背景:放射治疗是癌症的重要治疗方式之一。出色的放射治疗计划在很大程度上依赖于出色的剂量分布图,传统上是由经验丰富的物理学家反复试验和调整产生的。然而,这个过程既耗时又费力,它带有一定程度的主观性。现在,凭借深度学习的强大功能,我们能够更准确地预测剂量分布图,有效克服这些挑战。
方法:在本研究中,我们提出了一种新型的Swin-UMamba-Channel预测模型,该模型专门设计用于预测接受全乳房切除术后放疗的左乳腺癌患者的剂量分布.该模型集成了器官的解剖位置信息和射线角度信息,显著提高预测精度。通过对生成器(Swin-UMamba)和鉴别器的迭代训练,该模型可以生成与实际剂量紧密匹配的图像,协助物理学家快速创建DVH曲线并缩短治疗计划周期。我们的模型在预测精度方面表现出优异的性能,计算效率,和实用性,并通过与同类网络的对比实验进一步验证了其有效性。
结果:研究结果表明,我们的模型可以准确预测接受调强放疗(IMRT)的乳腺癌患者的临床剂量。预测剂量范围为0至50Gy,并与实际数据进行比较,它显示出很高的准确性,平均骰子相似系数为0.86。具体来说,计划目标体积的平均剂量变化率范围为0.28%至1.515%,右肺和左肺的平均剂量变化率分别为2.113%和0.508%,分别。值得注意的是,由于尺寸小,心脏和脊髓表现出相对较高的平均剂量变化率,达到3.208%和1.49%,分别。与类似的剂量研究相比,我们的模型展示了卓越的性能。此外,我们的模型拥有更少的参数,更低的计算复杂度,和更短的处理时间,进一步提高其实用性和效率。这些发现为我们的模型预测剂量的准确性和可靠性提供了有力的证据,为乳腺癌患者的IMRT提供重要的技术支持。
结论:本研究提出了一种新的Swin-UMamba-Channel剂量预测模型,其结果表明,它可以精确预测接受全乳房切除术和IMRT的左乳腺癌患者的目标区域的临床剂量。这些显著成绩为后续方案优化和质量控制提供了有价值的参考数据,为深度学习在放射治疗领域的应用开辟了新的道路。
BACKGROUND: Radiation therapy is one of the crucial treatment modalities for cancer. An excellent radiation therapy plan relies heavily on an outstanding dose distribution map, which is traditionally generated through repeated trials and adjustments by experienced physicists. However, this process is both time-consuming and labor-intensive, and it comes with a degree of subjectivity. Now, with the powerful capabilities of deep learning, we are able to predict dose distribution maps more accurately, effectively overcoming these challenges.
METHODS: In this study, we propose a novel Swin-UMamba-Channel prediction model specifically designed for predicting the dose distribution of patients with left breast cancer undergoing radiotherapy after total mastectomy. This model integrates anatomical position information of organs and ray angle information, significantly enhancing prediction accuracy. Through iterative training of the generator (Swin-UMamba) and discriminator, the model can generate images that closely match the actual dose, assisting physicists in quickly creating DVH curves and shortening the treatment planning cycle. Our model exhibits excellent performance in terms of prediction accuracy, computational efficiency, and practicality, and its effectiveness has been further verified through comparative experiments with similar networks.
RESULTS: The results of the study indicate that our model can accurately predict the clinical dose of breast cancer patients undergoing intensity-modulated radiation therapy (IMRT). The predicted dose range is from 0 to 50 Gy, and compared with actual data, it shows a high accuracy with an average Dice similarity coefficient of 0.86. Specifically, the average dose change rate for the planning target volume ranges from 0.28 % to 1.515 %, while the average dose change rates for the right and left lungs are 2.113 % and 0.508 %, respectively. Notably, due to their small sizes, the heart and spinal cord exhibit relatively higher average dose change rates, reaching 3.208 % and 1.490 %, respectively. In comparison with similar dose studies, our model demonstrates superior performance. Additionally, our model possesses fewer parameters, lower computational complexity, and shorter processing time, further enhancing its practicality and efficiency. These findings provide strong evidence for the accuracy and reliability of our model in predicting doses, offering significant technical support for IMRT in breast cancer patients.
CONCLUSIONS: This study presents a novel Swin-UMamba-Channel dose prediction model, and its results demonstrate its precise prediction of clinical doses for the target area of left breast cancer patients undergoing total mastectomy and IMRT. These remarkable achievements provide valuable reference data for subsequent plan optimization and quality control, paving a new path for the application of deep learning in the field of radiation therapy.