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Lung Tumor Growth Modeling in Patients with NSCLC Undergoing Radiotherapy ?

机译:NSCLC患者的肺肿瘤生长模拟

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This paper proposes two modeling approaches to predict lung tumor dynamics as an effect of radiotherapy. Real clinical information of non-small cell lung cancer (NSCLC) patients undergoing stereotactic body radiation therapy (SBRT) as the primary treatment method has been used for numerical simulations. The classical Gompertz model for tumor volume growth prediction was modified using a fractional parameter and combined with the linear-quadratic model to foresee the effect of SBRT on the targeted tumor. Another approach was implemented by following a pharmacokinetic-pharmacodynamic (PKPD) minimal compartmental model for single therapy with SBRT. Statistical analysis has been carried out to compare the two models. In terms of tumor growth prediction, obtained results indicated a decrease in the total tumor volume for both modeling approaches. A striking observation to emerge from the data comparison is the interesting perspective of fractional tools for further exploration in modeling tumor growth.
机译:本文提出了两种建模方法,以预测肺肿瘤动态作为放射疗法的影响。非小细胞肺癌(NSCLC)患者经过立体定向体放射治疗(SBRT)的真正临床信息,作为主要处理方法的数值模拟。使用分数参数进行修饰肿瘤体积生长预测的经典Gompertz模型,并结合线性二次模型,以预见SBRT对靶向肿瘤的影响。通过遵循药代动力学药物动力学(PKPD)的单一疗法,通过SBRT进行另一种方法来实施。已经进行了统计分析以比较两种模型。就肿瘤生长预测而言,得到的结果表明既有建模方法的总肿瘤体积减少。从数据比较中出现的醒目观察是肿瘤生长的进一步探索的分数工具的有趣视点。

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