Elisa Scalco

An automatic contour propagation method to follow parotid gland deformation during head-and-neck cancer tomotherapy

We developed an efficient technique to auto-propagate parotid gland contours from planning kVCT to daily MVCT images of head-and-neck cancer patients treated with helical tomotherapy. The method deformed a 3D surface mesh constructed from manual kVCT contours by B-spline free-form deformation to generate optimal and smooth contours. Deformation was calculated by elastic image registration between kVCT and MVCT images. Data from ten head-and-neck cancer patients were considered and manual contours by three observers were included in both kVCT and MVCT images. A preliminary inter-observer variability analysis demonstrated the importance of contour propagation in tomotherapy application: a high variability was reported in MVCT parotid volume estimation (p = 0.0176, ANOVA test) and a larger uncertainty of MVCT contouring compared with kVCT was demonstrated by DICE and volume variability indices (Wilcoxon signed rank test, p < 10(-4) for both indices). The performance analysis of our method showed no significant differences between automatic and manual contours in terms of volumes (p > 0.05, in a multiple comparison Tukey test), center-of-mass distances (p = 0.3043, ANOVA test), DICE values (p = 0.1672, Wilcoxon signed rank test) and average and maximum symmetric distances (p = 0.2043, p = 0.8228 Wilcoxon signed rank tests). Results suggested that our contour propagation method could successfully substitute human contouring on MVCT images.

Texture analysis for the assessment of structural changes in parotid glands induced by radiotherapy

BACKGROUND AND PURPOSE During radiotherapy (RT) for head-and-neck cancer, parotid glands undergo significant anatomic, functional and structural changes which could characterize pre-clinical signs of an increased risk of xerostomia. Texture analysis is proposed to assess structural changes of parotids induced by RT, and to investigate whether early variations of textural parameters (such as mean intensity and fractal dimension) can predict parotid shrinkage at the end of treatment. MATERIAL AND METHODS Textural parameters and volumes of 42 parotids from 21 patients treated with intensity-modulated RT for nasopharyngeal cancer were extracted from CT images. To individuate which parameters changed during RT, a Wilcoxon signed-rank test between textural indices (first and second RT week; first and last RT week) was performed. Discriminant analysis was applied to variations of these parameters in the first two weeks of RT to assess their power in predicting parotid shrinkage at the end of RT. RESULTS A significant decrease in mean intensity (1.7 HU and 3.8 HU after the second and last weeks, respectively) and fractal dimension (0.016 and 0.021) was found. Discriminant analysis, based on volume and fractal dimension, was able to predict the final parotid shrinkage (accuracy of 71.4%). CONCLUSION Textural features could be used in combination with volume to characterize structural modifications on parotid glands and to predict parotid shrinkage at the end of RT.

Comparative high-resolution pQCT analysis of femoral neck indicates different bone mass distribution in osteoporosis and osteoarthritis

SummaryOsteoarthritis is linked to a reduced risk of femoral fracture despite osteoporosis. Different bone distribution in the femoral neck in osteoarthritis and fracture was revealed using a peripheral quantitative computed tomography (pQCT) comparative analysis. Our findings sustain the presence of an adaptive mechanism of bone structure providing fracture protection in osteoarthritis.IntroductionAlthough osteoarthritis is associated with reduced femoral fracture risk, it does not protect from bone loss. We investigated whether adaptive mechanisms are present at the arthritic joint, leading to reduced fracture risk, despite the presence of low bone mass density.MethodsWe performed pQCT comparative analyses of human femoral neck specimens derived from 32 postmenopausal women who received hip prostheses for osteoarthritis (n = 19) or femoral fracture (n = 13) by applying an in-house automated software to extract bone structure descriptors, characterize trabecular and cortical bone distribution, and evaluate their mutual relationships.ResultsThe cortical bone volume and trabecular thickness were significantly (p < 0.05) higher in the osteoarthritis group than in the fracture group. Trabecular bone volume was also significantly (p < 0.05) higher in the osteoarthritis group than the fracture group at the inferior and anterior quadrants. Significance was maintained after adjusting for age, cortical bone volume, and cortical porosity thickness. Multiple linear regression analysis showed that thickness, volume, and apparent density of the trabecular region significantly (p < 0.05) correlated with the same cortical descriptors in osteoarthritis, but no significant relationship was found in the fracture group. Age differentially affected the mutual relationships in the two groups, showing a significant correlation with trabecular thickness in both groups and with apparent trabecular density only in femoral fracture group.ConclusionsStarting from these differences in the structural descriptors, our study sustains the presence of a compensatory mechanism in osteoarthritis to preserve the mechanical competence of bone structure, despite the loss of trabecular bone, underlying lower fracture risk.

