Anatomic structure-based deformable image registration of brachytherapy implants in the treatment of locally advanced cervix cancer.

PURPOSE: To examine the impact of anatomic structure-based image sets in deformable image registration (DIR) for cervical cancer patients. METHODS AND MATERIALS: CT examinations of 7 patients previously treated for locally advanced cervical cancer with external beam radiation therapy and from three to five fractions of high-dose-rate brachytherapy (HDR-BT) were used. Structure-based image sets were created from "free" structures already made for planning purposes, with each structure of interest assigned a unique, homogeneous Hounsfield number. Subsequent HDR fractions were registered to the pretreatment external beam radiation therapy and/or the first HDR fraction using commercially available software by rigid alignment (RIG) followed by DIR. Comparison methods included quantification of external contour displacement between source and target images and calculation of mean voxel displacement values. Registration results for structure-based image sets were then compared and contrasted to intensity-based registrations of the original grayscale images. RESULTS: Utilization of anatomic structure-based image sets resulted in better initial rigid matching (A-RIG) with more importance on applicator positioning and soft tissue structures. Subsequent DIR of anatomic structure-based images allowed for intermodality registrations, whereas all intermodality registrations using original CT images failed to produce anatomically feasible results. CONCLUSIONS: We have investigated the use of structure-based CT image sets for image registrations and have produced anatomically favorable registrations with excellent matching of external contours as compared to registrations of original grayscale images. Commercial software registrations using treatment-planning structures required no manual tweaking on a per-patient basis, suggesting results are reproducible and broadly applicable.

A novel, reproducible, and objective method for volumetric magnetic resonance imaging assessment of enhancing glioblastoma.

OBJECT: Robust methodology that allows objective, automated, and observer-independent measurements of brain tumor volume, especially after resection, is lacking. Thus, determination of tumor response and progression in neurooncology is unreliable. The objective of this study 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 MR images from 13 patients with glioblastoma were assessed using our method, by 2 neuroradiologists, 1 neurosurgeon, 1 neurosurgical resident, 1 nurse practitioner, and 1 medical student. The 2 neuroradiologists also performed traditional 1-dimensional (1D) and 2-dimensional (2D) measurements. Intraclass correlation coefficients (ICCs) assessed interobserver variability between measurements. Radiological response was determined using Response Evaluation Criteria In Solid Tumors (RECIST) guidelines and Macdonald criteria. Kappa statistics described interobserver variability of volumetric radiological response determinations. RESULTS: There was strong agreement for 1D (RECIST) and 2D (Macdonald) measurements between neuroradiologists (ICC = 0.42 and 0.61, respectively), but the agreement using the authors' novel automated approach was significantly stronger (ICC = 0.97). The volumetric method had the strongest agreement with regard to radiological response (κ = 0.96) when compared with 2D (κ = 0.54) or 1D (κ = 0.46) methods. Despite diverse levels of experience of the users of the volumetric method, measurements using the volumetric program remained remarkably consistent in all users (0.94). CONCLUSIONS: Interobserver variability using this new semi-automated method is less than the variability with traditional methods of tumor measurement. This new method is objective, quick, and highly reproducible among operators with varying levels of expertise. This approach should be further evaluated as a potential standard for response assessment based on contrast enhancement in brain tumors.

Voxel clustering for quantifying PET-based treatment response assessment.

PURPOSE: Imaging biomarkers are crucial in managing treatment options for cancer patients. They are extremely powerful tools since they allow personalized treatment assessment early during therapy by using repeated imaging to detect and quantify tumor response. Currently, treatment response assessment from consecutive imaging is measured by simple global measures that do not capture a tumor's heterogeneous response. The authors present an automated, multivoxel metric that groups voxels into clusters of changes for a local definition of radiation treatment efficiency from multiple PET imaging studies acquired at different time periods for assessing therapeutic response. METHODS: The algorithm employs level-set mathematics to extract changing features to classify voxels into response patterns. First, pretreatment and post-treatment PET images were aligned using a deformable registration to correct for posture and soft tissue changes. The detailed mapping was modeled by free form deformations B-spline optimized using the limited memory L-BFGS algorithm. The posture-corrected datasets are then subtracted to produce an image of molecular changes embedded with noise. Once images were aligned and subtracted, a segmentation algorithm combining the concepts of voxel and distance-based techniques classified voxels into patterns of signal reduction or enhancement. Although signal reduction is evidence of successful treatment, signal-enhancing regions are an indication of treatment failure. For an in depth analysis of potential treatment errors, patterns of signal enhancement were correlated with the radiation treatment dose and anatomical structures from the treatment plan using image registration methods. RESULTS: The algorithm was retrospectively applied to PET∕CT and radiotherapy (RT) oncology data from an NCI-sponsored clinical trial (81 clinical cases from RTOG 0522 Trial) for combined drug and radiation therapy in head and neck carcinomas. This clinical trial dataset presented a realistic environment for implementing and validating our algorithm to correlate local response as observed in serial PET with delivered dose. The technique was instrumental in detecting geographical and segmentation misses on the actual clinical cases by providing accurate voxel-by-voxel analysis of metabolic changes. Results of the level-set based clustering algorithm are saved as a detailed report of enhancing∕nonenhancing regions and their location, and can be further displayed as a colorwash laid over the original anatomy for in depth analysis. CONCLUSIONS: The automated technique was instrumental in analyzing treatment response in the clinical cases and provided an useful tool for accurate, outcome-based response assessment of the radiation treatment process. The developed method is general and should be extendable to other high-resolution diagnostic imaging with minor modifications.

