Exploring the complexities of radiomics: an in-depth analysis of a machine learning pipeline for predicting rectal cancer therapy response using MRI

Developing radiomic biomarkers remains challenging due to the variability in imaging protocols across centres and the lack of standardised methodologies. This study evaluates the impact of different technical decisions in radiomics pipelines using multiparametric magnetic resonance imaging data from six centres for predicting therapy response in rectal cancer. Preprocessing, feature extraction, normalisation strategies, and machine learning (ML) models were assessed for robustness and generalisability. Key findings demonstrated that the preprocessing significantly enhanced the feature reproducibility and the importance of the selected ones over the complexity of the ML classifier. This analysis highlights the necessity of a unique pipeline and the complexity of radiomics biomarkers in real-world settings, particularly when handling highly imbalanced datasets. Several insights and methodologies have been presented that may support towards more conscious decisions when implementing radiomic systems. Future efforts should focus on integrating clinical/genomic/pathomics data to improve the predictive capabilities and facilitate the introduction into clinical practice.

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Publication Details

Journal
Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization
Published
2025-09-25
DOI
https://doi.org/10.1080/21681163.2025.2564261
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Exploring the complexities of radiomics: an in-depth analysis of a machine learning pipeline for predicting rectal cancer therapy response using MRI

Jovana Panić, Antonio Esposito, Gabriella Balestra, Samanta Rosati et al.
Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization
Radiomics and Machine Learning in Medical Imaging
article

Exploring the complexities of radiomics: an in-depth analysis of a machine learning pipeline for predicting rectal cancer therapy response using MRI

Jovana Panić, Antonio Esposito, Gabriella Balestra, Samanta Rosati, Alfonso Ragozzino, Arianna Defeudis, Salvatore Siena, Angelo Vanzulli, Serena Dell’Aversana, Riccardo Faletti, Daniele Regge, Marco Gatti, Anna Palmisano, Valentina Giannini, Lorenzo Vassallo, Stefano Cirillo
article en

Abstract

Developing radiomic biomarkers remains challenging due to the variability in imaging protocols across centres and the lack of standardised methodologies. This study evaluates the impact of different technical decisions in radiomics pipelines using multiparametric magnetic resonance imaging data from six centres for predicting therapy response in rectal cancer. Preprocessing, feature extraction, normalisation strategies, and machine learning (ML) models were assessed for robustness and generalisability. Key findings demonstrated that the preprocessing significantly enhanced the feature reproducibility and the importance of the selected ones over the complexity of the ML classifier. This analysis highlights the necessity of a unique pipeline and the complexity of radiomics biomarkers in real-world settings, particularly when handling highly imbalanced datasets. Several insights and methodologies have been presented that may support towards more conscious decisions when implementing radiomic systems. Future efforts should focus on integrating clinical/genomic/pathomics data to improve the predictive capabilities and facilitate the introduction into clinical practice.

Computer Methods in Biomechanics and Biomedical Engineering Imaging & VisualizationVol. 13(1)
Vita-Salute San Raffaele University (IT), University of Milan (IT), Azienda Ospedaliera Citta' della Salute e della Scienza di Torino (IT), A. O. Ordine Mauriziano di Torino (IT), Turin Polytechnic University (UZ), Istituti di Ricovero e Cura a Carattere Scientifico (IT), Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele (IT), University of Turin (IT), The University of Osaka (JP)
Openalex Percentile: Top 61%
Radiomics and Machine Learning in Medical Imaging
DOIOpen Access PDFOpenAlex
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