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  1. Article: A Critical Analysis of the Robustness of Radiomics to Variations in Segmentation Methods in

    Pasini, Giovanni / Russo, Giorgio / Mantarro, Cristina / Bini, Fabiano / Richiusa, Selene / Morgante, Lucrezia / Comelli, Albert / Russo, Giorgio Ivan / Sabini, Maria Gabriella / Cosentino, Sebastiano / Marinozzi, Franco / Ippolito, Massimo / Stefano, Alessandro

    Diagnostics (Basel, Switzerland)

    2023  Volume 13, Issue 24

    Abstract: Background: Radiomics shows promising results in supporting the clinical decision process, and much effort has been put into its standardization, thus leading to the Imaging Biomarker Standardization Initiative (IBSI), that established how radiomics ... ...

    Abstract Background: Radiomics shows promising results in supporting the clinical decision process, and much effort has been put into its standardization, thus leading to the Imaging Biomarker Standardization Initiative (IBSI), that established how radiomics features should be computed. However, radiomics still lacks standardization and many factors, such as segmentation methods, limit study reproducibility and robustness.
    Aim: We investigated the impact that three different segmentation methods (manual, thresholding and region growing) have on radiomics features extracted from
    Conclusions: Our study showed that segmentation methods influence radiomics features and that Shape features were the least reproducible (average ICC: 0.27), while GLCM features the most reproducible. Moreover, feature reproducibility changed depending on segmentation type, resulting in 51.18% of LoG features exhibiting excellent reproducibility (range average ICC: 0.68-0.87) and 47.85% of wavelet features exhibiting poor reproducibility that varied between wavelet sub-bands (range average ICC: 0.34-0.80) and resulted in the LLL band showing the highest average ICC (0.80). Finally, model performance showed that region growing led to the highest accuracy (74.49%), improved sensitivity (84.38%) and AUC (79.20%) in contrast with manual segmentation.
    Language English
    Publishing date 2023-12-11
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662336-5
    ISSN 2075-4418
    ISSN 2075-4418
    DOI 10.3390/diagnostics13243640
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Biodistribution Assessment of a Novel

    Pavone, Anna Maria / Benfante, Viviana / Giaccone, Paolo / Stefano, Alessandro / Torrisi, Filippo / Russo, Vincenzo / Serafini, Davide / Richiusa, Selene / Pometti, Marco / Scopelliti, Fabrizio / Ippolito, Massimo / Giannone, Antonino Giulio / Cabibi, Daniela / Asti, Mattia / Vettorato, Elisa / Morselli, Luca / Merone, Mario / Lunardon, Marcello / Andrighetto, Alberto /
    Tuttolomondo, Antonino / Cammarata, Francesco Paolo / Verona, Marco / Marzaro, Giovanni / Mastrotto, Francesca / Parenti, Rosalba / Russo, Giorgio / Comelli, Albert

    Life (Basel, Switzerland)

    2024  Volume 14, Issue 3

    Abstract: The aim of the present study consists of the evaluation of the biodistribution of a ... ...

    Abstract The aim of the present study consists of the evaluation of the biodistribution of a novel
    Language English
    Publishing date 2024-03-20
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662250-6
    ISSN 2075-1729
    ISSN 2075-1729
    DOI 10.3390/life14030409
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: A New Preclinical Decision Support System Based on PET Radiomics: A Preliminary Study on the Evaluation of an Innovative

    Benfante, Viviana / Stefano, Alessandro / Comelli, Albert / Giaccone, Paolo / Cammarata, Francesco Paolo / Richiusa, Selene / Scopelliti, Fabrizio / Pometti, Marco / Ficarra, Milene / Cosentino, Sebastiano / Lunardon, Marcello / Mastrotto, Francesca / Andrighetto, Alberto / Tuttolomondo, Antonino / Parenti, Rosalba / Ippolito, Massimo / Russo, Giorgio

    Journal of imaging

    2022  Volume 8, Issue 4

    Abstract: 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 ... ...

