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Renner A, Gulyas I, Buschmann M, Heilemann G, Knäusl B, Heilmann M, Widder J, Georg D, Trnková P. Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data. Phys Imaging Radiat Oncol 2024; 30:100594. [PMID: 38883146 PMCID: PMC11176922 DOI: 10.1016/j.phro.2024.100594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 05/17/2024] [Accepted: 05/25/2024] [Indexed: 06/18/2024] Open
Abstract
Background and purpose Active breathing motion management in radiotherapy consists of motion monitoring, quantification and mitigation. It is impacted by associated latencies of a few 100 ms. Artificial neural networks can successfully predict breathing motion and eliminate latencies. However, they require usually a large dataset for training. The objective of this work was to demonstrate that explicitly encoding the cyclic nature of the breathing signal into the training data enables significant reduction of training datasets which can be obtained from healthy volunteers. Material and methods Seventy surface scanner breathing signals from 25 healthy volunteers in anterior-posterior direction were used for training and validation (ratio 4:1) of long short-term memory models. The model performance was compared to a model using decomposition into phase, amplitude and a time-dependent baseline. Testing of the models was performed on 55 independent breathing signals in anterior-posterior direction from surface scanner (35 lung, 20 liver) of 30 patients with a mean breathing amplitude of (5.9 ± 6.7) mm. Results Using the decomposed breathing signal allowed for a reduction of the absolute root-mean square error (RMSE) from 0.34 mm to 0.12 mm during validation. Testing using patient data yielded an average absolute RMSE of the breathing signal of (0.16 ± 0.11) mm with a prediction horizon of 500 ms. Conclusion It was demonstrated that a motion prediction model can be trained with less than 100 datasets of healthy volunteers if breathing cycle parameters are considered. Applied to 55 patients, the model predicted breathing motion with a high accuracy.
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Affiliation(s)
- Andreas Renner
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University of Vienna, Austria
| | - Ingo Gulyas
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- MedAustron Ion Therapy Center, Wiener Neustadt, Austria
| | - Martin Buschmann
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Gerd Heilemann
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University of Vienna, Austria
| | - Barbara Knäusl
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University of Vienna, Austria
- MedAustron Ion Therapy Center, Wiener Neustadt, Austria
| | - Martin Heilmann
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Joachim Widder
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Dietmar Georg
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University of Vienna, Austria
- MedAustron Ion Therapy Center, Wiener Neustadt, Austria
| | - Petra Trnková
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
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Mayo CS, Feng MU, Brock KK, Kudner R, Balter P, Buchsbaum JC, Caissie A, Covington E, Daugherty EC, Dekker AL, Fuller CD, Hallstrom AL, Hong DS, Hong JC, Kamran SC, Katsoulakis E, Kildea J, Krauze AV, Kruse JJ, McNutt T, Mierzwa M, Moreno A, Palta JR, Popple R, Purdie TG, Richardson S, Sharp GC, Satomi S, Tarbox LR, Venkatesan AM, Witztum A, Woods KE, Yao Y, Farahani K, Aneja S, Gabriel PE, Hadjiiski L, Ruan D, Siewerdsen JH, Bratt S, Casagni M, Chen S, Christodouleas JC, DiDonato A, Hayman J, Kapoor R, Kravitz S, Sebastian S, Von Siebenthal M, Bosch W, Hurkmans C, Yom SS, Xiao Y. 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. Int J Radiat Oncol Biol Phys 2023; 117:533-550. [PMID: 37244628 PMCID: PMC10741247 DOI: 10.1016/j.ijrobp.2023.05.033] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 05/17/2023] [Accepted: 05/19/2023] [Indexed: 05/29/2023]
Abstract
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. The Big Data Science Committee adopted an iterative, cyclical approach to engaging stakeholders beyond its membership to optimize the integration of diverse perspectives from the community. RESULTS We developed the Operational Ontology for Oncology (O3), which identified 42 key elements, 359 attributes, 144 value sets, and 155 relationships ranked in relative importance of clinical significance, likelihood of availability in EHRs, and the ability to modify routine clinical processes to permit aggregation. Recommendations are provided for best use and development of the O3 to 4 constituencies: device manufacturers, centers of clinical care, researchers, and professional societies. CONCLUSIONS O3 is designed to extend and interoperate with existing global infrastructure and data science standards. The implementation of these recommendations will lower the barriers for aggregation of information that could be used to create large, representative, findable, accessible, interoperable, and reusable data sets to support the scientific objectives of grant programs. The construction of comprehensive "real-world" data sets and application of advanced analytical techniques, including artificial intelligence, holds the potential to revolutionize patient management and improve outcomes by leveraging increased access to information derived from larger, more representative data sets.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Dan Ruan
- University of California, Los Angeles
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- University of California, San Francisco
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Kapoor R, Sleeman WC, Ghosh P, Palta J. Infrastructure tools to support an effective Radiation Oncology Learning Health System. J Appl Clin Med Phys 2023; 24:e14127. [PMID: 37624227 PMCID: PMC10562037 DOI: 10.1002/acm2.14127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 08/26/2023] Open
Abstract
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.
