3D deep convolution neural network for radiation pneumonitis prediction following stereotactic body radiotherapy.

In this study, we investigated 3D convolutional neural networks (CNNs) with input from radiographic and dosimetric datasets of primary lung tumors and surrounding lung volumes to predict the likelihood of radiation pneumonitis (RP). Pre-treatment, 3- and 6-month follow-up computed tomography (CT) and 3D dose datasets from one hundred and ninety-three NSCLC patients treated with stereotactic body radiotherapy (SBRT) were retrospectively collected and analyzed for this study. DenseNet-121 and ResNet-50 models were selected for this study as they are deep neural networks and have been proven to have high accuracy for complex image classification tasks. Both were modified with 3D convolution and max pooling layers to accept 3D datasets. We used a minority class oversampling approach and data augmentation to address the challenges of data imbalance and data scarcity. We built two sets of models for classification of three (No RP, Grade 1 RP, Grade 2 RP) and two (No RP, Yes RP) classes as outputs. The 3D DenseNet-121 models performed better (F1 score [0.81], AUC [0.91] [three class]; F1 score [0.77], AUC [0.84] [two class]) than the 3D ResNet-50 models (F1 score [0.54], AUC [0.72] [three-class]; F1 score [0.68], AUC [0.71] [two-class]) (p = 0.017 for three class predictions). We also attempted to identify salient regions within the input 3D image dataset via integrated gradient (IG) techniques to assess the relevance of the tumor surrounding volume for RP stratification. These techniques appeared to indicate the significance of the tumor and surrounding regions in the prediction of RP. Overall, 3D CNNs performed well to predict clinical RP in our cohort based on the provided image sets and radiotherapy dose information.

Infrastructure tools to support an effective Radiation Oncology Learning Health System.

PURPOSE: Radiation Oncology Learning Health System (RO-LHS) is a promising approach to improve the quality of care by integrating clinical, dosimetry, treatment delivery, research data in real-time. This paper describes a novel set of tools to support the development of a RO-LHS and the current challenges they can address. METHODS: We present a knowledge graph-based approach to map radiotherapy data from clinical databases to an ontology-based data repository using FAIR concepts. This strategy ensures that the data are easily discoverable, accessible, and can be used by other clinical decision support systems. It allows for visualization, presentation, and data analyses of valuable information to identify trends and patterns in patient outcomes. We designed a search engine that utilizes ontology-based keyword searching, synonym-based term matching that leverages the hierarchical nature of ontologies to retrieve patient records based on parent and children classes, connects to the Bioportal database for relevant clinical attributes retrieval. To identify similar patients, a method involving text corpus creation and vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) are employed, using cosine similarity and distance metrics. RESULTS: The data pipeline and tool were tested with 1660 patient clinical and dosimetry records resulting in 504 180 RDF (Resource Description Framework) tuples and visualized data relationships using graph-based representations. Patient similarity analysis using embedding models showed that the Word2Vec model had the highest mean cosine similarity, while the GloVe model exhibited more compact embeddings with lower Euclidean and Manhattan distances. CONCLUSIONS: The framework and tools described support the development of a RO-LHS. By integrating diverse data sources and facilitating data discovery and analysis, they contribute to continuous learning and improvement in patient care. The tools enhance the quality of care by enabling the identification of cohorts, clinical decision support, and the development of clinical studies and machine learning programs in radiation oncology.

An Unusual Case of Hyperthyroidism.