Texture analysis of medical images for radiotherapy applications.

The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.

Early changes of parotid density and volume predict modifications at the end of therapy and intensity of acute xerostomia

PurposeTo quantitatively assess the predictive power of early variations of parotid gland volume and density on final changes at the end of therapy and, possibly, on acute xerostomia during IMRT for head-neck cancer.Materials and methodsData of 92 parotids (46 patients) were available. Kinetics of the changes during treatment were described by the daily rate of density (rΔρ) and volume (rΔvol) variation based on weekly diagnostic kVCT images. Correlation between early and final changes was investigated as well as the correlation with prospective toxicity data (CTCAEv3.0) collected weekly during treatment for 24/46 patients.ResultsA higher rΔρ was observed during the first compared to last week of treatment (−0,50 vs −0,05HU, p-value = 0.0001). Based on early variations, a good estimation of the final changes may be obtained (Δρ: AUC = 0.82, p = 0.0001; Δvol: AUC = 0.77, p = 0.0001).Both early rΔρ and rΔvol predict a higher “mean” acute xerostomia score (≥ median value, 1.57; p-value = 0.01). Median early density rate changes for patients with mean xerostomia score ≥ / < 1.57 were −0.98 vs −0.22 HU/day respectively (p = 0.05).ConclusionsEarly density and volume variations accurately predict final changes of parotid glands. A higher longitudinally assessed score of acute xerostomia is well predicted by higher rΔρ and rΔvol in the first two weeks of treatment: best cut-off values were −0.50 HU/day and −380 mm3/day for rΔρ and rΔvol respectively. Further studies are necessary to definitively assess the potential of early density/volume changes in identifying more sensitive patients at higher risk of experiencing xerostomia.ZusammenfassungZielZiel der Studie ist die Untersuchung der prädiktiven Aussagekraft von frühen Veränderungen in Volumen und Dichte der Ohrspeicheldrüse in Bezug auf die finale Verformung zum Ende der Therapie sowie das Risiko von Xerostomie während der intesitätsmodulierten Strahlentherapie (IMRT) bei Kopf und Hals Tumoren.Materialien und MethodenDie Studie umfasst 46 Patienten (92 Speicheldrüsen). Für 24 Patienten wurden prospektiv gesammelte Toxizitätsdaten (CTCAEv3.0) während der Therapie ausgewertet. Die Reaktion auf die Behandlung wurde beobachtet anhand der tägliche Veränderung der Dichte (rΔρ) sowie des Volumens (rΔvol) der Ohrspeicheldrüsen. Die Beziehung zwischen frühen und späten Veränderungen, sowie der Zusammenhang mit den wöchentlich dokumentieren Toxitätsdaten wurde für 24 der 46 Fälle untersucht.ErgebnisseAm Anfang der Therapie wurde ein höheres rΔρ beobachtet (−0,50 vs −0,05HU, p-Wert = 0,0001) als gegen Therapieende. Basierend auf frühen Veränderungen kann die finale Verformung der Speicheldrüsen gut abgeschätzt werden (Δρ: AUC = 0.82, p = 0.0001; Δvol: AUC = 0.77, p = 0.0001). Sowohl der frühe rΔρ- als auch der rΔvol-Wert sagen eine höheres „mittlere“ Auftreten von Xerostomie für den Durchschnitt der untersuchten Population vorher (1.57; p-Wert = 0.01, log. Regression). Die mittlere frühe Veränderung der Dichte für Patienten mit mittlerem Xerostomie-Wert ≥ / < 1.57 waren −0.98 bzw. −0.22 HU (p = 0.05, Mann–Whitney-Test).SchlussfolgerungFrühe Dichte- und Volumenvariationen können präzise letztendliche Änderungen der Ohrspeicheldrüse vorhersagen. Eine akuten Xerostomie kann gut durch größere rΔρ- und rΔvol-Werte in den ersten zwei Behandlungswochen vorhergesagt werden: die beste Cut-off-Werte für rΔρ und rΔvol waren jeweils −0.50 HU/ Tag und −380 mm3/day. Weitere Studien sind notwendig, um das ganze Potenzial der frühen Dichte/ Volumen Änderungen für die Identifizierung sensibler Patienten mit einemerhöhten Mundtrockenheitsrisiko zu beurteilen.

