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

DIAGNOSTICS (2023)

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Article Radiology, Nuclear Medicine & Medical Imaging

Automated field-in-field whole brain radiotherapy planning

Kai Huang et al.

Summary: We developed an automatic field-in-field (FIF) solution for whole-brain radiotherapy (WBRT) planning, which creates a homogeneous dose distribution by minimizing hotspots and results in clinically acceptable plans. The algorithm was tested on 17 whole-brain patients and produced high-quality plans in an average of 16 minutes without user intervention. 76.5% of the auto-plans were clinically acceptable, and all plans were clinically acceptable after minor edits.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2023)

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Article Oncology

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

Mary P. Gronberg et al.

Summary: This study used deep learning-based dose prediction to evaluate the quality of head and neck plans and identify suboptimal plans. The predictions were compared to high-quality plans created using RapidPlan knowledge-based planning. The study showed that deep learning can accurately predict high-quality dose distributions, which can be used for automated assessment of plan quality.

PRACTICAL RADIATION ONCOLOGY (2023)

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Article Oncology

Comprehensive Quantitative Evaluation of Variability in Magnetic Resonance-Guided Delineation of Oropharyngeal Gross Tumor Volumes and High-Risk Clinical Target Volumes: An R-IDEAL Stage 0 Prospective Study

Carlos E. Cardenas et al.

Summary: This study evaluated the manual delineations of tumor and target volumes in oropharyngeal cancer patients using magnetic resonance scans. The results showed significant variability among the observers in their delineations. While there was more consistency in defining normal tissue contours, further guidance and training are needed to reduce delineation variation and ensure standardization of target design.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Deep learning-based auto-segmentation of clinical target volumes for radiotherapy treatment of cervical cancer

Chen-Ying Ma et al.

Summary: The study evaluated a deep learning-based auto-segmentation algorithm for contouring clinical target volumes in cervical cancer treatment, showing improved accuracy and efficiency in delineating radiation targets.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process

Jingwei Duan et al.

Summary: This study evaluates the performance of a commercial AI-based contouring model in the automated prostate treatment planning process. The results demonstrate good geometric accuracy and clinical acceptability of the AI contours, and the automated treatment plans based on these contours are comparable to those based on reference contours.

MEDICAL PHYSICS (2022)

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Article Oncology

Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer

Maria Kawula et al.

Summary: The aim of this study was to investigate the impact of state-of-the-art 3D U-Net-generated organ delineations on dose optimization in radiation therapy for prostate cancer patients. The study found no strong statistically significant correlation between geometric and dosimetric metrics. It highlighted the importance of adding dosimetric analysis to the standard geometric evaluation.

RADIATION ONCOLOGY (2022)

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Article Oncology

Clinical Use of A Priori Knowledge of Organ-At-Risk Sparing During Radiation Therapy Treatment for Oropharyngeal Cancer: Dosimetric and Patient Reported Outcome Improvements

David Fried et al.

Summary: This study investigated the dosimetric and clinical effects of treatment planners having prior knowledge of the maximum achievable dose sparing for OARs in patients with oropharynx cancer receiving intensity modulated radiation therapy. The use of a tool generating estimates of maximum achievable dose sparing for OARs (FDVH) was associated with improved sparing of OARs and better patient-reported outcomes compared to baseline. This study highlights the importance of using FDVH to improve treatment planning and patient outcomes in radiation therapy for oropharynx cancer.

PRACTICAL RADIATION ONCOLOGY (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Knowledge-based planning for the radiation therapy treatment plan quality assurance for patients with head and neck cancer

Wenhua Cao et al.

Summary: This study investigates the feasibility of using a knowledge-based planning technique to detect poor quality VMAT plans for patients with head and neck cancer. The results show that the prediction models can accurately assess plan quality, identify suboptimal plans, and assist in the clinical workflow for individual patients.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Automatic contouring QA method using a deep learning-based autocontouring system

Dong Joo Rhee et al.

