Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma

Abstract

Background Glioblastoma is an aggressive and highly infiltrative brain cancer. Standard surgical resection is guided by enhancement on postcontrast T1-weighted (T1) magnetic resonance imaging (MRI), which is insufficient for delineating surrounding infiltrating tumor. Objective To develop imaging biomarkers that delineate areas of tumor infiltration and predict early recurrence in peritumoral tissue. Such markers would enable intensive, yet targeted, surgery and radiotherapy, thereby potentially delaying recurrence and prolonging survival. Methods Preoperative multiparametric MRIs (T1, T1-Gad, T2-weighted [T2], T2-fluid-attenuated inversion recovery [FLAIR], diffusion tensor imaging (DTI), and dynamic susceptibility contrast-enhanced [DSC]-MRI) from 31 patients were combined using machine learning methods, thereby creating predictive spatial maps of infiltrated peritumoral tissue. Cross validation was used in the retrospective cohort to achieve generalizable biomarkers. Subsequently, the imaging signatures learned from the retrospective study were used in a replication cohort of 34 new patients. Spatial maps representing likelihood of tumor infiltration and future early recurrence were compared to regions of recurrence on postresection follow-up studies with pathology confirmation. Results This technique produced predictions of early recurrence with a mean area under the curve (AUC) of 0.84, sensitivity of 91%, specificity of 93%, and odds ratio estimates of 9.29 (99% CI, 8.95–9.65) for tissue predicted to be heavily infiltrated in the replication study. Regions of tumor recurrence were found to have subtle, yet fairly distinctive multiparametric imaging signatures when analyzed quantitatively by pattern analysis and machine learning. Conclusion Visually imperceptible imaging patterns discovered via multiparametric pattern analysis methods were found to estimate the extent of infiltration and location of future tumor recurrence, paving the way for improved targeted treatment.

Accuracy of High-Field Intraoperative MRI in the Detectability of Residual Tumor in Glioma Grade IV Resections

“…In accordance with Coburger et al and Akbari et al, [1,28,29] we believe that a multimodality approach including T2, FLAIR, DTI and spectroscopic imaging as well as dynamic T1-weighted imaging will further improve the sensitivity and specificity of ioMRI and might lead to an improved detection rate and that advanced imaging like dynamic T1-weighed imaging might increase the accura-▶ Table 2 The sensitivity for all tumors was calculated as 95 % and the specificity as 69.5 %. The negative predictive value was 88 %, and the positive predictive value was 86 %.…”

“…Showing equal sensitivity ioMRI appears more specific in primary than in recurrent glioblastoma. cy of ioMRI in the future [27,29]. Dynamic contrast enhancement may provide better differentiation between contrast-enhancing tissue and leakage and DTI may reveal additional information about tumor margins [28].…”

Advanced Magnetic Resonance Imaging in Glioblastoma: A Review

“…29 Diffusion and perfusion parameters, when combined with standard MR sequences, may allow radiation oncologists to better characterize the highest-risk regions to include in high-dose target volumes, utilizing macroscopically visible features 30 as well as radiomic features. 31 Voxel-based MR spectroscopy (MRS) and whole-brain spectroscopic MRI (sMRI) may identify regions of tumor infiltration and areas at high risk of recurrence; 32 regions with metabolic abnormalities on sMRI are correlated with intraoperative tissue samples showing increased immunohistochemical staining for neoplastic cells. 33 …”

Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma

“…The principal component features of temporal perfusion dynamics have been related to recurrence and infiltration [24, 42], as well as molecular characteristics [6, 7, 10, 44]. Furthermore, the textural features available in brain-CaPTk capture characteristics of the local micro-architecture of tissue and have already demonstrated predictive and prognostic value [9, 38, 41, 44, 45].…”

“…Immediate future plans include the integration of various other specialized diagnostic analysis tools for glioblastoma, such as prediction of survival [9], potential recurrence [42], and characterization into distinct imaging subtypes [41], as well as application of existing brain-CaPTk components in other neurological diseases, i.e. meningioma and multiple sclerosis.…”