{"title":"放射组学:强大的工具","authors":"Shahid Kamal","doi":"10.21089/njhs.91.0001","DOIUrl":null,"url":null,"abstract":"The Artificial Intelligence based technique of radiomics is an evolving modality that employs data characterization algorithms to extract a variety of useful features from medical images [1].Tumoral patterns are revealed that cannot be appreciated by the naked eye [2]. This can aid in assessing prognosis and gauging the response of tumor cells to therapy [3]. Various imaging modalities like CT, PET MR provide essential raw data. Extraction tools then use the raw data volumes to analyze pixel/voxel characteristics. Using these images, “volumes of interest” can be generated since such segmentation entails handling and processing of large image data; automatic and semiautomatic segmentation algorithms are employed enabling automation [4]. However, thorough testing and quality assurance are vital to ensure that the algorithm used is not only accurate, consistent, and reproducible but also time-efficient [5].","PeriodicalId":441304,"journal":{"name":"National Journal of Health Sciences","volume":"23 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Radiomics: A Powerful Tool\",\"authors\":\"Shahid Kamal\",\"doi\":\"10.21089/njhs.91.0001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Artificial Intelligence based technique of radiomics is an evolving modality that employs data characterization algorithms to extract a variety of useful features from medical images [1].Tumoral patterns are revealed that cannot be appreciated by the naked eye [2]. This can aid in assessing prognosis and gauging the response of tumor cells to therapy [3]. Various imaging modalities like CT, PET MR provide essential raw data. Extraction tools then use the raw data volumes to analyze pixel/voxel characteristics. Using these images, “volumes of interest” can be generated since such segmentation entails handling and processing of large image data; automatic and semiautomatic segmentation algorithms are employed enabling automation [4]. However, thorough testing and quality assurance are vital to ensure that the algorithm used is not only accurate, consistent, and reproducible but also time-efficient [5].\",\"PeriodicalId\":441304,\"journal\":{\"name\":\"National Journal of Health Sciences\",\"volume\":\"23 3\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"National Journal of Health Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21089/njhs.91.0001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"National Journal of Health Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21089/njhs.91.0001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
基于人工智能的放射组学技术是一种不断发展的模式,它采用数据表征算法从医学图像中提取各种有用的特征[1]。这有助于评估预后和衡量肿瘤细胞对治疗的反应[3]。CT 和 PET MR 等各种成像模式提供了重要的原始数据。然后,提取工具使用原始数据卷分析像素/体素特征。使用这些图像可以生成 "感兴趣体块",因为这种分割需要处理大量图像数据;自动和半自动分割算法的采用实现了自动化[4]。然而,彻底的测试和质量保证对于确保所使用的算法不仅准确、一致、可重复,而且省时高效至关重要[5]。
The Artificial Intelligence based technique of radiomics is an evolving modality that employs data characterization algorithms to extract a variety of useful features from medical images [1].Tumoral patterns are revealed that cannot be appreciated by the naked eye [2]. This can aid in assessing prognosis and gauging the response of tumor cells to therapy [3]. Various imaging modalities like CT, PET MR provide essential raw data. Extraction tools then use the raw data volumes to analyze pixel/voxel characteristics. Using these images, “volumes of interest” can be generated since such segmentation entails handling and processing of large image data; automatic and semiautomatic segmentation algorithms are employed enabling automation [4]. However, thorough testing and quality assurance are vital to ensure that the algorithm used is not only accurate, consistent, and reproducible but also time-efficient [5].