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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: Fully 3D Active Surface with Machine Learning for PET Image Segmentation.

Comelli, Albert

Journal of imaging

2020 Volume 6, Issue 11

Abstract: In order to tackle three-dimensional tumor volume reconstruction from Positron Emission Tomography (PET) images, most of the existing algorithms rely on the segmentation of independent PET slices. To exploit cross-slice information, typically overlooked ... ...

Abstract	In order to tackle three-dimensional tumor volume reconstruction from Positron Emission Tomography (PET) images, most of the existing algorithms rely on the segmentation of independent PET slices. To exploit cross-slice information, typically overlooked in these 2D implementations, I present an algorithm capable of achieving the volume reconstruction directly in 3D, by leveraging an active surface algorithm. The evolution of such surface performs the segmentation of the whole stack of slices simultaneously and can handle changes in topology. Furthermore, no artificial stop condition is required, as the active surface will naturally converge to a stable topology. In addition, I include a machine learning component to enhance the accuracy of the segmentation process. The latter consists of a forcing term based on classification results from a discriminant analysis algorithm, which is included directly in the mathematical formulation of the energy function driving surface evolution. It is worth noting that the training of such a component requires minimal data compared to more involved deep learning methods. Only eight patients (i.e., two lung, four head and neck, and two brain cancers) were used for training and testing the machine learning component, while fifty patients (i.e., 10 lung, 25 head and neck, and 15 brain cancers) were used to test the full 3D reconstruction algorithm. Performance evaluation is based on the same dataset of patients discussed in my previous work, where the segmentation was performed using the 2D active contour. The results confirm that the active surface algorithm is superior to the active contour algorithm, outperforming the earlier approach on all the investigated anatomical districts with a dice similarity coefficient of 90.47 ± 2.36% for lung cancer, 88.30 ± 2.89% for head and neck cancer, and 90.29 ± 2.52% for brain cancer. Based on the reported results, it can be claimed that the migration into a 3D system yielded a practical benefit justifying the effort to rewrite an existing 2D system for PET imaging segmentation.
Language	English
Publishing date	2020-10-23
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/jimaging6110113
Database	MEDical Literature Analysis and Retrieval System OnLINE

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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: Recent Developments in Nanoparticle Formulations for Resveratrol Encapsulation as an Anticancer Agent.

Ali, Muhammad / Benfante, Viviana / Di Raimondo, Domenico / Salvaggio, Giuseppe / Tuttolomondo, Antonino / Comelli, Albert

Pharmaceuticals (Basel, Switzerland)

2024 Volume 17, Issue 1

Abstract: Resveratrol is a polyphenolic compound that has gained considerable attention in the past decade due to its multifaceted therapeutic potential, including anti-inflammatory and anticancer properties. However, its anticancer efficacy is impeded by low ... ...

Abstract	Resveratrol is a polyphenolic compound that has gained considerable attention in the past decade due to its multifaceted therapeutic potential, including anti-inflammatory and anticancer properties. However, its anticancer efficacy is impeded by low water solubility, dose-limiting toxicity, low bioavailability, and rapid hepatic metabolism. To overcome these hurdles, various nanoparticles such as organic and inorganic nanoparticles, liposomes, polymeric nanoparticles, dendrimers, solid lipid nanoparticles, gold nanoparticles, zinc oxide nanoparticles, zeolitic imidazolate frameworks, carbon nanotubes, bioactive glass nanoparticles, and mesoporous nanoparticles were employed to deliver resveratrol, enhancing its water solubility, bioavailability, and efficacy against various types of cancer. Resveratrol-loaded nanoparticle or resveratrol-conjugated nanoparticle administration exhibits excellent anticancer potency compared to free resveratrol. This review highlights the latest developments in nanoparticle-based delivery systems for resveratrol, focusing on the potential to overcome limitations associated with the compound's bioavailability and therapeutic effectiveness.
Language	English
Publishing date	2024-01-18
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2193542-7
ISSN	1424-8247
ISSN	1424-8247
DOI	10.3390/ph17010126
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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Article: Quantitative Evaluation by Digital Pathology of Immunohistochemical Expression of CK7, CK19, and EpCAM in Advanced Stages of NASH.

Cabibi, Daniela / Giannone, Antonino Giulio / Quattrocchi, Alberto / Calvaruso, Vincenza / Porcasi, Rossana / Di Grusa, Domenico / Pavone, Anna Maria / Comelli, Albert / Petta, Salvatore

Biomedicines

2024 Volume 12, Issue 2

Abstract: 1) Background: Nonalcoholic Steatohepatitis/Nonalcoholic Fatty Liver Disease (NASH/NAFLD) is the most recurrent chronic liver disease. NASH could present with a cholestatic (C) or hepatic (H) pattern of damage. Recently, we observed that increased ... ...

Abstract	(1) Background: Nonalcoholic Steatohepatitis/Nonalcoholic Fatty Liver Disease (NASH/NAFLD) is the most recurrent chronic liver disease. NASH could present with a cholestatic (C) or hepatic (H) pattern of damage. Recently, we observed that increased Epithelial Cell Adhesion Molecule (EpCAM) expression was the main immunohistochemical feature to distinguish C from H pattern in NASH. (2) Methods: In the present study, we used digital pathology to compare the quantitative results of digital image analysis by QuPath software (Q-results), with the semi-quantitative results of observer assessment (S-results) for cytokeratin 7 and 19, (CK7, CK19) as well as EpCAM expression. Patients were classified into H or C group on the basis of the ratio between alanine transaminase (ALT) and alkaline phosphatase (ALP) values, using the "R-ratio formula". (3) Results: Q- and S-results showed a significant correlation for all markers (
Language	English
Publishing date	2024-02-16
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2720867-9
ISSN	2227-9059
ISSN	2227-9059
DOI	10.3390/biomedicines12020440
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: 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: A Radioactive-Free Method for the Thorough Analysis of the Kinetics of Cell Cytotoxicity.

Coronnello, Claudia / Busà, Rosalia / Cicero, Luca / Comelli, Albert / Badami, Ester

Journal of imaging

2021 Volume 7, Issue 11

Abstract: The cytotoxic activity of T cells and Natural Killer cells is usually measured with the chromium release assay (CRA), which involves the use of 51Chromium ( ...

Abstract	The cytotoxic activity of T cells and Natural Killer cells is usually measured with the chromium release assay (CRA), which involves the use of 51Chromium (
Language	English
Publishing date	2021-10-23
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/jimaging7110222
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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