A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities

Adaptive radiation therapy (ART) is an advanced field of radiation oncology. Image-guided radiation therapy (IGRT) methods can support daily setup and assess anatomical variations during therapy, which could prevent incorrect dose distribution and unexpected toxicities. A re-planning to correct these anatomical variations should be done daily/weekly, but to be applicable to a large number of patients, still require time consumption and resources. Using unsupervised machine learning on retrospective data, we have developed a predictive network, to identify patients that would benefit of a re-planning.1200 MVCT of 40 head and neck (H&N) cases were re-contoured, automatically, using deformable hybrid registration and structures mapping. Deformable algorithm and MATLAB(®) homemade machine learning process, developed, allow prediction of criticalities for Tomotherapy treatments.Using retrospective analysis of H&N treatments, we have investigated and predicted tumor shrinkage and organ at risk (OAR) deformations. Support vector machine (SVM) and cluster analysis have identified cases or treatment sessions with potential criticalities, based on dose and volume discrepancies between fractions. During 1st weeks of treatment, 84% of patients shown an output comparable to average standard radiation treatment behavior. Starting from the 4th week, significant morpho-dosimetric changes affect 77% of patients, suggesting need for re-planning. The comparison of treatment delivered and ART simulation was carried out with receiver operating characteristic (ROC) curves, showing monotonous increase of ROC area.Warping methods, supported by daily image analysis and predictive tools, can improve personalization and monitoring of each treatment, thereby minimizing anatomic and dosimetric divergences from initial constraints

Expert system classifier for adaptive radiation therapy in prostate cancer

A classifier-based expert system was developed to compare delivered and planned radiation therapy in prostate cancer patients. Its aim is to automatically identify patients that can benefit from an adaptive treatment strategy. The study predominantly addresses dosimetric uncertainties and critical issues caused by motion of hollow organs. 1200 MVCT images of 38 prostate adenocarcinoma cases were analyzed. An automatic daily re-contouring of structures (i.e. rectum, bladder and femoral heads), rigid/deformable registration and dose warping was carried out to simulate dose and volume variations during therapy. Support vector machine, K-means clustering algorithms and similarity index analysis were used to create an unsupervised predictive tool to detect incorrect setup and/or morphological changes as a consequence of inadequate patient preparation due to stochastic physiological changes, supporting clinical decision-making. After training on a dataset that was considered sufficiently dosimetrically stable, the system identified two equally sized macro clusters with distinctly different volumetric and dosimetric baseline properties and defined thresholds for these two clusters. Application to the test cohort resulted in 25% of the patients located outside the two macro clusters thresholds and which were therefore suspected to be dosimetrically unstable. In these patients, over the treatment course, mean volumetric changes of 30 and 40% for rectum and bladder were detected which possibly represents values justifying adjustment of patient preparation, frequent re-planning or a plan-of-the-day strategy. Based on our research, by combining daily IGRT images with rigid/deformable registration and dose warping, it is possible to apply a machine learning approach to the clinical setting obtaining useful information for a decision regarding an individualized adaptive strategy. Especially for treatments influenced by the movement of hollow organs, this could reduce inadequate treatments and possibly reduce toxicity, thereby increasing overall RT efficacy

A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities

Adaptive radiation therapy (ART) is an advanced field of radiation oncology. Image-guided radiation therapy (IGRT) methods can support daily setup and assess anatomical variations during therapy, which could prevent incorrect dose distribution and unexpected toxicities. A re-planning to correct these anatomical variations should be done daily/weekly, but to be applicable to a large number of patients, still require time consumption and resources. Using unsupervised machine learning on retrospective data, we have developed a predictive network, to identify patients that would benefit of a re-planning.1200 MVCT of 40 head and neck (H&N) cases were re-contoured, automatically, using deformable hybrid registration and structures mapping. Deformable algorithm and MATLAB(\uae) homemade machine learning process, developed, allow prediction of criticalities for Tomotherapy treatments.Using retrospective analysis of H&N treatments, we have investigated and predicted tumor shrinkage and organ at risk (OAR) deformations. Support vector machine (SVM) and cluster analysis have identified cases or treatment sessions with potential criticalities, based on dose and volume discrepancies between fractions. During 1st weeks of treatment, 84% of patients shown an output comparable to average standard radiation treatment behavior. Starting from the 4th week, significant morpho-dosimetric changes affect 77% of patients, suggesting need for re-planning. The comparison of treatment delivered and ART simulation was carried out with receiver operating characteristic (ROC) curves, showing monotonous increase of ROC area.Warping methods, supported by daily image analysis and predictive tools, can improve personalization and monitoring of each treatment, thereby minimizing anatomic and dosimetric divergences from initial constraints

Intra-fraction motion in IMRT, VMAT and helical tomotherapy: In vivo dosimetry using TLD and LEGO phantom

