Deep Learning-Based Auto-Segmentation of Planning Target Volume for Total Marrow and Lymph Node Irradiation

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.Comment: arXiv admin note: text overlap with arXiv:2304.0235

Segmentation of Planning Target Volume in CT Series for Total Marrow Irradiation Using U-Net

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

Impact of the Extremities Positioning on the Set-Up Reproducibility for the Total Marrow Irradiation Treatment

Total marrow (lymph node) irradiation (TMI/TMLI) delivery requires more time than standard radiotherapy treatments. The patient's extremities, through the joints, can experience large movements. The reproducibility of TMI/TMLI patients' extremities was evaluated to find the best positioning and reduce unwanted movements. Eighty TMI/TMLI patients were selected (2013-2022). During treatment, a cone-beam computed tomography (CBCT) was performed for each isocenter to reposition the patient. CBCT-CT pairs were evaluated considering: (i) online vector shift (OVS) that matched the two series; (ii) residual vector shift (RVS) to reposition the patient's extremities; (iii) qualitative agreement (range 1-5). Patients were subdivided into (i) arms either leaning on the frame or above the body; (ii) with or without a personal cushion for foot positioning. The Mann-Whitney test was considered (p < 0.05 significant). Six-hundred-twenty-nine CBCTs were analyzed. The median OVS was 4.0 mm, with only 1.6% of cases ranked < 3, and 24% of RVS > 10 mm. Arms leaning on the frame had significantly smaller RVS than above the body (median: 8.0 mm/6.0 mm, p < 0.05). Using a personal cushion for the feet significantly improved the RVS than without cushions (median: 8.5 mm/1.8 mm, p < 0.01). The role and experience of the radiotherapy team are fundamental to optimizing the TMI/TMLI patient setup

Internal Guidelines for Reducing Lymph Node Contour Variability in Total Marrow and Lymph Node Irradiation

Background: The total marrow and lymph node irradiation (TMLI) target includes the bones, spleen, and lymph node chains, with the latter being the most challenging structures to contour. We evaluated the impact of introducing internal contour guidelines to reduce the inter- and intraobserver lymph node delineation variability in TMLI treatments. Methods: A total of 10 patients were randomly selected from our database of 104 TMLI patients so as to evaluate the guidelines' efficacy. The lymph node clinical target volume (CTV_LN) was recontoured according to the guidelines (CTV_LN_GL_RO1) and compared to the historical guidelines (CTV_LN_Old). Both topological (i.e., Dice similarity coefficient (DSC)) and dosimetric (i.e., V95 (the volume receiving 95% of the prescription dose) metrics were calculated for all paired contours. Results: The mean DSCs were 0.82 ± 0.09, 0.97 ± 0.01, and 0.98 ± 0.02, respectively, for CTV_LN_Old vs. CTV_LN_GL_RO1, and between the inter- and intraobserver contours following the guidelines. Correspondingly, the mean CTV_LN-V95 dose differences were 4.8 ± 4.7%, 0.03 ± 0.5%, and 0.1 ± 0.1%. Conclusions: The guidelines reduced the CTV_LN contour variability. The high target coverage agreement revealed that historical CTV-to-planning-target-volume margins were safe, even if a relatively low DSC was observed

Dataset related to article "Impact of the Extremities Positioning on the Set-Up Reproducibility for the Total Marrow Irradiation Treatment"

<p>Abstract</p><p>Total marrow (lymph node) irradiation (TMI/TMLI) delivery requires more time than standard radiotherapy treatments. The patient's extremities, through the joints, can experience large movements. The reproducibility of TMI/TMLI patients' extremities was evaluated to find the best positioning and reduce unwanted movements. Eighty TMI/TMLI patients were selected (2013-2022). During treatment, a cone-beam computed tomography (CBCT) was performed for each isocenter to reposition the patient. CBCT-CT pairs were evaluated considering: (i) online vector shift (OVS) that matched the two series; (ii) residual vector shift (RVS) to reposition the patient's extremities; (iii) qualitative agreement (range 1-5). Patients were subdivided into (i) arms either leaning on the frame or above the body; (ii) with or without a personal cushion for foot positioning. The Mann-Whitney test was considered (p < 0.05 significant). Six-hundred-twenty-nine CBCTs were analyzed. The median OVS was 4.0 mm, with only 1.6% of cases ranked < 3, and 24% of RVS > 10 mm. Arms leaning on the frame had significantly smaller RVS than above the body (median: 8.0 mm/6.0 mm, p < 0.05). Using a personal cushion for the feet significantly improved the RVS than without cushions (median: 8.5 mm/1.8 mm, p < 0.01). The role and experience of the radiotherapy team are fundamental to optimizing the TMI/TMLI patient setup.</p><p> </p&gt