showing 38 related works from this author
K-nearest neighbor driving active contours to delineate biological tumor volumes
2019
Abstract An algorithm for tumor delineation in positron emission tomography (PET) is presented. Segmentation is achieved by a local active contour algorithm, integrated and optimized with the k-nearest neighbor (KNN) classification method, which takes advantage of the stratified k-fold cross-validation strategy. The proposed approach is evaluated considering the delineation of cancers located in different body districts (i.e. brain, head and neck, and lung), and considering different PET radioactive tracers. Data are pre-processed in order to be expressed in terms of standardized uptake value, the most widely used PET quantification index. The algorithm uses an initial, operator selected re…
A Predictive System to Classify Preoperative Grading of Rectal Cancer Using Radiomics Features
2022
Although preoperative biopsy of rectal cancer (RC) is an essential step for confirmation of diagnosis, it currently fails to provide prognostic information to the clinician beyond a rough estimation of tumour grade. In this study we used a risk classification to stratified patient in low-risk and high-risk patients in relation to the disease free survival and the overall survival using histopathological post-operative features. The purpose of this study was to evaluate if low-risk and high-risk RC can be distinguished using a CT-based radiomics model. We retrospectively reviewed the preoperative abdominal contrast-enhanced CT of 40 patients with RC. CT portal-venous phase was used for manua…
Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging
2021
Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardwar…
Unsupervised tissue classification of brain MR images for voxel-based morphometry analysis
2016
In this article, a fully unsupervised method for brain tissue segmentation of T1-weighted MRI 3D volumes is proposed. The method uses the Fuzzy C-Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro-radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial-and-error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro-Spinal Fluid in …
Building a statistical surveillance dashboard for COVID-19 infection worldwide
2020
When a pandemic like the current novel coronavirus (COVID-19) breaks out, it is important that authorities, healthcare organizations and official decision makers, have in place an effective monitoring system to promptly analyze data, create new insights into problematic areas and generate actionable knowledge for fact-based decision making. The aim of this article is to describe an initial work focused on building a comprehensive statistical surveillance dashboard for the epidemic of COVID-19, which can be exploited also for future needs. We propose novel ways of exploring, analyzing and presenting data, using metrics that have not been used previously. We also show the steps necessary to b…
Radiomics Analyses of Schwannomas in the Head and Neck: A Preliminary Analysis
2022
The purpose of this preliminary study was to evaluate the differences in Magnetic Resonance Imaging (MRI)-based radiomics analysis between cerebellopontine angle neurinomas and schwannomas originating from other locations in the neck spaces. Twenty-six patients with available MRI exams and head and neck schwannomas were included. Lesions were manually segmented on the precontrast and postcontrast T1 sequences. The radiomics features were extracted by using PyRadiomics software, and a total of 120 radiomics features were obtained from each segmented tumor volume. An operator-independent hybrid descriptive‐inferential method was adopted for the selection and reduction of the features, while d…
PI-RADS 3 Lesions: Role of Prostate MRI Texture Analysis in the Identification of Prostate Cancer
2021
Abstract Purpose To determine the diagnostic performance of texture analysis of prostate MRI for the diagnosis of prostate cancer among Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions. Materials and Methods Forty-three patients with at least 1 PI-RADS 3 lesion on prostate MRI performed between June 2016 and January 2019 were retrospectively included. Reference standard was pathological analysis of radical prostatectomy specimens or MRI-targeted biopsies. Texture analysis extraction of target lesions was performed on axial T2-weighted images and apparent diffusion coefficient (ADC) maps using a radiomic software. Lesions were categorized as prostate cancer (Gleason score [GS] …
Oral Palmitoylethanolamide Treatment Is Associated with Reduced Cutaneous Adverse Effects of Interferon-β1a and Circulating Proinflammatory Cytokines…
2016
Palmitoylethanolamide (PEA) is an endogenous lipid mediator known to reduce pain and inflammation. However, only limited clinical studies have evaluated the effects of PEA in neuroinflammatory and neurodegenerative diseases. Multiple sclerosis (MS) is a chronic autoimmune and inflammatory disease of the central nervous system. Although subcutaneous administration of interferon (IFN)-β1a is approved as first-line therapy for the treatment of relapsing–remitting MS (RR-MS), its commonly reported adverse events (AEs) such as pain, myalgia, and erythema at the injection site, deeply affect the quality of life (QoL) of patients with MS. In this randomized, double-blind, placebo-controlled study,…
