1 INTRODUCTION

Radiomics, the high‐throughput extraction and analysis of features from medical images, is a promising field for characterizing tumor phenotype and normal tissue injury post‐radiotherapy. Radiomics provides unique opportunities to identify predictive and prognostic imaging biomarkers in noninvasive imaging assays providing so‐called digital biopsies that can be acquired throughout the whole course of cancer treatment. Radiomics have been proved to be associated with underlying gene expression and therapy response, which is an area currently referred to as radiogenomics. Multimodality imaging biomarkers extracted from positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), and images in other medical modalities have been shown to have discriminative power for cancer treatment outcome prognosis and prediction. For example, F‐fluoro‐2‐deoxy‐D‐glucose (FDG)‐PET images are the standard of care in tumor quantification of head and neck radiation therapy (RT) and will likely remain so for the foreseeable future. Metabolic tumor volume, defined as the volume of tumor tissues with increase and heterogeneous FDG uptakes, is an important prognostic factor in many malignancies. The radiomics features can complement known first order imaging biomarkers and provide further insights beyond those revealed to naked eyes from medical images.

During the past years, there has been tremendous growth in the radiomics field leading to improved performances in cancer diagnosis, cancer staging, tumor classification, treatment outcome prediction, patient survival, and other clinical practice, compared to other simple clinical biomarkers such as tumor staging, tumor size, human papillomavirus (HPV) status, etc. Clinical applications of radiomics have been widely investigated as well.1, 2 Radiomics yield great promise to support clinical practice and achieve many promising results. There are many publications and special issues dedicated to the usage of radiomics to support clinical applications in combination with recent spread of advanced machine learning methods.3, 4 Yet questions remain if the development of radiomics makes it ready for prospective clinical use. Herein, we brought in two medical physics experts both of whom have extensive knowledge in clinical practice and radiomics research. Dr. Hua Li is taking the proposition that “Radiomics poses more challenges than opportunities for clinical practice in cancer management,” whereas Dr. Issam El Naqa argues against it.

Dr. Hua Li is currently a research associate professor in the Department of Bioengineering at University of Illinois at Urbana‐Champaign and a clinical medical physicist at Carle Cancer Center, Carle Foundation Hospital, Urbana, IL. Before joining UIUC and Carle, she was an associate professor in the Department of Radiation Oncology at Washington University in Saint Louis. Dr. Li is certified in Therapeutic, Diagnostic, and Nuclear medical physics by the American Board of Radiology. She has conducted active research in developing advanced machine learning, pattern recognition, and image analysis techniques for applications in radiation therapy and diagnostic imaging. Her current research projects include radiomics‐based prognostic model of cervical cancer habitats, multimodal biomarkers for personalized oropharyngeal cancer treatment, and task‐based image quality assessment and optimization in radiation therapy. Her research projects are funded by the National Institute of Health (NIH).

Dr. EI Naqa worked as a Professor and associate member in Applied Physics and the Michigan institute of data science. He recently accepted the position of founding chair of the department of Machine Learning at Moffitt Cancer Center, Tampa, Fl, where will officially start later this summer. He is a certified Medical Physicist by the American Board of Radiology. He is a recognized authority in the fields of machine learning, data analytics, and oncology outcomes modeling and has published extensively in these areas with more than 180+ peer‐reviewed journal publications and four edited textbooks. He has been a senior member and fellow of several academic and professional societies. His research has been funded by several federal and private grants in Canada and the USA and served on national and international study sections. He acts as a peer‐reviewer and editorial board member for several leading international journals in his areas of expertise.

中文翻译：

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癌症管理放射组学的现状：临床实践的挑战与机遇。

1 简介

放射组学是对医学图像特征的高通量提取和分析，是表征肿瘤表型和放疗后正常组织损伤的一个有前途的领域。放射组学提供了独特的机会，可以在无创成像分析中识别预测和预后成像生物标志物，提供所谓的数字活检，可以在整个癌症治疗过程中获取。放射组学已被证明与潜在的基因表达和治疗反应相关，这是一个目前被称为放射基因组学的领域。从正电子发射断层扫描 (PET)、计算机断层扫描 (CT)、磁共振成像 (MRI) 和其他医学模式中的图像中提取的多模态成像生物标志物已被证明对癌症治疗结果的预后和预测具有鉴别力。例如，F-fluoro-2-deoxy-D-glucose (FDG)-PET 图像是头颈部放射治疗 (RT) 肿瘤量化的护理标准，并且在可预见的未来可能会继续如此。代谢肿瘤体积，定义为具有增加和异质性 FDG 摄取的肿瘤组织体积，是许多恶性肿瘤的重要预后因素。放射组学特征可以补充已知的一阶成像生物标志物，并提供比肉眼从医学图像中看到的更深入的见解。是许多恶性肿瘤的重要预后因素。放射组学特征可以补充已知的一阶成像生物标志物，并提供比肉眼从医学图像中看到的更深入的见解。是许多恶性肿瘤的重要预后因素。放射组学特征可以补充已知的一阶成像生物标志物，并提供比肉眼从医学图像中看到的更深入的见解。

在过去的几年中，与其他简单的临床生物标志物（如肿瘤分期）相比，放射组学领域取得了巨大的进步，在癌症诊断、癌症分期、肿瘤分类、治疗结果预测、患者生存和其他临床实践方面的表现有所提高、肿瘤大小、人乳头瘤病毒 (HPV) 状态等。放射组学的临床应用也得到了广泛研究。1, 2放射组学有望支持临床实践并取得许多有希望的结果。有许多出版物和特刊致力于使用放射组学来支持临床应用，并结合最近传播的高级机器学习方法。3, 4然而，如果放射组学的发展使其为前瞻性临床应用做好准备，问题仍然存在。在此，我们引进了两位在临床实践和放射组学研究方面具有丰富知识的医学物理专家。Hua Li 博士认为“放射组学在癌症管理的临床实践中带来的挑战多于机遇”，而 Issam El Naqa 博士反对这一观点。

Hua Li 博士目前是伊利诺伊大学香槟分校生物工程系的研究副教授，以及伊利诺伊州厄巴纳市卡尔基金会医院卡尔癌症中心的临床医学物理学家。在加入 UIUC 和 Carle 之前，她是圣路易斯华盛顿大学放射肿瘤学系的副教授。李博士获得了美国放射学委员会的治疗、诊断和核医学物理学认证。她在开发先进的机器学习、模式识别和图像分析技术方面进行了积极的研究，以应用于放射治疗和诊断成像。她目前的研究项目包括基于放射组学的宫颈癌栖息地预后模型、用于个性化口咽癌治疗的多模式生物标志物、以及放射治疗中基于任务的图像质量评估和优化。她的研究项目由美国国立卫生研究院 (NIH) 资助。

EI Naqa 博士曾担任应用物理学和密歇根数据科学研究所的教授和副会员。他最近接受了佛罗里达州坦帕市莫菲特癌症中心机器学习系创始主席的职位，该中心将于今年夏天晚些时候正式启动。他是美国放射学委员会认证的医学物理学家。他是机器学习、数据分析和肿瘤结果建模领域公认的权威，并在这些领域发表了大量文章，发表了 180 多篇同行评审的期刊出版物和四本编辑的教科书。他是多个学术和专业协会的高级会员和研究员。他的研究得到了加拿大和美国的多项联邦和私人资助，并在国家和国际研究部门任职。