Elastic registration based on particle filter in radiotherapy images with brain deformations

This paper presents the evaluation of the accuracy of an elastic registration algorithm, based on the particle filter and an optical flow process. The algorithm is applied in brain CT and MRI simulated image datasets, and MRI images from a real clinical radiotherapy case. To validate registration accuracy, standard indices for registration accuracy assessment were calculated: the dice similarity coefficient (DICE), the average symmetric distance (ASD) and the maximal distance between pixels (Dmax). The results showed that this registration process has good accuracy, both qualitatively and quantitatively, suggesting that this method may be considered as a good new option for radiotherapy applications like patients follow up treatment.

Early prediction of radiotherapy-induced parotid shrinkage and toxicity based on CT radiomics and fuzzy classification

MOTIVATION Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers. In previous works, clinical, dosimetric and image-based features were considered separately, to find different possible predictors of parotid shrinkage. On the other hand, a few works reported possible image-based predictors of xerostomia, while the combination of different types of features has been little addressed. OBJECTIVE This paper proposes the application of a novel machine learning approach, based on both statistics and fuzzy logic, aimed at the classification of patients at risk of i) parotid gland shrinkage and ii) 12-months xerostomia. Both problems are addressed with the aim of individuating predictors and models to classify respective outcomes. METHODS Knowledge is extracted from a real dataset of radiotherapy patients, by means of a recently developed method named Likelihood-Fuzzy Analysis, based on the representation of statistical information by fuzzy rule-based models. This method enables to manage heterogeneous variables and missing data, and to obtain interpretable fuzzy models presenting good generalization power (thus high performance), and to measure classification confidence. Numerous features are extracted to characterize patients, coming from different sources, i.e. clinical features, dosimetric parameters, and radiomics-based measures obtained by texture analysis of Computed Tomography images. A learning approach based on the composition of simple models in a more complicated one allows to consider the features separately, in order to identify predictors and models to use when only some data source is available, and obtaining more accurate results when more information can be combined. RESULTS Regarding parotid shrinkage, a number of good predictors is detected, some already known and confirmed here, and some others found here, in particular among radiomics-based features. A number of models are also designed, some using single features and others involving models composition to improve classification accuracy. In particular, the best model to be used at the initial treatment stage, and another one applicable at the half treatment stage are identified. Regarding 12-months toxicity, some possible predictors are detected, in particular among radiomics-based features. Moreover, the relation between final parotid shrinkage rate and 12-months xerostomia is evaluated. The method is compared to the naïve Bayes classifier, which reveals similar results in terms of classification accuracy and best predictors. The interpretable fuzzy rule-based models are explicitly presented, and the dependence between predictors and outcome is explained, thus furnishing in some cases helpful insights about the considered problems. CONCLUSION Thanks to the performance and interpretability of the fuzzy classification method employed, predictors of both parotid shrinkage and xerostomia are detected, and their influence on each outcome is revealed. Moreover, models for predicting parotid shrinkage at initial and half radiotherapy stages are found.

Assessment and clinical validation of margins for adaptive simultaneous integrated boost in neo-adjuvant radiochemotherapy for rectal cancer.