Hybrid descriptive-inferential method for key feature selection in prostate cancer radiomics
2021
In healthcare industry 4.0, a big role is played by radiomics. Radiomics concerns the extraction and analysis of quantitative information not visible to the naked eye, even by expert operators, from biomedical images. Radiomics involves the management of digital images as data matrices, with the aim of extracting a number of morphological and predictive variables, named features, using automatic or semi-automatic methods. Multidisciplinary methods as machine learning and deep learning are fully involved in this field. However, the large number of features requires efficient and effective core methods for their selection, in order to avoid bias or misinterpretations problems. In this work, t…
Radiomics: A New Biomedical Workflow to Create a Predictive Model
2020
‘Radiomics’ is utilized to improve the prediction of patient overall survival and/or outcome. Target segmentation, feature extraction, feature selection, and classification model are the fundamental blocks of a radiomics workflow. Nevertheless, these blocks can be affected by several issues, i.e. high inter- and intra-observer variability. To overcome these issues obtaining reproducible results, we propose a novel radiomics workflow to identify a relevant prognostic model concerning a real clinical problem. In the specific, we propose an operator-independent segmentation system with the consequent automatic extraction of radiomics features, and a novel feature selection approach to create a…
An ontology-based retrieval system for mammographic reports
2015
In healthcare domain it can be useful to compare unstructured free-text clinical reports in order to enable the search for similar and/or relevant clinical cases. In data mining and text analysis tasks, the cosine similarity is usually used for texts comparison purposes. It is usually performed by computing the standard document vector cosine similarity between the two vectors representing the report pair under analysis. In this paper a novel system based on text pre-processing techniques and a modelled medical knowledge, using an improved radiological ontology, is proposed. Medical terms organized in a hierarchical tree can assess semantic similarity relationships between unstructured repo…
Choline PET/CT Features to Predict Survival Outcome in High Risk Prostate Cancer Restaging: A Preliminary Machine-Learning Radiomics Study
2020
Background Radiomic features are increasingly utilized to evaluate tumor heterogeneity in PET imaging but to date its role has not been investigated for Cho-PET in prostate cancer. The potential application of radiomics features analysis using a machine-learning radiomics algorithm was evaluated to select 18F-Cho PET/CT imaging features to predict disease progression in PCa. Methods We retrospectively analyzed high-risk PCa patients who underwent restaging 18F-Cho PET/CT from November 2013 to May 2018. 18F-Cho PET/CT studies and related structures containing volumetric segmentations were imported in the "CGITA" toolbox to extract imaging features from each lesion. A Machine-learning model h…
A kernel support vector machine based technique for Crohnâs disease classification in human patients
2017
In this paper a new technique for classification of patients affected by Crohnâs disease (CD) is proposed. The proposed technique is based on a Kernel Support Vector Machine (KSVM) and it adopts a Stratified K-Fold Cross-Validation strategy to enhance the KSVM classifier reliability. Traditional manual classification methods require radiological expertise and they usually are very time-consuming. Accordingly to three expert radiologists, a dataset composed of 300 patients has been selected for KSVM training and validation. Each patient was codified by 22 extracted qualitative features and classified as Positive or Negative as the related histological specimen result showed the CD. The eff…
Robustness of PET Radiomics Features: Impact of Co-Registration with MRI
2021
Radiomics holds great promise in the field of cancer management. However, the clinical application of radiomics has been hampered by uncertainty about the robustness of the features extracted from the images. Previous studies have reported that radiomics features are sensitive to changes in voxel size resampling and interpolation, image perturbation, or slice thickness. This study aims to observe the variability of positron emission tomography (PET) radiomics features under the impact of co-registration with magnetic resonance imaging (MRI) using the difference percentage coefficient, and the Spearman’s correlation coefficient for three groups of images: (i) original PET, (ii) PET after co-…
Automatic multi-seed detection for MR breast image segmentation
2017
In this paper an automatic multi-seed detection method for magnetic resonance (MR) breast image segmentation is presented. The proposed method consists of three steps: (1) pre-processing step to locate three regions of interest (axillary and sternal regions); (2) processing step to detect maximum concavity points for each region of interest; (3) breast image segmentation step. Traditional manual segmentation methods require radiological expertise and they usually are very tiring and time-consuming. The approach is fast because the multi-seed detection is based on geometric properties of the ROI. When the maximum concavity points of the breast regions have been detected, region growing and m…
