Department of Radiation Oncology (Maastro), GROW - School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands.
Research group NuTeC, Centre for Environmental Sciences, Hasselt University, Diepenbeek, Belgium.
Phys Med Biol. 2023 Jul 24;68(15). doi: 10.1088/1361-6560/ace308.
. A novel solution is required for accurate 3D bioluminescence tomography (BLT) based glioblastoma (GBM) targeting. The provided solution should be computationally efficient to support real-time treatment planning, thus reducing the x-ray imaging dose imposed by high-resolution micro cone-beam CT.. A novel deep-learning approach is developed to enable BLT-based tumor targeting and treatment planning for orthotopic rat GBM models. The proposed framework is trained and validated on a set of realistic Monte Carlo simulations. Finally, the trained deep learning model is tested on a limited set of BLI measurements of real rat GBM models.. Bioluminescence imaging (BLI) is a 2D non-invasive optical imaging modality geared toward preclinical cancer research. It can be used to monitor tumor growth in small animal tumor models effectively and without radiation burden. However, the current state-of-the-art does not allow accurate radiation treatment planning using BLI, hence limiting BLI's value in preclinical radiobiology research.. The proposed solution can achieve sub-millimeter targeting accuracy on the simulated dataset, with a median dice similarity coefficient (DSC) of 61%. The provided BLT-based planning volume achieves a median encapsulation of more than 97% of the tumor while keeping the median geometrical brain coverage below 4.2%. For the real BLI measurements, the proposed solution provided median geometrical tumor coverage of 95% and a median DSC of 42%. Dose planning using a dedicated small animal treatment planning system indicated good BLT-based treatment planning accuracy compared to ground-truth CT-based planning, where dose-volume metrics for the tumor fall within the limit of agreement for more than 95% of cases.. The combination of flexibility, accuracy, and speed of the deep learning solutions make them a viable option for the BLT reconstruction problem and can provide BLT-based tumor targeting for the rat GBM models.
一种新的解决方案是基于准确的 3D 生物发光断层成像（BLT）的脑胶质瘤（GBM）靶向治疗所必需的。该解决方案应该计算效率高，以支持实时治疗计划，从而降低高分辨率微锥束 CT 成像的 X 射线剂量。开发了一种新的深度学习方法，以实现基于 BLT 的肿瘤靶向和治疗计划，用于原位大鼠 GBM 模型。该框架在一组真实的蒙特卡罗模拟上进行了训练和验证。最后，在有限的真实大鼠 GBM 模型 BLI 测量数据集上测试了经过训练的深度学习模型。生物发光成像（BLI）是一种 2D 非侵入性光学成像方式，专门用于临床前癌症研究。它可以用于有效地监测小动物肿瘤模型中的肿瘤生长，并且没有辐射负担。然而，目前的技术水平不允许使用 BLI 进行准确的放射治疗计划，从而限制了 BLI 在临床前放射生物学研究中的价值。所提出的解决方案可以在模拟数据集上实现亚毫米级的靶向精度，中位数骰子相似系数（DSC）为 61%。基于 BLT 的规划体积提供了超过 97%的肿瘤包裹，同时保持中位数几何脑覆盖低于 4.2%。对于真实的 BLI 测量，所提出的解决方案提供了中位数几何肿瘤覆盖率为 95%和中位数 DSC 为 42%。使用专用的小动物治疗计划系统进行剂量计划表明，与基于地面真相 CT 的计划相比，基于 BLT 的治疗计划具有良好的准确性，其中肿瘤的剂量体积指标在超过 95%的情况下都在协议范围内。深度学习解决方案的灵活性、准确性和速度的结合使它们成为 BLT 重建问题的可行选择，并可以为大鼠 GBM 模型提供基于 BLT 的肿瘤靶向。
