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P812: PREDICTING HIGH-RISK DISEASE BIOLOGY USING ARTIFICIAL INTELLIGENCE BASED FDG PET/CT RADIOMICS IN NEWLY DIAGNOSED MULTIPLE MYELOMA
| Autores principales: | , , , , , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Lippincott Williams & Wilkins
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10430489/ http://dx.doi.org/10.1097/01.HS9.0000970152.05861.06 |
| _version_ | 1785090975957254144 |
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| author | Shreve, Jacob Charalampous, Charalampos Kourelis, Taxiarchis Pritchett, Josh Hwa, Yi Paludo, Jonas Gonsalves, Wilson Lin, Yi Shah, Mithun Hobbs, Miriam Cook, Joselle Lacy, Martha Dispenzieri, Angela Broski, Stephen Rajkumar, Vincent Kumar, Shaji Binder, Moritz |
| author_facet | Shreve, Jacob Charalampous, Charalampos Kourelis, Taxiarchis Pritchett, Josh Hwa, Yi Paludo, Jonas Gonsalves, Wilson Lin, Yi Shah, Mithun Hobbs, Miriam Cook, Joselle Lacy, Martha Dispenzieri, Angela Broski, Stephen Rajkumar, Vincent Kumar, Shaji Binder, Moritz |
| author_sort | Shreve, Jacob |
| collection | PubMed |
| description | |
| format | Online Article Text |
| id | pubmed-10430489 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2023 |
| publisher | Lippincott Williams & Wilkins |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-104304892023-08-17 P812: PREDICTING HIGH-RISK DISEASE BIOLOGY USING ARTIFICIAL INTELLIGENCE BASED FDG PET/CT RADIOMICS IN NEWLY DIAGNOSED MULTIPLE MYELOMA Shreve, Jacob Charalampous, Charalampos Kourelis, Taxiarchis Pritchett, Josh Hwa, Yi Paludo, Jonas Gonsalves, Wilson Lin, Yi Shah, Mithun Hobbs, Miriam Cook, Joselle Lacy, Martha Dispenzieri, Angela Broski, Stephen Rajkumar, Vincent Kumar, Shaji Binder, Moritz Hemasphere Posters Lippincott Williams & Wilkins 2023-08-08 /pmc/articles/PMC10430489/ http://dx.doi.org/10.1097/01.HS9.0000970152.05861.06 Text en Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access Abstract Book distributed under the Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) which allows third parties to download the articles and share them with others as long as they credit the author and the Abstract Book, but they cannot change the content in any way or use them commercially. |
| spellingShingle | Posters Shreve, Jacob Charalampous, Charalampos Kourelis, Taxiarchis Pritchett, Josh Hwa, Yi Paludo, Jonas Gonsalves, Wilson Lin, Yi Shah, Mithun Hobbs, Miriam Cook, Joselle Lacy, Martha Dispenzieri, Angela Broski, Stephen Rajkumar, Vincent Kumar, Shaji Binder, Moritz P812: PREDICTING HIGH-RISK DISEASE BIOLOGY USING ARTIFICIAL INTELLIGENCE BASED FDG PET/CT RADIOMICS IN NEWLY DIAGNOSED MULTIPLE MYELOMA |
| title | P812: PREDICTING HIGH-RISK DISEASE BIOLOGY USING ARTIFICIAL INTELLIGENCE BASED FDG PET/CT RADIOMICS IN NEWLY DIAGNOSED MULTIPLE MYELOMA |
| title_full | P812: PREDICTING HIGH-RISK DISEASE BIOLOGY USING ARTIFICIAL INTELLIGENCE BASED FDG PET/CT RADIOMICS IN NEWLY DIAGNOSED MULTIPLE MYELOMA |
| title_fullStr | P812: PREDICTING HIGH-RISK DISEASE BIOLOGY USING ARTIFICIAL INTELLIGENCE BASED FDG PET/CT RADIOMICS IN NEWLY DIAGNOSED MULTIPLE MYELOMA |
| title_full_unstemmed | P812: PREDICTING HIGH-RISK DISEASE BIOLOGY USING ARTIFICIAL INTELLIGENCE BASED FDG PET/CT RADIOMICS IN NEWLY DIAGNOSED MULTIPLE MYELOMA |
| title_short | P812: PREDICTING HIGH-RISK DISEASE BIOLOGY USING ARTIFICIAL INTELLIGENCE BASED FDG PET/CT RADIOMICS IN NEWLY DIAGNOSED MULTIPLE MYELOMA |
| title_sort | p812: predicting high-risk disease biology using artificial intelligence based fdg pet/ct radiomics in newly diagnosed multiple myeloma |
| topic | Posters |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10430489/ http://dx.doi.org/10.1097/01.HS9.0000970152.05861.06 |
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