Cargando…
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...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
---|---|
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 |
_version_ | 1785015032168316928 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10052019 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT carlinigianluca effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT gaudianocaterina effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT golfieririta effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT curtinico effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT biondiriccardo effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT bianchilorenzo effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT schiavinariccardo effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT giunchifrancesca effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT faggionilorenzo effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT giampierienrico effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT merlottialessandra effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT dalloliodaniele effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT salaclaudia effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT pandolfisara effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT remondinidaniel effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT rusticiarianna effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT pastoreluigivincenzo effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT scarpettileonardo effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT bortolanibarbara effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT cercenellilaura effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT brunocillaeugenio effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT marcelliemanuela effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT coppolafrancesca effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer AT castellanigastone effectivenessofradiomiczotfeaturesintheautomateddiscriminationofoncocytomafromclearcellrenalcancer |