessed. Statistical Tests Uniparametric Cox regression; Kaplan-Meier test; Mann-Whitney test. Results The rCBV(max) derived from the HAT, LAT, and IPE habitats were significantly associated with patient OS (P < 0.05; hazard ratio [HR]: 1.05, 1.11, 1.28, respectively). Moreover, these markers can stratify patients into "moderate-" and "high-vascular" groups (P < 0.05). The Mann-Whitney test did not find significant differences among most of the centers in markers (HAT: P = 0.02-0.685; LAT: P = 0.010-0.769; IPE: P = 0.093-0.939; VPE: P = 0.016-1.000). Data Conclusion The rCBV(max) calculated in HAT, LAT, and IPE habitats have been validated as clinically relevant prognostic biomarkers for glioblastoma patients in the pretreatment stage. This study demonstrates the robustness of the hemodynamic tissue signature (HTS) habitats to assess the GBM vascular heterogeneity and their association with patient prognosis independently of intercenter variability. Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019.This work was partially supported by: MTS4up project (National Plan for Scientific and Technical Research and Innovation 2013-2016, No. DPI2016-80054-R) (to J.M.G.G.); H2020-SC1-2016-CNECT Project (No. 727560) (to J.M.G.G.) and H2020-SC1-BHC-2018-2020 (No. 825750) (to J.M.G.G.); M.A.T was supported by DPI2016-80054-R (Programa Estatal de Promocion del Talento y su Empleabilidad en I + D + i). The data acquisition and curation of the Oslo University Hospital was supported by: the European Research Council (ERC) under the European Union's Horizon 2020 (Grant Agreement No. 758657), the South-Eastern Norway Regional Health Authority Grants 2017073 and 2013069, and the Research Council of Norway Grants 261984 (to K.E.E.). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research. E.F.G. was supported by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 844646. Figure 1 was designed by the Science Artist Elena Poritskaya.Álvarez-Torres, MDM.; Juan-Albarracín, J.; Fuster García, E.; Bellvís-Bataller, F.; Lorente, D.; Reynés, G.; Font De Mora, J.... (2020). Robust association between vascular habitats and patient prognosis in glioblastoma: an international retrospective multicenter study. Journal of Magnetic Resonance Imaging. 51(5):1478-1486. https://doi.org/10.1002/jmri.2695814781486515Louis, D. N., Perry, A., Reifenberger, G., von Deimling, A., Figarella-Branger, D., Cavenee, W. K., … Ellison, D. W. (2016). The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathologica, 131(6), 803-820. doi:10.1007/s00401-016-1545-1Gately, L., McLachlan, S., Dowling, A., & Philip, J. (2017). Life beyond a diagnosis of glioblastoma: a systematic review of the literature. Journal of Cancer Survivorship, 11(4), 447-452. doi:10.1007/s11764-017-0602-7Bae, S., Choi, Y. S., Ahn, S. S., Chang, J. H., Kang, S.-G., Kim, E. H., … Lee, S.-K. (2018). Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction. Radiology, 289(3), 797-806. doi:10.1148/radiol.2018180200Akbari, H., Macyszyn, L., Da, X., Wolf, R. L., Bilello, M., Verma, R., … Davatzikos, C. (2014). Pattern Analysis of Dynamic Susceptibility Contrast-enhanced MR Imaging Demonstrates Peritumoral Tissue Heterogeneity. Radiology, 273(2), 502-510. doi:10.1148/radiol.14132458Weis, S. M., & Cheresh, D. A. (2011). Tumor angiogenesis: molecular pathways and therapeutic targets. Nature Medicine, 17(11), 1359-1370. doi:10.1038/nm.2537De Palma, M., Biziato, D., & Petrova, T. V. (2017). Microenvironmental regulation of tumour angiogenesis. Nature Reviews Cancer, 17(8), 457-474. doi:10.1038/nrc.2017.51Jain, R., Poisson, L. M., Gutman, D., Scarpace, L., Hwang, S. N., Holder, C. A., … Flanders, A. (2014). Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor. Radiology, 272(2), 484-493. doi:10.1148/radiol.14131691Jensen, R. L., Mumert, M. L., Gillespie, D. L., Kinney, A. Y., Schabel, M. C., & Salzman, K. L. (2013). Preoperative dynamic contrast-enhanced MRI correlates with molecular markers of hypoxia and vascularity in specific areas of intratumoral microenvironment and is predictive of patient outcome. Neuro-Oncology, 16(2), 280-291. doi:10.1093/neuonc/not148Jena, A., Taneja, S., Gambhir, A., Mishra, A. K., D’souza, M. M., Verma, S. M., … Sogani, S. K. (2016). Glioma Recurrence Versus Radiation Necrosis. Clinical Nuclear Medicine, 41(5), e228-e236. doi:10.1097/rlu.0000000000001152Price, S. J., Young, A. M. H., Scotton, W. J., Ching, J., Mohsen, L. A., Boonzaier, N. R., … Larkin, T. J. (2015). Multimodal MRI can identify perfusion and metabolic changes in the invasive margin of glioblastomas. Journal of Magnetic Resonance Imaging, 43(2), 487-494. doi:10.1002/jmri.24996Chang, Y.-C. C., Ackerstaff, E., Tschudi, Y., Jimenez, B., Foltz, W., Fisher, C., … Stoyanova, R. (2017). Delineation of Tumor Habitats based on Dynamic Contrast Enhanced MRI. Scientific Reports, 7(1). doi:10.1038/s41598-017-09932-5Cui, Y., Tha, K. K., Terasaka, S., Yamaguchi, S., Wang, J., Kudo, K., … Li, R. (2016). Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images. Radiology, 278(2), 546-553. doi:10.1148/radiol.2015150358Juan-Albarracín, J., Fuster-Garcia, E., Pérez-Girbés, A., Aparici-Robles, F., Alberich-Bayarri, Á., Revert-Ventura, A., … García-Gómez, J. M. (2018). Glioblastoma: Vascular Habitats Detected at Preoperative Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging Predict Survival. Radiology, 287(3), 944-954. doi:10.1148/radiol.2017170845Fuster-Garcia, E., Juan-Albarracín, J., García-Ferrando, G. A., Martí-Bonmatí, L., Aparici-Robles, F., & García-Gómez, J. M. (2018). Improving the estimation of prognosis for glioblastoma patients by MR based hemodynamic tissue signatures. NMR in Biomedicine, 31(12), e4006. doi:10.1002/nbm.4006Abramson, R. G., Burton, K. R., Yu, J.-P. J., Scalzetti, E. M., Yankeelov, T. E., Rosenkrantz, A. B., … Subramaniam, R. M. (2015). Methods and Challenges in Quantitative Imaging Biomarker Development. Academic Radiology, 22(1), 25-32. doi:10.1016/j.acra.2014.09.001Stupp, R., Mason, W. P., van den Bent, M. J., Weller, M., Fisher, B., Taphoorn, M. J. B., … Mirimanoff, R. O. (2005). Radiotherapy plus Concomitant and Adjuvant Temozolomide for Glioblastoma. New England Journal of Medicine, 352(10), 987-996. doi:10.1056/nejmoa043330Wetzel, S. G., Cha, S., Johnson, G., Lee, P., Law, M., Kasow, D. L., … Xue, X. (2002). Relative Cerebral Blood Volume Measurements in Intracranial Mass Lesions: Interobserver and Intraobserver Reproducibility Study. Radiology, 224(3), 797-803. doi:10.1148/radiol.2243011014Schnack, H. G., van Haren, N. E. M., Hulshoff Pol, H. E., Picchioni, M., Weisbrod, M., Sauer, H., … Kahn, R. S. (2004). Reliability of brain volumes from multicenter MRI acquisition: A calibration study. Human Brain Mapping, 22(4), 312-320. doi:10.1002/hbm.20040De Guio, F., Jouvent, E., Biessels, G. J., Black, S. E., Brayne, C., Chen, C., … Chabriat, H. (2016). Reproducibility and variability of quantitative magnetic resonance imaging markers in cerebral small vessel disease. Journal of Cerebral Blood Flow & Metabolism, 36(8), 1319-1337. doi:10.1177/0271678x16647396Hirai, T., Murakami, R., Nakamura, H., Kitajima, M., Fukuoka, H., Sasao, A., … Yamashita, Y. (2008). Prognostic Value of Perfusion MR Imaging of High-Grade Astrocytomas: Long-Term Follow-Up Study. American Journal of Neuroradiology, 29(8), 1505-1510. doi:10.3174/ajnr.a1121Sawlani, R. N., Raizer, J., Horowitz, S. W., Shin, W., Grimm, S. A., Chandler, J. P., … Carroll, T. J. (2010). Glioblastoma: A Method for Predicting Response to Antiangiogenic Chemotherapy by Using MR Perfusion Imaging—Pilot Study. Radiology, 255(2), 622-628. doi:10.1148/radiol.10091341Hambardzumyan, D., & Bergers, G. (2015). Glioblastoma: Defining Tumor Niches. Trends in Cancer, 1(4), 252-265. doi:10.1016/j.trecan.2015.10.009Artzi, M., Bokstein, F., Blumenthal, D. T., Aizenstein, O., Liberman, G., Corn, B. W., & Ben Bashat, D. (2014). Differentiation between vasogenic-edema versus tumor-infiltrative area in patients with glioblastoma during bevacizumab therapy: A longitudinal MRI study. European Journal of Radiology, 83(7), 1250-1256. doi:10.1016/j.ejrad.2014.03.02

Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review

[EN] Purpose: To systematically review evidence regarding the association of multi-parametric biomarkers with clinical outcomes and their capacity to explain relevant subcompartments of gliomas. Materials and Methods: Scopus database was searched for original journal papers from January 1st, 2007 to February 20th , 2017 according to PRISMA. Four hundred forty-nine abstracts of papers were reviewed and scored independently by two out of six authors. Based on those papers we analyzed associations between biomarkers, subcompartments within the tumor lesion, and clinical outcomes. From all the articles analyzed, the twenty-seven papers with the highest scores were highlighted to represent the evidence about MR imaging biomarkers associated with clinical outcomes. Similarly, eighteen studies defining subcompartments within the tumor region were also highlighted to represent the evidence of MR imaging biomarkers. Their reports were critically appraised according to the QUADAS-2 criteria. Results: It has been demonstrated that multi-parametric biomarkers are prepared for surrogating diagnosis, grading, segmentation, overall survival, progression-free survival, recurrence, molecular profiling and response to treatment in gliomas. Quantifications and radiomics features obtained from morphological exams (T1, T2, FLAIR, T1c), PWI (including DSC and DCE), diffusion (DWI, DTI) and chemical shift imaging (CSI) are the preferred MR biomarkers associated to clinical outcomes. Subcompartments relative to the peritumoral region, invasion, infiltration, proliferation, mass effect and pseudo flush, relapse compartments, gross tumor volumes, and high-risk regions have been defined to characterize the heterogeneity. For the majority of pairwise cooccurrences, we found no evidence to assert that observed co-occurrences were significantly different from their expected co-occurrences (Binomial test with False Discovery Rate correction, alpha=0.05). The co-occurrence among terms in the studied papers was found to be driven by their individual prevalence and trends in the literature. Conclusion: Combinations of MR imaging biomarkers from morphological, PWI, DWI and CSI exams have demonstrated their capability to predict clinical outcomes in different management moments of gliomas. Whereas morphologic-derived compartments have been mostly studied during the last ten years, new multi-parametric MRI approaches have also been proposed to discover specific subcompartments of the tumors. MR biomarkers from those subcompartments show the local behavior within the heterogeneous tumor and may quantify the prognosis and response to treatment of gliomas.This work was supported by the Spanish Ministry for Investigation, Development and Innovation project with identification number DPI2016-80054-R.Oltra-Sastre, M.; Fuster García, E.; Juan -Albarracín, J.; Sáez Silvestre, C.; Perez-Girbes, A.; Sanz-Requena, R.; Revert-Ventura, A.... (2019). Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review. Current Medical Imaging Reviews. 15(10):933-947. https://doi.org/10.2174/1573405615666190109100503S9339471510Louis D.N.; Perry A.; Reifenberger G.; The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 2016,131(6),803-820Ostrom Q.T.; Gittleman H.; Fulop J.; CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008-2012. Neuro-oncol 2015,17(Suppl. 