    Abstract 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 template space (3D whole-body Digimouse atlas), and 108 radiomics features were extracted from seven organs (namely, heart, bladder, stomach, liver, spleen, kidney, and lung) to investigate possible changes over time in [64Cu]chelator biodistribution. The one-way analysis of variance and post hoc Tukey Honestly Significant Difference test revealed that, while heart, stomach, spleen, kidney, and lung districts showed a very low percentage of radiomics features with significant variations (p-value < 0.05) among the three groups of mice, a large number of features (greater than 60% and 50%, respectively) that varied significantly between groups were observed in bladder and liver, indicating a different in vivo uptake of the 64Cu-labeled chelator over time. The proposed methodology may improve the method of calculating the [64Cu]chelator biodistribution and open the way towards a decision support system in the field of new radiopharmaceuticals used in preclinical imaging trials.
    Language English
    Publishing date 2022-03-30
    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/jimaging8040092
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Feasibility on the Use of Radiomics Features of 11[C]-MET PET/CT in Central Nervous System Tumours: Preliminary Results on Potential Grading Discrimination Using a Machine Learning Model.

    Russo, Giorgio / Stefano, Alessandro / Alongi, Pierpaolo / Comelli, Albert / Catalfamo, Barbara / Mantarro, Cristina / Longo, Costanza / Altieri, Roberto / Certo, Francesco / Cosentino, Sebastiano / Sabini, Maria Gabriella / Richiusa, Selene / Barbagallo, Giuseppe Maria Vincenzo / Ippolito, Massimo

    Current oncology (Toronto, Ont.)

    2021  Volume 28, Issue 6, Page(s) 5318–5331

    Abstract: Background/aim: Nowadays, Machine Learning (ML) algorithms have demonstrated remarkable progress in image-recognition tasks and could be useful for the new concept of precision medicine in order to help physicians in the choice of therapeutic strategies ...

    Abstract Background/aim: Nowadays, Machine Learning (ML) algorithms have demonstrated remarkable progress in image-recognition tasks and could be useful for the new concept of precision medicine in order to help physicians in the choice of therapeutic strategies for brain tumours. Previous data suggest that, in the central nervous system (CNS) tumours, amino acid PET may more accurately demarcate the active disease than paramagnetic enhanced MRI, which is currently the standard method of evaluation in brain tumours and helps in the assessment of disease grading, as a fundamental basis for proper clinical patient management. The aim of this study is to evaluate the feasibility of ML on 11[C]-MET PET/CT scan images and to propose a radiomics workflow using a machine-learning method to create a predictive model capable of discriminating between low-grade and high-grade CNS tumours.
    Materials and methods: In this retrospective study, fifty-six patients affected by a primary brain tumour who underwent 11[C]-MET PET/CT were selected from January 2016 to December 2019. Pathological examination was available in all patients to confirm the diagnosis and grading of disease. PET/CT acquisition was performed after 10 min from the administration of 11C-Methionine (401-610 MBq) for a time acquisition of 15 min. 11[C]-MET PET/CT images were acquired using two scanners (24 patients on a Siemens scan and 32 patients on a GE scan). Then, LIFEx software was used to delineate brain tumours using two different semi-automatic and user-independent segmentation approaches and to extract 44 radiomics features for each segmentation. A novel mixed descriptive-inferential sequential approach was used to identify a subset of relevant features that correlate with the grading of disease confirmed by pathological examination and clinical outcome. Finally, a machine learning model based on discriminant analysis was used in the evaluation of grading prediction (low grade CNS vs. high-grade CNS) of 11[C]-MET PET/CT.
    Results: The proposed machine learning model based on (i) two semi-automatic and user-independent segmentation processes, (ii) an innovative feature selection and reduction process, and (iii) the discriminant analysis, showed good performance in the prediction of tumour grade when the volumetric segmentation was used for feature extraction. In this case, the proposed model obtained an accuracy of ~85% (AUC ~79%) in the subgroup of patients who underwent Siemens tomography scans, of 80.51% (AUC 65.73%) in patients who underwent GE tomography scans, and of 70.31% (AUC 64.13%) in the whole patients' dataset (Siemens and GE scans).
    Conclusions: This preliminary study on the use of an ML model demonstrated to be feasible and able to select radiomics features of 11[C]-MET PET with potential value in prediction of grading of disease. Further studies are needed to improve radiomics algorithms to personalize predictive and prognostic models and potentially support the medical decision process.
    MeSH term(s) Brain Neoplasms/diagnostic imaging ; Feasibility Studies ; Humans ; Machine Learning ; Positron Emission Tomography Computed Tomography ; Retrospective Studies
    Language English
    Publishing date 2021-12-12
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 1236972-x
    ISSN 1718-7729 ; 1198-0052
    ISSN (online) 1718-7729
    ISSN 1198-0052
    DOI 10.3390/curroncol28060444
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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    Jg. 1995 - 2021: Lesesall (2.OG)
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  5. Article ; Online: Proton boron capture therapy (PBCT) induces cell death and mitophagy in a heterotopic glioblastoma model.

