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Book ; Online: Image Processing and Analysis for Preclinical and Clinical Applications

Stefano, Alessandro / Comelli, Albert / Vernuccio, Federica

2022

Keywords	Research & information: general ; Chemistry ; deep learning ; segmentation ; prostate ; MRI ; ENet ; UNet ; ERFNet ; radiomics ; gamma knife ; imaging quantification ; [11C]-methionine positron emission tomography ; cancer ; atrial fibrillation ; 4D-flow ; stasis ; pulmonary vein ablation ; convolutional neural network ; transfer learning ; maxillofacial fractures ; computed tomography images ; radiography ; xenotransplant ; cancer cells ; zebrafish image analysis ; in vivo assay ; convolutional neural network (CNN) ; magnetic resonance imaging (MRI) ; neoadjuvant chemoradiation therapy (nCRT) ; pathologic complete response (pCR) ; rectal cancer ; radiomics feature robustness ; PET/MRI co-registration ; image registration ; fundus image ; feature extraction ; glomerular filtration rate ; Gate's method ; renal depth ; computed tomography ; computer-aided diagnosis ; medical-image analysis ; automated prostate-volume estimation ; abdominal ultrasound images ; image-patch voting ; soft tissue sarcoma ; volume estimation ; artificial intelligence ; Basal Cell Carcinoma ; skin lesion ; classification ; colon ; positron emission tomography-computed tomography ; nuclear medicine ; image pre-processing ; high-level synthesis ; X-ray pre-processing ; pipelined architecture ; n/a
Language	English
Size	1 electronic resource (228 pages)
Publisher	MDPI - Multidisciplinary Digital Publishing Institute
Publishing place	Basel
Document type	Book ; Online
Note	English
HBZ-ID	HT030374370
ISBN	9783036550145 ; 3036550143
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images.

Stefano, Alessandro / Comelli, Albert

Journal of imaging

2021 Volume 7, Issue 8

Abstract: Background: In the field of biomedical imaging, radiomics is a promising approach that aims to provide quantitative features from images. It is highly dependent on accurate identification and delineation of the volume of interest to avoid mistakes in ... ...

Abstract	Background: In the field of biomedical imaging, radiomics is a promising approach that aims to provide quantitative features from images. It is highly dependent on accurate identification and delineation of the volume of interest to avoid mistakes in the implementation of the texture-based prediction model. In this context, we present a customized deep learning approach aimed at addressing the real-time, and fully automated identification and segmentation of COVID-19 infected regions in computed tomography images. Methods: In a previous study, we adopted ENET, originally used for image segmentation tasks in self-driving cars, for whole parenchyma segmentation in patients with idiopathic pulmonary fibrosis which has several similarities to COVID-19 disease. To automatically identify and segment COVID-19 infected areas, a customized ENET, namely C-ENET, was implemented and its performance compared to the original ENET and some state-of-the-art deep learning architectures. Results: The experimental results demonstrate the effectiveness of our approach. Considering the performance obtained in terms of similarity of the result of the segmentation to the gold standard (dice similarity coefficient ~75%), our proposed methodology can be used for the identification and delineation of COVID-19 infected areas without any supervision of a radiologist, in order to obtain a volume of interest independent from the user. Conclusions: We demonstrated that the proposed customized deep learning model can be applied to rapidly identify, and segment COVID-19 infected regions to subsequently extract useful information for assessing disease severity through radiomics analyses.
Language	English
Publishing date	2021-08-04
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2824270-1
ISSN	2313-433X ; 2313-433X
ISSN (online)	2313-433X
ISSN	2313-433X
DOI	10.3390/jimaging7080131
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Editorial: Radiomics and radiogenomics in genitourinary oncology: artificial intelligence and deep learning applications.

Stefano, Alessandro / Bertelli, Elena / Comelli, Albert / Gatti, Marco / Stanzione, Arnaldo

Frontiers in radiology

2023 Volume 3, Page(s) 1325594

Language	English
Publishing date	2023-12-18
Publishing country	Switzerland
Document type	Editorial
ISSN	2673-8740
ISSN (online)	2673-8740
DOI	10.3389/fradi.2023.1325594
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Both eyes open

Bova, Paolo / Di Stefano, Alessandro / Han, The Anh

Vigilant Incentives help Regulatory Markets improve AI Safety

2023

Abstract: In the context of rapid discoveries by leaders in AI, governments must consider how to design regulation that matches the increasing pace of new AI capabilities. Regulatory Markets for AI is a proposal designed with adaptability in mind. It involves ... ...

