Vasari Scoring System in Discerning between Different Degrees of Glioma and IDH Status Prediction: A Possible Machine Learning Application?
(1) The aim of our study is to evaluate the capacity of the Visually AcceSAble Rembrandt Images (VASARI) scoring system in discerning between the different degrees of glioma and Isocitrate Dehydrogenase (IDH) status predictions, with a possible application in machine learning. (2) A retrospective st...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/9/4/75 |
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| author | Laura Gemini Mario Tortora Pasqualina Giordano Maria Evelina Prudente Alessandro Villa Ottavia Vargas Maria Francesca Giugliano Francesco Somma Giulia Marchello Carmela Chiaramonte Marcella Gaetano Federico Frio Eugenio Di Giorgio Alfredo D’Avino Fabio Tortora Vincenzo D’Agostino Alberto Negro |
| author_facet | Laura Gemini Mario Tortora Pasqualina Giordano Maria Evelina Prudente Alessandro Villa Ottavia Vargas Maria Francesca Giugliano Francesco Somma Giulia Marchello Carmela Chiaramonte Marcella Gaetano Federico Frio Eugenio Di Giorgio Alfredo D’Avino Fabio Tortora Vincenzo D’Agostino Alberto Negro |
| author_sort | Laura Gemini |
| collection | DOAJ |
| description | (1) The aim of our study is to evaluate the capacity of the Visually AcceSAble Rembrandt Images (VASARI) scoring system in discerning between the different degrees of glioma and Isocitrate Dehydrogenase (IDH) status predictions, with a possible application in machine learning. (2) A retrospective study was conducted on 126 patients with gliomas (M/F = 75/51; mean age: 55.30), from which we obtained their histological grade and molecular status. Each patient was analyzed with all 25 features of VASARI, blinded by two residents and three neuroradiologists. The interobserver agreement was assessed. A statistical analysis was conducted to evaluate the distribution of the observations using a box plot and a bar plot. We then performed univariate and multivariate logistic regressions and a Wald test. We also calculated the odds ratios and confidence intervals for each variable and the evaluation matrices with receiver operating characteristic (ROC) curves in order to identify cut-off values that are predictive of a diagnosis. Finally, we did the Pearson correlation test to see if the variables grade and IDH were correlated. (3) An excellent ICC estimate was obtained. For the grade and IDH status prediction, there were statistically significant results by evaluation of the degree of post-contrast impregnation (F4) and the percentage of impregnated area (F5), not impregnated area (F6), and necrotic (F7) tissue. These models showed good performances according to the area under the curve (AUC) values (>70%). (4) Specific MRI features can be used to predict the grade and IDH status of gliomas, with important prognostic implications. The standardization and improvement of these data (aim: AUC > 80%) can be used for programming machine learning software. |
| first_indexed | 2024-03-11T04:53:21Z |
| format | Article |
| id | doaj.art-c6517ee257b94d499066a9be62e6e201 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-11T04:53:21Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-c6517ee257b94d499066a9be62e6e2012023-11-17T19:53:39ZengMDPI AGJournal of Imaging2313-433X2023-03-01947510.3390/jimaging9040075Vasari Scoring System in Discerning between Different Degrees of Glioma and IDH Status Prediction: A Possible Machine Learning Application?Laura Gemini0Mario Tortora1Pasqualina Giordano2Maria Evelina Prudente3Alessandro Villa4Ottavia Vargas5Maria Francesca Giugliano6Francesco Somma7Giulia Marchello8Carmela Chiaramonte9Marcella Gaetano10Federico Frio11Eugenio Di Giorgio12Alfredo D’Avino13Fabio Tortora14Vincenzo D’Agostino15Alberto Negro16Department of Advanced Biomedical Sciences, University “Federico II”, Via Pansini, 80131 Naples, ItalyDepartment of Advanced Biomedical Sciences, University “Federico II”, Via Pansini, 80131 Naples, ItalyOncology Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, ItalyNeuroradiology Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, ItalyNeurosurgery Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, ItalyNeuroradiology Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, ItalyRadiotherapy Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, ItalyNeuroradiology Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, ItalyCNRS, Laboratoire