Summary: This study aimed to determine the most accurate similarity metric when using an independent system to verify automatically generated contours. By comparing various similarity metrics, it was found that surface DSC with a thickness of 1, 2, or 3 mm can accurately distinguish clinically acceptable and unacceptable contours.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2022)

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Review Radiology, Nuclear Medicine & Medical Imaging

Plan quality in radiotherapy treatment planning - Review of the factors and challenges

Christian Ronn Hansen et al.

Summary: A high-quality treatment plan involves collaboration between clinical and technical experts, with a focus on understanding clinical requirements and limitations. Factors influencing plan quality include treatment planning system commissioning, quality assurance, and plan deliverability. Ongoing developments in treatment planning techniques and evidence-based science continue to shape the evolution of high-quality treatment plans.

JOURNAL OF MEDICAL IMAGING AND RADIATION ONCOLOGY (2022)

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Article Computer Science, Artificial Intelligence

Head and neck tumor segmentation in PET/CT: The HECKTOR challenge

Valentin Oreiller et al.

Summary: This paper presents the post-analysis of the first edition of the HEad and neCK TumOR (HECKTOR) challenge, which focused on automatic segmentation of head and neck tumors in combined FDG-PET and CT images. A total of 64 teams participated in the challenge, and the best method achieved a high Dice Score Coefficient (DSC) and outperformed human inter-observer agreement and other methods.

MEDICAL IMAGE ANALYSIS (2022)

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Article Oncology

Deep Learning for Per-Fraction Automatic Segmentation of Gross Tumor Volume (GTV) and Organs at Risk (OARs) in Adaptive Radiotherapy of Cervical Cancer

Adrian L. Breto et al.

Summary: MRI-guided online adaptive radiotherapy can improve target coverage and OARs sparing in radiation therapy. This study successfully trained a deep learning CNN to automatically segment GTV and OARs in MRI scans of LACC patients. Further improvement is expected with more cases.

FRONTIERS IN ONCOLOGY (2022)

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Article Oncology

Evaluation of Auto-Contouring and Dose Distributions for Online Adaptive Radiation Therapy of Patients With Locally Advanced Lung Cancers

Weihua Mao et al.

Summary: This retrospective study evaluates the accuracy of automatically mapped structures and dosimetric consequences of daily online adaptive radiation therapy (ART) for lung cancer treatments. The study shows that Ethos automatic contouring for lung cancer is clinically acceptable and online adaptive radiation therapy significantly improves target coverage and spares organs-at-risk.

PRACTICAL RADIATION ONCOLOGY (2022)

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Article Oncology

Using Failure Mode and Effects Analysis to Evaluate Risk in the Clinical Adoption of Automated Contouring and Treatment Planning Tools

Kelly A. Nealon et al.

Summary: The study used the failure mode and effects analysis (FMEA) approach to evaluate and mitigate the risks in deploying an automated radiation therapy contouring and treatment planning tool. Through the analysis, specific errors and high-risk failure modes were identified, leading to workflow modifications and training resource enhancements. The findings demonstrate the effectiveness of FMEA in assessing and reducing risks associated with automated planning tools.

PRACTICAL RADIATION ONCOLOGY (2022)

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Article Multidisciplinary Sciences

Multi-organ segmentation of abdominal structures from non-contrast and contrast enhanced CT images

Cenji Yu et al.

Summary: The article introduces a deep learning-based tool for automated segmentation of upper abdominal organs at risk, achieving satisfactory results and providing accurate contours for tumor treatment planning.

SCIENTIFIC REPORTS (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Development and validation of a checklist for use with automatically generated radiotherapy plans

Kelly A. Nealon et al.

Summary: Using a customized checklist can improve the rate of error detection during the review of automatically generated radiotherapy plans, leading to enhanced patient safety.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Automation of radiation treatment planning for rectal cancer

Kai Huang et al.