INTRODUCTION: Thyroid gland dysfunction greatly alters the hemodynamics of the body resulting in major changes in cardiac output, blood pressure and pulmonary vascular resistance amongst others. Hyperthyroidism is associated with an increased morbidity and mortality from cardiovascular disease. Thyrotoxicosis is commonly associated with exacerbation of underlying coronary heart disease, with atrial fibrillation and systolic dysfunction. It is less well appreciated that hyperthyroidism is also associated with pulmonary arterial hypertension (PAH) and right heart failure. MATERIALS: History -We present a 46 years old female, Presented to our hospital with complaints of Breathlessness on exertion since 3 months gradually progressed from MMRC grade 1 to grade 4 over the period of 2 months without any diurnal/postural variation Cough with expectoration since 3 weeks associated with weight loss. RESULT: Examination-Patient is severely malnourished with BMI 11.6 kg/m2 . Bilateral multiple cervical lymph nodes palpable, 6-8 in number discrete, mobile, soft consistency, measuring 1 cm in size changes. Thyroid is symmetrically enlarged, soft in consistency, moving with deglutition, Systemic examination-Apex beat palpable at 5th intercostal space 2 cm lateral to the MCL with normal character Parasternal heave grade 3+ Palpable P2+ A high pitched, rumbling, pansystolic murmur of grade 3, non radiating heard best with the diaphragm of stethoscope with patient in supine position. Unique features-Both thyroid lobes appear enlarged in size and show homogeneously increased radiotracer uptake. CONCLUSION: IMPRESSION- Well-visualized thyroid gland with increased trapping function. In the given clinical context scan findings favour hyperthyroid status-Graves'disease. Take home message-Hyperthyroidism is a reversible cause of pulmonary hypertension.

Automated data abstraction for quality surveillance and outcome assessment in radiation oncology.

Rigorous radiotherapy quality surveillance and comprehensive outcome assessment require electronic capture and automatic abstraction of clinical, radiation treatment planning, and delivery data. We present the design and implementation framework of an integrated data abstraction, aggregation, and storage, curation, and analytics software: the Health Information Gateway and Exchange (HINGE), which collates data for cancer patients receiving radiotherapy. The HINGE software abstracts structured DICOM-RT data from the treatment planning system (TPS), treatment data from the treatment management system (TMS), and clinical data from the electronic health records (EHRs). HINGE software has disease site-specific "Smart" templates that facilitate the entry of relevant clinical information by physicians and clinical staff in a discrete manner as part of the routine clinical documentation. Radiotherapy data abstracted from these disparate sources and the smart templates are processed for quality and outcome assessment. The predictive data analyses are done on using well-defined clinical and dosimetry quality measures defined by disease site experts in radiation oncology. HINGE application software connects seamlessly to the local IT/medical infrastructure via interfaces and cloud services and performs data extraction and aggregation functions without human intervention. It provides tools to assess variations in radiation oncology practices and outcomes and determines gaps in radiotherapy quality delivered by each provider.

A Machine Learning method for relabeling arbitrary DICOM structure sets to TG-263 defined labels.

PURPOSE: To present a Machine Learning pipeline for automatically relabeling anatomical structure sets in the Digital Imaging and Communications in Medicine (DICOM) format to a standard nomenclature that will enable data abstraction for research and quality improvement. METHODS: DICOM structure sets from approximately 1200 lung and prostate cancer patients across 40 treatment centers were used to build predictive models to automate the relabeling of clinically specified structure labels to standardized labels as defined by the American Association of Physics in Medicine's (AAPM) Task Group 263 (TG-263). Volumetric bitmaps were created based on the delineated volumes and were combined with associated bony anatomy data to build feature vectors. Feature reduction was performed with singular value decomposition and the resulting vectors were used for predicting the label of each structure using five different classifier algorithms on the Apache Spark platform with 5-fold cross-validation. Undersampling methods were used to deal with underlying class imbalance that hindered the performance of classifiers. Experiments were performed on both a curated version of the data, which included only annotated structures, and the non-curated data that included all structures from the original treatment plans. RESULTS: Random Forest provided the highest accuracies with F1 scores of 98.77 for lung and 95.06 for prostate on the curated data sets. Scores were lower with 95.67 for lung and 90.22 for prostate on the non-curated data sets, highlighting some of the challenges of classifying real clinical data. Including bony anatomy data and pooling information from all structures for the same patient both increased accuracies. In some cases, undersampling with k-Means clustering for class balancing improved classifier accuracy but in all experiments it significantly reduced run time compared to random undersampling. CONCLUSION: This work shows that structure sets can be relabeled using our approach with accuracies over 95% for many structure types when presented with curated data. Although accuracies dropped when using the full non-curated data sets, some structure types were still correctly labeled over 90% of the time. With similar results obtained on an external test data set, we can infer that the proposed models are likely to work on other clinical data sets.