    Cammarata, Francesco Paolo / Torrisi, Filippo / Vicario, Nunzio / Bravatà, Valentina / Stefano, Alessandro / Salvatorelli, Lucia / D'Aprile, Simona / Giustetto, Pierangela / Forte, Giusi Irma / Minafra, Luigi / Calvaruso, Marco / Richiusa, Selene / Cirrone, Giuseppe Antonio Pablo / Petringa, Giada / Broggi, Giuseppe / Cosentino, Sebastiano / Scopelliti, Fabrizio / Magro, Gaetano / Porro, Danilo /
    Libra, Massimo / Ippolito, Massimo / Russo, Giorgio / Parenti, Rosalba / Cuttone, Giacomo

    Communications biology

    2023  Volume 6, Issue 1, Page(s) 388

    Abstract: Despite aggressive therapeutic regimens, glioblastoma (GBM) represents a deadly brain tumor with significant aggressiveness, radioresistance and chemoresistance, leading to dismal prognosis. Hypoxic microenvironment, which characterizes GBM, is ... ...

    Abstract Despite aggressive therapeutic regimens, glioblastoma (GBM) represents a deadly brain tumor with significant aggressiveness, radioresistance and chemoresistance, leading to dismal prognosis. Hypoxic microenvironment, which characterizes GBM, is associated with reduced therapeutic effectiveness. Moreover, current irradiation approaches are limited by uncertain tumor delineation and severe side effects that comprehensively lead to unsuccessful treatment and to a worsening of the quality of life of GBM patients. Proton beam offers the opportunity of reduced side effects and a depth-dose profile, which, unfortunately, are coupled with low relative biological effectiveness (RBE). The use of radiosensitizing agents, such as boron-containing molecules, enhances proton RBE and increases the effectiveness on proton beam-hit targets. We report a first preclinical evaluation of proton boron capture therapy (PBCT) in a preclinical model of GBM analyzed via μ-positron emission tomography/computed tomography (μPET-CT) assisted live imaging, finding a significant increased therapeutic effectiveness of PBCT versus proton coupled with an increased cell death and mitophagy. Our work supports PBCT and radiosensitizing agents as a scalable strategy to treat GBM exploiting ballistic advances of proton beam and increasing therapeutic effectiveness and quality of life in GBM patients.
    MeSH term(s) Humans ; Glioblastoma/drug therapy ; Glioblastoma/radiotherapy ; Glioblastoma/pathology ; Protons ; Boron ; Mitophagy ; Quality of Life ; Radiation-Sensitizing Agents/pharmacology ; Cell Death ; Tumor Microenvironment
    Chemical Substances Protons ; Boron (N9E3X5056Q) ; Radiation-Sensitizing Agents
    Language English
    Publishing date 2023-04-08
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2399-3642
    ISSN (online) 2399-3642
    DOI 10.1038/s42003-023-04770-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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