Summary: This study successfully developed an automated workflow for 3DCRT treatment planning for rectal cancer, combining deep learning aperture predictions and forward-planning algorithms. The generated apertures and optimized field-in-field planning resulted in clinically acceptable plans for the majority of patients.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Clinical acceptability of fully automated external beam radiotherapy for cervical cancer with three different beam delivery techniques

Dong Joo Rhee et al.

Summary: The purpose of this study was to automate CT-based cervical cancer radiotherapy by automating contouring and planning for three different treatment techniques. The authors developed auto-planning algorithms combined with a previously developed auto-contouring system. The results showed that approximately 90% of the automatically generated plans were clinically acceptable for all three planning techniques.

MEDICAL PHYSICS (2022)

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Article Oncology

Multicenter comparison of measures for quantitative evaluation of contouring in radiotherapy

Mark J. Gooding et al.

Summary: This study aimed to assess the variation in output when applying different implementations of quantitative measures in radiation oncology. The survey results showed significant differences in the reported results between institutions due to variations in definitions and implementations of contouring measures. Therefore, caution must be taken when comparing quantitative results from different studies, and validation of such tools is necessary before their use.

PHYSICS & IMAGING IN RADIATION ONCOLOGY (2022)

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Article Oncology

Inter-observer variability of organ contouring for preclinical studies with cone beam Computed Tomography imaging

Georgios Lappas et al.

Summary: This study evaluated the interobserver variability in manual contouring of normal tissues in rodents. The results showed that the interobserver variability was relatively small for normal tissues in the thorax and head regions, which can serve as a basis for developing an automated delineation method.

PHYSICS & IMAGING IN RADIATION ONCOLOGY (2022)

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Article Oncology

Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring of Bladder and Rectum for Prostate Radiation Therapy

W. Jeffrey Zabel et al.

Summary: Auto-contouring can reduce workload, interobserver variation, and time associated with manual contouring. Using deep-learning auto-contouring for bladder and rectum contour generation reduced contouring time without negatively affecting editing times, contour geometry, or clinically relevant dose-volume metrics. This study contributes to the evidence that deep-learning methods are a viable solution for organ-at-risk contouring in radiation therapy.

PRACTICAL RADIATION ONCOLOGY (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

When Does Physician Use of AI Increase Liability?

Kevin Tobia et al.

Summary: An experimental study involving 2000 U.S. adults found that physicians can reduce liability by accepting AI systems' recommendations for standard care, but rejecting nonstandard care recommendations does not have the same protective effect.

JOURNAL OF NUCLEAR MEDICINE (2021)

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Review Oncology

Automation in radiotherapy treatment planning: Examples of use in clinical practice and future trends for a complete automated workflow

P. Meyer et al.

Summary: Modern radiotherapy treatment planning has become a key research topic with the automation of planning processes, leading to the implementation of commercial solutions in clinical departments. However, the diverse commercial solutions based on different methods suggest varying practices across different centers. With the emergence of artificial intelligence, particularly deep learning, the future of radiotherapy planning practices is expected to undergo lasting and profound transformation.

CANCER RADIOTHERAPIE (2021)

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Article Oncology

Automatic Segmentation Using Deep Learning to Enable Online Dose Optimization During Adaptive Radiation Therapy of Cervical Cancer

Bastien Rigaud et al.

Summary: This study explored deep learning models for automatic segmentation to support daily online dose optimization strategies for cervical cancer IMRT, aiming to reduce toxicity events. The investigated models provided auto-segmentation of cervical anatomy with similar performances on two institutional data sets and reasonable dosimetric accuracies, opening the door to automatic online dose optimization for advanced adaptive radiation therapy strategies.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

A systematically compiled set of quantitative metrics to describe spatial characteristics of radiotherapy dose distributions and aid in treatment planning

Laura Patricia Kaplan et al.

