Transformer-Based Approach to Melanoma Detection.

Melanoma is a malignant cancer type which develops when DNA damage occurs (mainly due to environmental factors such as ultraviolet rays). Often, melanoma results in intense and aggressive cell growth that, if not caught in time, can bring one toward death. Thus, early identification at the initial stage is fundamental to stopping the spread of cancer. In this paper, a ViT-based architecture able to classify melanoma versus non-cancerous lesions is presented. The proposed predictive model is trained and tested on public skin cancer data from the ISIC challenge, and the obtained results are highly promising. Different classifier configurations are considered and analyzed in order to find the most discriminating one. The best one reached an accuracy of 0.948, sensitivity of 0.928, specificity of 0.967, and AUROC of 0.948.

Epicardial and thoracic subcutaneous fat texture analysis in patients undergoing cardiac CT.

Introduction: The aim of our study was to evaluate the feasibility of texture analysis of epicardial fat (EF) and thoracic subcutaneous fat (TSF) in patients undergoing cardiac CT (CCT). Materials and methods: We compared a consecutive population of 30 patients with BMI ≤25 kg/m2 (Group A, 60.6 ± 13.7 years) with a control population of 30 patients with BMI >25 kg/m2 (Group B, 63.3 ± 11 years). A dedicated computer application for quantification of EF and a texture analysis application for the study of EF and TSF were employed. Results: The volume of EF was higher in group B (mean 116.1 cm3 vs. 86.3 cm3, p = 0.014), despite no differences were found neither in terms of mean density (-69.5 ± 5 HU vs. -68 ± 5 HU, p = 0.28), nor in terms of quartiles distribution (Q1, p = 0.83; Q2, p = 0.22, Q3, p = 0.83, Q4, p = 0.34). The discriminating parameters of the histogram class were mean (p = 0.02), 0,1st (p = 0.001), 10th (p = 0.002), and 50th percentiles (p = 0.02). DifVarnc was the discriminating parameter of the co-occurrence matrix class (p = 0.007).The TSF thickness was 15 ± 6 mm in group A and 19.5 ± 5 mm in group B (p = 0.003). The TSF had a mean density of -97 ± 19 HU in group A and -95.8 ± 19 HU in group B (p = 0.75). The discriminating parameters of texture analysis were 10th (p = 0.03), 50th (p = 0.01), 90th percentiles (p = 0.04), S(0,1)SumAverg (p = 0.02), S(1,-1)SumOfSqs (p = 0.02), S(3,0)Contrast (p = 0.03), S(3,0)SumAverg (p = 0.02), S(4,0)SumAverg (p = 0.04), Horzl_RLNonUni (p = 0.02), and Vertl_LngREmph (p = 0.0005). Conclusions: Texture analysis provides distinctive radiomic parameters of EF and TSF. EF and TSF had different radiomic features as the BMI varies.

Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images.

Radiomic analysis allows for the detection of imaging biomarkers supporting decision-making processes in clinical environments, from diagnosis to prognosis. Frequently, the original set of radiomic features is augmented by considering high-level features, such as wavelet transforms. However, several wavelets families (so called kernels) are able to generate different multi-resolution representations of the original image, and which of them produces more salient images is not yet clear. In this study, an in-depth analysis is performed by comparing different wavelet kernels and by evaluating their impact on predictive capabilities of radiomic models. A dataset composed of 1589 chest X-ray images was used for COVID-19 prognosis prediction as a case study. Random forest, support vector machine, and XGBoost were trained (on a subset of 1103 images) after a rigorous feature selection strategy to build-up the predictive models. Next, to evaluate the models generalization capability on unseen data, a test phase was performed (on a subset of 486 images). The experimental findings showed that Bior1.5, Coif1, Haar, and Sym2 kernels guarantee better and similar performance for all three machine learning models considered. Support vector machine and random forest showed comparable performance, and they were better than XGBoost. Additionally, random forest proved to be the most stable model, ensuring an appropriate balance between sensitivity and specificity.

Basalt Fiber Hybridization Effects on High-Performance Sisal-Reinforced Biocomposites.