Abstract	In the context of rapid discoveries by leaders in AI, governments must consider how to design regulation that matches the increasing pace of new AI capabilities. Regulatory Markets for AI is a proposal designed with adaptability in mind. It involves governments setting outcome-based targets for AI companies to achieve, which they can show by purchasing services from a market of private regulators. We use an evolutionary game theory model to explore the role governments can play in building a Regulatory Market for AI systems that deters reckless behaviour. We warn that it is alarmingly easy to stumble on incentives which would prevent Regulatory Markets from achieving this goal. These 'Bounty Incentives' only reward private regulators for catching unsafe behaviour. We argue that AI companies will likely learn to tailor their behaviour to how much effort regulators invest, discouraging regulators from innovating. Instead, we recommend that governments always reward regulators, except when they find that those regulators failed to detect unsafe behaviour that they should have. These 'Vigilant Incentives' could encourage private regulators to find innovative ways to evaluate cutting-edge AI systems.
Keywords	Computer Science - Artificial Intelligence ; Computer Science - Computers and Society ; Computer Science - Computer Science and Game Theory ; Computer Science - Multiagent Systems ; Economics - General Economics
Subject code	303
Publishing date	2023-03-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Phenotyping the Histopathological Subtypes of Non-Small-Cell Lung Carcinoma: How Beneficial Is Radiomics?

Pasini, Giovanni / Stefano, Alessandro / Russo, Giorgio / Comelli, Albert / Marinozzi, Franco / Bini, Fabiano

Diagnostics (Basel, Switzerland)

2023 Volume 13, Issue 6

Abstract: The aim of this study was to investigate the usefulness of radiomics in the absence of well-defined standard guidelines. Specifically, we extracted radiomics features from multicenter computed tomography (CT) images to differentiate between the four ... ...

Abstract	The aim of this study was to investigate the usefulness of radiomics in the absence of well-defined standard guidelines. Specifically, we extracted radiomics features from multicenter computed tomography (CT) images to differentiate between the four histopathological subtypes of non-small-cell lung carcinoma (NSCLC). In addition, the results that varied with the radiomics model were compared. We investigated the presence of the batch effects and the impact of feature harmonization on the models' performance. Moreover, the question on how the training dataset composition influenced the selected feature subsets and, consequently, the model's performance was also investigated. Therefore, through combining data from the two publicly available datasets, this study involves a total of 152 squamous cell carcinoma (SCC), 106 large cell carcinoma (LCC), 150 adenocarcinoma (ADC), and 58 no other specified (NOS). Through the matRadiomics tool, which is an example of Image Biomarker Standardization Initiative (IBSI) compliant software, 1781 radiomics features were extracted from each of the malignant lesions that were identified in CT images. After batch analysis and feature harmonization, which were based on the ComBat tool and were integrated in matRadiomics, the datasets (the harmonized and the non-harmonized) were given as an input to a machine learning modeling pipeline. The following steps were articulated: (i) training-set/test-set splitting (80/20); (ii) a Kruskal-Wallis analysis and LASSO linear regression for the feature selection; (iii) model training; (iv) a model validation and hyperparameter optimization; and (v) model testing. Model optimization consisted of a 5-fold cross-validated Bayesian optimization, repeated ten times (inner loop). The whole pipeline was repeated 10 times (outer loop) with six different machine learning classification algorithms. Moreover, the stability of the feature selection was evaluated. Results showed that the batch effects were present even if the voxels were resampled to an isotropic form and whether feature harmonization correctly removed them, even though the models' performances decreased. Moreover, the results showed that a low accuracy (61.41%) was reached when differentiating between the four subtypes, even though a high average area under curve (AUC) was reached (0.831). Further, a NOS subtype was classified as almost completely correct (true positive rate ~90%). The accuracy increased (77.25%) when only the SCC and ADC subtypes were considered, as well as when a high AUC (0.821) was obtained-although harmonization decreased the accuracy to 58%. Moreover, the features that contributed the most to models' performance were those extracted from wavelet decomposed and Laplacian of Gaussian (LoG) filtered images and they belonged to the texture feature class.. In conclusion, we showed that our multicenter data were affected by batch effects, that they could significantly alter the models' performance, and that feature harmonization correctly removed them. Although wavelet features seemed to be the most informative features, an absolute subset could not be identified since it changed depending on the training/testing splitting. Moreover, performance was influenced by the chosen dataset and by the machine learning methods, which could reach a high accuracy in binary classification tasks, but could underperform in multiclass problems. It is, therefore, essential that the scientific community propose a more systematic radiomics approach, focusing on multicenter studies, with clear and solid guidelines to facilitate the translation of radiomics to clinical practice.
Language	English
Publishing date	2023-03-18
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics13061167
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI.

