Wu Chengyue, Lima Ernesto A B F, Stowers Casey E, Xu Zhan, Yam Clinton, Son Jong Bum, Ma Jingfei, Rauch Gaiane M, Yankeelov Thomas E
Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
NPJ Digit Med. 2025 Apr 7;8(1):195. doi: 10.1038/s41746-025-01579-1.
We developed a practical framework to construct digital twins for predicting and optimizing triple-negative breast cancer (TNBC) response to neoadjuvant chemotherapy (NAC). This study employed 105 TNBC patients from the ARTEMIS trial (NCT02276443, registered on 10/21/2014) who received Adriamycin/Cytoxan (A/C)-Taxol (T). Digital twins were established by calibrating a biology-based mathematical model to patient-specific MRI data, which accurately predicted pathological complete response (pCR) with an AUC of 0.82. We then used each patient's twin to theoretically optimize outcome by identifying their optimal A/C-T schedule from 128 options. The patient-specifically optimized treatment yielded a significant improvement in pCR rate of 20.95-24.76%. Retrospective validation was conducted by virtually treating the twins with AC-T schedules from historical trials and obtaining identical observations on outcomes: bi-weekly A/C-T outperforms tri-weekly A/C-T, and weekly/bi-weekly T outperforms tri-weekly T. This proof-of-principle study demonstrates that our digital twin framework provides a practical methodology to identify patient-specific TNBC treatment schedules.
我们开发了一个实用框架，用于构建数字孪生模型，以预测和优化三阴性乳腺癌（TNBC）对新辅助化疗（NAC）的反应。本研究采用了来自ARTEMIS试验（NCT02276443，于2014年10月21日注册）的105例TNBC患者，这些患者接受了阿霉素/环磷酰胺（A/C）-紫杉醇（T）治疗。通过将基于生物学的数学模型校准到患者特异性MRI数据来建立数字孪生模型，该模型以0.82的曲线下面积（AUC）准确预测了病理完全缓解（pCR）。然后，我们使用每个患者的数字孪生模型，从128种方案中确定其最佳A/C-T方案，从理论上优化治疗结果。患者特异性优化治疗使pCR率显著提高了20.95 - 24.76%。通过虚拟地用历史试验中的AC-T方案治疗数字孪生模型并获得相同的结局观察结果进行回顾性验证：每两周一次的A/C-T方案优于每三周一次的A/C-T方案，每周/每两周一次的T方案优于每三周一次的T方案。这项原理验证研究表明，我们的数字孪生框架提供了一种实用方法，可用于确定患者特异性的TNBC治疗方案。
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