  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.
