Machine Learning With Radiation Oncology Big Data
Radiation oncology is uniquely positioned to harness the power of big data as vast amounts of data are generated at an unprecedented pace for individual patients in imaging studies and radiation treatments worldwide. The big data encountered in the radiotherapy clinic may include patient demographic...
Sábháilte in:
| Príomhchruthaitheoirí: | , , |
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| Formáid: | Online |
| Teanga: | Béarla |
| Foilsithe / Cruthaithe: |
Frontiers Media SA
2021
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| Ábhair: | |
| Rochtain ar líne: | 32088 |
| Clibeanna: |
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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| _version_ | 1869528398621048832 |
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| author | Lei Xing Issam El Naqa Jun Deng |
| author_browse | Issam El Naqa Jun Deng Lei Xing |
| author_facet | Lei Xing Issam El Naqa Jun Deng |
| author_sort | Lei Xing |
| collection | Directory of Open Access Books |
| description | Radiation oncology is uniquely positioned to harness the power of big data as vast amounts of data are generated at an unprecedented pace for individual patients in imaging studies and radiation treatments worldwide. The big data encountered in the radiotherapy clinic may include patient demographics stored in the electronic medical record (EMR) systems, plan settings and dose volumetric information of the tumors and normal tissues generated by treatment planning systems (TPS), anatomical and functional information from diagnostic and therapeutic imaging modalities (e.g., CT, PET, MRI and kVCBCT) stored in picture archiving and communication systems (PACS), as well as the genomics, proteomics and metabolomics information derived from blood and tissue specimens. Yet, the great potential of big data in radiation oncology has not been fully exploited for the benefits of cancer patients due to a variety of technical hurdles and hardware limitations. With recent development in computer technology, there have been increasing and promising applications of machine learning algorithms involving the big data in radiation oncology. This research topic is intended to present novel technological breakthroughs and state-of-the-art developments in machine learning and data mining in radiation oncology in recent years. |
| format | Online |
| id | doab-20.500.12854ir-52519 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Frontiers Media SA |
| publisherStr | Frontiers Media SA |
| record_format | ojs |
| spelling | doab-20.500.12854ir-525192024-03-31T13:09:58Z Machine Learning With Radiation Oncology Big Data Lei Xing Issam El Naqa Jun Deng R5-920 RC254-282 deep learning precision medicine Radiation Oncology big data machine learning artificial intelligence personalized medicine thema EDItEUR::M Medicine and Nursing Radiation oncology is uniquely positioned to harness the power of big data as vast amounts of data are generated at an unprecedented pace for individual patients in imaging studies and radiation treatments worldwide. The big data encountered in the radiotherapy clinic may include patient demographics stored in the electronic medical record (EMR) systems, plan settings and dose volumetric information of the tumors and normal tissues generated by treatment planning systems (TPS), anatomical and functional information from diagnostic and therapeutic imaging modalities (e.g., CT, PET, MRI and kVCBCT) stored in picture archiving and communication systems (PACS), as well as the genomics, proteomics and metabolomics information derived from blood and tissue specimens. Yet, the great potential of big data in radiation oncology has not been fully exploited for the benefits of cancer patients due to a variety of technical hurdles and hardware limitations. With recent development in computer technology, there have been increasing and promising applications of machine learning algorithms involving the big data in radiation oncology. This research topic is intended to present novel technological breakthroughs and state-of-the-art developments in machine learning and data mining in radiation oncology in recent years. 2021-02-11T18:29:34Z 2021-02-11T18:29:34Z 2019-01-23 14:53:43 2019 book 32088 16648714 9782889457304 https://directory.doabooks.org/handle/20.500.12854/52519 eng Frontiers Research Topics image/jpeg Attribution 4.0 International https://www.frontiersin.org/research-topics/6126/machine-learning-with-radiation-oncology-big-data Frontiers Media SA 10.3389/978-2-88945-730-4 10.3389/978-2-88945-730-4 bf5ce210-e72e-4860-ba9b-c305640ff3ae 9782889457304 146 open access |
| spellingShingle | R5-920 RC254-282 deep learning precision medicine Radiation Oncology big data machine learning artificial intelligence personalized medicine thema EDItEUR::M Medicine and Nursing Lei Xing Issam El Naqa Jun Deng Machine Learning With Radiation Oncology Big Data |
| title | Machine Learning With Radiation Oncology Big Data |
| title_full | Machine Learning With Radiation Oncology Big Data |
| title_fullStr | Machine Learning With Radiation Oncology Big Data |
| title_full_unstemmed | Machine Learning With Radiation Oncology Big Data |
| title_short | Machine Learning With Radiation Oncology Big Data |
| title_sort | machine learning with radiation oncology big data |
| topic | R5-920 RC254-282 deep learning precision medicine Radiation Oncology big data machine learning artificial intelligence personalized medicine thema EDItEUR::M Medicine and Nursing |
| topic_facet | R5-920 RC254-282 deep learning precision medicine Radiation Oncology big data machine learning artificial intelligence personalized medicine thema EDItEUR::M Medicine and Nursing |
| url | 32088 |
| work_keys_str_mv | AT leixing machinelearningwithradiationoncologybigdata AT issamelnaqa machinelearningwithradiationoncologybigdata AT jundeng machinelearningwithradiationoncologybigdata |