Institute of Imaging Science, Vanderbilt University, Nashville, TN 37212, USA.
Sci Transl Med. 2013 May 29;5(187):187ps9. doi: 10.1126/scitranslmed.3005686.
Current mathematical models of tumor growth are limited in their clinical application because they require input data that are nearly impossible to obtain with sufficient spatial resolution in patients even at a single time point--for example, extent of vascularization, immune infiltrate, ratio of tumor-to-normal cells, or extracellular matrix status. Here we propose the use of emerging, quantitative tumor imaging methods to initialize a new generation of predictive models. In the near future, these models could be able to forecast clinical outputs, such as overall response to treatment and time to progression, which will provide opportunities for guided intervention and improved patient care.
目前的肿瘤生长数学模型在临床应用中受到限制，因为它们需要输入数据，而即使在单一时间点，这些数据也几乎不可能以足够的空间分辨率从患者中获得——例如，血管生成程度、免疫浸润程度、肿瘤与正常细胞的比例或细胞外基质状态。在这里，我们建议使用新兴的定量肿瘤成像方法来启动新一代预测模型。在不久的将来，这些模型可以预测临床结果，例如对治疗的总体反应和进展时间，这将为指导干预和改善患者护理提供机会。
