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Over the last fifty years, a number of higher energy X‐ray sources have been suggested as alternatives for the usual AlKα source found in the first commercial XPS systems and still the standard anode material for XPS today. This paper reviews the development of a number of such sources, predominantly in the authors' laboratory, and the rationale behind the desire to extend the binding energy range of the technique. The achromatic sources SiKα, ZrLα and TiKα are described along with monochromatic sources AgLα and CrKβ, both based on the standard quartz monochromator geometry but taking higher orders of diffraction. The driving force for much of this development was the desire to probe deeper core levels and CCC Auger transitions. These could be combined into initial or final state Auger parameters as described in much of the work cited in this review. The highest energy source considered is the CuKα source based around an external X‐ray tube, which provides much insight into the electronic structure of steels by measurement of the Fe1s and FeKLL peaks. The last decade or so has seen a significant increase of interest in HAXPES, and all manufacturers of turn‐key XPS instruments offer HAXPES options of one form or another, there are three dedicated HAXPES systems commercially available, which are very briefly described. [ABSTRACT FROM AUTHOR]

Background: Inaccurate manual organ delineation is one of the high‐risk failure modes in radiation treatment. Numerous automated contour quality assurance (QA) systems have been developed to assess contour acceptability; however, manual inspection of flagged cases is a time‐consuming and challenging process, and can lead to users overlooking the exact error location. Purpose: Our aim is to develop and validate a contour QA system that can effectively detect and visualize subregional contour errors, both qualitatively and quantitatively. Methods/Materials: A novel contour subregion error detection (CSED) system was developed using subregional surface distance discrepancies between manual and deep learning auto‐segmentation (DLAS) contours. A validation study was conducted using a head and neck public dataset containing 339 cases and evaluated according to knowledge‐based pass criteria derived from a clinical training dataset of 60 cases. A blind qualitative evaluation was conducted, comparing the results from the CSED system with manual labels. Subsequently, the CSED‐flagged cases were re‐examined by a radiation oncologist. Results: The CSED system could visualize the diverse types of subregional contour errors qualitatively and quantitatively. In the validation dataset, the CSED system resulted in true positive rates (TPR) of 0.814, 0.800, and 0.771; false positive rates (FPR) of 0.310, 0.267, and 0.298; and accuracies of 0.735, 0.759, and 0.730, for brainstem and left and right parotid contours, respectively. The CSED‐assisted manual review caught 13 brainstem, 19 left parotid, and 21 right parotid contour errors missed by conventional human review. The TPR/FPR/accuracy of the CSED‐assisted manual review improved to 0.836/0.253/0.784, 0.831/0.171/0.830, and 0.808/0.193/0.807 for each structure, respectively. Further, the time savings achieved through CSED‐assisted review improved by 75%, with the time for review taking 24.81 ± 12.84, 26.75 ± 10.41, and 28.71 ± 13.72 s for each structure, respectively. Conclusions: The CSED system enables qualitative and quantitative detection, localization, and visualization of manual segmentation subregional errors utilizing DLAS contours as references. The use of this system has been shown to help reduce the risk of high‐risk failure modes resulting from inaccurate organ segmentation. [ABSTRACT FROM AUTHOR]

Background: Contouring error is one of the top failure modes in radiation treatment. Multiple efforts have been made to develop tools to automatically detect segmentation errors. Deep learning‐based auto‐segmentation (DLAS) has been used as a baseline for flagging manual segmentation errors, but those efforts are limited to using only one or two contour comparison metrics. Purpose: The purpose of this research is to develop an improved contouring quality assurance system to identify and flag manual contouring errors. Methods and materials: DLAS contours were used as a reference to compare with manually segmented contours. A total of 27 geometric agreement metrics were determined from the comparisons between the two segmentation approaches. Feature selection was performed to optimize the training of a machine learning classification model to identify potential contouring errors. A public dataset with 339 cases was used to train and test the classifier. Four independent classifiers were trained using five‐fold cross validation, and the predictions from each classifier were ensembled using soft voting. The trained model was validated on a held‐out testing dataset. An additional independent clinical dataset with 60 cases was used to test the generalizability of the model. Model predictions were reviewed by an expert to confirm or reject the findings. Results: The proposed machine learning multiple features (ML‐MF) approach outperformed traditional nonmachine‐learning‐based approaches that are based on only one or two geometric agreement metrics. The machine learning model achieved recall (precision) values of 0.842 (0.899), 0.762 (0.762), 0.727 (0.842), and 0.773 (0.773) for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively compared to 0.526 (0.909), 0.619 (0.765), 0.682 (0.882), 0.773 (0.568) for an approach based solely on Dice similarity coefficient values. In the external validation dataset, 66.7, 93.3, 94.1, and 58.8% of flagged cases were confirmed to have contouring errors by an expert for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively. Conclusions: The proposed ML‐MF approach, which includes multiple geometric agreement metrics to flag manual contouring errors, demonstrated superior performance in comparison to traditional methods. This method is easy to implement in clinical practice and can help to reduce the significant time and labor costs associated with manual segmentation and review. [ABSTRACT FROM AUTHOR]