The increasing attention given to environmental protection, largely through specific regulations on environmental impact and the recycling of materials, has led to a considerable interest of researchers in biocomposites, materials consisting of bio-based or green polymer matrixes reinforced by natural fibers. Among the various reinforcing natural fibers, sisal fibers are particularly promising for their good mechanical properties, low specific weight and wide availability on the current market. As proven in literature by various authors, the hybridization of biocomposites by synthetical fibers or different natural fibers can lead to an interesting improvement of the mechanical properties or, in turn, of the strength against environmental agents. Consequently, this can lead to a significant enlargement of their practical applications, in particular from quite common non-structural applications (dashboards, fillings, soundproofing, etc.) towards semi-structural (panels, etc.) and structural applications (structural elements of civil construction and/or machine components). Hybridizations with natural fibers or with ecofriendly basalt fibers are the most interesting ones, since they permit the improvement of the biocomposite's performance without an appreciable increment on environmental impact, as occurs instead for synthetic fiber hybridizations that are also widely proposed in the literature. In order to further increase the mechanical performance and, above all, to reduce the aging effects on high-performance sisal-reinforced biocomposites due to environmental agents, the hybridization of such biocomposites with basalt fibers are studied with tensile, compression and delamination tests performed by varying the exposition to environmental agents. In brief, the experimental analysis has shown that hybridization can lead to further enhancements of mechanical performance (strength and stiffness) that increase with basalt volume fraction and can lead to appreciable reductions in the aging effects on mechanical performance by simple hybridization of the surface laminae. Therefore, such a hybridization can be advantageously used in all practical outdoor applications in which high-performance sisal biocomposites can be exposed to significant environmental agents (temperature, humidity, UV).

3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients.

RATIONALE AND OBJECTIVES: To develop and validate a radiomic model, with radiomic features extracted from breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) from a 1.5T scanner, for predicting the malignancy of masses with enhancement. Images were acquired using an 8-channel breast coil in the axial plane. The rationale behind this study is to show the feasibility of a radiomics-powered model that could be integrated into the clinical practice by exploiting only standard-of-care DCE-MRI with the goal of reducing the required image pre-processing (ie, normalization and quantitative imaging map generation). MATERIALS AND METHODS: 107 radiomic features were extracted from a manually annotated dataset of 111 patients, which was split into discovery and test sets. A feature calibration and pre-processing step was performed to find only robust non-redundant features. An in-depth discovery analysis was performed to define a predictive model: for this purpose, a Support Vector Machine (SVM) was trained in a nested 5-fold cross-validation scheme, by exploiting several unsupervised feature selection methods. The predictive model performance was evaluated in terms of Area Under the Receiver Operating Characteristic (AUROC), specificity, sensitivity, PPV and NPV. The test was performed on unseen held-out data. RESULTS: The model combining Unsupervised Discriminative Feature Selection (UDFS) and SVMs on average achieved the best performance on the blinded test set: AUROC = 0.725±0.091, sensitivity = 0.709±0.176, specificity = 0.741±0.114, PPV = 0.72±0.093, and NPV = 0.75±0.114. CONCLUSION: In this study, we built a radiomic predictive model based on breast DCE-MRI, using only the strongest enhancement phase, with promising results in terms of accuracy and specificity in the differentiation of malignant from benign breast lesions.

Advanced Computational Methods for Oncological Image Analysis.

The Special Issue "Advanced Computational Methods for Oncological Image Analysis", published for the Journal of Imaging, covered original research papers about state-of-the-art and novel algorithms and methodologies, as well as applications of computational methods for oncological image analysis, ranging from radiogenomics to deep learning [...].

Biological and Mechanical Characterization of the Random Positioning Machine (RPM) for Microgravity Simulations.

The rapid improvement of space technologies is leading to the continuous increase of space missions that will soon bring humans back to the Moon and, in the coming future, toward longer interplanetary missions such as the one to Mars. The idea of living in space is charming and fascinating; however, the space environment is a harsh place to host human life and exposes the crew to many physical challenges. The absence of gravity experienced in space affects many aspects of human biology and can be reproduced in vitro with the help of microgravity simulators. Simulated microgravity (s-µg) is applied in many fields of research, ranging from cell biology to physics, including cancer biology. In our study, we aimed to characterize, at the biological and mechanical level, a Random Positioning Machine in order to simulate microgravity in an in vitro model of Triple-Negative Breast Cancer (TNBC). We investigated the effects played by s-µg by analyzing the change of expression of some genes that drive proliferation, survival, cell death, cancer stemness, and metastasis in the human MDA-MB-231 cell line. Besides the mechanical verification of the RPM used in our studies, our biological findings highlighted the impact of s-µg and its putative involvement in cancer progression.

A Computational Study on Temperature Variations in MRgFUS Treatments Using PRF Thermometry Techniques and Optical Probes.

