0000000001278710
AUTHOR
Alessandro Stefano
showing 41 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…
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…
A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning
2017
The aim of this study is to combine Biological Target Volume (BTV) segmentation and Gross Target Volume (GTV) segmentation in stereotactic neurosurgery.Our goal is to enhance Clinical Target Volume (CTV) definition, including metabolic and morphologic information, for treatment planning and patient follow-up.We propose a fully automatic approach for multimodal PET and MR image segmentation. This method is based on the Random Walker (RW) and Fuzzy C-Means clustering (FCM) algorithms. 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 presented, considering volume…
79. Amyloid-PET analysis based on tissue probability maps
2018
Purpose The regional quantification of amyloid burden is crucial for the clinical diagnosis of Alzheimer’s disease [1]. The best method to evaluate regional amyloid deposition in PET is through the use MR imaging for brain space normalization. However, since MR imaging is not always available in the clinical practice, a MR-less methodology is needed in order to compute semi-quantitative and analyze regional amyloid burden. Methods Forty-four patients with clinical evidence of dementia, underwent 18F-Florbetaben PET (FBB-PET), FDG-PET, neuropsychological assessment and cerebrospinal fluid analysis. We implemented a methodology that uses SPM12 to import and normalize the FBB-PET images in Mon…
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…
Biological target volume segmentation for radiotherapy treatment planning
2016
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…
Assessment of Lung Cancer Development in Idiopathic Pulmonary Fibrosis Patients Using Quantitative High-Resolution Computed Tomography:A Retrospectiv…
2020
Purpose The purpose of this study was to investigate histogram-based quantitative high-resolution computed tomography (HRCT) indexes in the assessment of lung cancer (LC) development in idiopathic pulmonary fibrosis (IPF) patients. Materials and methods From IPF databases of 2 national respiratory centers, we retrospectively studied patients with and without LC development-respectively, divided into Group A (n=16) and Group B (n=33). The extent of fibrotic disease was quantified on baseline and follow-up HRCT examinations using kurtosis, skewness, percentage of high attenuation area (HAA%), and percentage of fibrotic area (FA%). These indexes were compared between the 2 groups using the Man…
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-…
Potential clinical value of quantitative fluorine-18-fluorodeoxyglucose-PET/computed tomography using a graph-based method analysis in evaluation of …
2019
Objectives To identify the clinical relevance of incidentally detected lesions (IDLs) in the gastrointestinal tract (GIT) with 18F-FDG PET/CT and to assess the potential benefit of using semiquantitative PET measures to discern malignant from benign lesions. Methods Forty-one patients who underwent F-FDG PET/CT scans during the oncologic follow-up, revealing abnormal incidental 18F-FDG accumulations in the GIT were included in this retrospective analysis. Incidental PET/CT findings were correlated with endoscopic and histological findings. Semiquantitative PET values (SUVmax, SUVmean, SULpeak, and TLG) were evaluated by using a new graph-based method. Two sample t-test analysis has been per…
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 …
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…
Using anatomic and metabolic imaging in stereotactic radio neuro-surgery treatments
2016
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…
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…
18F-Florbetaben PET/CT to Assess Alzheimer's Disease: A new Analysis Method for Regional Amyloid Quantification.
