Synthetic Megavoltage Cone Beam Computed Tomography Image Generation for Improved Contouring Accuracy of Cardiac Pacemakers

In this study, we aimed to enhance the contouring accuracy of cardiac pacemakers by improving their visualization using deep learning models to predict MV CBCT images based on kV CT or CBCT images. Ten pacemakers and four thorax phantoms were included, creating a total of 35 combinations. Each combination was imaged on a Varian Halcyon (kV/MV CBCT images) and Siemens SOMATOM CT scanner (kV CT images). Two generative adversarial network (GAN)-based models, cycleGAN and conditional GAN (cGAN), were trained to generate synthetic MV (sMV) CBCT images from kV CT/CBCT images using twenty-eight datasets (80%). The pacemakers in the sMV CBCT images and original MV CBCT images were manually delineated and reviewed by three users. The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were used to compare contour accuracy. Visual inspection showed the improved visualization of pacemakers on sMV CBCT images compared to original kV CT/CBCT images. Moreover, cGAN demonstrated superior performance in enhancing pacemaker visualization compared to cycleGAN. The mean DSC, HD95, and MSD for contours on sMV CBCT images generated from kV CT/CBCT images were 0.91 ± 0.02/0.92 ± 0.01, 1.38 ± 0.31 mm/1.18 ± 0.20 mm, and 0.42 ± 0.07 mm/0.36 ± 0.06 mm using the cGAN model. Deep learning-based methods, specifically cycleGAN and cGAN, can effectively enhance the visualization of pacemakers in thorax kV CT/CBCT images, therefore improving the contouring precision of these devices.

Impact of microscopic extrathyroidal extension on differentiated thyroid cancer post-surgical risk of recurrence: a retrospective analysis

PURPOSE

In the last edition of the American Joint Committee on Cancer (AJCC) staging system, differentiated thyroid cancers (DTC) showing microscopic extrathyroidal extension (mETE) are considered comparable to intrathyroidal cancers for their clinical behavior and prognosis. The aim of the study is to evaluate the impact of this updated assessment of T, when applied to the postoperative recurrence risk stratification, according to the American Thyroid Association Guidelines (ATA-RR).

METHODS

One-hundred DTC patients who underwent total thyroidectomy were retrospectively evaluated. The downstaging of mETE was introduced in the definition of T, and the updated classification defined as modified ATA-RR (ATAm-RR). For each patient, post-surgical basal and stimulated thyroglobulin (Tg) levels, neck ultrasound (US) and post-ablative 131-I whole body scan (WBS) reports were considered. The predictive performance (PP) of disease recurrence was calculated both for each single parameter, as well as for all of them.

RESULTS

According to ATAm-RR classification, 19/100 patients (19%) were downstaged. ATA-RR proved a significant PP for disease recurrence (DR) (sensitivity 75.0%, specificity 63.0%, p = 0.023). However, ATAm-RR performed slightly better due to an increased specificity (sensitivity 75.0%, specificity 83.7%, p < 0.001). For both classifications, the PP was optimal when all the above-mentioned predictive parameters were considered.

CONCLUSION

Our results suggest that the new assessment of T considering mETE resulted in a downgrading of ATA-RR class in a significant number of patients. This provides a better PP for disease recurrence, and the best PP was obtained when considering the whole predictive variables together.

A real-time contouring feedback tool for consensus-based contour training

Purpose

Variability in contouring structures of interest for radiotherapy continues to be challenging. Although training can reduce such variability, having radiation oncologists provide feedback can be impractical. We developed a contour training tool to provide real-time feedback to trainees, thereby reducing variability in contouring.

Methods

We developed a novel metric termed localized signed square distance (LSSD) to provide feedback to the trainee on how their contour compares with a reference contour, which is generated real-time by combining trainee contour and multiple expert radiation oncologist contours. Nine trainees performed contour training by using six randomly assigned training cases that included one test case of the heart and left ventricle (LV). The test case was repeated 30 days later to assess retention. The distribution of LSSD maps of the initial contour for the training cases was combined and compared with the distribution of LSSD maps of the final contours for all training cases. The difference in standard deviations from the initial to final LSSD maps, ΔLSSD, was computed both on a per-case basis and for the entire group.

