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

Author(s):

Miquel Oltra-Sastre*, Elies Fuster-Garcia, Javier Juan-Albarracin, Carlos Sáez, Alexandre Perez-Girbes, Roberto Sanz-Requena, Antonio Revert-Ventura, Antonio Mocholi, Javier Urchueguia, Antonio Hervas, Gaspar Reynes, Jaime Font-de-Mora, Jose Muñoz-Langa, Carlos Botella, Fernando Aparici, Luis Marti-Bonmati and Juan M Garcia-Gomez   Pages 1 - 15 ( 15 )

Abstract:


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 co-occurrences, 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, α=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.

Keywords:

Magnetic resonance imaging, Magnetic Resonance Spectroscopy, Computer-assisted Image Processing, Tumor Biomarkers, Patient Outcome Assessment

Affiliation:

Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de Valencia, Camino de Vera s/n, 46022 Valencia, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de Valencia, Camino de Vera s/n, 46022 Valencia, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de Valencia, Camino de Vera s/n, 46022 Valencia, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de Valencia, Camino de Vera s/n, 46022 Valencia, GIBI230 (Grupo de Investigación Biomédica en Imagen), Instituto de Investigación Sanitaria (IIS) Hospital la Fe, Hospital Quironsalud Valencia, Valencia, Hospital de Manises, Valencia, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de Valencia, Camino de Vera s/n, 46022 Valencia, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de Valencia, Camino de Vera s/n, 46022 Valencia, Instituto de Matemática Multidisciplinar (IMM) , Universitat Politècnica de Valencia , Valencia, Grupo de Investigación Clínica y Traslacional del Cáncer, Instituto de Investigación Sanitaria (IIS) Hospital la Fe, Grupo de Investigación Clínica y Traslacional del Cáncer, Instituto de Investigación Sanitaria (IIS) Hospital la Fe, GIBI230 (Grupo de Investigación Biomédica en Imagen), Instituto de Investigación Sanitaria (IIS) Hospital la Fe 3Hospital Quironsalud Valencia, Valencia, GIBI230 (Grupo de Investigación Biomédica en Imagen), Instituto de Investigación Sanitaria (IIS) Hospital la Fe, GIBI230 (Grupo de Investigación Biomédica en Imagen), Instituto de Investigación Sanitaria (IIS) Hospital la Fe, GIBI230 (Grupo de Investigación Biomédica en Imagen), Instituto de Investigación Sanitaria (IIS) Hospital la Fe, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de Valencia, Camino de Vera s/n, 46022 Valencia



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