Best practice guidelines for use of reference points in radiation oncology information systems to aggregate longitudinal dosimetric data.

Purpose/objectives: Tracking patient dose in radiation oncology is challenging due to disparate electronic systems from various vendors. Treatment planning systems (TPS), radiation oncology information systems (ROIS), and electronic health records (EHR) lack uniformity, complicating dose tracking and reporting. To address this, we examined practices in multiple radiation oncology settings and proposed guidelines for current systems.

Machine learning automated treatment planning for online magnetic resonance guided adaptive radiotherapy of prostate cancer.

Background And Purpose: No best practices currently exist for achieving high quality radiation therapy (RT) treatment plan adaptation during magnetic resonance (MR) guided RT of prostate cancer. This study validates the use of machine learning (ML) automated RT treatment plan adaptation and benchmarks it against current clinical RT plan adaptation methods.

Materials And Methods: We trained an atlas-based ML automated treatment planning model using reference MR RT treatment plans (42.

A Prospective Study of Machine Learning-Assisted Radiation Therapy Planning for Patients Receiving 54 Gy to the Brain.

Purpose: The capacity for machine learning (ML) to facilitate radiation therapy (RT) planning for primary brain tumors has not been described. We evaluated ML-assisted RT planning with regard to clinical acceptability, dosimetric outcomes, and planning efficiency for adults and children with primary brain tumors.

Methods And Materials: In this prospective study, children and adults receiving 54 Gy fractionated RT for a primary brain tumor were enrolled.

Operational Ontology for Oncology (O3): A Professional Society-Based, Multistakeholder, Consensus-Driven Informatics Standard Supporting Clinical and Research Use of Real-World Data From Patients Treated for Cancer.

Purpose: The ongoing lack of data standardization severely undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), radiation oncology information systems, treatment planning systems, and other cancer care and outcomes databases. We sought to create a standardized ontology for clinical data, social determinants of health, and other radiation oncology concepts and interrelationships.

Methods And Materials: The American Association of Physicists in Medicine's Big Data Science Committee was initiated in July 2019 to explore common ground from the stakeholders' collective experience of issues that typically compromise the formation of large inter- and intra-institutional databases from EHRs.

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

To establish an open framework for developing plan optimization models for knowledge-based planning (KBP).Our framework includes radiotherapy treatment data (i.e.

Domain adaptation of automated treatment planning from computed tomography to magnetic resonance.

Machine learning (ML) based radiation treatment planning addresses the iterative and time-consuming nature of conventional inverse planning. Given the rising importance of magnetic resonance (MR) only treatment planning workflows, we sought to determine if an ML based treatment planning model, trained on computed tomography (CT) imaging, could be applied to MR through domain adaptation.In this study, MR and CT imaging was collected from 55 prostate cancer patients treated on an MR linear accelerator.

SuPART: supervised projective adapted resonance theory for automatic quality assurance approval of radiotherapy treatment plans.

Radiotherapy is a common treatment modality for the treatment of cancer, where treatments must be carefully designed to deliver appropriate dose to targets while avoiding healthy organs. The comprehensive multi-disciplinary quality assurance (QA) process in radiotherapy is designed to ensure safe and effective treatment plans are delivered to patients. However, the plan QA process is expensive, often time-intensive, and requires review of large quantities of complex data, potentially leading to human error in QA assessment.

A pilot study of machine-learning based automated planning for primary brain tumours.

Purpose: High-quality radiotherapy (RT) planning for children and young adults with primary brain tumours is essential to minimize the risk of late treatment effects. The feasibility of using automated machine-learning (ML) to aid RT planning in this population has not previously been studied.

Methods And Materials: We developed a ML model that identifies learned relationships between image features and expected dose in a training set of 95 patients with a primary brain tumour treated with focal radiotherapy to a dose of 54 Gy in 30 fractions.

Understanding machine learning classifier decisions in automated radiotherapy quality assurance.

