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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.

Deformable registration has migrated from a research topic to a widely used clinical tool that can improve radiotherapeutic treatment accuracy by tracking anatomical changes. Although various mathematical formulations have been reported in the literature and implemented in commercial software, we lack a straightforward method to verify a given solution in routine clinical use. We propose a metric using concepts derived from vector analysis that complements the standard evaluation tools to identify unrealistic wrappings in a displacement field. At the heart of the proposed procedure is identification of vortexes in the displacement field that do not correspond to underlying anatomical changes. Vortexes are detected and their intensity quantified using the CURL operator and presented as a vortex map overlaid on the original anatomy for rapid identification of problematic regions. We show application of the proposed metric on clinical scenarios of adaptive radiotherapy and treatment response assessment, where the CURL operator quantitatively detected errors in the displacement field and identified problematic regions that were invisible to classical voxel‐based evaluation methods. Unrealistic warping not visible to standard voxel‐based solution assessment can produce erroneous results when the deformable solution is applied on a secondary dataset, such as dose matrix in adaptive therapy or PET data for treatment response assessment. The proposed metric for evaluating deformable registration provides increased usability and accuracy of detecting unrealistic deformable registration solutions when compared to standard intensity‐based approaches. It is computationally efficient and provides a valuable platform for the clinical acceptance of image‐guided radiotherapy.PACS numbers: 87.57.nj; 87.55.Qr; 87.57.cp

Objective Robust methodology that allows objective, automated, and observer-independent measurements of brain tumor volume, especially postresection, is lacking; hence, determination of tumor response and progression in neuro-oncology is unreliable. Our objective was to determine if a semi-automated, volumetric method for quantifying enhancing tissue would perform with high reproducibility and low interobserver variability. Methods Fifty-seven scans from 13 patients with glioblastoma (GBM) were assessed, using our method, by two neuroradiologists, one neurosurgeon, one neurosurgery resident, one nurse practitioner, and one medical student. The two neuroradiologists also performed traditional one-dimensional and two-dimensional measurements. Intraclass correlation coefficients (ICC) assessed interobserver variability between measurements. Radiographic response was determined using Response Evaluation Criteria In Solid Tumors (RECIST) guidelines and Macdonald criteria. Kappa statistics described interobserver variability of volumetric radiographic response determinations. Results There was strong agreement for one-dimensional (RECIST) and two-dimensional (Macdonald) measurements between neuroradiologists (ICC=0.42 and 0.61, respectively), but the agreement using our novel automated approach was significantly stronger (ICC=0.97). Our volumetric method had the strongest agreement with regard to radiographic response (kappa=0.96) when compared with two-dimensional (0.54) or one-dimensional (0.46) methods. Despite diverse levels of experience, measurements using our volumetric program by all users remained remarkably high (0.94). Conclusion Interobserver variability with our semiautomated method is less than the variability with traditional methods of tumor measurement. It is objective, quick, and highly reproducible among operators with varying expertise. This approach should be further evaluated as a potential standard for response assessment based on contrast enhancement in brain tumors.

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