دورية أكاديمية
Bladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence.
| العنوان: | Bladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence. |
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| المؤلفون: | Moore NS; Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA., McWilliam A; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK., Aneja S; Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA. Electronic address: sanjay.aneja@yale.edu. |
| المصدر: | Seminars in radiation oncology [Semin Radiat Oncol] 2023 Jan; Vol. 33 (1), pp. 70-75. |
| نوع المنشور: | Journal Article; Review |
| اللغة: | English |
| بيانات الدورية: | Publisher: W.B. Saunders Country of Publication: United States NLM ID: 9202882 Publication Model: Print Cited Medium: Internet ISSN: 1532-9461 (Electronic) Linking ISSN: 10534296 NLM ISO Abbreviation: Semin Radiat Oncol Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Philadelphia, PA : W.B. Saunders, c1991- |
| مواضيع طبية MeSH: | Radiation Oncology*/methods , Urinary Bladder Neoplasms*/diagnostic imaging , Urinary Bladder Neoplasms*/radiotherapy, Humans ; Artificial Intelligence ; Prognosis ; Reproducibility of Results |
| مستخلص: | Machine learning (ML) and artificial intelligence (AI) have demonstrated potential to improve the care of radiation oncology patients. Here we review recent advances applicable to the care of bladder cancer, with an eye towards studies that may suggest next steps in clinical implementation. Algorithms have been applied to clinical records, pathology, and radiology data to generate accurate predictive models for prognosis and clinical outcomes. AI has also shown increasing utility for auto-contouring and efficient creation of workflows involving multiple treatment plans. As technologies progress towards routine clinical use for bladder cancer patients, we also discuss emerging methods to improve interpretability and reliability of algorithms. (Copyright © 2022 Elsevier Inc. All rights reserved.) |
| تواريخ الأحداث: | Date Created: 20221214 Date Completed: 20221216 Latest Revision: 20230127 |
| رمز التحديث: | 20240628 |
| DOI: | 10.1016/j.semradonc.2022.10.009 |
| PMID: | 36517196 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 1532-9461 |
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| DOI: | 10.1016/j.semradonc.2022.10.009 |