Summary: This study compared different metrics for spatial dose properties and provided a systematically selected set of metrics to comprehensively quantify spatial dose distribution properties. Results showed substantial differences in the responses of metrics to simulated dose variations, with some metrics unable to accurately quantify certain changes.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2021)

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Article Oncology

The impact of organ-at-risk contour variations on automatically generated treatment plans for NSCLC

Femke Vaassen et al.

Summary: This study evaluated the dosimetric differences between treatment plans optimized using various organ-at-risk (OAR) contouring methods. The results showed that dose differences arising from automatic contour variations were of a similar magnitude or lower than those from intra-observer contour variability. Recommendations include correcting delineation errors when the heart overlaps with the PTV for Heart D-mean and checking contours if they are close to the PTV for D-max parameters.

RADIOTHERAPY AND ONCOLOGY (2021)

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Article Oncology

Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy

Elaine Cha et al.

Summary: The study evaluated the clinical utility of deep learning-based autosegmentation for MR-based prostate radiotherapy planning, finding that this technology improved efficiency, although some autocontours still required major clinically significant edits. Geometric indices correlated weakly with contouring time, but had no relationship with quality scores.

RADIOTHERAPY AND ONCOLOGY (2021)

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Article Oncology

Clinical Acceptability of Automated Radiation Treatment Planning for Head and Neck Cancer Using the Radiation Planning Assistant

Adenike Olanrewaju et al.

Summary: Automated radiation treatment planning was compared to manually generated clinical plans for head and neck cancer patients, showing similar quality and acceptance by physician reviewers. The majority of reviewers found both plans acceptable, with a higher percentage favoring the autoplan.

PRACTICAL RADIATION ONCOLOGY (2021)

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Article Oncology

Using Auto-Segmentation to Reduce Contouring and Dose Inconsistency in Clinical Trials: The Simulated Impact on RTOG 0617

Maria Thor et al.

Summary: This study applied auto-contouring using deep learning to resegment hearts in the RTOG 0617 dose escalation trial, revealing significantly higher heart doses compared to previous reports. Deep learning technology is likely to reduce contouring and dose inconsistencies, thereby enhancing the quality of clinical radiation therapy trials.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2021)

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Article Oncology

Randomized multi-reader evaluation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networks

Shao-Lun Lu et al.

Summary: The study found that in brain tumor SRS, AI-assisted contouring significantly improved inter-reader agreement and lesion detection sensitivity, especially for less experienced physicians. AI assistance not only increased contouring accuracy, but also improved work efficiency, with a notable impact on time-saving.

NEURO-ONCOLOGY (2021)

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Review Psychology, Multidisciplinary

Human, All Too Human? An All-Around Appraisal of the Artificial Intelligence Revolution in Medical Imaging

Francesca Coppola et al.

Summary: Artificial intelligence has seen significant growth, benefiting the medical field by improving diagnostic accuracy and personalized treatment options. However, concerns have been raised about AI potentially replacing healthcare professionals. AI offers advantages but also raises psychological, ethical, and legal issues that need to be addressed.

FRONTIERS IN PSYCHOLOGY (2021)

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Review Oncology

Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review

Michael Sherer et al.

Summary: Advances in AI-based methods have led to the development of auto-segmentation systems in radiotherapy. However, there is no uniform standard for evaluating their efficacy. Common evaluation techniques include geometric overlap, dosimetric parameters, and clinical rating scales, with many geometric indices showing weak correlation with clinical endpoints. A multi-domain evaluation is necessary to assess the clinical readiness of auto-segmentation for radiation treatment planning.

RADIOTHERAPY AND ONCOLOGY (2021)

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Article Oncology

A Preliminary Experience of Implementing Deep-Learning Based Auto-Segmentation in Head and Neck Cancer: A Study on Real-World Clinical Cases

Yang Zhong et al.

Summary: This study validated a U-net-based CNN for automatic delineation of OARs in head and neck cancer, showing potential for clinical application in terms of implementation and evaluation.

FRONTIERS IN ONCOLOGY (2021)

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Article Health Care Sciences & Services

Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

Stanislav Nikolov et al.