Comparative evaluation of antimicrobial efficacy of various intracanal medicament in young permanent teeth: An in vivo study.

INTRODUCTION: Bacteria and their byproducts are key contributors to the onset and perpetuation of pulpoperiapical pathosis. Intracanal medication is vital in achieving successful endodontic outcomes as it targets and eradicates remaining microorganisms following biomechanical preparation. AIM AND OBJECTIVE: The aim of the study was to compare and evaluate the antimicrobial efficacy of calcium hydroxide (CH) paste, triple antibiotic paste (TAP), and probiotics (PBs) as intracanal medicament in 12-17-year-old children undergoing root canal treatment for the management of infected pulpal tissues in young permanent teeth. MATERIALS AND METHODS: A total of 30 patients aged 12-17 years indicated for endodontic therapy in maxillary incisors and with no systemic complications were selected. They were randomly divided into three groups, i.e., Group I - CH group, Group II - TAP, and Group III - PB allocating 10 teeth in each group. After access opening, the first sample (S1) was collected by inserting a paper point into the root canal, the second sample (S2) was collected immediately after biomechanical preparation, and the third sample (S3) was collected after 7 days, i.e., postintracanal medication. Samples were sent for microbiological analysis to assess the microbial count, and statistical analysis was done for the obtained data. RESULTS: The three intracanal medicaments were successful in reducing the microbial counts of Enterococcus faecalis in the infected root canals. However, according to the results of the study, the PB group demonstrated greater effectiveness against E. faecalis compared to the CH group and displayed similar antimicrobial efficacy as the TAP group. CONCLUSION: PB exhibited antimicrobial efficacy comparable to TAP but greater than Ca (OH) 2 paste. Hence, PB can be utilized as an intracanal medicament in young permanent teeth.

Pneumonia in Geriatric Patients and Prediction of Mortality Based on the Pneumonia Severity Index (PSI), CURB-65, Frailty Index (FI), and FI-Lab21 Scores.

Background Elderly individuals have higher rates of morbidity, death, and financial burden due to community-acquired pneumonia (CAP). Objectives The study aimed to assess the outcomes of geriatric pneumonia patients and the prediction of mortality based on the pneumonia severity index (PSI), CURB-65 (confusion, urea, respiratory rate, blood pressure, and 65-year-old score), frailty index (frailty index), and FI-Lab21 (21-item frailty index based on laboratory) scores. Methods A prospective observational study was conducted on 100 elderly patients (≥ 65 years) with CAP. PSI, CURB-65, FI, and FI-Lab21 scores were determined. The outcome measures were 30-day mortality and the risk factors of mortality. The mortality predictive value of scores were compared. Results The mean age of the study subjects was 72.14 ± 6.1 years. Specifically, 76 (76%) were male, and 24 (24%) were females. During the follow-up, there was a 30-day mortality rate of 57%. On performing multivariate regression, the PSI score and severely frail were significant independent risk factors of mortality, with an odds ratio of 1.046 and 52.213, respectively. Area under the ROC curve (AUC) showed that the performance of the PSI score (AUC: 0.952; 95% CI: 0.910-0.994), CURB-65 score (AUC: 0.936; 95% CI: 0.893-0.978), and severely frail (AUC: 0.907; 95% CI: 0.851-0.962) was outstanding, while FI-Lab21 (AUC: 0.515; 95% CI: 0.400-0.631) was non-significant. Among all the parameters, the PSI score was the best predictor of mortality at the cutoff points of >121 with a diagnostic accuracy of 92%. Conclusion CAP in the elderly carries a high mortality rate. Out of PSI, CURB-65, FI, and FI-Lab21 scores, the PSI holds the best predicting ability for mortality.