J.A. Dieudonné, Inria, Universitè Côte d’Azur, Avenue Valrose, 06108 Nice, FranceNeurosurgery Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, ItalyRadiotherapy Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, ItalyNeurosurgery Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, ItalyNuclear Medicine Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, ItalyPathological Anatomy Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, ItalyDepartment of Advanced Biomedical Sciences, University “Federico II”, Via Pansini, 80131 Naples, ItalyNeuroradiology Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, ItalyNeuroradiology Unit, Ospedale del Mare ASL NA1 Centro, Via Enrico Russo, 80147 Naples, Italy(1) The aim of our study is to evaluate the capacity of the Visually AcceSAble Rembrandt Images (VASARI) scoring system in discerning between the different degrees of glioma and Isocitrate Dehydrogenase (IDH) status predictions, with a possible application in machine learning. (2) A retrospective study was conducted on 126 patients with gliomas (M/F = 75/51; mean age: 55.30), from which we obtained their histological grade and molecular status. Each patient was analyzed with all 25 features of VASARI, blinded by two residents and three neuroradiologists. The interobserver agreement was assessed. A statistical analysis was conducted to evaluate the distribution of the observations using a box plot and a bar plot. We then performed univariate and multivariate logistic regressions and a Wald test. We also calculated the odds ratios and confidence intervals for each variable and the evaluation matrices with receiver operating characteristic (ROC) curves in order to identify cut-off values that are predictive of a diagnosis. Finally, we did the Pearson correlation test to see if the variables grade and IDH were correlated. (3) An excellent ICC estimate was obtained. For the grade and IDH status prediction, there were statistically significant results by evaluation of the degree of post-contrast impregnation (F4) and the percentage of impregnated area (F5), not impregnated area (F6), and necrotic (F7) tissue. These models showed good performances according to the area under the curve (AUC) values (>70%). (4) Specific MRI features can be used to predict the grade and IDH status of gliomas, with important prognostic implications. The standardization and improvement of these data (aim: AUC > 80%) can be used for programming machine learning software.https://www.mdpi.com/2313-433X/9/4/75magnetic resonancegliomaVASARIgrade predictionIDH |
| spellingShingle | Laura Gemini Mario Tortora Pasqualina Giordano Maria Evelina Prudente Alessandro Villa Ottavia Vargas Maria Francesca Giugliano Francesco Somma Giulia Marchello Carmela Chiaramonte Marcella Gaetano Federico Frio Eugenio Di Giorgio Alfredo D’Avino Fabio Tortora Vincenzo D’Agostino Alberto Negro Vasari Scoring System in Discerning between Different Degrees of Glioma and IDH Status Prediction: A Possible Machine Learning Application? Journal of Imaging magnetic resonance glioma VASARI grade prediction IDH |
| title | Vasari Scoring System in Discerning between Different Degrees of Glioma and IDH Status Prediction: A Possible Machine Learning Application? |
| title_full | Vasari Scoring System in Discerning between Different Degrees of Glioma and IDH Status Prediction: A Possible Machine Learning Application? |
| title_fullStr | Vasari Scoring System in Discerning between Different Degrees of Glioma and IDH Status Prediction: A Possible Machine Learning Application? |
| title_full_unstemmed | Vasari Scoring System in Discerning between Different Degrees of Glioma and IDH Status Prediction: A Possible Machine Learning Application? |
| title_short | Vasari Scoring System in Discerning between Different Degrees of Glioma and IDH Status Prediction: A Possible Machine Learning Application? |
| title_sort | vasari scoring system in discerning between different degrees of glioma and idh status prediction a possible machine learning application |
| topic | magnetic resonance glioma VASARI grade prediction IDH |
| url | https://www.mdpi.com/2313-433X/9/4/75 |
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