4),iv1-iv62Yachida S.; Jones S.; Bozic I.; Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 2010,467(7319),1114-1117Gerlinger M.; Rowan A.J.; Horswell S.; Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012,366(10),883-892Sottoriva A.; Spiteri I.; Piccirillo S.G.M.; Intratumor heterogeneityin human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci USA 2013,110(10),4009-4014Whiting P.F.; Rutjes A.W.; Westwood M.E.; QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011,155(8),529-536Stupp R.; Mason W.P.; van den Bent M.J.; Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 2005,352(10),987-996Ponte K.F.; Berro D.H.; Collet S.; In vivo relationship between hypoxia and angiogenesis in human glioblastoma: a multimodal imaging study. J Nucl Med 2017,58(10),1574-1579Pope W.B.; Kim H.J.; Huo J.; Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology 2009,252(1),182-189Mörén L.; Bergenheim A.T.; Ghasimi S.; Brännström T.; Johansson M.; Antti H.; Metabolomic screening of tumor tissue and serum in glioma patients reveals diagnostic and prognostic information. Metabolites 2015,5(3),502-520Prager A.J.; Martinez N.; Beal K.; Omuro A.; Zhang Z.; Young R.J.; Diffusion and perfusion MRI to differentiate treatment-related changes including pseudoprogression from recurrent tumors in high-grade gliomas with histopathologic evidence. AJNR Am J Neuroradiol 2015,36(5),877-885Kickingereder P.; Burth S.; Wick A.; Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 2016,280(3),880-889Yoo R-E.; Choi S.H.; Cho H.R.; Tumor blood flow from arterial spin labeling perfusion MRI: a key parameter in distinguishing high-grade gliomas from primary cerebral lymphomas, and in predicting genetic biomarkers in high-grade gliomas. J Magn Reson Imaging 2013,38(4),852-860Liberman G.; Louzoun Y.; Aizenstein O.; Automatic multi-modal MR tissue classification for the assessment of response to bevacizumab in patients with glioblastoma. Eur J Radiol 2013,82(2),e87-e94Ramadan S.; Andronesi O.C.; Stanwell P.; Lin A.P.; Sorensen A.G.; Mountford C.E.; Use of in vivo two-dimensional MR spectroscopy to compare the biochemistry of the human brain to that of glioblastoma. Radiology 2011,259(2),540-549Xintao H.; Wong K.K.; Young G.S.; Guo L.; Wong S.T.; Support vector machine multi-parametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma. J Magn Reson Imaging 2011,33(2),296Ingrisch M.; Schneider M.J.; Nörenberg D.; Radiomic Analysis reveals prognostic information in T1-weighted baseline magnetic resonance imaging in patients with glioblastoma. Invest Radiol 2017,52(6),360-366Ulyte A.; Katsaros V.K.; Liouta E.; Prognostic value of preoperative dynamic contrast-enhanced MRI perfusion parameters for high-grade glioma patients. Neuroradiology 2016,58(12),1197-1208O’Neill A.F.; Qin L.; Wen P.Y.; de Groot J.F.; Van den Abbeele A.D.; Yap J.T.; Demonstration of DCE-MRI as an early pharmacodynamic biomarker of response to VEGF Trap in glioblastoma. J Neurooncol 2016,130(3),495-503Kickingereder P.; Bonekamp D.; Nowosielski M.; Radiogenomics of glioblastoma: machine learning-based classification of molecular characteristics by using multiparametric and multiregional mr imaging features. Radiology 2016,281(3),907-918Roberto S-R.; Antonio R-V.; Luis M-B.; Angel A-B.; Gracián G-M.; Quantitative mr perfusion parameters related to survival time in high-grade gliomas. European Radiology 2013,23(12),3456-3465Jain R.; Poisson L.; Narang J.; Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers. Radiology 2013,267(1),212-220Fathi K.A.; Mohseni M.; Rezaei S.; Bakhshandehpour G.; Saligheh R.H.; Multi-parametric (ADC/PWI/T2-W) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme. MAGMA 2015,28(1),13-22Caulo M.; Panara V.; Tortora D.; Data-driven grading of brain gliomas: a multiparametric MR imaging study. Radiology 2014,272(2),494-503Alexiou G.A.; Zikou A.; Tsiouris S.; Comparison of diffusion tensor, dynamic susceptibility contrast MRI and (99m)Tc-Tetrofosmin brain SPECT for the detection of recurrent high-grade glioma. Magn Reson Imaging 2014,32(7),854-859Van Cauter S.; De Keyzer F.; Sima D.M.; Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas. Neuro-oncol 2014,16(7),1010-1021Seeger A.; Braun C.; Skardelly