AAPM task group report 288: Recommendations for guiding radiotherapy event narratives.

Incident reporting and learning systems provide an opportunity to identify systemic vulnerabilities that contribute to incidents and potentially degrade quality. The narrative of an incident is intended to provide a clear, easy to understand description of an incident. Unclear, incomplete or poorly organized narratives compromise the ability to learn from them. This report provides guidance for drafting effective narratives, with particular attention to the use of narratives in incident reporting and learning systems (IRLS). Examples are given that compare effective and less than effective narratives. This report is mostly directed to organizations that maintain IRLS, but also may be helpful for individuals who desire to write a useful narrative for entry into such a system. Recommendations include the following: (1) Systems should allow a one- or two-sentence, free-text synopsis of an incident without guessing at causes; (2) Information included should form a sequence of events with chronology; and (3) Reporting and learning systems should consider using the headings suggested to guide the reporter through the narrative: (a) incident occurrences and actions by role; (b) prior circumstances and actions; (c) method by which the incident was identified; (d) equipment related details if relevant; (e) recovery actions by role; (f) relevant time span between responses; (g) and how individuals affected during or immediately after incident. When possible and appropriate, supplementary information including relevant data elements should be included using numerical scales or drop-down choices outside of the narrative. Information that should not be included in the narrative includes: (a) patient health information (PHI); (b) conjecture or blame; (c) jargon abbreviations or details without specifying their significance; (d) causal analysis.

Consensus Quality Measures and Dose Constraints for Prostate Cancer From the Veterans Affairs Radiation Oncology Quality Surveillance Program and American Society for Radiation Oncology Expert Panel.

PURPOSE: There are no agreed upon measures to comprehensively determine the quality of radiation oncology (RO) care delivered for prostate cancer. Consequently, it is difficult to assess the implementation of scientific advances and adherence to best practices in routine clinical practice. To address this need, the US Department of Veterans Affairs (VA) National Radiation Oncology Program established the VA Radiation Oncology Quality Surveillance (VA ROQS) Program to develop clinical quality measures to assess the quality of RO care delivered to Veterans with cancer. This article reports the prostate cancer consensus measures. METHODS AND MATERIALS: The VA ROQS Program contracted with the American Society for Radiation Oncology to commission a Blue Ribbon Panel of prostate cancer experts to develop a set of evidence-based measures and performance expectations. From February to June 2021, the panel developed quality, aspirational, and surveillance measures for (1) initial consultation and workup, (2) simulation, treatment planning, and delivery, and (3) follow-up. Dose-volume histogram (DVH) constraints to be used as quality measures for definitive and post-prostatectomy radiation therapy were selected. The panel also identified the optimal Common Terminology Criteria for Adverse Events, version 5.0 (CTCAE V5.0), toxicity terms to assess in follow-up. RESULTS: Eighteen prostate-specific measures were developed (13 quality, 2 aspirational, and 3 surveillance). DVH metrics tailored to conventional, moderately hypofractionated, and ultrahypofractionated regimens were identified. Decision trees to determine performance for each measure were developed. Eighteen CTCAE V5.0 terms were selected in the sexual, urinary, and gastrointestinal domains as highest priority for assessment during follow-up. CONCLUSIONS: This set of measures and DVH constraints serves as a tool for assessing the comprehensive quality of RO care for prostate cancer. These measures will be used for ongoing quality surveillance and improvement among veterans receiving care across VA and community sites. These measures can also be applied to clinical settings outside of those serving veterans.

Consensus Quality Measures and Dose Constraints for Breast Cancer From the Veterans Affairs Radiation Oncology Quality Surveillance Program and American Society for Radiation Oncology Expert Panel.

