El-Habashy Dina M, Wahid Kareem A, He Renjie, McDonald Brigid, Rigert Jillian, Mulder Samuel J, Lim Tze Yee, Wang Xin, Yang Jinzhong, Ding Yao, Naser Mohamed A, Ng Sweet Ping, Bahig Houda, Salzillo Travis C, Preston Kathryn E, Abobakr Moamen, Shehata Mohamed A, Elkhouly Enas A, Alagizy Hagar A, Hegazy Amira H, Mohammadseid Mustefa, Terhaard Chris, Philippens Marielle, Rosenthal David I, Wang Jihong, Lai Stephen Y, Dresner Alex, Christodouleas John C, Mohamed Abdallah Sherif Radwan, Fuller Clifton D
Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Department of Clinical Oncology and Nuclear Medicine, Menoufia University, Shebin Elkom, Egypt.
medRxiv. 2023 May 5:2023.05.04.23289527. doi: 10.1101/2023.05.04.23289527.
We aim to characterize the serial quantitative apparent diffusion coefficient (ADC) changes of the target disease volume using diffusion-weighted imaging (DWI) acquired weekly during radiation therapy (RT) on a 1.5T MR-Linac and correlate these changes with tumor response and oncologic outcomes for head and neck squamous cell carcinoma (HNSCC) patients as part of a programmatic R-IDEAL biomarker characterization effort.
Thirty patients with pathologically confirmed HNSCC who received curative-intent RT at the University of Texas MD Anderson Cancer Center, were included in this prospective study. Baseline and weekly Magnetic resonance imaging (MRI) (weeks 1-6) were obtained, and various ADC parameters (mean, 5 , 10 , 20 , 30 , 40 , 50 , 60 , 70 , 80 , 90 and 95 percentile) were extracted from the target regions of interest (ROIs). Baseline and weekly ADC parameters were correlated with response during RT, loco-regional control, and the development of recurrence using the Mann-Whitney U test. The Wilcoxon signed-rank test was used to compare the weekly ADC versus baseline values. Weekly volumetric changes (Δvolume) for each ROI were correlated with ΔADC using Spearman's Rho test. Recursive partitioning analysis (RPA) was performed to identify the optimal ΔADC threshold associated with different oncologic outcomes.
There was an overall significant rise in all ADC parameters during different time points of RT compared to baseline values for both gross primary disease volume (GTV-P) and gross nodal disease volumes (GTV-N). The increased ADC values for GTV-P were statistically significant only for primary tumors achieving complete remission (CR) during RT. RPA identified GTV-P ΔADC 5 percentile >13% at the 3 week of RT as the most significant parameter associated with CR for primary tumor during RT (p <0.001). Baseline ADC parameters for GTV-P and GTV-N didn't significantly correlate with response to RT or other oncologic outcomes. There was a significant decrease in residual volume of both GTV-P & GTV-N throughout the course of RT. Additionally, a significant negative correlation between mean ΔADC and Δvolume for GTV-P at the 3 and 4 week of RT was detected (r = -0.39, p = 0.044 & r = -0.45, p = 0.019, respectively).
Assessment of ADC kinetics at regular intervals throughout RT seems to be correlated with RT response. Further studies with larger cohorts and multi-institutional data are needed for validation of ΔADC as a model for prediction of response to RT.
作为一项系统性R-IDEAL生物标志物特征研究工作的一部分,我们旨在利用在1.5T MR直线加速器上放疗(RT)期间每周采集的扩散加权成像(DWI)来描述目标疾病体积的系列定量表观扩散系数(ADC)变化,并将这些变化与头颈部鳞状细胞癌(HNSCC)患者的肿瘤反应和肿瘤学结局相关联。
本前瞻性研究纳入了得克萨斯大学MD安德森癌症中心30例经病理证实的接受根治性放疗的HNSCC患者。获取基线及每周(第1 - 6周)的磁共振成像(MRI),并从感兴趣目标区域(ROI)提取各种ADC参数(均值、第5、10、20、30、40、50、60、70、80、90和95百分位数)。使用Mann-Whitney U检验将基线及每周的ADC参数与放疗期间的反应、局部区域控制和复发情况相关联。使用Wilcoxon符号秩检验比较每周的ADC与基线值。使用Spearman秩相关检验将每个ROI的每周体积变化(Δ体积)与ΔADC相关联。进行递归划分分析(RPA)以确定与不同肿瘤学结局相关的最佳ΔADC阈值。
与基线值相比,在放疗的不同时间点,原发性大体肿瘤体积(GTV-P)和大体淋巴结疾病体积(GTV-N)的所有ADC参数总体上均有显著升高。仅对于放疗期间实现完全缓解(CR)的原发性肿瘤,GTV-P的ADC值升高具有统计学意义。RPA确定放疗第3周时GTV-P的ΔADC第5百分位数>13%是放疗期间原发性肿瘤CR的最显著相关参数(p <0.001)。GTV-P和GTV-N的基线ADC参数与放疗反应或其他肿瘤学结局无显著相关性。在整个放疗过程中,GTV-P和GTV-N的残余体积均显著减小。此外,在放疗第3周和第4周时,检测到GTV-P的平均ΔADC与Δ体积之间存在显著负相关(r = -0.39,p = 0.044;r = -0.45,p = 0.019)。
在整个放疗过程中定期评估ADC动力学似乎与放疗反应相关。需要进一步开展更大样本量的队列研究和多机构数据研究以验证ΔADC作为预测放疗反应模型的有效性。