Laudicella, Riccardo / Comelli, Albert / Schwyzer, Moritz / Stefano, Alessandro / Konukoglu, Ender / Messerli, Michael / Baldari, Sergio / Eberli, Daniel / Burger, Irene A

La Radiologia medica

2024

Abstract: Purpose: High PSMA expression might be correlated with structural characteristics such as growth patterns on histopathology, not recognized by the human eye on MRI images. Deep structural image analysis might be able to detect such differences and ... ...

Abstract	Purpose: High PSMA expression might be correlated with structural characteristics such as growth patterns on histopathology, not recognized by the human eye on MRI images. Deep structural image analysis might be able to detect such differences and therefore predict if a lesion would be PSMA positive. Therefore, we aimed to train a neural network based on PSMA PET/MRI scans to predict increased prostatic PSMA uptake based on the axial T2-weighted sequence alone. Material and methods: All patients undergoing simultaneous PSMA PET/MRI for PCa staging or biopsy guidance between April 2016 and December 2020 at our institution were selected. To increase the specificity of our model, the prostatic beds on PSMA PET scans were dichotomized in positive and negative regions using an SUV threshold greater than 4 to generate a PSMA PET map. Then, a C-ENet was trained on the T2 images of the training cohort to generate a predictive prostatic PSMA PET map. Results: One hundred and fifty-four PSMA PET/MRI scans were available (133 [ Conclusion: Increased prostatic PSMA uptake on PET might be estimated based on T2 MRI alone. Further investigation with larger cohorts and external validation is needed to assess whether PSMA uptake can be predicted accurately enough to help in the interpretation of mpMRI.
Language	English
Publishing date	2024-05-03
Publishing country	Italy
Document type	Journal Article
ZDB-ID	205751-7
ISSN	1826-6983 ; 0033-8362
ISSN (online)	1826-6983
ISSN	0033-8362
DOI	10.1007/s11547-024-01820-z
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Development and Implementation of an Innovative Framework for Automated Radiomics Analysis in Neuroimaging.

Camastra, Chiara / Pasini, Giovanni / Stefano, Alessandro / Russo, Giorgio / Vescio, Basilio / Bini, Fabiano / Marinozzi, Franco / Augimeri, Antonio

Journal of imaging

2024 Volume 10, Issue 4

Abstract: Radiomics represents an innovative approach to medical image analysis, enabling comprehensive quantitative evaluation of radiological images through advanced image processing and Machine or Deep Learning algorithms. This technique uncovers intricate data ...

Abstract	Radiomics represents an innovative approach to medical image analysis, enabling comprehensive quantitative evaluation of radiological images through advanced image processing and Machine or Deep Learning algorithms. This technique uncovers intricate data patterns beyond human visual detection. Traditionally, executing a radiomic pipeline involves multiple standardized phases across several software platforms. This could represent a limit that was overcome thanks to the development of the matRadiomics application. MatRadiomics, a freely available, IBSI-compliant tool, features its intuitive Graphical User Interface (GUI), facilitating the entire radiomics workflow from DICOM image importation to segmentation, feature selection and extraction, and Machine Learning model construction. In this project, an extension of matRadiomics was developed to support the importation of brain MRI images and segmentations in NIfTI format, thus extending its applicability to neuroimaging. This enhancement allows for the seamless execution of radiomic pipelines within matRadiomics, offering substantial advantages to the realm of neuroimaging.
Language	English
Publishing date	2024-04-22
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2824270-1
ISSN	2313-433X ; 2313-433X
ISSN (online)	2313-433X
ISSN	2313-433X
DOI	10.3390/jimaging10040096
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model.

Pasini, Giovanni / Bini, Fabiano / Russo, Giorgio / Comelli, Albert / Marinozzi, Franco / Stefano, Alessandro

Journal of imaging

2022 Volume 8, Issue 8

Abstract: Radiomics aims to support clinical decisions through its workflow, which is divided into: (i) target identification and segmentation, (ii) feature extraction, (iii) feature selection, and (iv) model fitting. Many radiomics tools were developed to fulfill ...