Structural and metabolic imaging are fundamental for diagnosis, treatment and follow-up in oncology. Beyond the well-established diagnostic imaging applications, ultrasounds are currently emerging in the clinical practice as a noninvasive technology for therapy. Indeed, the sound waves can be used to increase the temperature inside the target solid tumors, leading to apoptosis or necrosis of neoplastic tissues. The Magnetic resonance-guided focused ultrasound surgery (MRgFUS) technology represents a valid application of this ultrasound property, mainly used in oncology and neurology. In this paper; patient safety during MRgFUS treatments was investigated by a series of experiments in a tissue-mimicking phantom and performing ex vivo skin samples, to promptly identify unwanted temperature rises. The acquired MR images, used to evaluate the temperature in the treated areas, were analyzed to compare classical proton resonance frequency (PRF) shift techniques and referenceless thermometry methods to accurately assess the temperature variations. We exploited radial basis function (RBF) neural networks for referenceless thermometry and compared the results against interferometric optical fiber measurements. The experimental measurements were obtained using a set of interferometric optical fibers aimed at quantifying temperature variations directly in the sonication areas. The temperature increases during the treatment were not accurately detected by MRI-based referenceless thermometry methods, and more sensitive measurement systems, such as optical fibers, would be required. In-depth studies about these aspects are needed to monitor temperature and improve safety during MRgFUS treatments.

A quantum-inspired classifier for clonogenic assay evaluations.

Recent advances in Quantum Machine Learning (QML) have provided benefits to several computational processes, drastically reducing the time complexity. Another approach of combining quantum information theory with machine learning-without involving quantum computers-is known as Quantum-inspired Machine Learning (QiML), which exploits the expressive power of the quantum language to increase the accuracy of the process (rather than reducing the time complexity). In this work, we propose a large-scale experiment based on the application of a binary classifier inspired by quantum information theory to the biomedical imaging context in clonogenic assay evaluation to identify the most discriminative feature, allowing us to enhance cell colony segmentation. This innovative approach offers a two-fold result: (1) among the extracted and analyzed image features, homogeneity is shown to be a relevant feature in detecting challenging cell colonies; and (2) the proposed quantum-inspired classifier is a novel and outstanding methodology, compared to conventional machine learning classifiers, for the evaluation of clonogenic assays.

New Concept in Bioderived Composites: Biochar as Toughening Agent for Improving Performances and Durability of Agave-Based Epoxy Biocomposites.

Biocomposites are increasingly used in the industry for the replacement of synthetic materials, thanks to their good mechanical properties, being lightweight, and having low cost. Unfortunately, in several potential fields of structural application their static strength and fatigue life are not high enough. For this reason, several chemical treatments on the fibers have been proposed in literature, although still without fully satisfactory results. To overcome this drawback, in this study we present a procedure based on the addition of a carbonaceous filler to a green epoxy matrix reinforced by Agave sisalana fibers. Among all carbon-based materials, biochar was selected for its environmental friendliness, along with its ability to improve the mechanical properties of polymers. Different percentages of biochar, 1, 2, and 4 wt %, were finely dispersed into the resin using a mixer and a sonicator, then a compression molding process coupled with an optimized thermomechanical cure process was used to produce a short fiber biocomposite with Vf = 35%. Systematic experimental tests have shown that the presence of biochar, in the amount 2 wt %, has significant effects on the matrix and fiber interphase, and leads to an increase of up to three orders of magnitude in the fatigue life, together with an appreciable improvement in static tensile strength.

Analysis of the Parameters Affecting the Stiffness of Short Sisal Fiber Biocomposites Manufactured by Compression-Molding.

The use of natural fiber-based composites is on the rise in many industries. Thanks to their eco-sustainability, these innovative materials make it possible to adapt the production of components, systems and machines to the increasingly stringent regulations on environmental protection, while at the same time reducing production costs, weight and operating costs. Optimizing the mechanical properties of biocomposites is an important goal of applied research. In this work, using a new numerical approach, the effects of the volume fraction, average length, distribution of orientation and curvature of fibers on the Young's modulus of a biocomposite reinforced with short natural fibers were studied. Although the proposed approach could be applied to any biocomposite, sisal fibers and an eco-sustainable thermosetting matrix (green epoxy) were considered in both simulations and the associated experimental assessment. The results of the simulations showed the following effects of the aforementioned parameters on Young's modulus: a linear growth with the volume fraction, nonlinear growth as the length of the fibers increased, a reduction as the average curvature increased and an increase in stiffness in the x-y plane as the distribution of fiber orientation in the z direction decreased.

ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy.

Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7× speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96, calculated against the manual cell counting, on the two tested datasets.

A semi-automatic approach for epicardial adipose tissue segmentation and quantification on cardiac CT scans.

Many studies have shown that epicardial fat is associated with a higher risk of heart diseases. Accurate epicardial adipose tissue quantification is still an open research issue. Considering that manual approaches are generally user-dependent and time-consuming, computer-assisted tools can considerably improve the result repeatability as well as reduce the time required for performing an accurate segmentation. Unfortunately, fully automatic strategies might not always identify the Region of Interest (ROI) correctly. Moreover, they could require user interaction for handling unexpected events. This paper proposes a semi-automatic method for Epicardial Fat Volume (EFV) segmentation and quantification. Unlike supervised Machine Learning approaches, the method does not require any initial training or modeling phase to set up the system. As a further key novelty, the method also yields a subdivision into quartiles of the adipose tissue density. Quartile-based analysis conveys information about fat densities distribution, enabling an in-depth study towards a possible correlation between fat amounts, fat distribution, and heart diseases. Experimental tests were performed on 50 Calcium Score (CaSc) series and 95 Coronary Computed Tomography Angiography (CorCTA) series. Area-based and distance-based metrics were used to evaluate the segmentation accuracy, by obtaining Dice Similarity Coefficient (DSC) = 93.74% and Mean Absolute Distance (MAD) = 2.18 for CaSc, as well as DSC = 92.48% and MAD = 2.87 for CorCTA. Moreover, the Pearson and Spearman coefficients were computed for quantifying the correlation between the ground-truth EFV and the corresponding automated measurement, by obtaining 0.9591 and 0.9490 for CaSc, and 0.9513 and 0.9319 for CorCTA, respectively. In conclusion, the proposed EFV quantification and analysis method represents a clinically useable tool assisting the cardiologist to gain insights into a specific clinical scenario and leading towards personalized diagnosis and therapy.

Radiosensitizing effect of curcumin-loaded lipid nanoparticles in breast cancer cells.

In breast cancer (BC) care, radiotherapy is considered an efficient treatment, prescribed both for controlling localized tumors or as a therapeutic option in case of inoperable, incompletely resected or recurrent tumors. However, approximately 90% of BC-related deaths are due to the metastatic tumor progression. Then, it is strongly desirable to improve tumor radiosensitivity using molecules with synergistic action. The main aim of this study is to develop curcumin-loaded solid nanoparticles (Cur-SLN) in order to increase curcumin bioavailability and to evaluate their radiosensitizing ability in comparison to free curcumin (free-Cur), by using an in vitro approach on BC cell lines. In addition, transcriptomic and metabolomic profiles, induced by Cur-SLN treatments, highlighted networks involved in this radiosensitization ability. The non tumorigenic MCF10A and the tumorigenic MCF7 and MDA-MB-231 BC cell lines were used. Curcumin-loaded solid nanoparticles were prepared using ethanolic precipitation and the loading capacity was evaluated by UV spectrophotometer analysis. Cell survival after treatments was evaluated by clonogenic assay. Dose-response curves were generated testing three concentrations of free-Cur and Cur-SLN in combination with increasing doses of IR (2-9 Gy). IC50 value and Dose Modifying Factor (DMF) was measured to quantify the sensitivity to curcumin and to combined treatments. A multi-"omic" approach was used to explain the Cur-SLN radiosensitizer effect by microarray and metobolomic analysis. We have shown the efficacy of the Cur-SLN formulation as radiosensitizer on three BC cell lines. The DMFs values, calculated at the isoeffect of SF = 50%, showed that the Luminal A MCF7 resulted sensitive to the combined treatments using increasing concentration of vehicled curcumin Cur-SLN (DMF: 1,78 with 10 µM Cur-SLN.) Instead, triple negative MDA-MB-231 cells were more sensitive to free-Cur, although these cells also receive a radiosensitization effect by combination with Cur-SLN (DMF: 1.38 with 10 µM Cur-SLN). The Cur-SLN radiosensitizing function, evaluated by transcriptomic and metabolomic approach, revealed anti-oxidant and anti-tumor effects. Curcumin loaded- SLN can be suggested in future preclinical and clinical studies to test its concomitant use during radiotherapy treatments with the double implications of being a radiosensitizing molecule against cancer cells, with a protective role against IR side effects.

A novel framework for MR image segmentation and quantification by using MedGA.