Results

For every training case, statistically significant ΔLSSD were observed for both the heart and LV. When all initial and final LSSD maps were aggregated for the training cases, before training, the mean LSSD ([range], standard deviation) was -0.8 mm ([-37.9, 34.9], 4.2) and 0.3 mm ([-25.1, 32.7], 4.8) for heart and LV, respectively. These were reduced to -0.1 mm ([-16.2, 7.3], 0.8) and 0.1 mm ([-6.6, 8.3], 0.7) for the final LSSD maps during the contour training sessions. For the retention case, the initial and final LSSD maps of the retention case were aggregated and were -1.5 mm ([-22.9, 19.9], 3.4) and -0.2 mm ([-4.5, 1.5], 0.7) for the heart and 1.8 mm ([-16.7, 34.5], 5.1) and 0.2 mm ([-3.9, 1.6],0.7) for the LV.

Conclusions

A tool that uses real-time contouring feedback was developed and successfully used for contour training of nine trainees. In all cases, the utility was able to guide the trainee and ultimately reduce the variability of the trainee's contouring.

The species coalescent indicates possible bat and pangolin origins of the COVID-19 pandemic

A consensus species tree is reconstructed from 11 gene trees for human, bat, and pangolin beta coronaviruses from samples taken early in the pandemic (prior to April 1, 2020). Using coalescent theory, the shallow (short branches relative to the hosts) consensus species tree provides evidence of recent gene flow events between bat and pangolin beta coronaviruses predating the zoonotic transfer to humans. The consensus species tree was also used to reconstruct the ancestral sequence of human SARS-CoV-2, which was 2 nucleotides different from the Wuhan sequence. The time to most recent common ancestor was estimated to be Dec 8, 2019 with a bat origin. Some human, bat, and pangolin coronavirus lineages found in China are phylogenetically distinct, a rare example of a class II phylogeography pattern (Avise et al. in Ann Rev Eco Syst 18:489-422, 1987). The consensus species tree is a product of evolutionary factors, providing evidence of repeated zoonotic transfers between bat and pangolin as a reservoir for future zoonotic transfers to humans.

Resection cavity auto-contouring for patients with pediatric medulloblastoma using only CT information

PURPOSE

Target delineation for radiation therapy is a time-consuming and complex task. Autocontouring gross tumor volumes (GTVs) has been shown to increase efficiency. However, there is limited literature on post-operative target delineation, particularly for CT-based studies. To this end, we trained a CT-based autocontouring model to contour the post-operative GTV of pediatric patients with medulloblastoma.

METHODS

One hundred four retrospective pediatric CT scans were used to train a GTV auto-contouring model. Eighty patients were then preselected for contour visibility, continuity, and location to train an additional model. Each GTV was manually annotated with a visibility score based on the number of slices with a visible GTV (1 = < 25%, 2 = 25-50%, 3 = > 50-75%, and 4 = > 75-100%). Contrast and the contrast-to-noise ratio (CNR) were calculated for the GTV contour with respect to a cropped background image. Both models were tested on the original and pre-selected testing sets. The resulting surface and overlap metrics were calculated comparing the clinical and autocontoured GTVs and the corresponding clinical target volumes (CTVs).

RESULTS

Eighty patients were pre-selected to have a continuous GTV within the posterior fossa. Of these, 7, 41, 21, and 11 were visibly scored as 4, 3, 2, and 1, respectively. The contrast and CNR removed an additional 11 and 20 patients from the dataset, respectively. The Dice similarity coefficients (DSC) were 0.61 ± 0.29 and 0.67 ± 0.22 on the models without pre-selected training data and 0.55 ± 13.01 and 0.83 ± 0.17 on the models with pre-selected data, respectively. The DSC on the CTV expansions were 0.90 ± 0.13.

CONCLUSION

We successfully automatically contoured continuous GTVs within the posterior fossa on scans that had contrast > ± 10 HU. CT-Based auto-contouring algorithms have potential to positively impact centers with limited MRI access.

Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'?

Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.

Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans

PURPOSE

This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans.

METHODS AND MATERIALS

A total of 245 volumetric modulated arc therapy HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included computed tomography images, target prescriptions, and contours for targets and organs at risk (OARs). The model's performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared with manual flags by 3 HN radiation oncologists.