M.; Comparison of three different MR perfusion techniques and MR spectroscopy for multiparametric assessment in distinguishing recurrent high-grade gliomas from stable disease. Acad Radiol 2013,20(12),1557-1565Chawalparit O.; Sangruchi T.; Witthiwej T.; Diagnostic performance of advanced mri in differentiating high-grade from low-grade gliomas in a setting of routine service. J Med Assoc Thai 2013,96(10),1365-1373Li Y.; Lupo J.M.; Parvataneni R.; Survival analysis in patients with newly diagnosed glioblastoma using pre- and postradiotherapy MR spectroscopic imaging. Neuro-oncol 2013,15(5),607-617Shankar J.J.S.; Woulfe J.; Silva V.D.; Nguyen T.B.; Evaluation of perfusion CT in grading and prognostication of high-grade gliomas at diagnosis: a pilot study. AJR Am J Roentgenol 2013,200(5)Zinn P.O.; Mahajan B.; Sathyan P.; Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme. PLoS One 2011,6(10)Matsusue E.; Fink J.R.; Rockhill J.K.; Ogawa T.; Maravilla K.R.; Distinction between glioma progression and post-radiation change by combined physiologic MR imaging. Neuroradiology 2010,52(4),297-306Juan-Albarracín J.; Fuster-Garcia E.; Manjón J.V.; Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification. PLoS One 2015,10(5)Itakura H.; Achrol A.S.; Mitchell L.A.; Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Sci Transl Med 2015,7(303)Ion-Margineanu A.; Van Cauter S.; Sima D.M.; Tumour relapse prediction using multiparametric MR data recorded during follow-up of GBM patients. BioMed Res Int 2015,2015Durst C.R.; Raghavan P.; Shaffrey M.E.; Multimodal MR imaging model to predict tumor infiltration in patients with gliomas. Neuroradiology 2014,56(2),107-115Yoon J.H.; Kim J.H.; Kang W.J.; Grading of cerebral glioma with multi-parametric MR Imaging and 18F-FDG-PET: concordance and accuracy. European Radiol 2014,24(2),380-389Demerath T.; Simon-Gabriel C.P.; Kellner E.; Mesoscopic imaging of glioblastomas: are diffusion, perfusion and spectroscopic measures influenced by the radiogenetic phenotype? Neuroradiol J 2017,30(1),36-47Qin L.; Li X.; Stroiney A.; Advanced MRI assessment to predict benefit of anti-programmed cell death 1 protein immunotherapy response in patients with recurrent glioblastoma. Neuroradiology 2017,59(2),135-145Boult J.K.R.; Borri M.; Jury A.; Investigating intracranial tumour growth patterns with multiparametric MRI incorporating Gd-DTPA and USPIO-enhanced imaging. NMR Biomed 2016,29(11),1608-1617Server A.; Kulle B.; Gadmar Ø.B.; Josefsen R.; Kumar T.; Nakstad P.H.; Measurements of diagnostic examination performance using quantitative apparent diffusion coefficient and proton MR spectroscopic imaging in the preoperative evaluation of tumor grade in cerebral gliomas. Eur J Radiol 2011,80(2),462-470Chang P.D.; Chow D.S.; Yang P.H.; Filippi C.G.; Lignelli A.; Predicting glioblastoma recurrence by early changes in the apparent diffusion coefficient value and signal intensity on FLAIR images. AJR Am J Roentgenol 2017,208(1),57-65Yi C.; Shangjie R.; Volume of high-risk intratumoralsubregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma. Eur Radiol 2017,27,3583-3592Khalifa J.; Tensaouti F.; Chaltiel L.; Identification of a candidate biomarker from perfusion MRI to anticipate glioblastoma progression after chemoradiation. Eur Radiol 2016,26(11),4194-4203Prateek P.; Jay P.; Partovi S.; Madabhushi A.; Tiwari P.; Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastomamultiforme: preliminary findings. Eur Radiol 2017,27(10),4188-4197Lemasson B.; Chenevert T.L.; Lawrence T.S.; Impact of perfusion map analysis on early survival prediction accuracy in glioma patients. Transl Oncol 2013,6(6),766-774Inano R.; Oishi N.; Kunieda T.; Visualization of heterogeneity and regional grading of gliomas by multiple features using magnetic resonance-based clustered images. Sci Rep 2016,6,30344Delgado-Goñi T.; Ortega-Martorell S.; Ciezka M.; MRSI-based molecular imaging of therapy response to