Monte Carlo (MC) independent dose calculations are often based on phase‐space files (PSF), as they can accurately represent particle characteristics. PSF generally are large and create a bottleneck in computation time. In addition, the number of independent particles is limited by the PSF, preventing further reduction of statistical uncertainty. The purpose of this study is to develop and validate a virtual source model (VSM) to address these limitations. Particles from existing PSF for the Varian TrueBeam medical linear accelerator 6X, 6XFFF, 10X, and 10XFFF beam configurations were tallied, analyzed, and used to generate a dual‐source photon VSM that includes electron contamination. The particle density distribution, kinetic energy spectrum, particle direction, and the correlations between characteristics were computed. The VSM models for each beam configuration were validated with water phantom measurements as well as clinical test cases against the original PSF. The new VSM requires 67 MB of disk space for each beam configuration, compared to 50 GB for the PSF from which they are based and effectively remove the bottleneck set by the PSF. At 3% MC uncertainty, the VSM approach reduces the calculation time by a factor of 14 on our server. MC doses obtained using the VSM approach were compared against PSF‐generated doses in clinical test cases and measurements in a water phantom using a gamma index analysis. For all tests, the VSMs were in excellent agreement with PSF doses and measurements (>90% passing voxels between doses and measurements). Results of this study indicate the successful derivation and implementation of a VSM model for Varian Linac that significantly saves computation time without sacrificing accuracy for independent dose calculation. [ABSTRACT FROM AUTHOR]

A newly-developed platform, the Illumina TruSeq Methyl Capture EPIC library prep (TruSeq EPIC), builds on the content of the Infinium MethylationEPIC Beadchip Microarray (EPIC-array) and leverages the power of next-generation sequencing for targeted bisulphite sequencing. We empirically examined the performance of TruSeq EPIC and EPIC-array in assessing genome-wide DNA methylation in breast tissue samples. TruSeq EPIC provided data with a much higher density in the regions when compared to EPIC-array (~2.74 million CpGs with at least 10X coverage vs ~752 K CpGs, respectively). Approximately 398 K CpGs were common and measured across the two platforms in every sample. Overall, there was high concordance in methylation levels between the two platforms (Pearson correlation r = 0.98, P < 0.0001). However, we observed that TruSeq EPIC measurements provided a wider dynamic range and likely a higher quantitative sensitivity for CpGs that were either hypo- or hyper-methylated (β close to 0 or 1, respectively). In addition, when comparing different breast tissue types TruSeq EPIC identified more differentially methylated CpGs than EPIC-array, not only out of additional sites interrogated by TruSeq EPIC alone, but also out of common sites interrogated by both platforms. Our results suggest that both platforms show high reproducibility and reliability in genome-wide DNA methylation profiling, while TruSeq EPIC had a significant improvement over EPIC-array regarding genomic resolution and coverage. The wider dynamic range and likely higher precision of the estimates by the TruSeq EPIC may lead to the identification of novel differentially methylated markers that are associated with disease risk. [ABSTRACT FROM AUTHOR]

A systematic bias in TomoTherapy output calibration was reported by the Imaging and Radiation Oncology Core Houston (IROC‐H) after analyzing intensity‐modulated radiation therapy (IMRT) credentialing results from hundreds of TomoTherapy units. Multiple theories were developed to explain this observation. One theory was that the use of a solid water "cheese" phantom instead of real water in the calibration measurement was the culprit. A phantom filled with distilled water was built to investigate whether our TomoTherapy was miscalibrated due to the use of a solid water phantom. A miscalibration of −1.47% was detected on our TomoTherapy unit. It is found that despite following the vendor's updated recommendation on computed tomography (CT) number to density calibration, the cheese phantom was still mapped to a density of 1.028 g/cm3, rather than the 1.01 g/cm3 value reported in literature. When the density of the cheese phantom was modified to 1.01 g/cm3 in the treatment planning system, the measurement also indicated that our TomoTherapy machine was miscalibrated by −1.52%, agreeing with the real water phantom findings. Our single‐institution finding showed that the cheese phantom density assignment can introduce greater than 1% errors in the TomoTherapy absolute dose calibration. It is recommended that the absolute dose calibration for TomoTherapy be performed either in real water or in the cheese phantom with the density in TPS overridden as 1.01 g/cm3. [ABSTRACT FROM AUTHOR]

We investigated the differences in growth and rates of recurrence of the botryoid odontogenic cyst (BOC) and the less aggressive lateral periodontal cyst (LPC) and gingival cyst of the adult (GCA). We compared the immunohistochemical expression of selected biomarkers of apoptosis and proliferation and of regulators of their activity. Sections from archival paraffin blocks of 15 BOCs, six GCAs, six LPCs, and three odontogenic keratocysts (OKCs) were processed for immunohistochemical localization of Bcl-2, caspase-3, p53 and Ki-67. Labeled and unlabeled epithelial cells were counted and differences in the mean labeling index (LI) were evaluated statistically. The only significant differences in LI were for the anti-apoptotic marker, Bcl-2; the hierarchy was BOC > OKC > LPC > GCA. In two BOCs, 97% of the cells, and in all OKCs, all of the basal cells were labeled with Bcl-2. Otherwise, cells labeled with Bcl-2, p53 and caspase-3 were scattered among the basal and intermediate epithelial cell layers. Ki-67 labeled almost exclusively basal cells in the BOCs, LPCs and GCAs, and both basal and intermediate layer cells in the OKCs. Our findings suggest that while there was no significant difference in replicative potential of the GCAs, LPCs and BOCs, factors that influence apoptosis may be partially responsible for the more aggressive behavior of BOCs and OKCs. [ABSTRACT FROM AUTHOR]