Abstract	Radiomics aims to support clinical decisions through its workflow, which is divided into: (i) target identification and segmentation, (ii) feature extraction, (iii) feature selection, and (iv) model fitting. Many radiomics tools were developed to fulfill the steps mentioned above. However, to date, users must switch different software to complete the radiomics workflow. To address this issue, we developed a new free and user-friendly radiomics framework, namely matRadiomics, which allows the user: (i) to import and inspect biomedical images, (ii) to identify and segment the target, (iii) to extract the features, (iv) to reduce and select them, and (v) to build a predictive model using machine learning algorithms. As a result, biomedical images can be visualized and segmented and, through the integration of Pyradiomics into matRadiomics, radiomic features can be extracted. These features can be selected using a hybrid descriptive-inferential method, and, consequently, used to train three different classifiers: linear discriminant analysis, k-nearest neighbors, and support vector machines. Model validation is performed using k-fold cross-Validation and k-fold stratified cross-validation. Finally, the performance metrics of each model are shown in the graphical interface of matRadiomics. In this study, we discuss the workflow, architecture, application, future development of matRadiomics, and demonstrate its working principles in a real case study with the aim of establishing a reference standard for the whole radiomics analysis, starting from the image visualization up to the predictive model implementation.
Language	English
Publishing date	2022-08-18
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2824270-1
ISSN	2313-433X ; 2313-433X
ISSN (online)	2313-433X
ISSN	2313-433X
DOI	10.3390/jimaging8080221
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Anti-Arthritic and Anti-Cancer Activities of Polyphenols: A Review of the Most Recent In Vitro Assays.

Ali, Muhammad / Benfante, Viviana / Stefano, Alessandro / Yezzi, Anthony / Di Raimondo, Domenico / Tuttolomondo, Antonino / Comelli, Albert

Life (Basel, Switzerland)

2023 Volume 13, Issue 2

Abstract: Polyphenols have gained widespread attention as they are effective in the prevention and management of various diseases, including cancer diseases (CD) and rheumatoid arthritis (RA). They are natural organic substances present in fruits, vegetables, and ... ...

Abstract	Polyphenols have gained widespread attention as they are effective in the prevention and management of various diseases, including cancer diseases (CD) and rheumatoid arthritis (RA). They are natural organic substances present in fruits, vegetables, and spices. Polyphenols interact with various kinds of receptors and membranes. They modulate different signal cascades and interact with the enzymes responsible for CD and RA. These interactions involve cellular machinery, from cell membranes to major nuclear components, and provide information on their beneficial effects on health. These actions provide evidence for their pharmaceutical exploitation in the treatment of CD and RA. In this review, we discuss different pathways, modulated by polyphenols, which are involved in CD and RA. A search of the most recent relevant publications was carried out with the following criteria: publication date, 2012-2022; language, English; study design, in vitro; and the investigation of polyphenols present in extra virgin olive, grapes, and spices in the context of RA and CD, including, when available, the underlying molecular mechanisms. This review is valuable for clarifying the mechanisms of polyphenols targeting the pathways of senescence and leading to the development of CD and RA treatments. Herein, we focus on research reports that emphasize antioxidant properties.
Language	English
Publishing date	2023-01-28
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2662250-6
ISSN	2075-1729
ISSN	2075-1729
DOI	10.3390/life13020361
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Social Diversity Reduces the Complexity and Cost of Fostering Fairness

Cimpeanu, Theodor / Di Stefano, Alessandro / Perret, Cedric / Han, The Anh

2022

Abstract: Institutions and investors are constantly faced with the challenge of appropriately distributing endowments. No budget is limitless and optimising overall spending without sacrificing positive outcomes has been approached and resolved using several ... ...

Abstract	Institutions and investors are constantly faced with the challenge of appropriately distributing endowments. No budget is limitless and optimising overall spending without sacrificing positive outcomes has been approached and resolved using several heuristics. To date, prior works have failed to consider how to encourage fairness in a population where social diversity is ubiquitous, and in which investors can only partially observe the population. Herein, by incorporating social diversity in the Ultimatum game through heterogeneous graphs, we investigate the effects of several interference mechanisms which assume incomplete information and flexible standards of fairness. We quantify the role of diversity and show how it reduces the need for information gathering, allowing us to relax a strict, costly interference process. Furthermore, we find that the influence of certain individuals, expressed by different network centrality measures, can be exploited to further reduce spending if minimal fairness requirements are lowered. Our results indicate that diversity changes and opens up novel mechanisms available to institutions wishing to promote fairness. Overall, our analysis provides novel insights to guide institutional policies in socially diverse complex systems.
Keywords	Computer Science - Multiagent Systems ; Computer Science - Artificial Intelligence ; Mathematics - Dynamical Systems ; Mathematics - Optimization and Control ; Nonlinear Sciences - Adaptation and Self-Organizing Systems
Publishing date	2022-11-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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