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PURPOSE: The purpose of the study was to assess the feasibility of performing intraoperative dosimetry for permanent prostate brachytherapy by combining transrectal ultrasound (TRUS) and fluoroscopy/cone beam CT [CBCT] images and accounting for the effect of prostate deformation. METHODS AND MATERIALS: 13 patients underwent TRUS and multiview two-dimensional fluoroscopic imaging partway through the implant, as well as repeat fluoroscopic imaging with the TRUS probe inserted and retracted, and finally three-dimensional CBCT imaging at the end of the implant. The locations of all the implanted seeds were obtained from the fluoroscopy/CBCT images and were registered to prostate contours delineated on the TRUS images based on a common subset of seeds identified on both image sets. Prostate contours were also deformed, using a finite-element model, to take into account the effect of the TRUS probe pressure. Prostate dosimetry parameters were obtained for fluoroscopic and CBCT-dosimetry approaches and compared with the standard-of-care Day-0 postimplant CT dosimetry. RESULTS: High linear correlation (R2 > 0.8) was observed in the measured values of prostate D90%, V100%, and V150%, between the two intraoperative dosimetry approaches. The prostate D90% and V100% obtained from intraoperative dosimetry methods were in agreement with the postimplant CT dosimetry. Only the prostate V150% was on average 4.1% (p-value <0.05) higher in the CBCT-dosimetry approach and 6.7% (p-value <0.05) higher in postimplant CT dosimetry compared with the fluoroscopic dosimetry approach. Deformation of the prostate by the ultrasound probe appeared to have a minimal effect on prostate dosimetry. CONCLUSIONS: The results of this study have shown that both of the proposed dosimetric evaluation approaches have potential for real-time intraoperative dosimetry.

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A novel approach for automatic localization of brachytherapy seeds in 3D transrectal ultrasound (TRUS) images, using machine learning based algorithm, is presented. 3D radiofrequency ultrasound signals were collected from 13 patients using the linear array of the TRUS probe during the brachytherapy procedure in which needles are used for insertion of stranded seeds. Gold standard for the location of seeds on TRUS data were obtained with the guidance of the complete reconstruction of the seed locations from multiple C-arm fluoroscopy views and used in the creation of the training set. We designed and trained a convolutional neural network (CNN) model that worked on 3D cubical sub-regions of the TRUS images, that will be referred to as patches, representing seed, non-seed within a needle track and non-seed elsewhere in the images. The models were trained with these patches to detect the needle track first and then the individual seeds within the needle track. A leave-one-out cross validation approach was used to test the model on the data from eight of the patients, for whom accurate seed locations were available from fluoroscopic imaging. The total inference time was about 7 min for needle track detection in each patient's image and approximately 1 min for seed detection in each needle, leading to a total seed detection time of less than 15 min. Our seed detection algorithm achieved [Formula: see text] precision, [Formula: see text] recall and [Formula: see text] F1_score. The results from our CNN-based method were compared to manual seed localization performed by an expert. The CNN model yielded higher precision (lower false discovery rate) compared to the manual method. The automated approach requires little modification to the current clinical setups and offers the prospect of application in real time intraoperative dosimetric analysis of the implant.

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PURPOSE: Current methods of low dose rate brachytherapy source strength verification for sources preloaded into needles consist of either assaying a small number of seeds from a separate sample belonging to the same lot used to load the needles or performing batch assays of a subset of the preloaded seed trains. Both of these methods are cumbersome and have the limitations inherent to sampling. The purpose of this work was to investigate an alternative approach that uses an image-based, autoradiographic system capable of the rapid and complete assay of all sources without compromising sterility. METHODS: The system consists of a flat panel image detector, an autoclavable needle holder, and software to analyze the detected signals. The needle holder was designed to maintain a fixed vertical spacing between the needles and the image detector, and to collimate the emissions from each seed. It also provides a sterile barrier between the needles and the imager. The image detector has a sufficiently large image capture area to allow several needles to be analyzed simultaneously.Several tests were performed to assess the accuracy and reproducibility of source strengths obtained using this system. Three different seed models (Oncura 6711 and 9011 (125)I seeds, and IsoAid Advantage (103)Pd seeds) were used in the evaluations. Seeds were loaded into trains with at least 1 cm spacing. RESULTS: Using our system, it was possible to obtain linear calibration curves with coverage factor k = 1 prediction intervals of less than ±2% near the centre of their range for the three source models. The uncertainty budget calculated from a combination of type A and type B estimates of potential sources of error was somewhat larger, yielding (k = 1) combined uncertainties for individual seed readings of 6.2% for (125)I 6711 seeds, 4.7% for (125)I 9011 seeds, and 11.0% for Advantage (103)Pd seeds. CONCLUSIONS: This study showed that a flat panel detector dosimetry system is a viable option for source strength verification in preloaded needles, as it is capable of measuring all of the sources intended for implantation. Such a system has the potential to directly and efficiently estimate individual source strengths, the overall mean source strength, and the positions within the seed-spacer train.

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PURPOSE/OBJECTIVES: To assess regional dose metrics as predictors of disease relapse after low-dose-rate prostate brachytherapy. METHODS AND MATERIALS: A cohort of 2000 consecutive patients with prostate cancer treated with iodine-125 low-dose-rate prostate brachytherapy monotherapy was implanted between July 1998 and November 2007. Within this group, 89 patients had disease relapse, 13 of whom had local failure by clinical or histopathologic criteria. Postoperative computed tomography scans were available for 96.3% of cases, resulting in a data set composed of 87 relapses (including all 13 local relapses) and 1839 nonrelapsed controls. Using the VariSeed 8.0.2 software, we divided the original prostate contours into four quadrants: anterior-superior (ASQ), posterior-superior, anterior-inferior (AIQ), and posterior-inferior. DVH-derived dosimetric parameters were calculated for the whole prostate and each quadrant. RESULTS: Gleason score, prostate-specific antigen, and the use of androgen-deprivation therapy were predictive of disease relapse in a multivariate Cox model. Whole prostate dose metrics did not predict biochemical or local relapse. Dosimetric coverage was sparsest in the ASQ. Despite low values, dose to the ASQ was not predictive of relapse, nor were doses to the remaining three quadrants. AIQ coverage was predictive of local relapse in multivariate Cox model (p = 0.042). However, the AIQ dose metrics exhibited a large variance and on bootstrap analysis, a p value of <0.05 was seen in only 51% of 1000 iterations. CONCLUSIONS: Consistent with previous reports, whole-prostate dose metrics were not predictive of disease relapse (any) or local relapse in our patients. Although significant ASQ underdosage relative to other prostate regions was observed, ASQ dose coverage did not correlate with relapse.

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