Vacuum-Assisted Wound Closure with Mesh-Mediated Fascial Traction Achieves Better Outcomes than Vacuum-Assisted Wound Closure Alone: A Comparative St…
2017
Background Open abdomen (OA) permits the application of damage control surgery principles when abdominal trauma, sepsis, severe acute peritonitis and abdominal compartmental syndrome (ACS) occur. Methods Non-traumatic patients treated with OA between January 2010 and December 2015 were identified in a prospective database, and the data collected were retrospectively reviewed. Patients’ records were collected from charts and the surgical and intensive care unit (ICU) registries. The Acosta ‘‘modified’’ technique was used to achieve fascial closure in vacuum-assisted wound closure and mesh-mediated fascial traction (VAWCM) patients. Sex, age, simplified acute physiology score II (SAPS II), ab…
Deep Learning Networks for Automatic Retroperitoneal Sarcoma Segmentation in Computerized Tomography
2022
The volume estimation of retroperitoneal sarcoma (RPS) is often difficult due to its huge dimensions and irregular shape; thus, it often requires manual segmentation, which is time-consuming and operator-dependent. This study aimed to evaluate two fully automated deep learning networks (ENet and ERFNet) for RPS segmentation. This retrospective study included 20 patients with RPS who received an abdominal computed tomography (CT) examination. Forty-nine CT examinations, with a total of 72 lesions, were included. Manual segmentation was performed by two radiologists in consensus, and automatic segmentation was performed using ENet and ERFNet. Significant differences between manual and automat…
Radiomics Analysis of Brain [18F]FDG PET/CT to Predict Alzheimer’s Disease in Patients with Amyloid PET Positivity: A Preliminary Report on the Appli…
2022
Background: Early in-vivo diagnosis of Alzheimer’s disease (AD) is crucial for accurate management of patients, in particular, to select subjects with mild cognitive impairment (MCI) that may evolve into AD, and to define other types of MCI non-AD patients. The application of artificial intelligence to functional brain [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography(CT) aiming to increase diagnostic accuracy in the diagnosis of AD is still undetermined. In this field, we propose a radiomics analysis on advanced imaging segmentation method Statistical Parametric Mapping (SPM)-based completed with a Machine-Learning (ML) application to predict the diagnosi…
Radiomics analysis of 18F-Choline PET/CT in the prediction of disease outcome in high-risk prostate cancer: an explorative study on machine learning …
2021
Objective: The aim of this study was (1) to investigate the application of texture analysis of choline PET/CT images in prostate cancer (PCa) patients and (2) to propose a machine-learning radiomics model able to select PET features predictive of disease progression in PCa patients with a same high-risk class at restaging. Material and methods: Ninety-four high-risk PCa patients who underwent restaging Cho-PET/CT were analyzed. Follow-up data were recorded for a minimum of 13 months after the PET/CT scan. PET images were imported in LIFEx toolbox to extract 51 features from each lesion. A statistical system based on correlation matrix and point-biserial-correlation coefficient has been impl…
Normal and Abnormal Tissue Classification in Positron Emission Tomography Oncological Studies
2018
Positron Emission Tomography (PET) imaging is increasingly used in radiotherapy environment as well as for staging and assessing treatment response. The ability to classify PET tissues, as normal versus abnormal tissues, is crucial for medical analysis and interpretation. For this reason, a system for classifying PET area is implemented and validated. The proposed classification is carried out using k-nearest neighbor (KNN) method with the stratified K-Fold Cross-Validation strategy to enhance the classifier reliability. A dataset of eighty oncological patients are collected for system training and validation. For every patient, lesion (abnormal tissue) and background (normal tissue around …
Assessing High-Order Interdependencies Through Static O-Information Measures Computed on Resting State fMRI Intrinsic Component Networks
2022
Resting state brain networks have reached a strong popularity in recent scientific endeavors due to their feasibility to characterize the metabolic mechanisms at the basis of neural control when the brain is not engaged in any task. The evaluation of these states, consisting in complex physiological processes employing a large amount of energy, is carried out from diagnostic images acquired through resting-state functionalmagnetic resonance (RS-fMRI) on different populations of subjects. In the present study, RS-fMRI signals from the WU-MinnHCP 1200 Subjects Data Release of the Human Connectome Project were studied with the aim of investigating the high order organizational structure of the…
Performance of Radiomics Features in the Quantification of Idiopathic Pulmonary Fibrosis from HRCT.
