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In external beam radiotherapy, conventional analysis of portal images in two dimensions (2D) is limited to verification of in-plane rotations and translations of the patient. We developed and clinically tested a new method for automatic quantification of the patient setup in three dimensions (3D) using one set of computed tomography (CT) data and two transmission images. These transmission images can be either a pair of simulator images or a pair of portal images. Our procedure adjusts the position and orientation of the CT data in order to maximize the distance through bone in the CT data along lines between the focus of the irradiation unit and bony structures in the transmission images. For this purpose, bony features are either automatically detected or manually delineated in the transmission images. The performance of the method was quantified by aligning randomly displaced CT data with transmission images simulated from digitally reconstructed radiographs. In addition, the clinical performance were assessed in a limited number of images of prostate cancer and parotid gland tumor treatments. The complete procedure takes less than 2 min on a 90-MHz Pentium PC. The alignment time is 50 s for portal images and 80 s for simulator images. The accuracy is about 1 mm and 1 degrees. Application to clinical cases demonstrated that the procedure provides essential information for the correction of setup errors in case of large rotations (typically larger than 2 degrees) in the setup. The 3D procedure was found to be robust for imperfections in the delineation of bony structures in the transmission images. Visual verification of the results remains, however, necessary. It can be concluded that our strategy for automatic analysis of patient setup in 3D is accurate and robust. The procedure is relatively fast and reduces the human workload compared with existing techniques for the quantification of patient setup in 3D. In addition, the procedure improves the accuracy of treatment verification in 2D in some cases where rotational deviations in the setup occur.

The purpose of this study is to quantify and optimize the performance of an automatic portal image analysis procedure under clinical conditions and to compare the performance with that of human operators. A new method, based on analysis of variance, is introduced to quantify the clinical performance of portal image analysis tools in terms of systematic and random variations. The automatic portal image analysis procedure is based on chamfer matching. Two image enhancement techniques have been investigated in the automatic procedure: morphological top-hat (MTH) transformation and multiscale medial axis (MMA) transformation. The performance of these enhancements was quantified and optimized as a function of filter size using images obtained from clinical treatment. All images used for this study were obtained from pelvic treatment fields by means of an electronic portal imaging device. The random variations in the alignment of AP fields are typically 0.5 mm and 0.5 degrees (1 SD) for both the human operators and the optimized automatic analysis procedure. Random variations in the alignment of lateral pelvic fields are typically twice as large for all operators. MMA enhancement yields smaller random variations than MTH enhancement for lateral fields, but the differences are marginal for AP fields. The optimized automatic analysis procedure has a success rate ranging from 99% for AP large fields to 96% for lateral fields and 85% for AP boost fields. The accuracy of the method is comparable with the accuracy of the human operators for most investigated fields. For lateral boost fields and simultaneous boost fields, the random variations of the automatic analysis are typically two times larger than the variations of the human operators. Automatic analysis is 4 to 20 times faster than human operators yielding a large reduction in work load.

Accurate characterization of breast tumors is important for the appropriate selection of therapy and monitoring of the response. For this purpose breast imaging and tissue biopsy are important aspects. In this study, a fully automated method for deformable registration of DCE-MRI and PET/CT of the breast is presented. The registration is performed using the CT component of the PET/CT and the pre-contrast T1-weighted non-fat suppressed MRI. Comparable patient setup protocols were used during the MRI and PET examinations in order to avoid having to make assumptions of biomedical properties of the breast during and after the application of chemotherapy. The registration uses a multi-resolution approach to speed up the process and to minimize the probability of converging to local minima. The validation was performed on 140 breasts (70 patients). From a total number of registration cases, 94.2% of the breasts were aligned within 4.0 mm accuracy (1 PET voxel). Fused information may be beneficial to obtain representative biopsy samples, which in turn will benefit the treatment of the patient.

Two computer methods for matching digital line drawings have been tested for automatic on-line verification of the radiation field shape during radiotherapy. This work is part of a research program aiming at automated inspection of on-line acquired digital portal images. Both methods, moment normalization and point distance minimization, compare the field edge detected in the portal image with the intended field edge and the beam shaping devices marked in the simulator image. Tests showed that the methods should be used together. First, shape deviations in the detected field edge are classified quickly, in less than a second (25 MHz 386 + 387 PC), as large (e.g., missing blocks) or small (e.g., shifts of a few mm) by moment normalization. Then the portal image is mapped to the simulator image by field edge alignment with a translation and magnification obtained from moment normalization and a rotation from point distance minimization. The mapped portal image and the simulator image juxtaposed on a monitor screen for visual inspection. Finally, the small field shape deviations are detected by an analysis of the relationship between the radiation field shape and the positioning of field shaping devices using point distance minimization.

Background - Neoadjuvant chemotherapy (NAC) is increasingly applied in stage II and III breast cancer. Response monitoring with magnetic resonance imaging (MRI) has been shown valuable, but knowledge of the breast cancer subtype is essential for correct interpretation of response assessment.(Loo et al, J Clin Oncol 29:660–6, 2011) The aim of the present study was to evaluate the relevance of breast cancer subtype for 18F-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET/CT) markers for monitoring of therapy response during NAC. Methods - Evaluation included 94 women with primary stage II or III breast cancer and measurable (quantifiable) FDG tumor uptake. FDG PET/CT scans were performed before and after six weeks of NAC using similar prone patient positioning. FDG uptake of the primary tumor was quantified using maximum standardized uptake values (SUVmax). Tumors were divided into three subtypes using immunohistochemistry: human epidermal growth factor receptor 2 (HER2) positive, estrogen receptor (ER) positive/HER2 negative and triple negative. Tumor response was assessed as presence of residual tumor in the surgery specimen (no response or partial response) or absence thereof (near complete or complete response). Multivariate regression analysis and receiver operating characteristic (ROC) analyses were employed to determine significant associations. Results - A (near) complete response at pathology was observed in 16 (73%) of 22 HER2 positive tumors, 5 (12%) of 43 ER positive/HER2 negative tumors and 20 (69%) of 29 triple negative tumors. In the multivariate regression analysis for the whole group, (near) complete response in the surgery specimen was significantly associated with relative reduction of SUVmax of the tumor between both scans and breast cancer subtype (area under the curve of the ROC curve 0.88 [95% confidence interval 0.81−0.95], p<0.001); no significant associations were found for FDG uptake at baseline and age. In a subgroup analysis of breast cancer subtype, a significant association was found between pathologic response and relative reduction of SUVmax for ER positive/HER2 negative and triple negative tumors (p=0.012 and p<0.001, respectively), but not for HER2 positive tumors (p=0.151). Conclusion - Knowledge of the breast cancer subtype appears relevant for the assessment of response to NAC with FDG PET/CT. Response monitoring with FDG PET/CT may predict a pathological response adequately in ER positive/HER2 negative and triple negative tumors, but seems less accurate in HER2 positive tumors. The reasons for these differences need to be elucidated in further investigations. Disclosure - This study was performed within the framework of CTMM, the Center for Translational Molecular Medicine (www.ctmm.nl), project Breast CARE (grant 030–104). Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P2-09-06.

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