PURPOSE: Using evidence-based radiation therapy to direct care for patients with breast cancer is critical to standardize practice, improve safety, and optimize outcomes. To address this need, the Veterans Affairs (VA) National Radiation Oncology Program (NROP) established the VA Radiation Oncology Quality Surveillance Program to develop clinical quality measures (QMs). The VA NROP contracted with the American Society for Radiation Oncology to commission 5 Blue Ribbon Panels for breast, lung, prostate, rectal, and head and neck cancers. METHODS AND MATERIALS: The Breast Cancer Blue Ribbon Panel experts worked collaboratively with the NROP to develop consensus QMs for use throughout the VA system, establishing a set of QMs for patients in several areas, including consultation and work-up; simulation, treatment planning, and treatment; and follow-up care. As part of this initiative, consensus dose-volume histogram (DVH) constraints were outlined. RESULTS: In total, 36 QMs were established. Herein, we review the process used to develop QMs and final consensus QMs pertaining to all aspects of radiation patient care, as well as DVH constraints. CONCLUSIONS: The QMs and expert consensus DVH constraints are intended for ongoing quality surveillance within the VA system and centers providing community care for Veterans. They are also available for use by greater non-VA community measures of quality care for patients with breast cancer receiving radiation.

Consensus Quality Measures and Dose Constraints for Lung Cancer From the Veterans Affairs Radiation Oncology Quality Surveillance Program and ASTRO Expert Panel.

PURPOSE: For patients with lung cancer, it is critical to provide evidence-based radiation therapy to ensure high-quality care. The US Department of Veterans Affairs (VA) National Radiation Oncology Program partnered with the American Society for Radiation Oncology (ASTRO) as part of the VA Radiation Oncology Quality Surveillance to develop lung cancer quality metrics and assess quality of care as a pilot program in 2016. This article presents recently updated consensus quality measures and dose-volume histogram (DVH) constraints. METHODS AND MATERIALS: A series of measures and performance standards were reviewed and developed by a Blue-Ribbon Panel of lung cancer experts in conjunction with ASTRO in 2022. As part of this initiative, quality, surveillance, and aspirational metrics were developed for (1) initial consultation and workup; (2) simulation, treatment planning, and treatment delivery; and (3) follow-up. The DVH metrics for target and organ-at-risk treatment planning dose constraints were also reviewed and defined. RESULTS: Altogether, a total of 19 lung cancer quality metrics were developed. There were 121 DVH constraints developed for various fractionation regimens, including ultrahypofractionated (1, 3, 4, or 5 fractions), hypofractionated (10 and 15 fractionations), and conventional fractionation (30-35 fractions). CONCLUSIONS: The devised measures will be implemented for quality surveillance for veterans both inside and outside of the VA system and will provide a resource for lung cancer-specific quality metrics. The recommended DVH constraints serve as a unique, comprehensive resource for evidence- and expert consensus-based constraints across multiple fractionation schemas.

Coronin 1A facilitates calcium mobilization and promotes astrocyte reactivity in HIV-1 neuropathogenesis.

Astrocyte reactivity, a phenomenon observed in a variety of neurodegenerative disorders, can have both beneficial and detrimental manifestations which significantly affect neuronal physiology. In neuroAIDS, reactive astrocytes have been observed to severely affect the neuronal population present in their vicinity. Calcium signaling plays a central role in mediating astrocyte reactivity. Coronin 1A, an actin-binding protein, majorly reported in hematopoietic cells, regulates cell activity in a calcium-dependent manner, but its role in astrocyte physiology and reactivity is largely unknown. Using a well-characterized primary culture of human astroglia and neurons, we explored the roles of coronin 1A in astrocyte physiology and its involvement in facilitating astrocyte reactivity. In this study, we report coronin 1A expression in human primary astrocytes and autopsy brain sections, and that it plays activity-dependent roles by facilitating calcium mobilization from the intracellular stores. HIV-1 Tat, a potent neurotoxicant that turns astrocytes reactive, augments coronin 1A expression, apart from affecting GFAP and pro-inflammatory molecules. Also, the autopsy brain tissue of HIV-1 infected individuals has a higher expression of coronin 1A. Downregulation of coronin 1A attenuated the HIV-1 Tat-induced deleterious effects of reactive astrocytes, measured as the upregulated expression of GFAP, pro-inflammatory molecules, and enhanced release of IL-6, and hence reduced astrocyte-mediated neurodegeneration. Our findings also suggest that out of a pool of dysregulated miRNAs studied by us, hsa-miR-92b-5p regulates coronin 1A expression under the effect of HIV-1 Tat. These findings highlight the novel roles of coronin 1A in regulating astrocyte activity in stimulated conditions and astrocyte reactivity observed in HIV-1 neuropathogenesis.