PURPOSE An adaptive concomitant boost (ACB) for the neo-adjuvant treatment of rectal cancer was clinically implemented. In this study population margins M(90,90) considering rectal deformation were derived for 10 consecutive patients treated at 18 × 2.3Gy with Helical Tomotherapy (HT) and prospectively validated on 20 additional patients treated with HT, delivering ACB in the last 6 fractions. METHODS Sectorial margins M(90,90) of the whole and second treatment parts were assessed for 90% population through a method combining the 90% coverage probability maps of rectal positions (CPC90%) with 3D local distance measurements between the CPC90% and a reference rectal contour. M(90,90) were compared with the margins M(90,90)(95%/99%), ensuring CPC90% coverage with 95%/99% confidence level. M(90,90) of the treatment second part were chosen as ACB margins which were clinically validated for each patient by means of %volume missing of CPC5/6 excluded by the ACB margins. RESULTS The whole treatment M(90,90) ranged between 1.9 mm and 9 mm in the lower-posterior and upper-anterior sectors, respectively. Regarding ACB, M(90,90) were 7 mm in the anterior direction and <5 mm elsewhere. M(90,90)(95%/99%) did not significantly differ from M(90,90). The %volume excluded by the ACB margin was<2% for all male and <5% for 9/10 female patients. The dosimetry impact on R_adapt for the patients with the largest residual error was negligible. CONCLUSIONS Local deformation measurements confirm an anisotropic motion of rectum once set-up error is rigidly corrected. Margins of 7 mm anterior and 5 mm elsewhere are adequate for ACB. Female patients show a slightly larger residual error.

A Comparative Evaluation of 3 Different Free-Form Deformable Image Registration and Contour Propagation Methods for Head and Neck MRI: The Case of Parotid Changes During Radiotherapy:

Purpose: To validate and compare the deformable image registration and parotid contour propagation process for head and neck magnetic resonance imaging in patients treated with radiotherapy using 3 different approaches—the commercial MIM, the open-source Elastix software, and an optimized version of it. Materials and Methods: Twelve patients with head and neck cancer previously treated with radiotherapy were considered. Deformable image registration and parotid contour propagation were evaluated by considering the magnetic resonance images acquired before and after the end of the treatment. Deformable image registration, based on free-form deformation method, and contour propagation available on MIM were compared to Elastix. Two different contour propagation approaches were implemented for Elastix software, a conventional one (DIR_Trx) and an optimized homemade version, based on mesh deformation (DIR_Mesh). The accuracy of these 3 approaches was estimated by comparing propagated to manual contours in terms of average symmetric distance, maximum symmetric distance, Dice similarity coefficient, sensitivity, and inclusiveness. Results: A good agreement was generally found between the manual contours and the propagated ones, without differences among the 3 methods; in few critical cases with complex deformations, DIR_Mesh proved to be more accurate, having the lowest values of average symmetric distance and maximum symmetric distance and the highest value of Dice similarity coefficient, although nonsignificant. The average propagation errors with respect to the reference contours are lower than the voxel diagonal (2 mm), and Dice similarity coefficient is around 0.8 for all 3 methods. Conclusion: The 3 free-form deformation approaches were not significantly different in terms of deformable image registration accuracy and can be safely adopted for the registration and parotid contour propagation during radiotherapy on magnetic resonance imaging. More optimized approaches (as DIR_Mesh) could be preferable for critical deformations.

Automatic segmentation of cortical and trabecular components of bone specimens acquired by pQCT

Peripheral Quantitative Computerized Tomography (pQCT) allows the acquisition of bone specimens with a spatial resolution adequate to visualize the 3D structure of the bone cortex and the trabecular network. At present, pQCT scanners are equipped with image processing software that limits the bone analysis in two dimensions and requires strong user interaction. In this work, a method is proposed to automatically segment, in 3D, cortical and trabecular components of bone specimens acquired by pQCT, in order to facilitate and enhance the quantitative evaluation of densitometric properties of the bone.