A Novel Method for Volumetric MRI Response Assessment of Enhancing Brain Tumors

Abstract

Current radiographic response criteria for brain tumors have difficulty describing changes surrounding postoperative resection cavities. Volumetric techniques may offer improved assessment, however usually are time-consuming, subjective and require expert opinion and specialized magnetic resonance imaging (MRI) sequences. We describe the application of a novel volumetric software algorithm that is nearly fully automated and uses standard T1 pre- and post-contrast MRI sequences. T1-weighted pre- and post-contrast images are automatically fused and normalized. The tumor region of interest is grossly outlined by the user. An atlas of the nasal mucosa is automatically detected and used to normalize levels of enhancement. The volume of enhancing tumor is then automatically calculated. We tested the ability of our method to calculate enhancing tumor volume with resection cavity collapse and when the enhancing tumor is obscured by subacute blood in a resection cavity. To determine variability in results, we compared narrowly-defined tumor regions with tumor regions that include adjacent meningeal enhancement and also compared different contrast enhancement threshold levels used for the automatic calculation of enhancing tumor volume. Our method quantified enhancing tumor volume despite resection cavity collapse. It detected tumor volume increase in the midst of blood products that incorrectly caused decreased measurements by other techniques. Similar trends in volume changes across scans were seen with inclusion or exclusion of meningeal enhancement and despite different automated thresholds for tissue enhancement. Our approach appears to overcome many of the challenges with response assessment of enhancing brain tumors and warrants further examination and validation.

Semiautomated Volumetric Measurement on Postcontrast MR Imaging for Analysis of Recurrent and Residual Disease in Glioblastoma Multiforme

“…Other studies have described similar computer-aided techniques in the evaluation of gliomas; however, validation against other measurement techniques is lacking. 15 Bidimensional techniques used in the MacDonald and RANO criteria are currently the most commonly used techniques for GBM assessment; however, studies have reported high interobserver and intraobserver variability, even among individuals with specialty training in neuroradiology. 24,25 The increased variability in diameter-based approaches likely stems from the inherent irregularities in GBM.…”

“…14 Application of computer-assisted volumetry (CAV) has been explored for the evaluation of gliomas; however, these studies have dealt with native nontreated disease and have not been validated against other measurement techniques. [15][16][17] In this study, we describe a novel CAV technique for assessment of tumor burden in the patient with GBM. Specifically, we describe the reliability and feasibility of this technique compared with traditional linear-based measurements in the patient with postresection GBM.…”

How Treatment Monitoring Is Influencing Treatment Decisions in Glioblastomas

“…Voxel-to-voxel image subtraction between T1-weighted images before and after contrast administration may detect subtle enhancement that can easily be interpreted as non-enhancement by visual analyses. The use of T1-subtraction maps may provide a more accurate assessment of true tumor extent in the context of antiangiogenic treatment [47•, 81,82].…”

Image Segmentation Methods and Applications in MRI Brain Images

“…This leads to the need for accurate brain magnetic resonance image (MRI) segmentation into gray matter (GM) [1], white matter (WM), cerebrospinal fluid (CSF), and also to define tumours or lesions if present [2,3].…”