Treatment Predicament for Pediatric Dentist: Gender-wise Comparative Correlation of Biological and Chronological Age in 8-15-year-old Children.

Introduction: The chronological age (CA) of a patient does not always correspond to the events of growth surge; therefore treatment strategies need good knowledge of biological markers. Aim: The aim of the present study was to investigate the relationships between the skeletal age (SA), dental age (DA), and CA along with the stages of calcification of teeth and the cervical vertebral maturity (CVM) stages in Indian subjects. Materials and methods: A sample of 100 pairs preexisting radiographs, both orthopantomogram and lateral cephalogram, of the individuals in the age-group of 8-15 years were procured and were analyzed for the level of dental and skeletal maturity using Demirjian scale and cervical vertebral maturity index, respectively. Results: A high correlation coefficient (r) was found to be 0.839 (p = 0) between chronological and dental age (DA), 0.833 (p = 0) between chronological and skeletal age (SA), and 0.730 (p = 0) between skeletal and DA. Conclusion: The current research showed that the overall correlation between all three ages was found to be high. It was found that the SA assessed by the CVM stages had a high correlation with the CA. Clinical significance: Within the limits of the present study, there exists a high degree of correlation between biological ages and chronological age, but still it is imperative for a correct assessment of biological age of individual patients for quality treatment outcomes. How to cite this article: Gandhi K, Malhotra R, Datta G, et al. Treatment Predicament for Pediatric Dentist: Gender-wise Comparative Correlation of Biological and Chronological Age in 8-15-year-old Children. Int J Clin Pediatr Dent 2022;15(5):569-574.

Consensus Quality Measures and Dose Constraints for Head and Neck Cancer with an emphasis on Oropharyngeal and Laryngeal Cancer from the Veterans Affairs Radiation Oncology Quality Surveillance Program and American Society for Radiation Oncology Expert Panel.

PURPOSE: Safeguarding high-quality care using evidence-based radiation therapy for patients with head and neck cancer is crucial to improving oncologic outcomes, including survival and quality of life. METHODS AND MATERIALS: The Veterans Administration (VA) National Radiation Oncology Program established the VA Radiation Oncology Quality Surveillance Program (VAROQS) to develop clinical quality measures (QM) in head and neck cancer. As part of the development of QM, the VA commissioned, along with the American Society for Radiation Oncology, a blue-ribbon panel comprising experts in head and neck cancer, to develop QM. RESULTS: We describe the methods used to develop QM and the final consensus QM, as well as aspirational and surveillance QM, which capture all aspects of the continuum of patient care from initial patient work-up, radiation treatment planning and delivery, and follow-up care, as well as dose volume constraints. CONCLUSION: These QM are intended for use as part of ongoing quality surveillance for veterans receiving radiation therapy throughout the VA as well as outside the VA. They may also be used by the non-VA community as a basic measure of quality care for head and neck cancer patients receiving radiation.

Consensus Quality Measures and Dose Constraints for Rectal Cancer From the Veterans Affairs Radiation Oncology Quality Surveillance Program and American Society for Radiation Oncology (ASTRO) Expert Panel.

