Radiotherapy (RT) is a key component in the treatment of various cancers, including Acute Lymphocytic Leukemia (ALL) and Acute Myelogenous Leukemia (AML). Precise delineation of organs at risk (OARs) and target areas is essential for effective treatment planning. Intensity Modulated Radiotherapy (IMRT) techniques, such as Total Marrow Irradiation (TMI) and Total Marrow and Lymph node Irradiation (TMLI), provide more precise radiation delivery compared to Total Body Irradiation (TBI). However, these techniques require time-consuming manual segmentation of structures in Computerized Tomography (CT) scans by the Radiation Oncologist (RO). In this paper, we present a deep learning-based auto-contouring method for segmenting Planning Target Volume (PTV) for TMLI treatment using the U-Net architecture. We trained and compared two segmentation models with two different loss functions on a dataset of 100 patients treated with TMLI at the Humanitas Research Hospital between 2011 and 2021. Despite challenges in lymph node areas, the best model achieved an average Dice score of 0.816 for PTV segmentation. Our findings are a preliminary but significant step towards developing a segmentation model that has the potential to save radiation oncologists a considerable amount of time. This could allow for the treatment of more patients, resulting in improved clinical practice efficiency and more reproducible contours.

翻译：放射治疗（RT）是治疗多种癌症（包括急性淋巴细胞白血病（ALL）和急性髓系白血病（AML））的关键组成部分。精确勾画危及器官（OARs）和靶区对于制定有效的治疗计划至关重要。相较于全身照射（TBI），调强放射治疗（IMRT）技术（如全骨髓照射（TMI）及全骨髓与淋巴结照射（TMLI））可实现更精准的辐射投递。然而，这些技术要求放射肿瘤科医生（RO）在计算机断层扫描（CT）图像中对结构进行耗时的手工分割。本文提出一种基于深度学习的自动勾画方法，利用U-Net架构对TMLI治疗中的计划靶区（PTV）进行分割。我们在Humanitas研究医院2011年至2021年间接受TMLI治疗的100例患者数据集上，训练并比较了采用两种不同损失函数的分割模型。尽管在淋巴结区域存在挑战，但最佳模型在PTV分割中取得了平均Dice系数0.816的结果。本研究是开发能显著节省放射肿瘤科医生时间的分割模型的初步但重要进展。该模型有望支持治疗更多患者，从而提高临床实践效率并获得更可重复的勾画结果。