Introduction: During free-breathing arbitrary phase, a mean motion reconstruction is acquired using not gated CT. The lack of knowledge of the tumor and organs at risk (OAR) location can generate possible random/systematic errors during RT treatment. A home-made anthropomorphic dynamic phantom was developed to assess, by TLD, the breathing of the lung district and to quantify the dose variation due to intra-fraction motion. Materials and Methods: Respiratory motion was simulated by a LEGO Mindstorms phantom, programmed in LabVIEW and equipped by 8 ribs, 1 OAR and 1 target with 4 degree of freedom. Within a treatment of 40 cGy, 3 planning strategies were compared. Static (S): CT acquired and plan delivered with phantom in static mode. Static-Real (SR): equal to the S condition but plan delivered with human breathing condition. Dynamic (D): 4DCT with breathing phantom and plan based on Maximum Intensity Projection (MIP) and the ITV generated by the junction of the target contoured in each phases. For each CT were planned and delivered an IMRT, VMAT and Helical plan to uniformly irradiate the target. Dosimetry was made using TLD GR-200 (LiF:Mg,Cu,P). Results: For each technique, data were normalized to the S IMRT plan. In SR condition, the dose delivered to the target was 89.2 ± 6.6%. Due to the motion of the target, TLD measure confirms the uncorrected dose distribution related with the plan. Between the 3 techniques, the Helical plan allows reaching a greater coverage probably due to the kinetic behavior with slow machine rotation. In D condition, the dose delivered to the target was 93.5 ± 5.1%. The internal motion was partially accounted with ITV density re-assignment. Conclusions: Respiratory motion is often assumed to be the same during CT and RT cycles. However, due to contraction of the thoracic diaphragm muscle, it can be slightly different. In this study, a dynamic phantom and TLD measures have quantified the error and dose distribution in a simulated lung treatment

Burden of premature atrial beats in middle-aged endurance athletes with and without lone atrial fibrillation versus sedentary controls

Background: The burden of premature atrial beats (PABs) at 24-h electrocardiographic (ECG) monitoring correlates with the risk of atrial fibrillation. It is unknown whether prolonged and intense exercise increases the burden of PABs, thus contributing to the higher prevalence of atrial fibrillation observed in middle-aged athletes. Methods: We compared the burden of PABs at 24-h ECG monitoring off therapy in 134 healthy middle-aged (30-60-year-old) competitive athletes who had practised 9 (7-11) h of endurance sports for 8 (4-15) consecutive years, 134 age- and gender-matched healthy sedentary individuals, and 66 middle-aged patients (20 athletes and 46 non-athletes) with 'lone' paroxysmal atrial fibrillation. Results: More than 50 PABs/24 h or ≥1 run of ≥3 PABs were recorded in 23/134 (17%) healthy athletes and in 29/134 (22%) sedentary controls (p = 0.61). Healthy athletes with frequent or repetitive PABs were older (median 50 years vs. 43 years, p < 0.01) and had practised sport for a longer time (median 10 years vs. 6 years, p = 0.03). At multivariable analysis only age (odds ratio 1.11, 95% confidence interval 1.04-1.20, p < 0.01) remained an independent predictor of a higher burden of PABs. Also among patients with 'lone' paroxysmal atrial fibrillation, there was no difference in the prevalence of >50 PABs/24 h or ≥1 run of ≥3 PABs between athletes (40%) and controls (48%, p = 0.74). Conclusions: Middle-aged endurance athletes, with or without paroxysmal atrial fibrillation, did not show a higher burden of PABs at 24-h ECG monitoring than sedentary controls. Age, but not intensity and duration of sports activity, predicted a higher burden of PABs among healthy athletes

SIS epidemiological model for adaptive RT: Forecasting the parotid glands shrinkage during tomotherapy treatment

Purpose: A susceptible-infected-susceptible (SIS) epidemic model was applied to radiation therapy (RT) treatments to predict morphological variations in head and neck (H&N) anatomy. Methods: 360 daily MVCT images of 12 H&N patients treated by tomotherapy were analyzed in this retrospective study. Deformable image registration (DIR) algorithms, mesh grids, and structure recontouring, implemented in the RayStation treatment planning system (TPS), were applied to assess the daily organ warping. The parotid's warping was evaluated using the epidemiological approach considering each vertex as a single subject and its deformed vector field (DVF) as an infection. Dedicated IronPython scripts were developed to export daily coordinates and displacements of the region of interest (ROI) from the TPS. MATLAB tools were implemented to simulate the SIS modeling. Finally, the fully trained model was applied to a new patient. Results: A QUASAR phantom was used to validate the model. The patients' validation was obtained setting 0.4 cm of vertex displacement as threshold and splitting susceptible (S) and infectious (I) cases. The correlation between the epidemiological model and the parotids' trend for further optimization of alpha and beta was carried out by Euclidean and dynamic time warping (DTW) distances. The best fit with experimental conditions across all patients (Euclidean distance of 4.09 +/- 1.12 and DTW distance of 2.39 +/- 0.66) was obtained setting the contact rate at 7.55 +/- 0.69 and the recovery rate at 2.45 +/- 0.26; birth rate was disregarded in this constant population. Conclusions: Combining an epidemiological model with adaptive RT (ART), the authors' novel approach could support image-guided radiation therapy (IGRT) to validate daily setup and to forecast anatomical variations. The SIS-ART model developed could support clinical decisions in order to optimize timing of replanning achieving personalized treatments. (C) 2016 American Association of Physicists in Medicine

Quality assurance multicenter comparison of different MR scanners for quantitative diffusion-weighted imaging

To propose a magnetic resonance imaging (MRI) quality assurance procedure that can be used for multicenter comparison of different MR scanners for quantitative diffusion-weighted imaging (DWI)