RESULTS

The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D_1%,D_95%, and D_99% across the targets were within -2.53% ± 1.34%, -0.42% ± 1.27%, and -0.12% ± 1.97%, respectively, and the OAR mean and maximum doses were within -0.33 ± 1.40 Gy and -0.96 ± 2.08 Gy, respectively. For the plan quality assessment study, radiation oncologists flagged 47 OARs for possible plan improvement. There was high interphysician variability; 83% of physician-flagged OARs were flagged by only one of 3 physicians. The comparative dose prediction model flagged 63 OARs, including 30 of 47 physician-flagged OARs.

CONCLUSIONS

Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality.

OpenKBP-Opt: an international and reproducible evaluation of 76 knowledge-based planning pipelines

Objective.To establish an open framework for developing plan optimization models for knowledge-based planning (KBP).Approach.Our framework includes radiotherapy treatment data (i.e. reference plans) for 100 patients with head-and-neck cancer who were treated with intensity-modulated radiotherapy. That data also includes high-quality dose predictions from 19 KBP models that were developed by different research groups using out-of-sample data during the OpenKBP Grand Challenge. The dose predictions were input to four fluence-based dose mimicking models to form 76 unique KBP pipelines that generated 7600 plans (76 pipelines × 100 patients). The predictions and KBP-generated plans were compared to the reference plans via: the dose score, which is the average mean absolute voxel-by-voxel difference in dose; the deviation in dose-volume histogram (DVH) points; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models.Main results.The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50-0.62, which indicates that the quality of the predictions was generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P< 0.05; one-sided Wilcoxon test) on 18 of 23 DVH points. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans, which satisfied 3.5% more criteria than the set of all dose predictions. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for an inverse planning model.Significance.This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. We found that the best performing models significantly outperformed the reference dose and dose predictions. In the interest of reproducibility, our data and code is freely available.

Development and validation of an algorithm to assist in the interpretation of the electrocardiogram

Funding Acknowledgements

Type of funding sources: None.

Background and Purpose

Interpretation of the electrocardiogram (ECG) is often poorly performed, affecting time critical patient care. There are no reliable methods that consistently bridge the gap between novices and competency. However, the development of a framework allowing a step-wise approach to interpret the ECG based on relevant signals might help. Indeed, in addition to expert teaching, a pilot study of this algorithm significantly improves sustained ECG interpretation in medical students. This study aimed to validate the ECG algorithm tool by confirming the appropriateness of its step-wise components through expert cardiologists.

Methods

A Modified Delphi Exercise protocol was used. Cardiologists were invited to participate in a questionnaire through an online survey tool. Expert participants evaluated each stage of the ECG algorithm through multiple levels of agreement and explained their decision making with the option of free-text comments. These were collated and analysed by the four authors and changes subsequently made. The process was repeated, with evaluation of the changes from each round until a predetermined level of agreement was achieved.

Results

This Modified Delphi Exercise produced two rounds. Overall, 55 responses from experts was achieved (first round = 33, round two = 22). The average agreement in round one was 85.8%  with 25 changes from 124 comments. Round two achieved 93.4% agreement with 12 changes from 57 comments. After discussion by four authors, a consensus was reached and a final algorithm was achieved. Figure 1 shows the bradycardia section of the final algorithm (tachycardia and ST/T-wave/ischaemia sections not shown).

Conclusions

We have validated an ECG algorithm through a rigorous development and review process in a multi-institutional and multi-national Modified Delphi study. The completed expert reviewed algorithm may be a safe, informative tool for novice users to improve ECG interpretation. However, further validation in user groups is now mandated to refine the tool. Abstract Figure 1- Algorithm one of three

Effects of alterations in positron emission tomography imaging parameters on radiomics features

Radiomics studies require large patient cohorts, which often include patients imaged using different imaging protocols. We aimed to determine the impact of variability in imaging protocol parameters and interscanner variability using a phantom that produced feature values similar to those of patients. Positron emission tomography (PET) scans of a Hoffman brain phantom were acquired on GE Discovery 710, Siemens mCT, and Philips Vereos scanners. A standard-protocol scan was acquired on each machine, and then each parameter that could be changed was altered individually. The phantom was contoured with 10 regions of interest (ROIs). Values for 45 features with 2 different preprocessing techniques were extracted for each image. To determine the impact of each parameter on the reliability of each radiomics feature, the intraclass correlation coefficient (ICC) was calculated with the ROIs as the subjects and the parameter values as the raters. For interscanner comparisons, we compared the standard deviation of each radiomics feature value from the standard-protocol images to the standard deviation of the same radiomics feature from PET scans of 224 patients with non-small cell lung cancer. When the pixel size was resampled prior to feature extraction, all features had good reliability (ICC > 0.75) for the field of view and matrix size. The time per bed position had excellent reliability (ICC > 0.9) on all features. When the filter cutoff was restricted to values below 6 mm, all features had good reliability. Similarly, when subsets and iterations were restricted to reasonable values used in clinics, almost all features had good reliability. The average ratio of the standard deviation of features on the phantom scans to that of the NSCLC patient scans was 0.73 using fixed-bin-width preprocessing and 0.92 using 64-level preprocessing. Most radiomics feature values had at least good reliability when imaging protocol parameters were within clinically used ranges. However, interscanner variability was about equal to interpatient variability; therefore, caution must be used when combining patients scanned on equipment from different vendors in radiomics data sets.