PURPOSE: Ensuring high quality, evidence-based radiation therapy for patients with cancer is of the upmost importance. To address this need, the Veterans Affairs (VA) Radiation Oncology Program partnered with the American Society for Radiation Oncology and established the VA Radiation Oncology Quality Surveillance program. As part of this ongoing effort to provide the highest quality of care for patients with rectal cancer, a blue-ribbon panel comprised of rectal cancer experts was formed to develop clinical quality measures. METHODS AND MATERIALS: The Rectal Cancer Blue Ribbon panel developed quality, surveillance, and aspirational measures for (a) initial consultation and workup, (b) simulation, treatment planning, and treatment, and (c) follow-up. Twenty-two rectal cancer specific measures were developed (19 quality, 1 aspirational, and 2 surveillance). In addition, dose-volume histogram constraints for conventional and hypofractionated radiation therapy were created. CONCLUSIONS: The quality measures and dose-volume histogram for rectal cancer serves as a guideline to assess the quality of care for patients with rectal cancer receiving radiation therapy. These quality measures will be used for quality surveillance for veterans receiving care both inside and outside the VA system to improve the quality of care for these patients.

Veterans Affairs Radiation Oncology Quality Surveillance Program and American Society for Radiation Oncology Quality Measures Initiative.

PURPOSE: Ensuring high quality, evidence-based radiation therapy for patients is of the upmost importance. As a part of the largest integrated health system in America, the Department of Veterans Affairs National Radiation Oncology Program (VA-NROP) established a quality surveillance initiative to address the challenge and necessity of providing the highest quality of care for veterans treated for cancer. METHODS AND MATERIALS: As part of this initiative, the VA-NROP contracted with the American Society for Radiation Oncology to commission 5 Blue Ribbon Panels for lung, prostate, rectal, breast, and head and neck cancers experts. This group worked collaboratively with the VA-NROP to develop consensus quality measures. In addition to the site-specific measures, an additional Blue Ribbon Panel comprised of the chairs and other members of the disease sites was formed to create 18 harmonized quality measures for all 5 sites (13 quality, 4 surveillance, and 1 aspirational). CONCLUSIONS: The VA-NROP and American Society for Radiation Oncology collaboration have created quality measures spanning 5 disease sites to help improve patient outcomes. These will be used for the ongoing quality surveillance of veterans receiving radiation therapy through the VA and its community partners.

The DNA-to-cytoplasm ratio broadly activates zygotic gene expression in Xenopus.

In multicellular animals, the first major event after fertilization is the switch from maternal to zygotic control of development. During this transition, zygotic gene transcription is broadly activated in an otherwise quiescent genome in a process known as zygotic genome activation (ZGA). In fast-developing embryos, ZGA often overlaps with the slowing of initially synchronous cell divisions at the mid-blastula transition (MBT). Initial studies of the MBT led to the nuclear-to-cytoplasmic ratio model where MBT timing is regulated by the exponentially increasing amounts of some nuclear component "N" titrated against a fixed cytoplasmic component "C." However, more recent experiments have been interpreted to suggest that ZGA is independent of the N/C ratio. To determine the role of the N/C ratio in ZGA, we generated Xenopus frog embryos with ∼3-fold differences in genomic DNA (i.e., N) by using X. tropicalis sperm to fertilize X. laevis eggs with or without their maternal genome. Resulting embryos have otherwise identical X. tropicalis genome template amounts, embryo sizes, and X. laevis maternal environments. We generated transcriptomic time series across the MBT in both conditions and used X. tropicalis paternally derived mRNA to identify a high-confidence set of exclusively zygotic transcripts. Both ZGA and the increase in cell-cycle duration are delayed in embryos with ∼3-fold less DNA per cell. Thus, DNA is an important component of the N/C ratio, which is a critical regulator of zygotic genome activation in Xenopus embryos.

Machine-Learning Models for Multicenter Prostate Cancer Treatment Plans.