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Affiliation(s)
- Rishabh Kapoor
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - William C Sleeman
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Preetam Ghosh
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Jatinder Palta
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
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Tomatis S, Mancosu P, Reggiori G, Lobefalo F, Gallo P, Lambri N, Paganini L, La Fauci F, Bresolin A, Parabicoli S, Pelizzoli M, Navarria P, Franzese C, Lenoci D, Scorsetti M. Twenty Years of Advancements in a Radiotherapy Facility: Clinical Protocols, Technology, and Management. Curr Oncol 2023; 30:7031-7042. [PMID: 37504370 PMCID: PMC10378035 DOI: 10.3390/curroncol30070510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 07/29/2023] Open
Abstract
BACKGROUND Hypo-fractionation can be an effective strategy to lower costs and save time, increasing patient access to advanced radiation therapy. To demonstrate this potential in practice within the context of temporal evolution, a twenty-year analysis of a representative radiation therapy facility from 2003 to 2022 was conducted. This analysis utilized comprehensive data to quantitatively evaluate the connections between advanced clinical protocols and technological improvements. The findings provide valuable insights to the management team, helping them ensure the delivery of high-quality treatments in a sustainable manner. METHODS Several parameters related to treatment technique, patient positioning, dose prescription, fractionation, equipment technology content, machine workload and throughput, therapy times and patients access counts were extracted from departmental database and analyzed on a yearly basis by means of linear regression. RESULTS Patients increased by 121 ± 6 new per year (NPY). Since 2010, the incidence of hypo-fractionation protocols grew thanks to increasing Linac technology. In seven years, both the average number of fractions and daily machine workload decreased by -0.84 ± 0.12 fractions/year and -1.61 ± 0.35 patients/year, respectively. The implementation of advanced dose delivery techniques, image guidance and high dose rate beams for high fraction doses, currently systematically used, has increased the complexity and reduced daily treatment throughput since 2010 from 40 to 32 patients per 8 h work shift (WS8). Thanks to hypo-fractionation, such an efficiency drop did not affect NPY, estimating 693 ± 28 NPY/WS8, regardless of the evaluation time. Each newly installed machine was shown to add 540 NPY, while absorbing 0.78 ± 0.04 WS8. The COVID-19 pandemic brought an overall reduction of 3.7% of patients and a reduction of 0.8 fractions/patient, to mitigate patient crowding in the department. CONCLUSIONS The evolution of therapy protocols towards hypo-fractionation was supported by the use of proper technology. The characteristics of this process were quantified considering time progression and organizational aspects. This strategy optimized resources while enabling broader access to advanced radiation therapy. To truly value the benefit of hypo-fractionation, a reimbursement policy should focus on the patient rather than individual treatment fractionation.
