Department of Biomedical Engineering, Wake Forest School of Medicine, 575 N. Patterson Ave., Suite 530, Winston-Salem, NC, 27101, USA.
Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Blacksburg, USA.
Sci Rep. 2022 Jul 9;12(1):11718. doi: 10.1038/s41598-022-15801-7.
Current tools to assess breast cancer response to neoadjuvant chemotherapy cannot reliably predict disease eradication, which if possible, could allow early cessation of therapy. In this work, we assessed the ability of an image data-driven mathematical modeling approach for dynamic characterization of breast cancer response to neoadjuvant therapy. We retrospectively analyzed patients enrolled in the I-SPY 2 TRIAL at the Atrium Health Wake Forest Baptist Comprehensive Cancer Center. Patients enrolled on the study received four MR imaging examinations during neoadjuvant therapy with acquisitions at baseline (T), 3-weeks/early-treatment (T), 12-weeks/mid-treatment (T), and completion of therapy prior to surgery (T). We use a biophysical mathematical model of tumor growth to generate spatial estimates of tumor proliferation to characterize the dynamics of treatment response. Using histogram summary metrics to quantify estimated tumor proliferation maps, we found strong correlation of mathematical model-estimated tumor proliferation with residual cancer burden, with Pearson correlation coefficients ranging from 0.88 and 0.97 between T and T, representing a significant improvement from conventional assessment methods of change in mean apparent diffusion coefficient and functional tumor volume. This data shows the significant promise of imaging-based biophysical mathematical modeling methods for dynamic characterization of patient-specific response to neoadjuvant therapy with correlation to residual disease outcomes.
目前用于评估新辅助化疗治疗乳腺癌反应的工具无法可靠地预测疾病的消除，如果可能的话，这将允许早期停止治疗。在这项工作中，我们评估了一种基于图像数据的数学建模方法来动态描述新辅助治疗中乳腺癌反应的能力。我们回顾性分析了在阿特利姆健康维克森林浸信会综合癌症中心的 I-SPY 2 试验中入组的患者。入组该研究的患者在新辅助治疗期间接受了四次磁共振成像检查，分别在基线（T）、治疗 3 周/早期（T）、治疗 12 周/中期（T）和手术前完成治疗（T）时进行采集。我们使用肿瘤生长的生物物理数学模型来生成肿瘤增殖的空间估计值，以描述治疗反应的动力学。使用直方图汇总指标来量化估计的肿瘤增殖图，我们发现数学模型估计的肿瘤增殖与残留癌负荷之间存在很强的相关性，T 和 T 之间的皮尔逊相关系数范围为 0.88 到 0.97，与传统的评估方法相比，平均表观扩散系数和功能肿瘤体积的变化有显著改善。这些数据表明，基于成像的生物物理数学建模方法在动态描述新辅助治疗中患者特异性反应方面具有很大的潜力，与残留疾病结局相关。
