RadiomiX for Radiomics Analysis: Automated Approaches to Overcome Challenges in Replicability
<b>Background/Objectives:</b> To simplify the decision-making process in radiomics by employing RadiomiX, an algorithm designed to automatically identify the best model combination and validate them across multiple environments was developed, thus enhancing the reliability of results. &l...
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MDPI AG
2025-08-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/15/1968 |
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| author | Harel Kotler Luca Bergamin Fabio Aiolli Elena Scagliori Angela Grassi Giulia Pasello Alessandra Ferro Francesca Caumo Gisella Gennaro |
| author_facet | Harel Kotler Luca Bergamin Fabio Aiolli Elena Scagliori Angela Grassi Giulia Pasello Alessandra Ferro Francesca Caumo Gisella Gennaro |
| author_sort | Harel Kotler |
| collection | DOAJ |
| description | <b>Background/Objectives:</b> To simplify the decision-making process in radiomics by employing RadiomiX, an algorithm designed to automatically identify the best model combination and validate them across multiple environments was developed, thus enhancing the reliability of results. <b>Methods</b>: RadiomiX systematically tests classifier and feature selection method combinations known to be suitable for radiomic datasets to determine the best-performing configuration across multiple train–test splits and K-fold cross-validation. The framework was validated on four public retrospective radiomics datasets including lung nodules, metastatic breast cancer, and hepatic encephalopathy using CT, PET/CT, and MRI modalities. Model performance was assessed using the area under the receiver-operating-characteristic curve (AUC) and accuracy metrics. <b>Results:</b> RadiomiX achieved superior performance across four datasets: LLN (AUC = 0.850 and accuracy = 0.785), SLN (AUC = 0.845 and accuracy = 0.754), MBC (AUC = 0.889 and accuracy = 0.833), and CHE (AUC = 0.837 and accuracy = 0.730), significantly outperforming original published models (<i>p</i> < 0.001 for LLN/SLN and <i>p</i> = 0.023 for MBC accuracy). When original published models were re-evaluated using ten-fold cross-validation, their performance decreased substantially: LLN (AUC = 0.783 and accuracy = 0.731), SLN (AUC = 0.748 and accuracy = 0.714), MBC (AUC = 0.764 and accuracy = 0.711), and CHE (AUC = 0.755 and accuracy = 0.677), further highlighting RadiomiX’s methodological advantages. <b>Conclusions:</b> Systematically testing model combinations using RadiomiX has led to significant improvements in performance. This emphasizes the potential of automated ML as a step towards better-performing and more reliable radiomic models. |
| format | Article |
| id | doaj-art-b6b59a7f2bb44e06a83f0ecf92af4ed6 |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-b6b59a7f2bb44e06a83f0ecf92af4ed62025-08-20T04:00:50ZengMDPI AGDiagnostics2075-44182025-08-011515196810.3390/diagnostics15151968RadiomiX for Radiomics Analysis: Automated Approaches to Overcome Challenges in ReplicabilityHarel Kotler0Luca Bergamin1Fabio Aiolli2Elena Scagliori3Angela Grassi4Giulia Pasello5Alessandra Ferro6Francesca Caumo7Gisella Gennaro8Breast Radiology, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, ItalyDepartment of Mathematics, University of Padova, 35128 Padua, ItalyDepartment of Mathematics, University of Padova, 35128 Padua, ItalyRadiology, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, ItalyClinical Research Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, ItalyMedical Oncology 2, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, ItalyMedical Oncology 2, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, ItalyBreast Radiology, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, ItalyBreast Radiology, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy<b>Background/Objectives:</b> To simplify the decision-making process in radiomics by employing RadiomiX, an algorithm designed to automatically identify the best model combination and validate them across multiple environments was developed, thus enhancing the reliability of results. <b>Methods</b>: RadiomiX systematically tests classifier and feature selection method combinations known to be suitable for radiomic datasets to determine the best-performing configuration across multiple train–test splits and K-fold cross-validation. The framework was validated on four public retrospective radiomics datasets including lung nodules, metastatic breast cancer, and hepatic encephalopathy using CT, PET/CT, and MRI modalities. Model performance was assessed using the area under the receiver-operating-characteristic curve (AUC) and accuracy metrics. <b>Results:</b> RadiomiX achieved superior performance across four datasets: LLN (AUC = 0.850 and accuracy = 0.785), SLN (AUC = 0.845 and accuracy = 0.754), MBC (AUC = 0.889 and accuracy = 0.833), and CHE (AUC = 0.837 and accuracy = 0.730), significantly outperforming original published models (<i>p</i> < 0.001 for LLN/SLN and <i>p</i> = 0.023 for MBC accuracy). When original published models were re-evaluated using ten-fold cross-validation, their performance decreased substantially: LLN (AUC = 0.783 and accuracy = 0.731), SLN (AUC = 0.748 and accuracy = 0.714), MBC (AUC = 0.764 and accuracy = 0.711), and CHE (AUC = 0.755 and accuracy = 0.677), further highlighting RadiomiX’s methodological advantages. <b>Conclusions:</b> Systematically testing model combinations using RadiomiX has led to significant improvements in performance. This emphasizes the potential of automated ML as a step towards better-performing and more reliable radiomic models.https://www.mdpi.com/2075-4418/15/15/1968radiomicslung cancerbreast cancerhepatic encephalopathymachine learningreplicability |
| spellingShingle | Harel Kotler Luca Bergamin Fabio Aiolli Elena Scagliori Angela Grassi Giulia Pasello Alessandra Ferro Francesca Caumo Gisella Gennaro RadiomiX for Radiomics Analysis: Automated Approaches to Overcome Challenges in Replicability Diagnostics radiomics lung cancer breast cancer hepatic encephalopathy machine learning replicability |
| title | RadiomiX for Radiomics Analysis: Automated Approaches to Overcome Challenges in Replicability |
| title_full | RadiomiX for Radiomics Analysis: Automated Approaches to Overcome Challenges in Replicability |
| title_fullStr | RadiomiX for Radiomics Analysis: Automated Approaches to Overcome Challenges in Replicability |
| title_full_unstemmed | RadiomiX for Radiomics Analysis: Automated Approaches to Overcome Challenges in Replicability |
| title_short | RadiomiX for Radiomics Analysis: Automated Approaches to Overcome Challenges in Replicability |
| title_sort | radiomix for radiomics analysis automated approaches to overcome challenges in replicability |
| topic | radiomics lung cancer breast cancer hepatic encephalopathy machine learning replicability |
| url | https://www.mdpi.com/2075-4418/15/15/1968 |
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