Matching and Homogenizing Convolution Kernels for Quantitative Studies in Computed Tomography

The sharpness of the kernels used for image reconstruction in computed tomography affects the values of the quantitative image features. We sought to identify the kernels that produce similar feature values to enable a more effective comparison of images produced using scanners from different manufactures. We also investigated a new image filter designed to change the kernel-related component of the frequency spectrum of a postreconstruction image from that of the initial kernel to that of a preferred kernel. A radiomics texture phantom was imaged using scanners from GE, Philips, Siemens, and Toshiba. Images were reconstructed multiple times, varying the kernel from smooth to sharp. The phantom comprised 10 cartridges of various textures. A semiautomated method was used to produce 8 × 2 × 2 cm regions of interest for each cartridge and for all scans. For each region of interest, 38 radiomics features from the categories intensity direct (n = 12), gray-level co-occurrence matrix (n = 21), and neighborhood gray-tone difference matrix (n = 5) were extracted. We then calculated the fractional differences of the features from those of the baseline kernel (GE Standard). To gauge the importance of the differences, we scaled them by the coefficient of variation of the same feature from a cohort of patients with non-small cell lung cancer. The noise power spectra for each kernel were estimated from the phantom's solid acrylic cartridge, and kernel-homogenization filters were developed from these estimates. The Philips C, Siemens B30f, and Toshiba FC24 kernels produced feature values most similar to GE Standard. The kernel homogenization filters reduced the median differences from baseline to less than 1 coefficient of variation in the patient population for all of the GE, Philips, and Siemens kernels except for GE Edge and Toshiba kernels. For prospective computed tomographic radiomics studies, the scanning protocol should specify kernels that have been shown to produce similar feature values. For retrospective studies, kernel homogenization filters can be designed and applied to reduce the kernel-related differences in the feature values.

Radiation-induced lung toxicity in mice irradiated in a strong magnetic field

Strong magnetic fields affect radiation dose deposition in MRI-guided radiation therapy systems, particularly at interfaces between tissues of differing densities such as those in the thorax. In this study, we evaluated the impact of a 1.5 T magnetic field on radiation-induced lung damage in C57L/J mice. We irradiated 140 mice to the whole thorax with parallel-opposed Co-60 beams to doses of 0, 9.0, 10.0, 10.5, 11.0, 12.0, or 13.0 Gy (20 mice per dose group). Ten mice per dose group were irradiated while a 1.5 T magnetic field was applied transverse to the radiation beam and ten mice were irradiated with the magnetic field set to 0 T. We compared survival and noninvasive assays of radiation-induced lung damage, namely respiratory rate and metrics derived from thoracic cone-beam CTs, between the two sets of mice. We report two main results. First, the presence of a transverse 1.5 T field during irradiation had no impact on survival of C57L/J mice. Second, there was a small but statistically significant effect on noninvasive assays of radiation-induced lung damage. These results provide critical safety data for the clinical introduction of MRI-guided radiation therapy systems.

Evaluation of adequacy of levo-thyroxine dosage in patients with differentiated thyroid carcinoma: correlation between morning and afternoon TSH determination

PURPOSE

The aim of this study was to judge the reliability of evaluating thyroid-stimulating hormone (TSH) and free thyroxine (f-T4) in the morning and afternoon in differentiated thyroid carcinoma (DTC) patients.

METHODS

We evaluated 153 DTC patients, aged 61 ± 13 years, in active follow-up in our center after primary treatments and under stabilized levo-thyroxine (L-T4) posology. In each patient, morning and afternoon examinations were performed 1-3 months apart. Blood samples were collected at 08:00-09:00 h and 15:00-16:00 h. TSH and f-T4 were evaluated in both samples. Thyroglobulin (Tg), Tg-antibodies and neck ultrasonography were also evaluated.

