Toma-Dasu Iuliana, Dasu Alexandru
Medical Radiation Physics, Stockholm University and Karolinska Institutet, P.O. Box 260, 171 76 Stockholm, Sweden.
Department of Radiation Physics and Department of Medical and Health Sciences, Linköping University, 581 83 Linköping, Sweden.
Comput Math Methods Med. 2015;2015:934380. doi: 10.1155/2015/934380. Epub 2015 Mar 5.
Functional and molecular imaging of tumours have offered the possibility of redefining the target in cancer therapy and individualising the treatment with a multidimensional approach that aims to target the adverse processes known to impact negatively upon treatment result. Following the first theoretical attempts to include imaging information into treatment planning, it became clear that the biological features of interest for targeting exhibit considerable heterogeneity with respect to magnitude, spatial, and temporal distribution, both within one patient and between patients, which require more advanced solutions for the way the treatment is planned and adapted. Combining multiparameter information from imaging with predictive information from biopsies and molecular analyses as well as in treatment monitoring of tumour responsiveness appears to be the key approach to maximise the individualisation of treatment. This review paper aims to discuss some of the key challenges for incorporating into treatment planning and optimisation the radiobiological features of the tumour derived from pretreatment PET imaging of tumour metabolism, proliferation, and hypoxia and combining them with intreatment monitoring of responsiveness and other predictive factors with the ultimate aim of individualising the treatment towards the maximisation of response.
肿瘤的功能和分子成像为重新定义癌症治疗靶点以及采用多维方法实现治疗个体化提供了可能，该方法旨在针对已知会对治疗结果产生负面影响的不良过程。在首次将成像信息纳入治疗计划的理论尝试之后，很明显，对于靶向治疗而言，感兴趣的生物学特征在患者体内以及患者之间，在大小、空间和时间分布方面都表现出相当大的异质性，这就需要在治疗计划和调整方式上采用更先进的解决方案。将来自成像的多参数信息与活检和分子分析的预测信息以及肿瘤反应性的治疗监测相结合，似乎是实现治疗个体化最大化的关键方法。这篇综述文章旨在讨论将肿瘤的放射生物学特征纳入治疗计划和优化过程中的一些关键挑战，这些特征源自肿瘤代谢、增殖和缺氧的治疗前PET成像，并将它们与反应性的治疗监测及其他预测因素相结合，最终目标是使治疗个体化以实现反应最大化。