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Affiliation(s)
- Stefano Tomatis
- Medical Physics Service, Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Pietro Mancosu
- Medical Physics Service, Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Giacomo Reggiori
- Medical Physics Service, Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Francesca Lobefalo
- Medical Physics Service, Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Pasqualina Gallo
- Medical Physics Service, Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Nicola Lambri
- Medical Physics Service, Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Lucia Paganini
- Medical Physics Service, Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Francesco La Fauci
- Medical Physics Service, Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Andrea Bresolin
- Medical Physics Service, Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Sara Parabicoli
- Medical Physics Service, Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Marco Pelizzoli
- Medical Physics Service, Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Pierina Navarria
- Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Ciro Franzese
- Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy
| | - Domenico Lenoci
- Development Strategic Initiatives Unit, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Marta Scorsetti
- Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy
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Chauhan RS, Pradhan A, Munshi A, Mohanti BK. Efficient and Reliable Data Extraction in Radiation Oncology using Python Programming Language: A Pilot Study. J Med Phys 2023; 48:13-18. [PMID: 37342597 PMCID: PMC10277304 DOI: 10.4103/jmp.jmp_12_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/27/2023] [Accepted: 03/02/2023] [Indexed: 06/23/2023] Open
Abstract
Background and Purpose In recent years, data science approaches have entered health-care systems such as radiology, pathology, and radiation oncology. In our pilot study, we developed an automated data mining approach to extract data from a treatment planning system (TPS) with high speed, maximum accuracy, and little human interaction. We compared the amount of time required for manual data extraction versus the automated data mining technique. Materials and Methods A Python programming script was created to extract specified parameters and features pertaining to patients and treatment (a total of 25 features) from TPS. We successfully implemented automation in data mining, utilizing the application programming interface environment provided by the external beam radiation therapy equipment provider for the whole group of patients who were accepted for treatment. Results This in-house Python-based script extracted selected features for 427 patients in 0.28 ± 0.03 min with 100% accuracy at an astonishing rate of 0.04 s/plan. Comparatively, manual extraction of 25 parameters took an average of 4.5 ± 0.33 min/plan, along with associated transcriptional and transpositional errors and missing data information. This new approach turned out to be 6850 times faster than the conventional approach. Manual feature extraction time increased by a factor of nearly 2.5 if we doubled the number of features extracted, whereas for the Python script, it increased by a factor of just 1.15. Conclusion We conclude that our in-house developed Python script can extract plan data from TPS at a far higher speed (>6000 times) and with the best possible accuracy compared to manual data extraction.
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Affiliation(s)
- Rohit Singh Chauhan
- Department of Physics, GLA University, Mathura, Uttar Pradesh, India
- Department of Radiation Oncology, Manipal Hospitals, Dwarka, New Delhi, India
| | - Anirudh Pradhan
- Centre for Cosmology, Astrophysics and Space Science, GLA University, Mathura, Uttar Pradesh, India
| | - Anusheel Munshi
- Department of Radiation Oncology, Manipal Hospitals, Dwarka, New Delhi, India
| | - Bidhu Kalyan Mohanti
- KIMS Cancer Centre, Kalinga Institute of Medical Sciences, KIIT University, Bhubaneswar, Odisha, India
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Gurjar M, Lindberg J, Björk-Eriksson T, Olsson C. Automated data extraction tool (DET) for external applications in radiotherapy. Tech Innov Patient Support Radiat Oncol 2022; 25:100194. [PMID: 36659909 PMCID: PMC9842687 DOI: 10.1016/j.tipsro.2022.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 10/18/2022] [Accepted: 12/06/2022] [Indexed: 01/09/2023] Open
Abstract
Oncological Information Systems (OIS) manage information in radiotherapy (RT) departments. Due to database structure limitations, stored information can rarely be directly used except for vendor-specific purposes. Our aim is to enable the use of such data in various external applications by creating a tool for automatic data extraction, cleaning and formatting. METHODS AND MATERIALS We used OIS data from a nine-linac RT department in Sweden (70 weeks, 2015-16). Extracted data included patients' referrals and appointments with details for RT sub-tasks. The data extraction tool to prepare the data for external use was built in C# programming language. It used excel-automation queries to remove unassigned/duplicated values, substitute missing data and perform application-specific calculations. Descriptive statistics were used to verify the output with the manually prepared dataset from the corresponding time period. RESULTS From the initial raw data, 2030 (51 %)/907 (23 %) patients had known curative and palliative treatment intent for 84 different cancer diagnoses. After removal of incomplete entries, 373 (10 %) patients had unknown treatment intents which were substituted based on the known curative/palliative ratio. Automatically- and manuallyprepared datasets differed < 1 % for Mould, Treatment planning, Quality assurance and ± 5 % for Fractions and Magnetic resonance imaging with overestimations in 80/140 (57 %) entries by the tool. CONCLUSION We successfully implemented a software tool to prepare ready-to-use OIS datasets for external applications. Our evaluations showed overall results close to the manually-prepared dataset. The time taken to prepare the dataset using our automated strategy can reduce the time for manual preparation from weeks to seconds.
