Yassine Abdul-Amir, Lilge Lothar, Betz Vaughn
Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Rd, Toronto, ON M5S3G8, Canada.
Princess Margaret Cancer Center, University Health Network, 101 College Street, Toronto, ON M5G1L7, Canada.
Biomed Opt Express. 2021 Aug 4;12(9):5401-5422. doi: 10.1364/BOE.431310. eCollection 2021 Sep 1.
With the continued development of non-toxic photosensitizer drugs, interstitial photodynamic therapy (iPDT) is showing more favorable outcomes in recent clinical trials. IPDT planning is crucial to further increase the treatment efficacy. However, it remains a major challenge to generate a high-quality, patient-specific plan due to uncertainty in tissue optical properties (OPs), and . These parameters govern how light propagates inside tissues, and any deviation from the planning-assumed values during treatment could significantly affect the treatment outcome. In this work, we increase the robustness of iPDT against OP variations by using machine learning models to recover the patient-specific OPs from light dosimetry measurements and then re-optimizing the diffusers' optical powers to adapt to these OPs in real time. Simulations on virtual brain tumor models show that reoptimizing the power allocation with the recovered OPs significantly reduces uncertainty in the predicted light dosimetry for all tissues involved.
随着无毒光敏剂药物的不断发展，间质光动力疗法（iPDT）在最近的临床试验中显示出更良好的效果。iPDT规划对于进一步提高治疗效果至关重要。然而，由于组织光学特性（OPs）存在不确定性等原因，生成高质量的、针对患者的治疗方案仍然是一项重大挑战。这些参数决定了光在组织内的传播方式，治疗过程中与规划假设值的任何偏差都可能显著影响治疗结果。在这项工作中，我们通过使用机器学习模型从光剂量测量中恢复患者特定的OPs，然后实时重新优化扩散器的光功率，以使其适应这些OPs，从而提高iPDT对OP变化的鲁棒性。在虚拟脑肿瘤模型上的模拟表明，利用恢复的OPs重新优化功率分配可显著降低所有相关组织预测光剂量的不确定性。
