In order to optimize the radiotherapy delivery for cancer treatment, especially when dealing with complex treatments such as Total Marrow and Lymph Node Irradiation (TMLI), the accurate contouring of the Planning Target Volume (PTV) is crucial. Unfortunately, relying on manual contouring for such treatments is time-consuming and prone to errors. In this paper, we investigate the application of Deep Learning (DL) to automate the segmentation of the PTV in TMLI treatment, building upon previous work that introduced a solution to this problem based on a 2D U-Net model. We extend the previous research (i) by employing the nnU-Net framework to develop both 2D and 3D U-Net models and (ii) by evaluating the trained models on the PTV with the exclusion of bones, which consist mainly of lymp-nodes and represent the most challenging region of the target volume to segment. Our result show that the introduction of nnU-NET framework led to statistically significant improvement in the segmentation performance. In addition, the analysis on the PTV after the exclusion of bones showed that the models are quite robust also on the most challenging areas of the target volume. Overall, our study is a significant step forward in the application of DL in a complex radiotherapy treatment such as TMLI, offering a viable and scalable solution to increase the number of patients who can benefit from this treatment.

翻译：为了优化癌症治疗的放疗实施，尤其是在处理全骨髓及淋巴结照射（TMLI）等复杂治疗方案时，精确勾画计划靶区（PTV）至关重要。然而，依赖人工勾画此类靶区既耗时又易出错。本文在前期基于2D U-Net模型引入解决方案的基础上，进一步探索了深度学习（DL）在TMLI治疗中自动分割PTV的应用。我们通过以下方式扩展了前期研究：（i）利用nnU-Net框架开发了2D和3D U-Net模型；（ii）在排除骨骼后的PTV区域（主要由淋巴结组成，是靶区中最具挑战性的部分）上评估了训练好的模型。结果表明，nnU-Net框架的引入显著提升了分割性能。此外，对排除骨骼后PTV的分析显示，模型在靶区最具挑战性的区域仍具有较强鲁棒性。总体而言，我们的研究是深度学习在TMLI等复杂放疗应用中迈出的重要一步，为提高该治疗受益患者数量提供了可行且可扩展的方案。