JOURNAL OF MEDICAL INTERNET RESEARCH (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Technical Note: Dose prediction for head and neck radiotherapy using a three-dimensional dense dilated U-net architecture

Mary P. Gronberg et al.

Summary: A 3D densely connected U-Net with dilated convolutions was developed to accurately predict radiation therapy dose distributions in head and neck patients. The team placed second in the OpenKBP challenge, with an average absolute difference of 2.56 Gy between predicted and clinical dose distributions, and high accuracy in target and organ at risk DVH metrics. This model shows promise for improving plan quality and reducing planning times in radiation therapy.

MEDICAL PHYSICS (2021)

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Article Biochemistry & Molecular Biology

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

Chris McIntosh et al.

Summary: The study demonstrates the potential impact of machine learning in healthcare delivery, with a random forest algorithm applied to therapeutic radiation therapy planning for prostate cancer. Machine-generated RT plans were found to be clinically acceptable in 89% of cases and were selected over human-generated plans in 72% of head-to-head comparisons, significantly reducing the time required for RT planning.

NATURE MEDICINE (2021)

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Article Oncology

Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers

Jordan Wong et al.

Summary: The implemented deep learning models for clinical radiotherapy planning showed minimal edits required for organs at risk (OAR) and clinical target volumes (CTV) with high user satisfaction. Despite low survey compliance, the majority of DC models performed positively in radiotherapy planning.

RADIATION ONCOLOGY (2021)

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Article Computer Science, Artificial Intelligence

Who Will Pay for AI?

Melissa M. Chen et al.

Summary: In 2020, the Centers for Medicare & Medicaid Services (CMS) in the United States established payment for artificial intelligence (AI) through two different systems – the Medicare Physician Fee Schedule (MPFS) and the Inpatient Prospective Payment System (IPPS). This included reimbursement for AI tools for diabetic retinopathy diagnosis and large-vessel occlusion stroke diagnosis and treatment. The article also proposes future payment pathways for AI technologies.

RADIOLOGY-ARTIFICIAL INTELLIGENCE (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

How Much Can Potential Jurors Tell Us About Liability for Medical Artificial Intelligence?

W. Nicholson Price et al.

JOURNAL OF NUCLEAR MEDICINE (2021)

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Article Oncology

Automated Contouring of Contrast and Noncontrast Computed Tomography Liver Images With Fully Convolutional Networks

Brian M. Anderson et al.

Summary: In this study, fully convolutional networks were used to auto-segment the liver, removing the time bottleneck for biomechanical modeling. The accuracy and qualitative performance of the auto-segmentations were evaluated, showing that in the majority of cases, auto-segmentations were preferred over manually defined contours.

ADVANCES IN RADIATION ONCOLOGY (2021)

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Review Oncology

Prospective Peer Review in Radiation Therapy Treatment Planning: Long -Term Results From a Longitudinal Study

Brett W. Cox et al.

PRACTICAL RADIATION ONCOLOGY (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

CT images with expert manual contours of thoracic cancer for benchmarking auto-segmentation accuracy

Jinzhong Yang et al.

MEDICAL PHYSICS (2020)

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Article Oncology

Clinical validation of a graphical method for radiation therapy plan quality assessment

Tiago Ventura et al.

RADIATION ONCOLOGY (2020)

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Article Biology

Dose-volume histogram analysis and clinical evaluation of knowledge-based plans with manual objective constraints for pharyngeal cancer

Takuya Uehara et al.

JOURNAL OF RADIATION RESEARCH (2020)

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Abdominal multi-organ auto-segmentation using 3D-patch-based deep convolutional neural network

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SCIENTIFIC REPORTS (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Head and neck cancer patient images for determining auto-segmentation accuracy in T2-weighted magnetic resonance imaging through expert manual segmentations

Carlos E. Cardenas et al.

MEDICAL PHYSICS (2020)

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Evaluation of Automatic Segmentation Model With Dosimetric Metrics for Radiotherapy of Esophageal Cancer

Ji Zhu et al.