2020
Background: Our study assesses the diagnostic value of different features extracted from high resolution computed tomography (HRCT) images of patients with idiopathic pulmonary fibrosis. These features are investigated over a range of HRCT lung volume measurements (in Hounsfield Units) for which no prior study has yet been published. In particular, we provide a comparison of their diagnostic value at different Hounsfield Unit (HU) thresholds, including corresponding pulmonary functional tests. Methods: We consider thirty-two patients retrospectively for whom both HRCT examinations and spirometry tests were available. First, we analyse the HRCT histogram to extract quantitative lung fibrosis…
Radiomics and Prostate MRI: Current Role and Future Applications
2021
Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine …
Active contour algorithm with discriminant analysis for delineating tumors in positron emission tomography.
2019
Abstract In the context of cancer delineation using positron emission tomography datasets, we present an innovative approach which purpose is to tackle the real-time, three-dimensional segmentation task in a full, or at least nearly full automatized way. The approach comprises a preliminary initialization phase where the user highlights a region of interest around the cancer on just one slice of the tomographic dataset. The algorithm takes care of identifying an optimal and user-independent region of interest around the anomalous tissue and located on the slice containing the highest standardized uptake value so to start the successive segmentation task. The three-dimensional volume is then…
OpenHVSR - Processing toolkit: Enhanced HVSR processing of distributed microtremor measurements and spatial variation of their informative content
2018
Abstract The investigation of seismic ambient noise (microtremor) in spectral ratio form, known as the Horizontal-to-Vertical Spectral Ratio technique, is extremely popular nowadays both to investigate large areas in a reduced amount of time, and to leverage a wider choice of low cost equipment. In general, measurements at multiple locations are collected to generate multiple, individual spectral ratio curves. Recently, however, there has been an increasing interest in spatially correlating informative content from different locations. Accordingly, we introduce a new computer program, “OpenHVSR – Processing Toolkit”, developed in Matlab (R2015b), specifically engineered to enhance data proc…
Diagnostic performance of qualitative and radiomics approach to parotid gland tumors: which is the added benefit of texture analysis?
