Wang Peng, Popovtzer Aron, Eisbruch Avraham, Cao Yue
Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan 48103, USA.
Med Phys. 2012 Aug;39(8):5277-85. doi: 10.1118/1.4737022.
To develop and investigate a method to identify, from dynamic contrast enhanced (DCE) MRI, significant subvolumes of tumors related to treatment outcomes.
A method, called global-initiated regularized local fuzzy clustering, was proposed to identify subvolumes of head-and-neck cancers (HNC) from heterogeneous distributions of tumor blood volume (BV) and blood flow (BF) for assessment of therapy response. BV and BF images, derived from DCE MRI, of 14 patients with advanced HNC were obtained before treatment and 2 weeks after the start of 7-week chemoradiation therapy (chemo-RT). The delineated subvolumes of tumors with low BV or BF before and during treatment were evaluated for their associations with local failure (LF). Receiver operating characteristic (ROC) analysis was used to assess performance of the method for prediction of local failure of HNC.
The sizes of the subvolumes of primary tumors with low BV, delineated by our method before and week 2 during treatment, were significantly greater in the patients with LF than with local control (LC) (p = 0.02 for pre-RT and 0.01 for week 2). While the total primary tumor volumes were reduced from baseline to week 2 during therapy to a similar extent for both the patients with LF and LC, the percentage decreases in the subvolumes of the primary tumors with low BV in the same time interval were significantly smaller for the patients with LF than those with LC (p < 0.05). ROC analysis shows that for any given sensitivity, the subvolume of the tumor with low BV week 2 during treatment has greater specificity for prediction of local failure than the pretreatment total tumor volume, the percentage change in the tumor volume week 2 during treatment, or the change in the averaged BV values of the entire tumor week 2 during therapy.
We developed a method to identify the significant subvolumes of primary tumors related to local failure. Large poorly perfused subvolumes of primary or nodal HNC before treatment and persisting during the early course of chemo-RT have the potential for prediction of local or regional failure, and could be candidates for local dose intensification.
开发并研究一种从动态对比增强(DCE)磁共振成像(MRI)中识别与治疗结果相关的肿瘤重要子体积的方法。
提出一种名为全局初始化正则化局部模糊聚类的方法,用于从肿瘤血容量(BV)和血流(BF)的异质分布中识别头颈癌(HNC)的子体积,以评估治疗反应。在治疗前以及7周放化疗(chemo-RT)开始2周后,获取了14例晚期HNC患者的DCE MRI衍生的BV和BF图像。评估治疗前和治疗期间BV或BF较低的肿瘤划定子体积与局部失败(LF)的相关性。采用受试者操作特征(ROC)分析来评估该方法预测HNC局部失败的性能。
我们的方法在治疗前和治疗第2周划定的BV较低的原发性肿瘤子体积大小,LF患者显著大于局部控制(LC)患者(放疗前p = 0.02,第2周p = 0.01)。虽然LF患者和LC患者的原发性肿瘤总体积从基线到治疗第2周均有相似程度的减小,但相同时间间隔内BV较低的原发性肿瘤子体积的减小百分比,LF患者显著小于LC患者(p < 0.05)。ROC分析表明,对于任何给定的敏感度,治疗第2周BV较低的肿瘤子体积预测局部失败的特异性高于治疗前肿瘤总体积、治疗第2周肿瘤体积的百分比变化或治疗第2周整个肿瘤平均BV值的变化。
我们开发了一种识别与局部失败相关的原发性肿瘤重要子体积的方法。治疗前原发性或淋巴结HNC的大的灌注不良子体积在放化疗早期持续存在,有可能预测局部或区域失败,并且可能是局部剂量强化的候选对象。