Current status of Radiomics for cancer management: Challenges versus opportunities for clinical practice.
Current status of Radiomics for cancer management: Challenges versus opportunities for clinical practice.
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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。
DOI：
10.1002/acm2.12982
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发表时间：
2020
影响因子：
2.1
通讯作者：
Rong,Yi
中科院分区：
文献类型：
--
作者：
Li,Hua;ElNaqa,Issam;Rong,Yi
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射治疗后正常组织损伤的一个有前途的领域。放射组学为在非侵入性成像检测中识别预测性和预后性成像生物标志物提供了独特的机会，提供了所谓的数字活检，可以在整个癌症治疗过程中获得。放射组学已被证明与潜在的基因表达和治疗反应有关，这是目前称为放射基因组学的一个领域。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预后和预测具有鉴别力。例如，F-氟-2-脱氧-D-葡萄糖（FDG）-PET图像是头颈部放射治疗（RT）肿瘤量化的标准治疗，并且在可预见的未来可能仍然如此。代谢性肿瘤体积，定义为肿瘤组织体积的增加和不均匀的FDG摄取，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，并提供超出从医学图像向裸眼揭示的那些的进一步的见解。在过去的几年中，放射组学领域取得了巨大的发展，与其他简单的临床生物标志物（如肿瘤分期、肿瘤大小、人乳头瘤病毒（HPV）状态等）相比，放射组学在癌症诊断、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中的表现得到了改善。放射组学的临床应用也得到了广泛的研究。1，2放射组学产生了巨大的承诺，以支持临床实践，并取得了许多有前途的结果。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，并结合最近传播的先进机器学习方法。3，4然而问题仍然存在，如果放射组学的发展使其准备好前瞻性的临床应用。在此，我们请来了两位医学物理专家，他们都在临床实践和放射组学研究方面拥有丰富的知识。李华博士的观点是“放射组学对癌症管理的临床实践带来的挑战大于机遇”，而Issam El Naqa博士则持反对意见。李华博士目前是伊利诺伊大学厄巴纳尚潘分校生物工程系的研究副教授，也是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，她是圣刘易斯的华盛顿大学放射肿瘤学系的副教授。李博士拥有美国放射学委员会颁发的治疗、诊断和核医学物理学认证。她积极研究开发先进的机器学习，模式识别和图像分析技术，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生境预后模型，个性化口咽癌治疗的多模式生物标志物，以及基于任务的图像质量评估和放射治疗优化。她的研究项目由美国国立卫生研究院（NIH）资助。
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).