temozolomide in preclinical glioblastoma using source analysis. NMR Biomed 2016,29(6),732-743Cui Y.; Tha K.K.; Terasaka S.; Prognostic imaging biomarkers in glioblastoma: development and independent validation on the basis of multiregion and quantitative analysis of MR images. Radiology 2016,278(2),546-553Price S.J.; Young A.M.H.; Scotton W.J.; Multimodal MRI can identify perfusion and metabolic changes in the invasive margin of glioblastomas. J Magn Reson Imaging 2016,43(2),487-494Sauwen N.; Acou M.; Van Cauter S.; Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI. Neuroimage Clin 2016,12,753-764Jena A.; Taneja S.; Gambhir A.; Glioma recurrence versus radiation necrosis: single-session multiparametric approach using simultaneous O-(2-18F-Fluoroethyl)-L-Tyrosine PET/MRI. Clin Nucl Med 2016,41(5),e228-e236Kim H.S.; Goh M.J.; Kim N.; Choi C.G.; Kim S.J.; Kim J.H.; Which combination of MR imaging modalities is best for predicting recurrent glioblastoma? Study of diagnostic accuracy and reproducibility. Radiology 2014,273(3),831-843Christoforidis G.A.; Yang M.; Abduljalil A.; “Tumoral pseudoblush” identified within gliomas at high-spatial-resolution ultrahigh-field-strength gradient-echo MR imaging corresponds to microvascularity at stereotactic biopsy. Radiology 2012,264(1),210-217Wang S.; Kim S.; Chawla S.; Differentiation between glioblastomas, solitary brain metastases, and primary cerebral lymphomas using diffusion tensor and dynamic susceptibility contrast-enhanced MR imaging. AJNR Am J Neuroradiol 2011,32(3),507-514Hanahan D.; Weinberg R.A.; Hallmarks of cancer: the next generation. Cell 2011,144(5),646-674Macdonald D.R.; Cascino T.L.; Schold S.C.; Cairncross J.G.; Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol 1990,8(7),1277-1280Wen P.Y.; Macdonald D.R.; Reardon D.A.; Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 2010,28(11),1963-1972Sorensen A.G.; Batchelor T.T.; Wen P.Y.; Zhang W-T.; Jain R.K.; Response criteria for glioma. Nat Clin Pract Oncol 2008,5(11),634-644Rosenkrantz A.B.; Friedman K.; Chandarana H.; Current status of hybrid PET/MRI in oncologic imaging. AJR Am J Roentgenol 2016,206(1),162-172Castiglioni I.; Gallivanone F.; Canevari C.; Hybrid PET/MRI for In vivo imaging of cancer: current clinical experiences and recent advances. Curr Med Imaging 2016,12,106Mainta I.C.; Perani D.; Delattre B.M.A.; FDG PET/MR imaging in major neurocognitive disorders. Curr Alzheimer Res 2017,14,186-197Marner L.; Henriksen O.M.; Lundemann M.; Larsen V.A.; Law I.; Clinical PET/MRI in neurooncology: opportunities and challenges from a single-institution perspective. Clin Transl Imaging 2017,5(2),135-149R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2015. Available from: https://www.R-project.org

New citrus selections from Cleopatra mandarin x Poncirus trifoliata with resistance to Tylenchulus semipenetrans Cobb

The response of four resistant selections of Cleopatra mandarin x Poncirus trifoliata to increasing inoculum densities of a population of the Mediterranean biotype of Tylenchulus semipenetrans was determined in microplots established at two sites. One-year-old trees of the resistant selections and of a susceptible Carrizo citrange were inoculated with 0, 1, 5 or 10 x 10(4) eggs per plant (Pi). Nematode reproduction on the resistant selections was consistently less than on Carrizo citrange. Increasing initial inoculum densities had no effect on the relative resistance in three selections (03.01.42, 03.01.5 and 03.01.16). Selection 03.01.42 was highly resistant (no females and eggs per g fresh root), and 03.01.5 and 03.01.16 both had good resistance (less than or equal to15% as many females and eggs per g fresh root as on Carrizo citrange). The fourth selection (03.01.18) expressed resistance at Pi 1 and 5 x 10(4) eggs per plant but was moderately susceptible at 10 x 10(4) eggs per plant (&gt;15% females and eggs per g fresh root). Deposits of a lignin or suberin-like material were more abundant in resistant selections 03.01.5 and 03.01.18 than in susceptible Carrizo citrange. The proportional increase in trunk diameter of inoculated and uninoculated trees of each rootstock was similar at both sites