2021
Objective: To investigate whether MRI-based texture analysis improves diagnostic performance for the diagnosis of parotid gland tumors compared to conventional radiological approach. Methods: Patients with parotid gland tumors who underwent salivary glands MRI between 2008 and 2019 were retrospectively selected. MRI analysis included a qualitative assessment by two radiologists (one of which subspecialized on head and neck imaging), and texture analysis on various sequences. Diagnostic performances including sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of qualitative features, radiologists’ diagnosis, and radiomic models were evaluated. Result…
A smart and operator independent system to delineate tumours in Positron Emission Tomography scans
2018
Abstract Positron Emission Tomography (PET) imaging has an enormous potential to improve radiation therapy treatment planning offering complementary functional information with respect to other anatomical imaging approaches. The aim of this study is to develop an operator independent, reliable, and clinically feasible system for biological tumour volume delineation from PET images. Under this design hypothesis, we combine several known approaches in an original way to deploy a system with a high level of automation. The proposed system automatically identifies the optimal region of interest around the tumour and performs a slice-by-slice marching local active contour segmentation. It automa…
Use of the KSVM-based system for the definition, validation and identification of the incisional hernia recurrence risk factors
2019
BACKGROUND: Incisional hernia is one of the most common complications after abdominal surgery with an incidence rate of 11 to 20% post laparotomy. Many different factors can be considered as risk factors of incisional hernia recurrence. The aim of this study is to confirm and to validate the incisional hernia recurrence risk factors and to identify and to validate new ones. METHODS: In the period from July 2007 to July 2017, 154 patients were selected and subjected to incisional hernia repair. The surgical operations were conducted under general anaesthesia. Patients received antibiotic prophylaxis when indicated, according to the hospital prophylaxis scheme. Inclusion criteria of the study…
A New Preclinical Decision Support System Based on PET Radiomics: A Preliminary Study on the Evaluation of an Innovative 64Cu-Labeled Chelator in Mou…
2022
The 64Cu-labeled chelator was analyzed in vivo by positron emission tomography (PET) imaging to evaluate its biodistribution in a murine model at different acquisition times. For this purpose, nine 6-week-old female Balb/C nude strain mice underwent micro-PET imaging at three different time points after 64Cu-labeled chelator injection. Specifically, the mice were divided into group 1 (acquisition 1 h after [64Cu] chelator administration, n = 3 mice), group 2 (acquisition 4 h after [64Cu]chelator administration, n = 3 mice), and group 3 (acquisition 24 h after [64Cu] chelator administration, n = 3 mice). Successively, all PET studies were segmented by means of registration with a standard te…
A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method
2020
AbstractBackgroundPositron Emission Tomography (PET) is increasingly utilized in radiomics studies for treatment evaluation purposes. Nevertheless, lesion volume identification in PET images is a critical and still challenging step in the process of radiomics, due to the low spatial resolution and high noise level of PET images. Currently, the biological target volume (BTV) is manually contoured by nuclear physicians, with a time expensive and operator-dependent procedure.This study aims to obtain BTVs from cerebral metastases in patients who underwent L-[11C]methionine (11C-MET) PET, using a fully automatic procedure and to use these BTVs to extract radiomics features to stratify between p…
Radiomics Analysis on Gadoxetate Disodium-Enhanced MRI Predicts Response to Transarterial Embolization in Patients with HCC
2022
Objectives: To explore the potential of radiomics on gadoxetate disodium-enhanced MRI for predicting hepatocellular carcinoma (HCC) response after transarterial embolization (TAE). Methods: This retrospective study included cirrhotic patients treated with TAE for unifocal HCC naïve to treatments. Each patient underwent gadoxetate disodium-enhanced MRI. Radiomics analysis was performed by segmenting the lesions on portal venous (PVP), 3-min transitional, and 20-min hepatobiliary (HBP) phases. Clinical data, laboratory variables, and qualitative features based on LI-RADSv2018 were assessed. Reference standard was based on mRECIST response criteria. Two different radiomics models were construc…
Brain magnetic resonance imaging radiomics features associated with hepatic encephalopathy in adult cirrhotic patients.
