Ariana M Familiar, Anahita Fathi Kazerooni, Arastoo Vossough, Jeffrey B Ware, Sina Bagheri, Nastaran Khalili, Hannah Anderson, Debanjan Haldar, Phillip B Storm, Adam C Resnick, Benjamin H Kann, Mariam Aboian, Cassie Kline, Michael Weller, Raymond Y Huang, Susan M Chang, Jason R Fangusaro, Lindsey M Hoffman, Sabine Mueller, Michael Prados, Ali Nabavizadeh
{"title":"实现小儿脑肿瘤测量的一致性:挑战、解决方案和基于人工智能的分割的作用。","authors":"Ariana M Familiar, Anahita Fathi Kazerooni, Arastoo Vossough, Jeffrey B Ware, Sina Bagheri, Nastaran Khalili, Hannah Anderson, Debanjan Haldar, Phillip B Storm, Adam C Resnick, Benjamin H Kann, Mariam Aboian, Cassie Kline, Michael Weller, Raymond Y Huang, Susan M Chang, Jason R Fangusaro, Lindsey M Hoffman, Sabine Mueller, Michael Prados, Ali Nabavizadeh","doi":"10.1093/neuonc/noae093","DOIUrl":null,"url":null,"abstract":"<p><p>MR imaging is central to the assessment of tumor burden and changes over time in neuro-oncology. Several response assessment guidelines have been set forth by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working groups in different tumor histologies; however, the visual delineation of tumor components using MRIs is not always straightforward, and complexities not currently addressed by these criteria can introduce inter- and intra-observer variability in manual assessments. Differentiation of non-enhancing tumors from peritumoral edema, mild enhancement from absence of enhancement, and various cystic components can be challenging; particularly given a lack of sufficient and uniform imaging protocols in clinical practice. Automated tumor segmentation with artificial intelligence (AI) may be able to provide more objective delineations, but rely on accurate and consistent training data created manually (ground truth). Herein, this paper reviews existing challenges and potential solutions to identifying and defining subregions of pediatric brain tumors (PBTs) that are not explicitly addressed by current guidelines. The goal is to assert the importance of defining and adopting criteria for addressing these challenges, as it will be critical to achieving standardized tumor measurements and reproducible response assessment in PBTs, ultimately leading to more precise outcome metrics and accurate comparisons among clinical studies.</p>","PeriodicalId":19377,"journal":{"name":"Neuro-oncology","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11376457/pdf/","citationCount":"0","resultStr":"{\"title\":\"Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation.\",\"authors\":\"Ariana M Familiar, Anahita Fathi Kazerooni, Arastoo Vossough, Jeffrey B Ware, Sina Bagheri, Nastaran Khalili, Hannah Anderson, Debanjan Haldar, Phillip B Storm, Adam C Resnick, Benjamin H Kann, Mariam Aboian, Cassie Kline, Michael Weller, Raymond Y Huang, Susan M Chang, Jason R Fangusaro, Lindsey M Hoffman, Sabine Mueller, Michael Prados, Ali Nabavizadeh\",\"doi\":\"10.1093/neuonc/noae093\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>MR imaging is central to the assessment of tumor burden and changes over time in neuro-oncology. 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Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation.
MR imaging is central to the assessment of tumor burden and changes over time in neuro-oncology. Several response assessment guidelines have been set forth by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working groups in different tumor histologies; however, the visual delineation of tumor components using MRIs is not always straightforward, and complexities not currently addressed by these criteria can introduce inter- and intra-observer variability in manual assessments. Differentiation of non-enhancing tumors from peritumoral edema, mild enhancement from absence of enhancement, and various cystic components can be challenging; particularly given a lack of sufficient and uniform imaging protocols in clinical practice. Automated tumor segmentation with artificial intelligence (AI) may be able to provide more objective delineations, but rely on accurate and consistent training data created manually (ground truth). Herein, this paper reviews existing challenges and potential solutions to identifying and defining subregions of pediatric brain tumors (PBTs) that are not explicitly addressed by current guidelines. The goal is to assert the importance of defining and adopting criteria for addressing these challenges, as it will be critical to achieving standardized tumor measurements and reproducible response assessment in PBTs, ultimately leading to more precise outcome metrics and accurate comparisons among clinical studies.
期刊介绍:
Neuro-Oncology, the official journal of the Society for Neuro-Oncology, has been published monthly since January 2010. Affiliated with the Japan Society for Neuro-Oncology and the European Association of Neuro-Oncology, it is a global leader in the field.
The journal is committed to swiftly disseminating high-quality information across all areas of neuro-oncology. It features peer-reviewed articles, reviews, symposia on various topics, abstracts from annual meetings, and updates from neuro-oncology societies worldwide.