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Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer

Background: Benign renal tumors, such as renal oncocytoma (RO), can be erroneously diagnosed as malignant renal cell carcinomas (RCC), because of their similar imaging features. Computer-aided systems leveraging radiomic features can be used to better discriminate benign renal tumors from the malign...

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Autores principales: Carlini, Gianluca, Gaudiano, Caterina, Golfieri, Rita, Curti, Nico, Biondi, Riccardo, Bianchi, Lorenzo, Schiavina, Riccardo, Giunchi, Francesca, Faggioni, Lorenzo, Giampieri, Enrico, Merlotti, Alessandra, Dall’Olio, Daniele, Sala, Claudia, Pandolfi, Sara, Remondini, Daniel, Rustici, Arianna, Pastore, Luigi Vincenzo, Scarpetti, Leonardo, Bortolani, Barbara, Cercenelli, Laura, Brunocilla, Eugenio, Marcelli, Emanuela, Coppola, Francesca, Castellani, Gastone
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10052019/
https://www.ncbi.nlm.nih.gov/pubmed/36983660
http://dx.doi.org/10.3390/jpm13030478
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author Carlini, Gianluca
Gaudiano, Caterina
Golfieri, Rita
Curti, Nico
Biondi, Riccardo
Bianchi, Lorenzo
Schiavina, Riccardo
Giunchi, Francesca
Faggioni, Lorenzo
Giampieri, Enrico
Merlotti, Alessandra
Dall’Olio, Daniele
Sala, Claudia
Pandolfi, Sara
Remondini, Daniel
Rustici, Arianna
Pastore, Luigi Vincenzo
Scarpetti, Leonardo
Bortolani, Barbara
Cercenelli, Laura
Brunocilla, Eugenio
Marcelli, Emanuela
Coppola, Francesca
Castellani, Gastone
author_facet Carlini, Gianluca
Gaudiano, Caterina
Golfieri, Rita
Curti, Nico
Biondi, Riccardo
Bianchi, Lorenzo
Schiavina, Riccardo
Giunchi, Francesca
Faggioni, Lorenzo
Giampieri, Enrico
Merlotti, Alessandra
Dall’Olio, Daniele
Sala, Claudia
Pandolfi, Sara
Remondini, Daniel
Rustici, Arianna
Pastore, Luigi Vincenzo
Scarpetti, Leonardo
Bortolani, Barbara
Cercenelli, Laura
Brunocilla, Eugenio
Marcelli, Emanuela
Coppola, Francesca
Castellani, Gastone
author_sort Carlini, Gianluca
collection PubMed
description Background: Benign renal tumors, such as renal oncocytoma (RO), can be erroneously diagnosed as malignant renal cell carcinomas (RCC), because of their similar imaging features. Computer-aided systems leveraging radiomic features can be used to better discriminate benign renal tumors from the malignant ones. The purpose of this work was to build a machine learning model to distinguish RO from clear cell RCC (ccRCC). Method: We collected CT images of 77 patients, with 30 cases of RO (39%) and 47 cases of ccRCC (61%). Radiomic features were extracted both from the tumor volumes identified by the clinicians and from the tumor’s zone of transition (ZOT). We used a genetic algorithm to perform feature selection, identifying the most descriptive set of features for the tumor classification. We built a decision tree classifier to distinguish between ROs and ccRCCs. We proposed two versions of the pipeline: in the first one, the feature selection was performed before the splitting of the data, while in the second one, the feature selection was performed after, i.e., on the training data only. We evaluated the efficiency of the two pipelines in cancer classification. Results: The ZOT features were found to be the most predictive by the genetic algorithm. The pipeline with the feature selection performed on the whole dataset obtained an average ROC AUC score of 0.87 ± 0.09. The second pipeline, in which the feature selection was performed on the training data only, obtained an average ROC AUC score of 0.62 ± 0.17. Conclusions: The obtained results confirm the efficiency of ZOT radiomic features in capturing the renal tumor characteristics. We showed that there is a significant difference in the performances of the two proposed pipelines, highlighting how some already published radiomic analyses could be too optimistic about the real generalization capabilities of the models.
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spelling pubmed-100520192023-03-30 Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer Carlini, Gianluca Gaudiano, Caterina Golfieri, Rita Curti, Nico Biondi, Riccardo Bianchi, Lorenzo Schiavina, Riccardo Giunchi, Francesca Faggioni, Lorenzo Giampieri, Enrico Merlotti, Alessandra Dall’Olio, Daniele Sala, Claudia Pandolfi, Sara Remondini, Daniel Rustici, Arianna Pastore, Luigi Vincenzo Scarpetti, Leonardo Bortolani, Barbara Cercenelli, Laura Brunocilla, Eugenio Marcelli, Emanuela Coppola, Francesca Castellani, Gastone J Pers Med Article Background: Benign renal tumors, such as renal oncocytoma (RO), can be erroneously diagnosed as malignant renal cell carcinomas (RCC), because of their similar imaging features. Computer-aided systems leveraging radiomic features can be used to better discriminate benign renal tumors from the malignant ones. The purpose of this work was to build a machine learning model to distinguish RO from clear cell RCC (ccRCC). Method: We collected CT images of 77 patients, with 30 cases of RO (39%) and 47 cases of ccRCC (61%). Radiomic features were extracted both from the tumor volumes identified by the clinicians and from the tumor’s zone of transition (ZOT). We used a genetic algorithm to perform feature selection, identifying the most descriptive set of features for the tumor classification. We built a decision tree classifier to distinguish between ROs and ccRCCs. We proposed two versions of the pipeline: in the first one, the feature selection was performed before the splitting of the data, while in the second one, the feature selection was performed after, i.e., on the training data only. We evaluated the efficiency of the two pipelines in cancer classification. Results: The ZOT features were found to be the most predictive by the genetic algorithm. The pipeline with the feature selection performed on the whole dataset obtained an average ROC AUC score of 0.87 ± 0.09. The second pipeline, in which the feature selection was performed on the training data only, obtained an average ROC AUC score of 0.62 ± 0.17. Conclusions: The obtained results confirm the efficiency of ZOT radiomic features in capturing the renal tumor characteristics. We showed that there is a significant difference in the performances of the two proposed pipelines, highlighting how some already published radiomic analyses could be too optimistic about the real generalization capabilities of the models. MDPI 2023-03-06 /pmc/articles/PMC10052019/ /pubmed/36983660 http://dx.doi.org/10.3390/jpm13030478 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Carlini, Gianluca
Gaudiano, Caterina
Golfieri, Rita
Curti, Nico
Biondi, Riccardo
Bianchi, Lorenzo
Schiavina, Riccardo
Giunchi, Francesca
Faggioni, Lorenzo
Giampieri, Enrico
Merlotti, Alessandra
Dall’Olio, Daniele
Sala, Claudia
Pandolfi, Sara
Remondini, Daniel
Rustici, Arianna
Pastore, Luigi Vincenzo
Scarpetti, Leonardo
Bortolani, Barbara
Cercenelli, Laura
Brunocilla, Eugenio
Marcelli, Emanuela
Coppola, Francesca
Castellani, Gastone
Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer
title Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer
title_full Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer
title_fullStr Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer
title_full_unstemmed Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer
title_short Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer
title_sort effectiveness of radiomic zot features in the automated discrimination of oncocytoma from clear cell renal cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10052019/
https://www.ncbi.nlm.nih.gov/pubmed/36983660
http://dx.doi.org/10.3390/jpm13030478
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