FRONTIERS IN ONCOLOGY (2020)

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Article Oncology

Analysis of Geometric Performance and Dosimetric Impact of Using Automatic Contour Segmentation for Radiotherapy Planning

Minsong Cao et al.

FRONTIERS IN ONCOLOGY (2020)

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A Blinded, Prospective Study of Error Detection During Physician Chart Rounds in Radiation Oncology

Wesley J. Talcott et al.

PRACTICAL RADIATION ONCOLOGY (2020)

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Article Oncology

Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy

Femke Vaassen et al.

PHYSICS & IMAGING IN RADIATION ONCOLOGY (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Inter-observer variability of manual contour delineation of structures in CT

Leo Joskowicz et al.

EUROPEAN RADIOLOGY (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Automatic multiorgan segmentation in thorax CT images using U-net-GAN

Xue Dong et al.

MEDICAL PHYSICS (2019)

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A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning

Dan Nguyen et al.

SCIENTIFIC REPORTS (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

A risk assessment of automated treatment planning and recommendations for clinical deployment

Kelly Kisling et al.

MEDICAL PHYSICS (2019)

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Automated treatment planning of postmastectomy radiotherapy

Kelly Kisling et al.

MEDICAL PHYSICS (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Evaluation of auto-planning in IMRT and VMAT for head and neck cancer

Zi Ouyang et al.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2019)

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Article Oncology

Advances in Auto-Segmentation

Carlos E. Cardenas et al.

SEMINARS IN RADIATION ONCOLOGY (2019)

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Article Oncology

Automated Radiotherapy Treatment Planning

Kevin L. Moore

SEMINARS IN RADIATION ONCOLOGY (2019)

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Article Oncology

Automated Closed- and Open-Loop Validation of Knowledge-Based Planning Routines Across Multiple Disease Sites

Robert Kaderka et al.

PRACTICAL RADIATION ONCOLOGY (2019)

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Article Oncology

Benefits of deep learning for delineation of organs at risk in head and neck cancer

J. van der Veen et al.

RADIOTHERAPY AND ONCOLOGY (2019)

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Article Computer Science, Interdisciplinary Applications

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

Hugo J. Kuijf et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2019)

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Review Oncology

Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future

Chunhao Wang et al.

TECHNOLOGY IN CANCER RESEARCH & TREATMENT (2019)

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Comparison of dose metrics between automated and manual radiotherapy planning for advanced stage non-small cell lung cancer with volumetric modulated arc therapy

Iris H. P. Creemers et al.

PHYSICS & IMAGING IN RADIATION ONCOLOGY (2019)

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Article Oncology

Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer

Tim Lustberg et al.

RADIOTHERAPY AND ONCOLOGY (2018)

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Article Radiology, Nuclear Medicine & Medical Imaging

Comparative evaluation of autocontouring in clinical practice: A practical method using the Turing test

Mark J. Gooding et al.

MEDICAL PHYSICS (2018)

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Article Radiology, Nuclear Medicine & Medical Imaging

Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017

Jinzhong Yang et al.

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A novel index for assessing treatment plan quality in stereotactic radiosurgery

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FMEA of manual and automated methods for commissioning a radiotherapy treatment planning system

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Increasing the power of tumour control and normal tissue complication probability modelling in radiotherapy: recent trends and current issues

Francesco Tommasino et al.

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An analysis of knowledge-based planning for stereotactic body radiation therapy of the spine

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

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SPIDERplan: A tool to support decision-making in radiation therapy treatment plan assessment

Tiago Ventura et al.

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Article Oncology

Uncertainties in target volume delineation in radiotherapy - are they relevant and what can we do about them?

Barbara Segedin et al.