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Affiliation(s)
- Mruga Gurjar
- Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden,Corresponding author
| | - Jesper Lindberg
- Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden,Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden,Regional Cancer Centre West, Western Sweden Healthcare Region, Gothenburg, Sweden
| | - Thomas Björk-Eriksson
- Regional Cancer Centre West, Western Sweden Healthcare Region, Gothenburg, Sweden,Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Caroline Olsson
- Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden,Regional Cancer Centre West, Western Sweden Healthcare Region, Gothenburg, Sweden
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Cui S, Tseng HH, Pakela J, Ten Haken RK, Naqa IE. Introduction to machine and deep learning for medical physicists. Med Phys 2020; 47:e127-e147. [PMID: 32418339 PMCID: PMC7331753 DOI: 10.1002/mp.14140] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 01/23/2020] [Accepted: 03/03/2020] [Indexed: 01/01/2023] Open
Abstract
Recent years have witnessed tremendous growth in the application of machine learning (ML) and deep learning (DL) techniques in medical physics. Embracing the current big data era, medical physicists equipped with these state-of-the-art tools should be able to solve pressing problems in modern radiation oncology. Here, a review of the basic aspects involved in ML/DL model building, including data processing, model training, and validation for medical physics applications is presented and discussed. Machine learning can be categorized based on the underlying task into supervised learning, unsupervised learning, or reinforcement learning; each of these categories has its own input/output dataset characteristics and aims to solve different classes of problems in medical physics ranging from automation of processes to predictive analytics. It is recognized that data size requirements may vary depending on the specific medical physics application and the nature of the algorithms applied. Data processing, which is a crucial step for model stability and precision, should be performed before training the model. Deep learning as a subset of ML is able to learn multilevel representations from raw input data, eliminating the necessity for hand crafted features in classical ML. It can be thought of as an extension of the classical linear models but with multilayer (deep) structures and nonlinear activation functions. The logic of going "deeper" is related to learning complex data structures and its realization has been aided by recent advancements in parallel computing architectures and the development of more robust optimization methods for efficient training of these algorithms. Model validation is an essential part of ML/DL model building. Without it, the model being developed cannot be easily trusted to generalize to unseen data. Whenever applying ML/DL, one should keep in mind, according to Amara's law, that humans may tend to overestimate the ability of a technology in the short term and underestimate its capability in the long term. To establish ML/DL role into standard clinical workflow, models considering balance between accuracy and interpretability should be developed. Machine learning/DL algorithms have potential in numerous radiation oncology applications, including automatizing mundane procedures, improving efficiency and safety of auto-contouring, treatment planning, quality assurance, motion management, and outcome predictions. Medical physicists have been at the frontiers of technology translation into medicine and they ought to be prepared to embrace the inevitable role of ML/DL in the practice of radiation oncology and lead its clinical implementation.
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Affiliation(s)
- Sunan Cui
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48103, USA; Applied Physics Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Huan-Hsin Tseng
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48103, USA
| | - Julia Pakela
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48103, USA; Applied Physics Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Randall K. Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48103, USA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48103, USA
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Kalet AM, Luk SMH, Phillips MH. Radiation Therapy Quality Assurance Tasks and Tools: The Many Roles of Machine Learning. Med Phys 2019; 47:e168-e177. [DOI: 10.1002/mp.13445] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 01/14/2019] [Accepted: 02/02/2019] [Indexed: 12/12/2022] Open
Affiliation(s)
- Alan M. Kalet
- Department of Radiation Oncology University of Washington Medical Center Seattle WA 98195 USA
| | - Samuel M. H. Luk
- Department of Radiation Oncology University of Washington Medical Center Seattle WA 98195 USA
| | - Mark H. Phillips
- Department of Radiation Oncology University of Washington Medical Center Seattle WA 98195 USA
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Mayo C. Community science and reaching the promise of big data in health care. Med Phys 2018; 45:e790-e792. [PMID: 30307633 DOI: 10.1002/mp.13140] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 08/15/2018] [Accepted: 08/16/2018] [Indexed: 11/10/2022] Open
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