Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors.
Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors.
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小儿髓母细胞瘤肿瘤的放射组学和放射基因组学的机会和进步。
DOI：
10.3390/diagnostics13172727
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发表时间：
2023-08-22
期刊：
影响因子：
3.6
通讯作者：
Tiwari, Pallavi
中科院分区：
文献类型：
--
作者：
Ismail, Marwa;Craig, Stephen;Ahmed, Raheel;de Blank, Peter;Tiwari, Pallavi
人工智能的最新进展极大地影响了医学成像领域，并极大地改善了数据分析计算算法的发展。在儿科神经肿瘤学领域，放射组学，从放射图像获得高维数据的过程，最近已被用于包括生存预测，分子分类和肿瘤类型分类的应用。同样，放射基因组学，或放射组学和基因组数据的整合，已经允许建立全面的计算模型，以更好地了解疾病的病因。虽然存在优秀的评论文章，放射组学和放射基因组学管道及其在成人实体瘤中的应用，在这篇评论文章中，我们特别回顾这些计算方法的背景下，小儿髓母细胞瘤肿瘤。基于我们通过PubMed和Google Scholar进行的系统性文献研究，我们提供了总共15篇文章的详细总结，这些文章在儿科髓母细胞瘤的背景下利用放射组学和放射基因组学分析进行生存预测、肿瘤分割和分子亚组分类。最后，我们阐明了当前的挑战与现有的方法，以及未来的方向和机会，使用这些计算放射组学和放射基因组学的方法为小儿髓母细胞瘤肿瘤。
Recent advances in artificial intelligence have greatly impacted the field of medical imaging and vastly improved the development of computational algorithms for data analysis. In the field of pediatric neuro-oncology, radiomics, the process of obtaining high-dimensional data from radiographic images, has been recently utilized in applications including survival prognostication, molecular classification, and tumor type classification. Similarly, radiogenomics, or the integration of radiomic and genomic data, has allowed for building comprehensive computational models to better understand disease etiology. While there exist excellent review articles on radiomics and radiogenomic pipelines and their applications in adult solid tumors, in this review article, we specifically review these computational approaches in the context of pediatric medulloblastoma tumors. Based on our systematic literature research via PubMed and Google Scholar, we provide a detailed summary of a total of 15 articles that have utilized radiomic and radiogenomic analysis for survival prognostication, tumor segmentation, and molecular subgroup classification in the context of pediatric medulloblastoma. Lastly, we shed light on the current challenges with the existing approaches as well as future directions and opportunities with using these computational radiomic and radiogenomic approaches for pediatric medulloblastoma tumors.
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DOI：
10.3174/ajnr.a5858
发表时间：
2018-12
期刊：
AJNR. American journal of neuroradiology
影响因子：
--
作者：
Ismail M;Hill V;Statsevych V;Huang R;Prasanna P;Correa R;Singh G;Bera K;Beig N;Thawani R;Madabhushi A;Aahluwalia M;Tiwari P
通讯作者：
Tiwari P
影响因子：
4.6
作者：
Grist JT;Withey S;Bennett C;Rose HEL;MacPherson L;Oates A;Powell S;Novak J;Abernethy L;Pizer B;Bailey S;Clifford SC;Mitra D;Arvanitis TN;Auer DP;Avula S;Grundy R;Peet AC
通讯作者：
Peet AC
影响因子：
3.8
作者：
Artzi, Moran;Gershov, Sapir;Ben Bashat, Dafna
通讯作者：
Ben Bashat, Dafna
影响因子：
4.7
作者：
Iyer, Sukanya;Ismail, Marwa;Tamrazi, Benita;Salloum, Ralph;de Blank, Peter;Margol, Ashley;Correa, Ramon;Chen, Jonathan;Bera, Kaustav;Statsevych, Volodymyr;Ho, Mai-Lan;Vaidya, Pranjal;Verma, Ruchika;Hawes, Debra;Judkins, Alexander;Fu, Pingfu;Madabhushi, Anant;Tiwari, Pallavi
通讯作者：
Tiwari, Pallavi
影响因子：
3.9
作者：
Dressler EV;Dolecek TA;Liu M;Villano JL
通讯作者：
Villano JL