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Article Oncology

Quantifying Unnecessary Normal Tissue Complication Risks due to Suboptimal Planning: A Secondary Study of RTOG 0126

Kevin L. Moore et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2015)

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Article Oncology

A Randomized Phase 2 Study Comparing 2 Stereotactic Body Radiation Therapy Schedules for Medically Inoperable Patients With Stage I Peripheral Non-Small Cell Lung Cancer: NRG Oncology RTOG 0915 (NCCTG N0927)

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Article Radiology, Nuclear Medicine & Medical Imaging

Quantitative comparison of automatic and manual IMRT optimization for prostate cancer: the benefits of DVH prediction

Yun Yang et al.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2015)

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Editorial Material Radiology, Nuclear Medicine & Medical Imaging

Evaluation of treatment plans using target and normal tissue DVHs is no longer appropriate

Christopher F. Njeh et al.

MEDICAL PHYSICS (2015)

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Article Radiology, Nuclear Medicine & Medical Imaging

Validation of clinical acceptability of an atlas-based segmentation algorithm for the delineation of organs at risk in head and neck cancer

Albert K. Hoang Duc et al.

MEDICAL PHYSICS (2015)

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Article Oncology

A dose-volume histogram based decision-support system for dosimetric comparison of radiotherapy treatment plans

J. C. L. Alfonso et al.

RADIATION ONCOLOGY (2015)

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Article Oncology

Multi-subject atlas-based auto-segmentation reduces interobserver variation and improves dosimetric parameter consistency for organs at risk in nasopharyngeal carcinoma: A multi-institution clinical study

Chang-Juan Tao et al.

RADIOTHERAPY AND ONCOLOGY (2015)

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Article Mathematical & Computational Biology

Impact of Dose and Sensitivity Heterogeneity on TCP

Kristin Wiklund et al.

COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE (2014)

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Article Oncology

Optimal set of grid size and angular increment for practical dose calculation using the dynamic conformal arc technique: a systematic evaluation of the dosimetric effects in lung stereotactic body radiation therapy

Ji-Yeon Park et al.

RADIATION ONCOLOGY (2014)

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Article Oncology

Prospective randomized double-blind study of atlas-based organ-at-risk autosegmentation-assisted radiation planning in head and neck cancer

Gary V. Walker et al.

RADIOTHERAPY AND ONCOLOGY (2014)

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Article Oncology

Correlation of contouring variation with modeled outcome for conformal non-small cell lung cancer radiotherapy

Michael G. Jameson et al.

RADIOTHERAPY AND ONCOLOGY (2014)

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Article Oncology

Treatment plan comparison using grading analysis based on clinical judgment

Kristoffer Peterson et al.

ACTA ONCOLOGICA (2013)

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Article Radiology, Nuclear Medicine & Medical Imaging

The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

Kenneth Clark et al.

JOURNAL OF DIGITAL IMAGING (2013)

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Review Oncology

Predicting outcomes in radiation oncology-multifactorial decision support systems

Philippe Lambin et al.

NATURE REVIEWS CLINICAL ONCOLOGY (2013)

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Article Radiology, Nuclear Medicine & Medical Imaging

A method to vectorize the dose distribution, the dose volume histogram and create a dose vector histogram

Charles S. Mayo et al.

MEDICAL PHYSICS (2013)

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Article Engineering, Biomedical

Segmentation editing improves efficiency while reducing inter-expert variation and maintaining accuracy for normal brain tissues in the presence of space-occupying lesions

M. A. Deeley et al.

PHYSICS IN MEDICINE AND BIOLOGY (2013)

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Article Oncology

How important is dosimetrist experience for intensity modulated radiation therapy? A comparative analysis of a head and neck case

Vikneswary Batumalai et al.

PRACTICAL RADIATION ONCOLOGY (2013)

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Article Oncology

Evaluation of Dosimetric Consequences of Seroma Contour Variability in Accelerated Partial Breast Irradiation Using a Constructed Representative Seroma Contour

Robert Kosztyla et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2012)

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Review Computer Science, Information Systems

Automation bias: a systematic review of frequency, effect mediators, and mitigators

Kate Goddard et al.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2012)

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Article Medicine, General & Internal

Ten Common Questions (and Their Answers) About Off-label Drug Use

Christopher M. Wittich et al.