BACKGROUND AND OBJECTIVES: Image segmentation represents one of the most challenging issues in medical image analysis to distinguish among different adjacent tissues in a body part. In this context, appropriate image pre-processing tools can improve the result accuracy achieved by computer-assisted segmentation methods. Taking into consideration images with a bimodal intensity distribution, image binarization can be used to classify the input pictorial data into two classes, given a threshold intensity value. Unfortunately, adaptive thresholding techniques for two-class segmentation work properly only for images characterized by bimodal histograms. We aim at overcoming these limitations and automatically determining a suitable optimal threshold for bimodal Magnetic Resonance (MR) images, by designing an intelligent image analysis framework tailored to effectively assist the physicians during their decision-making tasks. METHODS: In this work, we present a novel evolutionary framework for image enhancement, automatic global thresholding, and segmentation, which is here applied to different clinical scenarios involving bimodal MR image analysis: (i) uterine fibroid segmentation in MR guided Focused Ultrasound Surgery, and (ii) brain metastatic cancer segmentation in neuro-radiosurgery therapy. Our framework exploits MedGA as a pre-processing stage. MedGA is an image enhancement method based on Genetic Algorithms that improves the threshold selection, obtained by the efficient Iterative Optimal Threshold Selection algorithm, between the underlying sub-distributions in a nearly bimodal histogram. RESULTS: The results achieved by the proposed evolutionary framework were quantitatively evaluated, showing that the use of MedGA as a pre-processing stage outperforms the conventional image enhancement methods (i.e., histogram equalization, bi-histogram equalization, Gamma transformation, and sigmoid transformation), in terms of both MR image enhancement and segmentation evaluation metrics. CONCLUSIONS: Thanks to this framework, MR image segmentation accuracy is considerably increased, allowing for measurement repeatability in clinical workflows. The proposed computational solution could be well-suited for other clinical contexts requiring MR image analysis and segmentation, aiming at providing useful insights for differential diagnosis and prognosis.

Combined taxane-based chemotherapy and intensity-modulated radiotherapy with simultaneous integrated boost for gastroesophageal junction adenocarcinoma.

We describe our experience, gained over the past 3 years, in the treatment of gastroesophageal junction adenocarcinoma, whose incidence has been increasing in recent years. In our series, we present the results to a follow-up of about 2 years for a total of 18 patients, treated with a particularly intensive combination treatment. It consists of neoadjuvant induction chemotherapy with the protocol docetaxel-cisplatin-5-fluorouracil for four cycles, before a concomitant chemoradiotherapy treatment. During combined phase, patients received an intensity-modulated radiotherapy and a weekly cisplatin. We will present the data to a long follow-up time and we will discuss the literature, the integration with thoracoabdominal surgery and other specific issues of this pathology.

Single-institution experience of intensity-modulated radiotherapy for malignant pleural mesothelioma at University of Catania.

AIM: The multimodal approach to malignant pleural mesothelioma is gradually becoming the standard of care for this disease in patients with good performance status. Materials & methods: We report our experience concerning eight cases treated with the use of static step-and-shoot intensity-modulated radiotherapy to the whole pleural cavity, in patients already undergoing surgical and/or antiblastic therapy. Results & conclusion: Results at a median follow-up of 16 months showed a median survival from the initial treatment of 29 months, with lung toxicity of grade II reported only in two patients.

Area-based cell colony surviving fraction evaluation: A novel fully automatic approach using general-purpose acquisition hardware.

BACKGROUND: The current methodology for the Surviving Fraction (SF) measurement in clonogenic assay, which is a technique to study the anti-proliferative effect of treatments on cell cultures, involves manual counting of cell colony forming units. This procedure is operator-dependent and error-prone. Moreover, the identification of the exact colony number is often not feasible due to the high growth rate leading to the adjacent colony merging. As a matter of fact, conventional assessment does not deal with the colony size, which is generally correlated with the delivered radiation dose or the administered cytotoxic agent. METHOD: Considering that the Area Covered by Colony (ACC) is proportional to the colony number and size as well as to the growth rate, we propose a novel fully automatic approach exploiting Circle Hough Transform, to automatically detect the wells in the plate, and local adaptive thresholding, which calculates the percentage of ACC for the SF quantification. This measurement relies just on this covering percentage and does not consider the colony number, preventing inconsistencies due to intra- and inter-operator variability. RESULTS: To evaluate the accuracy of the proposed approach, we compared the SFs obtained by our automatic ACC-based method against the conventional counting procedure. The achieved results (r = 0.9791 and r = 0.9682 on MCF7 and MCF10A cells, respectively) showed values highly correlated with the measurements using the traditional approach based on colony number alone. CONCLUSIONS: The proposed computer-assisted methodology could be integrated in laboratory practice as an expert system for the SF evaluation in clonogenic assays.