A novel method for volumetric MRI response assessment of enhancing brain tumors.

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.

PET lesion segmentation using automated iso-intensity contouring in head and neck cancer.

To improve the objectivity of the integration of positron emission tomography (PET), we used the conformality index (CI) to measure the goodness of fit of a given PET iso-SUV (standardized uptake value) level with the GTV defined on PET (GTV(PET)) and CT (GTV(CT)). Twenty-two datasets involving 20 head and neck cancer patients were identified. GTV(PET) and GTV(CT) were delineated manually.An iso-intensity method was developed to automatically segment GTV(PET-ISO) using (a) SUV and (b) maximum intensity thresholding (% Max), over a range of intensities. For each intensity, GTV(PET-ISO) was compared to GTV(PET) using the conformality index CI(PET) (and, similarly, to GTV(CT) using CICT). Comparing GTV(PET) to GTV(PET-ISO) vs comparing GTV(CT) to GTV(PET-ISO), the average peak CI was 0.68 +/- 0.09 vs 0.49 +/- 0.12 (p < 0.001), the optimum iso-SUV was 2.7 +/- 0.7 vs 2.9 +/- 1.0 (p=0. 253), and the % Max SUV was 21.8% +/- 7.6% vs 23.8% +/- 8.6% (p=0. 310), respectively. The radiation oncologist's volumes corresponded to a lower iso-SUV (3.02 +/- 0.58 vs 4.36 +/- 0.77, p< 0.001) and lower % Max SUV (24.1 +/- 9.1% vs 34.3 +/- 11.2%, p<0.001) than those drawn by the nuclear medicine physician. Though manual editing may still be necessary, PET iso-contouring is one method to improve the objectivity of GTV definition in head and neck cancer patients. Iso-SUV's can also be used to study the differences between PET's role as a nuclear medicine diagnostic test versus a radiation oncology treatment planning tool.

Multileaf collimator-based linear accelerator radiosurgery: five-year efficiency analysis.

BACKGROUND: In 1989, Emory University initiated a linear accelerator (linac) radiosurgery program using circular collimators. In 2001, the program converted to a multileaf collimator. Since then, the treatment parameters of each patient have been stored in the record-and-verify system. Three major changes have occurred in the radiosurgery program in the past 6 years: in 2002, treatment was changed from static conformal beams to dynamic conformal arc (DCA) therapy, and all patients were imaged before treatment. Beginning in 2005, a linac was used, with the opportunity to treat at higher dose rates (600-1,000 monitor units/min). The aim of this study was to analyze the time required to deliver radiosurgery and the factors affecting treatment delivery. Benchmark data are provided for centers contemplating initiating linac radiosurgery programs. MATERIALS AND METHODS: Custom software was developed to mine the record-and-verify system database and automatically perform a chart review on patients who underwent stereotactic radiosurgery from March 2001 to October 2006. The software extracted 510 patients who underwent stereotactic radiosurgery, and the following information was recorded for each patient: treatment technique, treatment time (from initiation of imaging, if done, to completion of therapy), number of isocenters, number of fields, total monitor units, and dose rate. RESULTS: Of the 510 patients, 395 were treated with DCA therapy and 115 with static conformal beams. The average number of isocenters treated was 1.06 (range, 1-4). The average times to deliver treatment were 24.1 minutes for patients who underwent DCA therapy and 19.3 minutes for those treated with static conformal beams, reflecting the lack of imaging in the latter patients. Eighty percent of patients were treated in <30 minutes. For the patients who underwent DCA therapy, the times required to treat 1, 2, 3, and 4 isocenters were 23.9, 24.8, 33.1, and 37.8 minutes, respectively. Average beam-on time for these patients was 11.4 minutes. There has been no significant reduction in treatment delivery with the use of 1,000 monitor units/min, reflecting the fact that beam-on time is not the major determinant of overall treatment time. CONCLUSIONS: Multileaf collimator-based linac radiosurgery can be delivered efficiently in <30 minutes in the vast majority of patients. Given the limited treatment room utilization required for stereotactic radiosurgery treatments, this study calls into question the need for a dedicated radiosurgery unit for even busy treatment centers.