伽玛刀放射外科 (GKRS) 是一种成熟的放射治疗 (RT) 技术，用于治疗小尺寸脑肿瘤。它在每次治疗过程中施用高度集中的剂量，即使是很小的剂量错误也会对健康组织造成严重损害。它强调了 GKRS 对精确和细致精度的迫切需求。然而，GKRS 的规划过程复杂且耗时，严重依赖医学物理学家的专业知识。将深度学习方法纳入 GKRS 剂量预测可以减少这种依赖性，提高计划效率和同质性，简化临床工作流程，并减少患者滞后时间。尽管如此，使用现有模型进行精确的伽玛刀计划剂量分布预测仍然是一个重大挑战。其复杂性源于剂量分布的复杂性、CT 扫描的微妙对比以及剂量测定指标的相互依赖性。为了克服这些挑战，我们开发了一种采用混合加权优化方案的“级联深度监督”卷积神经网络（CDS-CNN）。我们的创新方法结合了多层次的深度监督和战略顺序多网络训练方法。它能够提取切片内和切片间特征，从而通过附加上下文信息实现更真实的剂量预测。 CDS-CNN 使用 105 名接受 GKRS 治疗的脑癌患者的数据进行训练和评估，其中 85 例用于训练，20 例用于测试。定量评估和统计分析表明，预测剂量分布与治疗计划系统（TPS）的参考剂量之间具有高度一致性。 3D 整体伽玛通过率 (GPR) 达到 97.15%±1.36%（3 mm/3%，10% 阈值），比之前使用 3D Dense U-Net 模型的最佳性能高出 2.53%。当根据更严格的标准（2 mm/3%、10%阈值和1 mm/3%、10%阈值）进行评估时，总体GPR仍达到96.53%±1.08%和95.03%±1.18%。此外，平均靶点覆盖率（TC）为98.33%±1.16%，剂量选择性（DS）为0.57±0.10，梯度指数（GI）为2.69±0.30，均匀性指数（HI）为1.79±0.09。与 3D Dense U-Net 相比，CDS-CNN 预测的 TC 提高了 3.5%，并且 CDS-CNN 的剂量预测在所有评估标准上都比 3D Dense U-Net 产生了更好的结果。实验结果表明，所提出的 CDS-CNN 模型在预测 GKRS 剂量分布方面优于其他模型，预测结果与 TPS 剂量非常匹配。© 2024。澳大利亚物理科学家和医学工程师学院。

Gamma Knife radiosurgery (GKRS) is a well-established technique in radiation therapy (RT) for treating small-size brain tumors. It administers highly concentrated doses during each treatment fraction, with even minor dose errors posing a significant risk of causing severe damage to healthy tissues. It underscores the critical need for precise and meticulous precision in GKRS. However, the planning process for GKRS is complex and time-consuming, heavily reliant on the expertise of medical physicists. Incorporating deep learning approaches for GKRS dose prediction can reduce this dependency, improve planning efficiency and homogeneity, streamline clinical workflows, and reduce patient lagging times. Despite this, precise Gamma Knife plan dose distribution prediction using existing models remains a significant challenge. The complexity stems from the intricate nature of dose distributions, subtle contrasts in CT scans, and the interdependence of dosimetric metrics. To overcome these challenges, we have developed a "Cascaded-Deep-Supervised" Convolutional Neural Network (CDS-CNN) that employs a hybrid-weighted optimization scheme. Our innovative method incorporates multi-level deep supervision and a strategic sequential multi-network training approach. It enables the extraction of intra-slice and inter-slice features, leading to more realistic dose predictions with additional contextual information. CDS-CNN was trained and evaluated using data from 105 brain cancer patients who underwent GKRS treatment, with 85 cases used for training and 20 for testing. Quantitative assessments and statistical analyses demonstrated high consistency between the predicted dose distributions and the reference doses from the treatment planning system (TPS). The 3D overall gamma passing rates (GPRs) reached 97.15% ± 1.36% (3 mm/3%, 10% threshold), surpassing the previous best performance by 2.53% using the 3D Dense U-Net model. When evaluated against more stringent criteria (2 mm/3%, 10% threshold, and 1 mm/3%, 10% threshold), the overall GPRs still achieved 96.53% ± 1.08% and 95.03% ± 1.18%. Furthermore, the average target coverage (TC) was 98.33% ± 1.16%, dose selectivity (DS) was 0.57 ± 0.10, gradient index (GI) was 2.69 ± 0.30, and homogeneity index (HI) was 1.79 ± 0.09. Compared to the 3D Dense U-Net, CDS-CNN predictions demonstrated a 3.5% improvement in TC, and CDS-CNN's dose prediction yielded better outcomes than the 3D Dense U-Net across all evaluation criteria. The experimental results demonstrated that the proposed CDS-CNN model outperformed other models in predicting GKRS dose distributions, with predictions closely matching the TPS doses.© 2024. Australasian College of Physical Scientists and Engineers in Medicine.