A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning.

BACKGROUND AND OBJECTIVES: Nowadays, clinical practice in Gamma Knife treatments is generally based on MRI anatomical information alone. However, the joint use of MRI and PET images can be useful for considering both anatomical and metabolic information about the lesion to be treated. In this paper we present a co-segmentation method to integrate the segmented Biological Target Volume (BTV), using [11C]-Methionine-PET (MET-PET) images, and the segmented Gross Target Volume (GTV), on the respective co-registered MR images. The resulting volume gives enhanced brain tumor information to be used in stereotactic neuro-radiosurgery treatment planning. GTV often does not match entirely with BTV, which provides metabolic information about brain lesions. For this reason, PET imaging is valuable and it could be used to provide complementary information useful for treatment planning. In this way, BTV can be used to modify GTV, enhancing Clinical Target Volume (CTV) delineation. METHODS: A novel fully automatic multimodal PET/MRI segmentation method for Leksell Gamma Knife® treatments is proposed. This approach improves and combines two computer-assisted and operator-independent single modality methods, previously developed and validated, to segment BTV and GTV from PET and MR images, respectively. In addition, the GTV is utilized to combine the superior contrast of PET images with the higher spatial resolution of MRI, obtaining a new BTV, called BTVMRI. A total of 19 brain metastatic tumors, undergone stereotactic neuro-radiosurgery, were retrospectively analyzed. A framework for the evaluation of multimodal PET/MRI segmentation is also presented. Overlap-based and spatial distance-based metrics were considered to quantify similarity concerning PET and MRI segmentation approaches. Statistics was also included to measure correlation among the different segmentation processes. Since it is not possible to define a gold-standard CTV according to both MRI and PET images without treatment response assessment, the feasibility and the clinical value of BTV integration in Gamma Knife treatment planning were considered. Therefore, a qualitative evaluation was carried out by three experienced clinicians. RESULTS: The achieved experimental results showed that GTV and BTV segmentations are statistically correlated (Spearman's rank correlation coefficient: 0.898) but they have low similarity degree (average Dice Similarity Coefficient: 61.87 ± 14.64). Therefore, volume measurements as well as evaluation metrics values demonstrated that MRI and PET convey different but complementary imaging information. GTV and BTV could be combined to enhance treatment planning. In more than 50% of cases the CTV was strongly or moderately conditioned by metabolic imaging. Especially, BTVMRI enhanced the CTV more accurately than BTV in 25% of cases. CONCLUSIONS: The proposed fully automatic multimodal PET/MRI segmentation method is a valid operator-independent methodology helping the clinicians to define a CTV that includes both metabolic and morphologic information. BTVMRI and GTV should be considered for a comprehensive treatment planning.

Influence of splanchnic artery stenosis on the in-hospital clinical course of elderly patients.

BACKGROUND: Chronic mesenteric ischaemia (CMI) has a long asymptomatic period, but little is known about the clinical implications of this phase of the disease, particularly in the elderly, who are most exposed to the condition. AIMS: The aim of the present observational study was to survey the in-hospital clinical course of elderly patients during the non-specific phase of the disease due to occlusion of at least one splanchnic artery. METHODS: For a median of 29 months, we followed up 85 patients aged 65 and over who, for various clinical reasons, had undergone computed tomographic and magnetic resonance angiography during 2010 at Padua Teaching Hospital, assessing economic impact and reasons for admission. RESULTS: Thirty-four of these patients had at least one occluded artery, and 68 % of them had at least one hospital admission. Elderly CMI patients were characterised by a higher number of admissions (median 2 vs 1 p = 0.05) and a higher cost (6044 vs 1733 Euros p = 0.04), but did not present typical gastrointestinal symptoms. The higher number of hospital admissions was not due to specific clinical risks (admitting wards: general medicine: 32 vs 29 %, p = 0.77; general surgery 8 vs 14 %, p = 0.73; vascular surgery: 26.5 vs 20 %, p = 0.46). CONCLUSIONS: In the asymptomatic phase of CMI, hospitalised elderly patients with at least one occluded splanchnic artery can be subject to a more challenging in-hospital clinical course.