RESULTS

According to clinical and laboratory examinations, 92% of patients were disease-free, 6% had biochemical disease, and 2% structural disease. L-T4 dosages (1.64 ± 0.38 µg/kg b.w.) proved the same on both occasions, despite slight changes in body weight or L-T4 posology in 15% of patients. Free-T4 values were significantly higher in the afternoon (21.5 ± 0.3 pmol/L) than in the morning (18.8 ± 0.4 pmol/L; P < 0.0001), whereas TSH values were statistically unchanged (morning 0.85 ± 0.25 mIU/L; afternoon 0.72 ± 0.20 mIU/L). There was a significant correlation (P < 0.0001) between the two TSH determinations in the same patients.

CONCLUSIONS

In DTC patients, follow-up examination consists of clinical and laboratory evaluations. The majority of patients have good disease control. Our study suggests that the adequacy of L-T4 therapy can be monitored equally well either in the morning or in the afternoon. Afternoon examinations can alleviate crowding in hospital ambulatories in the morning.

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

The Radiation Planning Assistant (RPA) is a system developed for the fully automated creation of radiotherapy treatment plans, including volume-modulated arc therapy (VMAT) plans for patients with head/neck cancer and 4-field box plans for patients with cervical cancer. It is a combination of specially developed in-house software that uses an application programming interface to communicate with a commercial radiotherapy treatment planning system. It also interfaces with a commercial secondary dose verification software. The necessary inputs to the system are a Treatment Plan Order, approved by the radiation oncologist, and a simulation computed tomography (CT) image, approved by the radiographer. The RPA then generates a complete radiotherapy treatment plan. For the cervical cancer treatment plans, no additional user intervention is necessary until the plan is complete. For head/neck treatment plans, after the normal tissue and some of the target structures are automatically delineated on the CT image, the radiation oncologist must review the contours, making edits if necessary. They also delineate the gross tumor volume. The RPA then completes the treatment planning process, creating a VMAT plan. Finally, the completed plan must be reviewed by qualified clinical staff.

Nanoshaping field emitters from glassy carbon sheets: a new functionality induced by H-plasma etching

This paper reports on the morphological and electrical characterization at the nanometer scale and the investigation of the field emission characteristics of glassy carbon (GC) plates which underwent H-induced physical/chemical processes occurring in a dual-mode MW-RF plasma reactor.

Intrafamilial variability of ZRS-associated syndrome: characterization of a mosaic ZRS mutation by pyrosequencing

During limb development, the spatio-temporal expression of sonic hedgehog (SHH) is driven by the Zone of polarizing activity Regulatory Sequence (ZRS), located 1 megabase upstream from SHH. Gain-of-function mutations of this enhancer, which cause ectopic expression of SHH, are known to be responsible for congenital limb malformations with variable expressivity, ranging from preaxial polydactyly or triphalangeal thumbs to polysyndactyly, which may also be associated with mesomelic deficiency. In this report, we describe a patient affected with mirror-image polydactyly of the four extremities and bilateral tibial deficiency. The proband's father had isolated preaxial polydactyly type II (PPD2). Using Sanger sequencing, a ZRS point mutation (NC_000007.14, g.156584153A>G, UCSC, Build hg.19) was only identified in the patient. However, pyrosequencing analysis enabled the detection of a 10% somatic mosaic in the blood and saliva from the father. To our knowledge, this is the first description of a ZRS mosaic mutation. This report highlights the complexity of genotype-phenotype correlation in ZRS-associated syndromes and the importance of detecting somatic mosaicism for accurate genetic counselling.

[Epigenetics in inflammatory systemic diseases]

In addition to analysis of the genetic code, in recent years more and more studies have concentrated on changes in the epigenetic code. Epigenetic mechanisms determine which genes in a cell are transcribed and thus form the phenotype of a cell. The epigenetic code can be changed by environmental influences, which allows cells to adapt to longstanding changes in the environment. Therefore, it is feasible to assume that epigenetic changes are the molecular basis for long-term effects of the environment on disease development. In particular in tumors and chronic inflammatory diseases epigenetic changes were found to correlate with disease severity and progression. Knowledge about these epigenetic changes might help that epigenetic modifications can be used in the future as biomarkers, prognostic factors and therapeutic targets.