Clinical factors, including T-stage, Gleason score, and baseline prostate-specific antigen, are used to stratify patients with prostate cancer (PCa) into risk groups. This provides prognostic information for a heterogeneous disease such as PCa and guides treatment selection. In this article, we hypothesize that nonclinical factors may also impact treatment selection and their adherence to treatment guidelines. A total of 552 patients with intermediate- and high-risk PCa treated with definitive radiation with or without androgen deprivation therapy (ADT) between 2010 and 2017 were identified from 34 medical centers within the Veterans Health Administration. Medical charts were manually reviewed, and details regarding each patient's clinical history and treatment were extracted. Support Vector Machine and Random forest-based classification was used to identify clinical and nonclinical predictors of adherence to the treatment guidelines from the National Comprehensive Cancer Network (NCCN). We created models for predicting both initial treatment intent and treatment alterations. Our results demonstrate that besides clinical factors, the center in which the patient was treated (nonclinical factor) played a significant role in adherence to NCCN guidelines. Furthermore, the treatment center served as an important predictor to decide on whether or not to prescribe ADT; however, it was not associated with ADT duration and weakly associated with treatment alterations. Such center-bias motivates further investigation on details of center-specific barriers to both NCCN guideline adherence and on oncological outcomes. In addition, we demonstrate that publicly available data sets, for example, that from Surveillance, Epidemiology, and End Results (SEERs), may not be well equipped to build such predictive models on treatment plans.

Multi-View Data Integration Methods for Radiotherapy Structure Name Standardization.

Standardization of radiotherapy structure names is essential for developing data-driven personalized radiotherapy treatment plans. Different types of data are associated with radiotherapy structures, such as the physician-given text labels, geometric (image) data, and Dose-Volume Histograms (DVH). Prior work on structure name standardization used just one type of data. We present novel approaches to integrate complementary types (views) of structure data to build better-performing machine learning models. We present two methods, namely (a) intermediate integration and (b) late integration, to combine physician-given textual structure name features and geometric information of structures. The dataset consisted of 709 prostate cancer and 752 lung cancer patients across 40 radiotherapy centers administered by the U.S. Veterans Health Administration (VA) and the Department of Radiation Oncology, Virginia Commonwealth University (VCU). We used randomly selected data from 30 centers for training and ten centers for testing. We also used the VCU data for testing. We observed that the intermediate integration approach outperformed the models with a single view of the dataset, while late integration showed comparable performance with single-view results. Thus, we demonstrate that combining different views (types of data) helps build better models for structure name standardization to enable big data analytics in radiation oncology.

Automatic Incident Triage in Radiation Oncology Incident Learning System.

The Radiotherapy Incident Reporting and Analysis System (RIRAS) receives incident reports from Radiation Oncology facilities across the US Veterans Health Affairs (VHA) enterprise and Virginia Commonwealth University (VCU). In this work, we propose a computational pipeline for analysis of radiation oncology incident reports. Our pipeline uses machine learning (ML) and natural language processing (NLP) based methods to predict the severity of the incidents reported in the RIRAS platform using the textual description of the reported incidents. These incidents in RIRAS are reviewed by a radiation oncology subject matter expert (SME), who initially triages some incidents based on the salient elements in the incident report. To automate the triage process, we used the data from the VHA treatment centers and the VCU radiation oncology department. We used NLP combined with traditional ML algorithms, including support vector machine (SVM) with linear kernel, and compared it against the transfer learning approach with the universal language model fine-tuning (ULMFiT) algorithm. In RIRAS, severities are divided into four categories; A, B, C, and D, with A being the most severe to D being the least. In this work, we built models to predict High (A & B) vs. Low (C & D) severity instead of all the four categories. Models were evaluated with macro-averaged precision, recall, and F1-Score. The Traditional ML machine learning (SVM-linear) approach did well on the VHA dataset with 0.78 F1-Score but performed poorly on the VCU dataset with 0.5 F1-Score. The transfer learning approach did well on both datasets with 0.81 F1-Score on VHA dataset and 0.68 F1-Score on the VCU dataset. Overall, our methods show promise in automating the triage and severity determination process from radiotherapy incident reports.