MAYO CLINIC PROCEEDINGS (2012)

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Article Radiology, Nuclear Medicine & Medical Imaging

Predicting dose-volume histograms for organs-at-risk in IMRT planning

Lindsey M. Appenzoller et al.

MEDICAL PHYSICS (2012)

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Article Oncology

Assessment of accuracy and efficiency of atlas-based autosegmentation for prostate radiotherapy in a variety of clinical conditions

I. Simmat et al.

STRAHLENTHERAPIE UND ONKOLOGIE (2012)

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Article Oncology

Variation in external beam treatment plan quality: An inter-institutional study of planners and planning systems

Benjamin E. Nelms et al.

PRACTICAL RADIATION ONCOLOGY (2012)

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Article Oncology

EXPERIENCE-BASED QUALITY CONTROL OF CLINICAL INTENSITY-MODULATED RADIOTHERAPY PLANNING

Kevin L. Moore et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2011)

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Article Oncology

ATLAS-BASED SEGMENTATION IMPROVES CONSISTENCY AND DECREASES TIME REQUIRED FOR CONTOURING POSTOPERATIVE ENDOMETRIAL CANCER NODAL VOLUMES

Amy V. Young et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2011)

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Article Oncology

Improved Planning Time and Plan Quality Through Multicriteria Optimization for Intensity-Modulated Radiotherapy

David L. Craft et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2011)

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Article Oncology

Technology Assessment of Automated Atlas Based Segmentation in Prostate Bed Contouring

Jeremiah Hwee et al.

RADIATION ONCOLOGY (2011)

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Article Oncology

USE OF NORMAL TISSUE COMPLICATION PROBABILITY MODELS IN THE CLINIC

Lawrence B. Marks et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2010)

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Article Oncology

THE LESSONS OF QUANTEC: RECOMMENDATIONS FOR REPORTING AND GATHERING DATA ON DOSE-VOLUME DEPENDENCIES OF TREATMENT OUTCOME

Andrew Jackson et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2010)

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Article Radiology, Nuclear Medicine & Medical Imaging

A computational tool for the efficient analysis of dose-volume histograms for radiation therapy treatment plans

Anil Pyakuryal et al.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2010)

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Article Computer Science, Interdisciplinary Applications

Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets

Tobias Heimann et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2009)

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Meeting Abstract Oncology

Increased Consistency and Time Savings for Delineation of Pelvic Lymph Nodes for Adjuvant Therapy of Endometrial Cancer

R. D. Ennis et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2009)

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Article Radiology, Nuclear Medicine & Medical Imaging

Patient geometry-driven information retrieval for IMRT treatment plan quality control

Binbin Wu et al.

MEDICAL PHYSICS (2009)

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Article Engineering, Biomedical

Reduced-order parameter optimization for simplifying prostate IMRT planning

Renzhi Lu et al.

PHYSICS IN MEDICINE AND BIOLOGY (2007)

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Article Oncology

Observer variation in target volume delineation of lung cancer related to radiation oncotogist-computer interaction: A 'Big Brother' evaluation

RJHM Steenbakkers et al.

RADIOTHERAPY AND ONCOLOGY (2005)

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Article Oncology

Do differences in target volume definition in prostate cancer lead to clinically relevant differences in normal tissue toxicity?

JE Livsey et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2004)

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Article Oncology

A simple and reliable index for scoring rival stereotactic radiosurgery plans

TH Wagner et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2003)

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Article Computer Science, Cybernetics

The role of trust in automation reliance

MT Dzindolet et al.

INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES (2003)

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Article Oncology

Optimization of intensity-modulated radiotherapy plans based on the equivalent uniform dose

QW Wu et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2002)

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Article Computer Science, Cybernetics

Accountability and automation bias

LJ Skitka et al.

INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES (2000)

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