The complexity of generating radiotherapy treatments demands a rigorous quality assurance (QA) process to ensure patient safety and to avoid clinically significant errors. Machine learning classifiers have been explored to augment the scope and efficiency of the traditional radiotherapy treatment planning QA process. However, one important gap in relying on classifiers for QA of radiotherapy treatment plans is the lack of understanding behind a specific classifier prediction.

Magnetic Resonance Imaging for Breast Tumor Bed Delineation: Computed Tomography Comparison and Sequence Variation.

Purpose: Our purpose was to investigate the interobserver variability in breast tumor bed delineation using magnetic resonance (MR) compared with computed tomography (CT) at baseline and to quantify the change in tumor bed volume between pretreatment and end-of-treatment MR for patients undergoing whole breast radiation therapy.

Methods And Materials: Forty-eight patients with breast cancer planned for whole breast radiation therapy underwent CT and MR (T1, T1 fat-suppression [T1fs], and T2) simulation in the supine treatment position before radiation therapy and MR (T1, T1fs, and T2) at the end of treatment in the same position. Two observers delineated 50 tumor beds on the CT and all MR sequences and assigned cavity visualization scores to the images.

OpenKBP: The open-access knowledge-based planning grand challenge and dataset.

Purpose: To advance fair and consistent comparisons of dose prediction methods for knowledge-based planning (KBP) in radiation therapy research.

Methods: We hosted OpenKBP, a 2020 AAPM Grand Challenge, and challenged participants to develop the best method for predicting the dose of contoured computed tomography (CT) images. The models were evaluated according to two separate scores: (a) dose score, which evaluates the full three-dimensional (3D) dose distributions, and (b) dose-volume histogram (DVH) score, which evaluates a set DVH metrics.

Performance stability evaluation of atlas-based machine learning radiation therapy treatment planning in prostate cancer.

Atlas-based machine learning (ML) for radiation therapy (RT) treatment planning is effective at tailoring dose distributions to account for unique patient anatomies by selecting the most appropriate patients from the training database (atlases) to inform dose prediction for new patients. However, variations in clinical practice between the training dataset and a new patient to be planned may impact ML performance by confounding atlas selection. In this study, we simulated various contouring practices in prostate cancer RT to investigate the impact of changing input data on atlas-based ML treatment planning.

Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer.

Machine learning (ML) holds great promise for impacting healthcare delivery; however, to date most methods are tested in 'simulated' environments that cannot recapitulate factors influencing real-world clinical practice. We prospectively deployed and evaluated a random forest algorithm for therapeutic curative-intent radiation therapy (RT) treatment planning for prostate cancer in a blinded, head-to-head study with full integration into the clinical workflow. ML- and human-generated RT treatment plans were directly compared in a retrospective simulation with retesting (n = 50) and a prospective clinical deployment (n = 50) phase.

Rapid Adaptation of Breast Radiation Therapy Use During the Coronavirus Disease 2019 Pandemic at a Large Academic Cancer Center in Canada.

Purpose: Mitigation strategies to balance the risk of coronavirus disease 2019 (COVID-19) infection against oncologic risk in patients with breast cancer undergoing radiation therapy have been deployed. To this end, shorter hypofractionated regimens have been recommended where appropriate, with prioritization of radiation therapy by oncologic risk and omission or deferral of radiation therapy for lower risk cases. Timely adoption of these measures reduces COVID-19 risk to both patients and health care workers and preserves resources.

An Innovative Learning Tool for Radiation Therapy Treatment Plan Evaluation: Implementation and Evaluation.

Purpose: To design, develop, and evaluate an interactive simulation-based learning tool for treatment plan evaluation for radiation oncology and medical physics residents to address gaps in learning.

Methods And Materials: We first conducted a needs assessment for optimal learning tool design and case selection. Next, we generated a curated database of cases with clinically unacceptable treatment plans accessible through an in-house developed interactive web-based digital imaging and communications in medicine-radiation therapy viewer.

