Abstract：
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.
摘要：
伽玛刀放射外科（GKRS）是放射治疗（RT）中一种公认的技术，用于治疗小尺寸的脑肿瘤。它在每个治疗阶段给予高度集中的剂量，即使是轻微的剂量误差也会对健康组织造成严重损害。它强调了GKRS对精确和细致的精度的关键需求。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。结合GKRS剂量预测的深度学习方法可以减少这种依赖性，提高规划效率和同质性，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。复杂性源于剂量分布的复杂性，CT扫描中的细微对比,以及剂量测定指标的相互依存关系。为了克服这些挑战，我们开发了一个“级联深度监督”卷积神经网络（CDS-CNN），它采用了混合加权优化方案。我们的创新方法包括多层次的深度监督和战略顺序多网络培训方法。它能够提取切片内和切片间特征，导致更现实的剂量预测与额外的上下文信息。使用来自105名接受GKRS治疗的脑癌患者的数据对CDS-CNN进行了训练和评估，85例用于培训，20例用于测试。定量评估和统计分析证明了来自治疗计划系统(TPS)的预测剂量分布和参考剂量之间的高度一致性。3D总体伽马通过率（GPR）达到97.15％±1.36％（3毫米/3％，10%阈值)，使用3DDenseU-Net模型，比以前的最佳性能高出2.53%。当根据更严格的标准(2mm/3%,10%阈值,和1毫米/3％，10%阈值)，总体GPR仍达到96.53%±1.08%和95.03%±1.18%。此外，平均目标覆盖率(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密集U网相比，CDS-CNN预测显示TC提高了3.5%，在所有评估标准中，CDS-CNN的剂量预测比3DDenseU-Net产生了更好的结果。实验结果表明，提出的CDS-CNN模型在预测GKRS剂量分布方面优于其他模型，预测与TPS剂量密切相关。