Enhanced oxidative stress and other potential biomarkers for retinopathy in type 2 diabetics: beneficial effects of the nutraceutic supplements

We have studied the global risk of retinopathy in a Mediterranean population of type 2 diabetes mellitus (T2DM) patients, according to clinical, biochemical, and lifestyle biomarkers. The effects of the oral supplementation containing antioxidants/omega 3 fatty acids (A/ω3) were also evaluated. Suitable participants were distributed into two main groups: (1) T2DMG (with retinopathy (+DR) or without retinopathy (-DR)) and (2) controls (CG). Participants were randomly assigned (+A/ω3) or not (-A/ω3) to the oral supplementation with a daily pill of Nutrof Omega (R) for 18 months. Data collected including demographics, anthropometrics, characteristics/lifestyle, ophthalmic examination (best corrected visual acuity, ocular fundus photographs, and retinal thickness as assessed by optical coherence tomography), and blood parameters (glucose, glycosylated hemoglobin, triglycerides, malondialdehyde, and total antioxidant capacity) were registered, integrated, and statistically processed by the SPSS 15.0 program. Finally, 208 participants (130 diabetics (68 +DR/62 -DR) and 78 controls) completed the follow-up. Blood analyses confirmed that the T2DMG+DR patients had significantly higher oxidative stress (p < 0.05), inflammatory (p < 0.05), and vascular (p < 0.001) risk markers than the T2DMG-DR and the CG. Furthermore, the A/ω3 oral supplementation positively changed the baseline parameters, presumptively by inducing metabolic activation and ameliorating the ocular health after 18 months of supplementation

Enhanced oxidative stress and other potential biomarkers for retinopathy in type 2 diabetics : beneficial effects of the nutraceutic supplements

CITATION: Roig-Revert, M. J. et al. 2015. Enhanced oxidative stress and other potential biomarkers for retinopathy in type 2 diabetics : beneficial effects of the nutraceutic supplements. BioMed Research International, 2015, Article ID 408180, doi:10.1155/2015/408180.The original publication is available at https://www.hindawi.com/journals/bmriWe have studied the global risk of retinopathy in a Mediterranean population of type 2 diabetes mellitus (T2DM) patients, according to clinical, biochemical, and lifestyle biomarkers. The effects of the oral supplementation containing antioxidants/omega 3 fatty acids (A/ω3) were also evaluated. Suitable participants were distributed into two main groups: (1) T2DMG (with retinopathy (+DR) or without retinopathy (−DR)) and (2) controls (CG). Participants were randomly assigned (+A/ω3) or not (−A/ω3) to the oral supplementation with a daily pill of Nutrof Omega (R) for 18 months. Data collected including demographics, anthropometrics, characteristics/lifestyle, ophthalmic examination (best corrected visual acuity, ocular fundus photographs, and retinal thickness as assessed by optical coherence tomography), and blood parameters (glucose, glycosylated hemoglobin, triglycerides, malondialdehyde, and total antioxidant capacity) were registered, integrated, and statistically processed by the SPSS 15.0 program. Finally, 208 participants (130 diabetics (68 +DR/62 −DR) and 78 controls) completed the follow-up. Blood analyses confirmed that the T2DMG+DR patients had significantly higher oxidative stress , inflammatory , and vascular risk markers than the T2DMG−DR and the CG. Furthermore, the A/ω3 oral supplementation positively changed the baseline parameters, presumptively by inducing metabolic activation and ameliorating the ocular health after 18 months of supplementation.https://www.hindawi.com/journals/bmri/2015/408180/Publisher's versio