The Role of Partial Breast Radiation in the Previously Radiated Breast.

Objectives: The aim of this study was to analyze breast cancer patients who previously had mantle-field or breast radiation (RT) followed by retreatment with external beam partial breast irradiation (EB PBI).

Materials And Methods: We retrospectively reviewed all women with newly diagnosed early-stage breast cancer treated with lumpectomy and partial breast irradiation between 2007 and 2017 who had undergone prior chest or breast RT.

Results: Of 11 patients recorded, 8 (73%) had Hodgkin lymphoma, and 3 (27%) had ipsilateral breast cancer diagnosis.

Vulnerabilities of radiomic signature development: The need for safeguards.

Purpose: Refinement of radiomic results and methodologies is required to ensure progression of the field. In this work, we establish a set of safeguards designed to improve and support current radiomic methodologies through detailed analysis of a radiomic signature.

Methods: A radiomic model (MW2018) was fitted and externally validated using features extracted from previously reported lung and head and neck (H&N) cancer datasets using gross-tumour-volume contours, as well as from images with randomly permuted voxel index values; i.

American Association of Physicists in Medicine Task Group 263: Standardizing Nomenclatures in Radiation Oncology.

A substantial barrier to the single- and multi-institutional aggregation of data to supporting clinical trials, practice quality improvement efforts, and development of big data analytics resource systems is the lack of standardized nomenclatures for expressing dosimetric data. To address this issue, the American Association of Physicists in Medicine (AAPM) Task Group 263 was charged with providing nomenclature guidelines and values in radiation oncology for use in clinical trials, data-pooling initiatives, population-based studies, and routine clinical care by standardizing: (1) structure names across image processing and treatment planning system platforms; (2) nomenclature for dosimetric data (eg, dose-volume histogram [DVH]-based metrics); (3) templates for clinical trial groups and users of an initial subset of software platforms to facilitate adoption of the standards; (4) formalism for nomenclature schema, which can accommodate the addition of other structures defined in the future. A multisociety, multidisciplinary, multinational group of 57 members representing stake holders ranging from large academic centers to community clinics and vendors was assembled, including physicists, physicians, dosimetrists, and vendors.

Guided undersampling classification for automated radiation therapy quality assurance of prostate cancer treatment.

Purpose: To test the use of well-studied and widely used classification methods alongside newly developed data-filtering techniques specifically designed for imbalanced-data classification in order to demonstrate proof of principle for an automated radiation therapy (RT) quality assurance process on prostate cancer treatment.

Methods: A series of acceptable (majority class, n = 61) and erroneous (minority class, n = 12) RT plans as well as a disjoint set of acceptable plans used to develop features (n = 273) were used to develop a dataset for testing. A series of five widely used imbalanced-data classification algorithms were tested with a modularized guided undersampling procedure that includes ensemble-outlier filtering and normalized-cut sampling.

Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method.

Recent works in automated radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present a probabilistic, atlas-based approach which predicts the dose for novel patients using a set of automatically selected most similar patients (atlases). The output is a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces the need to specify and tune dose-volume objectives.

Voxel-based dose prediction with multi-patient atlas selection for automated radiotherapy treatment planning.

Automating the radiotherapy treatment planning process is a technically challenging problem. The majority of automated approaches have focused on customizing and inferring dose volume objectives to be used in plan optimization. In this work we outline a multi-patient atlas-based dose prediction approach that learns to predict the dose-per-voxel for a novel patient directly from the computed tomography planning scan without the requirement of specifying any objectives.

Contextual Atlas Regression Forests: Multiple-Atlas-Based Automated Dose Prediction in Radiation Therapy.

Radiation therapy is an integral part of cancer treatment, but to date it remains highly manual. Plans are created through optimization of dose volume objectives that specify intent to minimize, maximize, or achieve a prescribed dose level to clinical targets and organs. Optimization is NP-hard, requiring highly iterative and manual initialization procedures.