2022
Abstract Purpose Hepatic encephalopathy (HE) is a potential complication of cirrhosis. Magnetic resonance imaging (MRI) may demonstrate hyperintense T1 signal in the globi pallidi. The purpose of this study was to evaluate the performance of MRI-based radiomic features for diagnosing and grading chronic HE in adult patients affected by cirrhosis. Methods Adult patients with and without cirrhosis underwent brain MRI with identical imaging protocol on a 3T scanner. Patients without history of chronic liver disease were the control population. HE grading was based on underlying liver disease, severity of clinical manifestation, and number of encephalopathic episodes. Texture analysis was perfo…
Artificial Intelligence Applications on Restaging [18F]FDG PET/CT in Metastatic Colorectal Cancer: A Preliminary Report of Morpho-Functional Radiomic…
2022
Featured Application Based on results defined in this study, new investigations might propose morpho-functional-based radiomics algorithms for risk stratification with possible impact on treatment management in colorectal cancer. The aim of this study was to investigate the application of [F-18]FDG PET/CT images-based textural features analysis to propose radiomics models able to early predict disease progression (PD) and survival outcome in metastatic colorectal cancer (MCC) patients after first adjuvant therapy. For this purpose, 52 MCC patients who underwent [F-18]FDGPET/CT during the disease restaging process after the first adjuvant therapy were analyzed. Follow-up data were recorded f…
253. An accurate and operator independent method for biological tumour volume segmentation
2018
Purpose The aim of this paper is to develop an operator independent method for biological tumour volume (BTV) delineation from Positron Emission Tomography (PET) images. BTV delineation is challenging because of the low spatial resolution and high noise level in PET images. In addition, BTV varies substantially depending on the method used to segment. Manual delineation is widely-used, but it is strongly user dependent. Methods The proposed method starts with the automatic identification of the PET slice with maximum Standardized Uptake Value (SUV). Then, a user- independent mask is obtained by a rough pre-segmentation step and it is used to perform the local active contour segmentation on …
Feature Dimensionality Reduction for Mammographic Report Classification
2016
The amount and the variety of available medical data coming from multiple and heterogeneous sources can inhibit analysis, manual interpretation, and use of simple data management applications. In this paper a deep overview of the principal algorithms for dimensionality reduction is carried out; moreover, the most effective techniques are applied on a dataset composed of 4461 mammographic reports is presented. The most useful medical terms are converted and represented using a TF-IDF matrix, in order to enable data mining and retrieval tasks. A series of query have been performed on the raw matrix and on the same matrix after the dimensionality reduction obtained using the most useful techni…
Deep learning approach for the segmentation of aneurysmal ascending aorta.
2020
Diagnosis of ascending thoracic aortic aneurysm (ATAA) is based on the measurement of the maximum aortic diameter, but size is not a good predictor of the risk of adverse events. There is growing interest in the development of novel image-derived risk strategies to improve patient risk management towards a highly individualized level. In this study, the feasibility and efficacy of deep learning for the automatic segmentation of ATAAs was investigated using UNet, ENet, and ERFNet techniques. Specifically, CT angiography done on 72 patients with ATAAs and different valve morphology (i.e., tricuspid aortic valve, TAV, and bicuspid aortic valve, BAV) were semi-automatically segmented with Mimic…
Deep Learning Network for Segmentation of the Prostate Gland With Median Lobe Enlargement in T2-weighted MR Images: Comparison With Manual Segmentati…
2021
Purpose: Aim of this study was to evaluate a fully automated deep learning network named Efficient Neural Network (ENet) for segmentation of prostate gland with median lobe enlargement compared to manual segmentation. Materials and Methods: One-hundred-three patients with median lobe enlargement on prostate MRI were retrospectively included. Ellipsoid formula, manual segmentation and automatic segmentation were used for prostate volume estimation using T2 weighted MRI images. ENet was used for automatic segmentation; it is a deep learning network developed for fast inference and high accuracy in augmented reality and automotive scenarios. Student t-test was performed to compare prostate vol…
IMAGE PROCESSING, SEGMENTATION AND MACHINE LEARNING MODELS TO CLASSIFY AND DELINEATE TUMOR VOLUMES TO SUPPORT MEDICAL DECISION
2020
Techniques for processing and analysing images and medical data have become the main’s translational applications and researches in clinical and pre-clinical environments. The advantages of these techniques are the improvement of diagnosis accuracy and the assessment of treatment response by means of quantitative biomarkers in an efficient way. In the era of the personalized medicine, an early and efficacy prediction of therapy response in patients is still a critical issue. In radiation therapy planning, Magnetic Resonance Imaging (MRI) provides high quality detailed images and excellent soft-tissue contrast, while Computerized Tomography (CT) images provides attenuation maps and very good…