14 research outputs found
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
Ensemble Methods for Multi-Organ Segmentation in CT Series
In the medical images field, semantic segmentation is one of the most
important, yet difficult and time-consuming tasks to be performed by
physicians. Thanks to the recent advancement in the Deep Learning models
regarding Computer Vision, the promise to automate this kind of task is getting
more and more realistic. However, many problems are still to be solved, like
the scarce availability of data and the difficulty to extend the efficiency of
highly specialised models to general scenarios. Organs at risk segmentation for
radiotherapy treatment planning falls in this category, as the limited data
available negatively affects the possibility to develop general-purpose models;
in this work, we focus on the possibility to solve this problem by presenting
three types of ensembles of single-organ models able to produce multi-organ
masks exploiting the different specialisations of their components. The results
obtained are promising and prove that this is a possible solution to finding
efficient multi-organ segmentation methods
Evaluation of plan complexity and dosimetric plan quality of total marrow and lymphoid irradiation using volumetric modulated arc therapy
PurposeTo assess the impact of the planner's experience and optimization algorithm on the plan quality and complexity of total marrow and lymphoid irradiation (TMLI) delivered by means of volumetric modulated arc therapy (VMAT) over 2010-2022 at our institute. MethodsEighty-two consecutive TMLI plans were considered. Three complexity indices were computed to characterize the plans in terms of leaf gap size, irregularity of beam apertures, and modulation complexity. Dosimetric points of the target volume (D2%) and organs at risk (OAR) (Dmean) were automatically extracted to combine them with plan complexity and obtain a global quality score (GQS). The analysis was stratified based on the different optimization algorithms used over the years, including a knowledge-based (KB) model. Patient-specific quality assurance (QA) using Portal Dosimetry was performed retrospectively, and the gamma agreement index (GAI) was investigated in conjunction with plan complexity. ResultsPlan complexity significantly reduced over the years (r = -0.50, p < 0.01). Significant differences in plan complexity and plan dosimetric quality among the different algorithms were observed. Moreover, the KB model allowed to achieve significantly better dosimetric results to the OARs. The plan quality remained similar or even improved during the years and when moving to a newer algorithm, with GQS increasing from 0.019 +/- 0.002 to 0.025 +/- 0.003 (p < 0.01). The significant correlation between GQS and time (r = 0.33, p = 0.01) indicated that the planner's experience was relevant to improve the plan quality of TMLI plans. Significant correlations between the GAI and the complexity metrics (r = -0.71, p < 0.01) were also found. ConclusionBoth the planner's experience and algorithm version are crucial to achieve an optimal plan quality in TMLI plans. Thus, the impact of the optimization algorithm should be carefully evaluated when a new algorithm is introduced and in system upgrades. Knowledge-based strategies can be useful to increase standardization and improve plan quality of TMLI treatments
Automatic planning of the lower extremities for total marrow irradiation using volumetric modulated arc therapy
Purpose Total marrow (and lymphoid) irradiation (TMI-TMLI) is limited by the couch travel range of modern linacs, which forces the treatment delivery to be split into two plans with opposite orientations: a head-first supine upper-body plan, and a feet-first supine lower extremities plan. A specific field junction is thus needed to obtain adequate target coverage in the overlap region of the two plans. In this study, an automatic procedure was developed for field junction creation and lower extremities plan optimization. Methods Ten patients treated with TMI-TMLI at our institution were selected retrospectively. The planning of the lower extremities was performed automatically. Target volume parameters (CTV_J-V-98% > 98%) at the junction region and several dose statistics (D-98%, D-mean, and D-2%) were compared between automatic and manual plans. The modulation complexity score (MCS) was used to assess plan complexity. Results The automatic procedure required 60-90 min, depending on the case. All automatic plans achieved clinically acceptable dosimetric results (CTV_J-V-98% > 98%), with significant differences found at the junction region, where D-mean and D-2% increased on average by 2.4% (p < 0.03) and 3.0% (p < 0.02), respectively. Similar plan complexity was observed (median MCS = 0.12). Since March 2022, the automatic procedure has been introduced in our clinic, reducing the TMI-TMLI simulation-to-delivery schedule by 2 days. Conclusion The developed procedure allowed treatment planning of TMI-TMLI to be streamlined, increasing efficiency and standardization, preventing human errors, while maintaining the dosimetric plan quality and complexity of manual plans. Automated strategies can simplify the future adoption and clinical implementation of TMI-TMLI treatments in new centers
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
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
Mixing Profiling in Red Winemaking: a Modelling Analysis.
Although winemaking practice is still mainly the fruit of experience with the backup of the technological achievements coming from oenological research, there is a strong motivation to pursue process modeling and simulation for equipment design, operation optimisation, and operator training. Red winemaking, in particular, is based on the simultaneous occurrence of many interacting phenomena, such as the alcoholic fermentation and the extraction from pomace of several compounds which are responsible of wine colour and taste.
This work presents a simplified mathematical modelling approach for the interpretation of the phenomena relevant to the transfer and modification of the main classes of polyphenolic substances characteristic of wine (anthocyanins, tannins and cinnamic acids) in relation to mixing and oxidative conditions prevailing in the vat
Improving Red Winemaking through Process Modelling
Red winemaking is based on the simultaneous occurrence of many interacting phenomena, such as the alcoholic fermentation and the extraction from pomace of several compounds which are responsible of wine colour and taste. This work presents some mathematical modelling approaches that can be used to describe the core phenomena of red winemaking (such as polyphenols leaching, polyphenols dispersion, and fermentation); the models were identified on full-scale realisations of some typical red vinification techniques on grapes belonging to cultivar Nebbiolo in different vintages and the relevant identified parameter set are reported
Segmentation of Planning Target Volume in CT Series for Total Marrow Irradiation Using U-Net
Multicentric evaluation of a machine learning model to streamline the radiotherapy patient specific quality assurance process
Purpose: Patient-specific quality assurance (PSQA) is performed to ensure that modulated treatment plans can be delivered as intended, but constitutes a substantial workload that could slow down the radiotherapy process and delay the start of clinical treatments. In this study, we investigated a machine learning (ML) tree-based ensemble model to predict the gamma passing rate (GPR) for volumetric modulated arc therapy (VMAT) plans. Materials and methods: 5622 VMAT plans from multiple treatment sites were selected from a database of Institution 1 and the ML model trained using 19 metrics. PSQA analyses were performed automatically using criteria 3%/1 mm (global normalization, absolute dose, 10% threshold) and 95% action limit. Model's performance was evaluated on an out-of-sample test set of Institution 1 and on two independent sets of measurements collected at Institution 2 and Institution 3. Mean absolute error (MAE), as well as the model's sensitivity and specificity, were computed. Results: The model obtained a MAE of 2.33%, 2.54% and 3.91% for the three Institutions, with a specificity of 0.90, 0.90 and 0.68, and a sensitivity of 0.61, 0.25, and 0.55, respectively. Small positive median values of the residuals (i.e., the difference between measurements and predictions) were observed for each Institution (0.95%, 1.66%, and 3.42%). Thus, the model's predictions were, on average, close to the real values and provided a conservative estimation of the GPR. Conclusions: ML models can be integrated into clinical practice to streamline the radiotherapy workflow, but they should be center-specific or thoroughly verified within centers before clinical use