2019
Background and purpose While AD can be definitively confirmed by postmortem histopathologic examination, in vivo imaging may improve the clinician's ability to identify AD at the earliest stage. The aim of the study was to test the performance of amyloid PET using new processing imaging algorithm for more precise diagnosis of AD. Methods Amyloid PET results using a new processing imaging algorithm (MRI-Less and AAL Atlas) were correlated with clinical, cognitive status, CSF analysis, and other imaging. The regional SUVR using the white matter of cerebellum as reference region and scores from clinical and cognitive tests were used to create ROC curves. Leave-one-out cross-validation was carr…
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…
Metabolic Response Assessment in Non-Small Cell Lung Cancer Patients after Platinum-Based Therapy: A Preliminary Analysis
2015
The purpose of this study was to evaluate the clinical value of PET (Positron Emission Tomography) for early prediction of tumor response to platinum-based therapy in patients with nonsmall cell lung cancer (NSCLC). The evaluation was carried out comparing the standard treatment response using RECIST (Response Evaluation Criteria in Solid Tumors) with metabolic treatment response according to European Organization for Research and Treatment of Cancer (EORTC) recommendations, PET Response Criteria in Solid Tumors (PERCIST), Total Lesion Glycolysis (TLG) and Metabolic Tumor Volume (MTV). Seventeen inoperable patients with stage IV NSCLC were enrolled between October 2011 and June 2013: PET st…
A fully automatic method for biological target volume segmentation of brain metastases
2016
Leksell Gamma Knife is a mini-invasive technique to obtain a complete destruction of cerebral lesions delivering a single high dose radiation beam. Positron Emission Tomography (PET) imaging is increasingly utilized for radiation treatment planning. Nevertheless, lesion volume delineation in PET datasets is challenging because of the low spatial resolution and high noise level of PET images. Nowadays, the biological target volume (BTV) is manually contoured on PET studies. This procedure is time expensive and operator-dependent. In this article, a fully automatic algorithm for the BTV delineation based on random walks (RW) on graphs is proposed. The results are compared with the outcomes of…
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 Graph-Based Method for PET Image Segmentation in Radiotherapy Planning: A Pilot Study
2013
Target volume delineation of Positron Emission Tomography (PET) images in radiation treatment planning is challenging because of the low spatial resolution and high noise level in PET data. The aim of this work is the devel- opment of an accurate and fast method for semi-automatic segmentation of me- tabolic regions on PET images. For this purpose, an algorithm for the biological tumor volume delineation based on random walks on graphs has been used. Va- lidation was first performed on phantoms containing spheres and irregular in- serts of different and known volumes, then tumors from a patient with head and neck cancer were segmented to discuss the clinical applicability of this algo- rith…
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…
An enhanced random walk algorithm for delineation of head and neck cancers in PET studies
2017
An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies:…
An automatic method for metabolic evaluation of gamma knife treatments
2015
Lesion volume delineation of Positron Emission Tomography images is challenging because of the low spatial resolution and high noise level. Aim of this work is the development of an operator independent segmentation method of metabolic images. For this purpose, an algorithm for the biological tumor volume delineation based on random walks on graphs has been used. Twenty-four cerebral tumors are segmented to evaluate the functional follow-up after Gamma Knife radiotherapy treatment. Experimental results show that the segmentation algorithm is accurate and has real-time performance. In addition, it can reflect metabolic changes useful to evaluate radiotherapy response in treated patients.
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 …
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…
Genotyping analysis and 18FDG uptake in breast cancer patients: a preliminary research
2013
Background: Diagnostic imaging plays a relevant role in the care of patients with breast cancer (BC). Positron Emission Tomography (PET) with 18F-fluoro-2-deoxy-D-glucose (FDG) has been widely proven to be a clinical tool suitable for BC detection and staging in which the glucose analog supplies metabolic information about the tumor. A limited number of studies, sometimes controversial, describe possible associations between FDG uptake and single nucleotide polymorphisms (SNPs). For this reason this field has to be explored and clarified. We investigated the association of SNPs in GLUT1, HIF-1a, EPAS1, APEX1, VEGFA and MTHFR genes with the FDG uptake in BC. Methods: In 26 caucasian individu…
Analysis of Metabolic Parameters Coming from Basal and Interim PET in Hodgkin Lymphoma
2018
Objective: Positron Emission Tomography (PET) with F-18-Fluoro-deoxy-glucose (FDG) emerged as a prognostic tool to predict treatment outcome in Hodgkin Lymphoma (HL). Moreover, a FDG-PET adapted strategy is currently assessed in clinical trial to minimize the toxic effect while maintaining the efficacy of treatment in HL. Purpose was to analyze the quantitative parameters to support the prognostic role of FDG-PET today based on the semi-quantitative Deauville 5-point Scale (D5-PS). Methods: This retrospective study included 53 patients diagnosed with advanced-stage HL between 2009 and 2014, enrolled in the PET response-adapted clinical trial HD 0607. FDG-PET was performed at baseline (PET0)…
An Automatic Method for PET Delineation of Cervical Tumors
2015
Aim: PET imaging is increasingly utilized for radiation treatment planning. Nevertheless, accurate segmentation of PET images is a complex and unresolved problem. Aim of this work is the development of an automatic segmentation method of Biological Target Volume (BTV) in patients with cervical cancer. Materials and methods: Random walks (RW) is a graph-based method that represents a DICOM (Digital Imaging and COmmunications in Medicine) image as a graph. The voxels are its nodes and the edges are defined by a cost function which maps a change in image intensity to edge weights. Then, RW partitions the nodes into target and background subsets. To create an automatic method starting from prev…