Abstract:94 Background: Prostate radiotherapy can be delivered using daily image-guided helical tomotherapy. Previous work has shown that contouring the rectum on the kV planning CT scan has a Jaccard conformity index (JCI) of 0.78 for different oncologists (inter-observer variability) and 0.82 for a single oncologist (intra-observer variability) (Lutgendorf-Caucig C et al. Feasibility of CBCT-based target and normal structure delineation in prostate cancer radiotherapy: multi-observer and image multi-modality study. … Show more
“…We have previously investigated interobserver contouring of this part of the rectum using deliberately extended image guidance scans. 59 We found that median Jaccard conformity index above the superior pelvic muscles was 0.84, interquartile range (IQR) 0.80–0.87. The corresponding value below the muscles was 0.62, IQR 0.53–0.72.…”
Objective:We sought to calculate accumulated dose (DA) to the rectum in patients treated with radiotherapy for prostate cancer. We were particularly interested in whether dose–surface maps (DSMs) provide additional information to dose–volume histograms (DVHs).Methods:Manual rectal contours were obtained for kilovoltage and daily megavoltage CT scans for 10 participants from the VoxTox study (380 scans). Daily delivered dose recalculation was performed using a ray-tracing algorithm. Delivered DVHs were summated to create accumulated DVHs. The rectum was considered as a cylinder, cut and unfolded to produce daily delivered DSMs; these were summated to produce accumulated DSMs.Results:Accumulated dose-volumes were different from planned in all participants. For one participant, all DA levels were higher and all volumes were larger than planned. For four participants, all DA levels were lower and all volumes were smaller than planned. For each of these four participants, ≥1% of pixels on the accumulated DSM received ≥5 Gy more than had been planned.Conclusion:Differences between accumulated and planned dose-volumes were seen in all participants. DSMs were able to identify differences between DA and planned dose that could not be appreciated from the DVHs. Further work is needed to extract the dose data embedded in the DSMs. These will be correlated with toxicity as part of the VoxTox Programme.Advances in knowledge:DSMs are able to identify differences between DA and planned dose that cannot be appreciated from DVHs alone and should be incorporated into future studies investigating links between DA and toxicity.
“…We have previously investigated interobserver contouring of this part of the rectum using deliberately extended image guidance scans. 59 We found that median Jaccard conformity index above the superior pelvic muscles was 0.84, interquartile range (IQR) 0.80–0.87. The corresponding value below the muscles was 0.62, IQR 0.53–0.72.…”
Objective:We sought to calculate accumulated dose (DA) to the rectum in patients treated with radiotherapy for prostate cancer. We were particularly interested in whether dose–surface maps (DSMs) provide additional information to dose–volume histograms (DVHs).Methods:Manual rectal contours were obtained for kilovoltage and daily megavoltage CT scans for 10 participants from the VoxTox study (380 scans). Daily delivered dose recalculation was performed using a ray-tracing algorithm. Delivered DVHs were summated to create accumulated DVHs. The rectum was considered as a cylinder, cut and unfolded to produce daily delivered DSMs; these were summated to produce accumulated DSMs.Results:Accumulated dose-volumes were different from planned in all participants. For one participant, all DA levels were higher and all volumes were larger than planned. For four participants, all DA levels were lower and all volumes were smaller than planned. For each of these four participants, ≥1% of pixels on the accumulated DSM received ≥5 Gy more than had been planned.Conclusion:Differences between accumulated and planned dose-volumes were seen in all participants. DSMs were able to identify differences between DA and planned dose that could not be appreciated from the DVHs. Further work is needed to extract the dose data embedded in the DSMs. These will be correlated with toxicity as part of the VoxTox Programme.Advances in knowledge:DSMs are able to identify differences between DA and planned dose that cannot be appreciated from DVHs alone and should be incorporated into future studies investigating links between DA and toxicity.
“…For daily MVCT scans, the rectum was identified using a locally developed autosegmentation system based on the Chan-Vese algorithm [32]. Manual contouring of the 4174 MVCT scans (over 62,000 slices) in this study would not have been practicable, and the autosegmentation system has previously been shown to fall within an acceptable range for intra-and inter-observer variability [33]. Dose was calculated directly from the MVCT images (and recalculated for kVCT images) using CheckTomo, an independent ray-tracing dose calculation algorithm [34,35].…”
Section: Voxtox Study Design and Patient Informationmentioning
Background and Purpose: Associations between dose and rectal toxicity in prostate radiotherapy are generally poorly understood. Evaluating spatial dose distributions to the rectal wall (RW) may lead to improvements in dose-toxicity modelling by incorporating geometric information, masked by dose-volume histograms. Furthermore, predictive power may be strengthened by incorporating the effects of interfraction motion into delivered dose calculations.Here we interrogate 3D dose distributions for patients with and without toxicity to identify rectal subregions at risk (SRR), and compare the discriminatory ability of planned and delivered dose. Material and Methods: Daily delivered dose to the rectum was calculated using image guidance scans, and accumulated at the voxel level using biomechanical finite element modelling. SRRs were statistically determined for rectal bleeding, proctitis, faecal incontinence and stool frequency from a training set (n = 139), and tested on a validation set (n = 47). Results: SRR patterns differed per endpoint. Analysing dose to SRRs improved discriminative ability with respect to the full RW for three of four endpoints. Training set AUC and OR analysis produced stronger toxicity associations from accumulated dose than planned dose. For rectal bleeding in particular, accumulated dose to the SRR (AUC 0.76) improved upon dose-toxicity associations derived from planned dose to the RW (AUC 0.63). However, validation results could not be considered significant. Conclusions: Voxel-level analysis of dose to the RW revealed SRRs associated with rectal toxicity, suggesting nonhomogeneous intra-organ radiosensitivity. Incorporating spatial features of accumulated delivered dose improved dose-toxicity associations. This may be an important tool for adaptive radiotherapy in the future.
“…[26,35,11,39,26,48,49,58,5] and references therein. Moreover, various applications were put forward for instance in optical flow [19], tomographic imaging [3], and medical imaging [59,14,15,12,51,20].…”
High-dimensional data classification is a fundamental task in machine learning and imaging science. In this paper, we propose a two-stage multiphase semi-supervised classification method for classifying high-dimensional data and unstructured point clouds. To begin with, a fuzzy classification method such as the standard support vector machine is used to generate a warm initialization. We then apply a two-stage approach named SaT (smoothing and thresholding) to improve the classification. In the first stage, an unconstraint convex variational model is implemented to purify and smooth the initialization, followed by the second stage which is to project the smoothed partition obtained at stage one to a binary partition. These two stages can be repeated, with the latest result as a new initialization, to keep improving the classification quality. We show that the convex model of the smoothing stage has a unique solution and can be solved by a specifically designed primal-dual algorithm whose convergence is guaranteed. We test our method and compare it with the state-of-the-art methods on several benchmark data sets. The experimental results demonstrate clearly that our method is superior in both the classification accuracy and computation speed for high-dimensional data and point clouds.
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