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.

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.

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).

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.

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.

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.

VA-Radiation Oncology Quality Surveillance Program.

Purpose: We sought to develop a quality surveillance program for approximately 15,000 US veterans treated at the 40 radiation oncology facilities at the Veterans Affairs (VA) hospitals each year.

Methods And Materials: State-of-the-art technologies were used with the goal to improve clinical outcomes while providing the best possible care to veterans. To measure quality of care and service rendered to veterans, the Veterans Health Administration established the VA Radiation Oncology Quality Surveillance program.

Effect of variations in atelectasis on tumor displacement during radiation therapy for locally advanced lung cancer.

Purpose: Atelectasis (AT), or collapsed lung, is frequently associated with central lung tumors. We investigated the variation of atelectasis volumes during radiation therapy and analyzed the effect of AT volume changes on the reproducibility of the primary tumor (PT) position.

Methods And Materials: Twelve patients with lung cancer who had AT and 10 patients without AT underwent repeated 4-dimensional fan beam computed tomography (CT) scans during radiation therapy per protocols that were approved by the institutional review board.

A longitudinal four-dimensional computed tomography and cone beam computed tomography dataset for image-guided radiation therapy research in lung cancer.

Purpose: To describe in detail a dataset consisting of serial four-dimensional computed tomography (4DCT) and 4D cone beam CT (4DCBCT) images acquired during chemoradiotherapy of 20 locally advanced, nonsmall cell lung cancer patients we have collected at our institution and shared publicly with the research community.

Acquisition And Validation Methods: As part of an NCI-sponsored research study 82 4DCT and 507 4DCBCT images were acquired in a population of 20 locally advanced nonsmall cell lung cancer patients undergoing radiation therapy. All subjects underwent concurrent radiochemotherapy to a total dose of 59.

A Voxel-by-Voxel Comparison of Deformable Vector Fields Obtained by Three Deformable Image Registration Algorithms Applied to 4DCT Lung Studies.

Background: Commonly used methods of assessing the accuracy of deformable image registration (DIR) rely on image segmentation or landmark selection. These methods are very labor intensive and thus limited to relatively small number of image pairs. The direct voxel-by-voxel comparison can be automated to examine fluctuations in DIR quality on a long series of image pairs.

Dose escalation for locally advanced lung cancer using adaptive radiation therapy with simultaneous integrated volume-adapted boost.

Purpose: To test the feasibility of a planned phase 1 study of image-guided adaptive radiation therapy in locally advanced lung cancer.

Methods And Materials: Weekly 4-dimensional fan beam computed tomographs (4D FBCT) of 10 lung cancer patients undergoing concurrent chemoradiation therapy were used to simulate adaptive radiation therapy: After an initial intensity modulated radiation therapy plan (0-30 Gy/2 Gy), adaptive replanning was performed on week 2 (30-50 Gy/2 Gy) and week 4 scans (50-66 Gy/2 Gy) to adjust for volume and shape changes of primary tumors and lymph nodes. Week 2 and 4 clinical target volumes (CTV) were deformably warped from the initial planning scan to adjust for anatomical changes.

Evaluation of 4-dimensional computed tomography to 4-dimensional cone-beam computed tomography deformable image registration for lung cancer adaptive radiation therapy.

Purpose: To evaluate 2 deformable image registration (DIR) algorithms for the purpose of contour mapping to support image-guided adaptive radiation therapy with 4-dimensional cone-beam CT (4DCBCT).

Methods And Materials: One planning 4D fan-beam CT (4DFBCT) and 7 weekly 4DCBCT scans were acquired for 10 locally advanced non-small cell lung cancer patients. The gross tumor volume was delineated by a physician in all 4D images.

A protocol to extend the longitudinal coverage of on-board cone-beam CT.

The longitudinal coverage of a LINAC-mounted CBCT scan is limited to the corresponding dimensional limits of its flat panel detector, which is often shorter than the length of the treatment field. These limits become apparent when fields are designed to encompass wide regions, as when providing nodal coverage. Therefore, we developed a novel protocol to acquire double orbit CBCT images using a commercial system, and combine the images to extend the longitudinal coverage for image-guided adaptive radiotherapy (IGART).

Classifying geometric variability by dominant eigenmodes of deformation in regressing tumours during active breath-hold lung cancer radiotherapy.

The purpose of this study is to develop and evaluate a lung tumour interfraction geometric variability classification scheme as a means to guide adaptive radiotherapy and improve measurement of treatment response. Principal component analysis (PCA) was used to generate statistical shape models of the gross tumour volume (GTV) for 12 patients with weekly breath hold CT scans. Each eigenmode of the PCA model was classified as 'trending' or 'non-trending' depending on whether its contribution to the overall GTV variability included a time trend over the treatment course.

Optimizing principal component models for representing interfraction variation in lung cancer radiotherapy.

Purpose: To optimize modeling of interfractional anatomical variation during active breath-hold radiotherapy in lung cancer using principal component analysis (PCA).

Methods: In 12 patients analyzed, weekly CT sessions consisting of three repeat intrafraction scans were acquired with active breathing control at the end of normal inspiration. The gross tumor volume (GTV) and lungs were delineated and reviewed on the first week image by physicians and propagated to all other images using deformable image registration.

Clinical evaluation of soft tissue organ boundary visualization on cone-beam computed tomographic imaging.

Purpose: Cone-beam computed tomographic images (CBCTs) are increasingly used for setup correction, soft tissue targeting, and image-guided adaptive radiotherapy. However, CBCT image quality is limited by low contrast and imaging artifacts. This analysis investigates the detectability of soft tissue boundaries in CBCT by performing a multiple-observer segmentation study.

Development of a population-based model of surface segmentation uncertainties for uncertainty-weighted deformable image registrations.

Purpose: To develop a population-based model of surface segmentation uncertainties for uncertainty-weighted surface-based deformable registrations.

Methods: The contours of the prostate, the bladder, and the rectum were manually delineated by five observers on fan beam CT images of four prostate cancer patients. First, patient-specific representations of structure segmentation uncertainties were derived by determining the interobserver variability (i.