D'Andrea Marco, Strolin Silvia, Ungania Sara, Cacciatore Alessandra, Bruzzaniti Vicente, Marconi Raffaella, Benassi Marcello, Strigari Lidia
Laboratory of Medical Physics and Expert Systems, Regina Elena National Cancer Institute, Rome, Italy.
Front Oncol. 2018 Jan 8;7:321. doi: 10.3389/fonc.2017.00321. eCollection 2017.
Lung tumors are often associated with a poor prognosis although different schedules and treatment modalities have been extensively tested in the clinical practice. The complexity of this disease and the use of combined therapeutic approaches have been investigated and the use of high dose-rates is emerging as effective strategy. Technological improvements of clinical linear accelerators allow combining high dose-rate and a more conformal dose delivery with accurate imaging modalities pre- and during therapy. This paper aims at reporting the state of the art and future direction in the use of radiobiological models and radiobiological-based optimizations in the clinical practice for the treatment of lung cancer. To address this issue, a search was carried out on PubMed database to identify potential papers reporting tumor control probability and normal tissue complication probability for lung tumors. Full articles were retrieved when the abstract was considered relevant, and only papers published in English language were considered. The bibliographies of retrieved papers were also searched and relevant articles included. At the state of the art, dose-response relationships have been reported in literature for local tumor control and survival in stage III non-small cell lung cancer. Due to the lack of published radiobiological models for SBRT, several authors used dose constraints and models derived for conventional fractionation schemes. Recently, several radiobiological models and parameters for SBRT have been published and could be used in prospective trials although external validations are recommended to improve the robustness of model predictive capability. Moreover, radiobiological-based functions have been used within treatment planning systems for plan optimization but the advantages of using this strategy in the clinical practice are still under discussion. Future research should be directed toward combined regimens, in order to potentially improve both local tumor control and survival. Indeed, accurate knowledge of the relevant parameters describing tumor biology and normal tissue response is mandatory to correctly address this issue. In this context, the role of medical physicists and the AAPM in the development of radiobiological models is crucial for the progress of developing specific tool for radiobiological-based optimization treatment planning.
尽管在临床实践中已经对不同的治疗方案和治疗方式进行了广泛测试，但肺肿瘤通常预后较差。人们已经对这种疾病的复杂性和联合治疗方法的使用进行了研究，高剂量率的使用正成为一种有效的策略。临床直线加速器的技术改进使得高剂量率与更适形的剂量输送相结合，并在治疗前和治疗期间采用精确的成像方式。本文旨在报告在肺癌临床治疗中使用放射生物学模型和基于放射生物学的优化方法的现状和未来方向。为了解决这个问题，我们在PubMed数据库中进行了搜索，以识别报告肺肿瘤的肿瘤控制概率和正常组织并发症概率的潜在论文。当摘要被认为相关时，检索全文，并且只考虑以英语发表的论文。还对检索到的论文的参考文献进行了搜索，并纳入了相关文章。在目前的技术水平下，文献中已经报道了局部肿瘤控制和III期非小细胞肺癌生存的剂量反应关系。由于缺乏已发表的立体定向体部放疗（SBRT）放射生物学模型，一些作者使用了剂量限制和从传统分割方案推导的模型。最近，已经发表了几种用于SBRT的放射生物学模型和参数，尽管建议进行外部验证以提高模型预测能力的稳健性，但这些模型和参数可用于前瞻性试验。此外，基于放射生物学的函数已被用于治疗计划系统中进行计划优化，但在临床实践中使用这种策略的优势仍在讨论中。未来的研究应针对联合治疗方案，以潜在地改善局部肿瘤控制和生存率。事实上，准确了解描述肿瘤生物学和正常组织反应的相关参数对于正确解决这个问题至关重要。在这种情况下，医学物理学家和医学物理与医学工程学会（AAPM）在放射生物学模型开发中的作用对于开发基于放射生物学的优化治疗计划的特定工具的进展至关重要。
