Articolo in rivista, 2023, ENG, 10.1016/j.radonc.2022.11.013
Carloni G.; Garibaldi C.; Marvaso G.; Volpe S.; Zaffaroni M.; Pepa M.; Isaksson L.J.; Colombo F.; Durante S.; Lo Presti G.; Raimondi S.; Spaggiari L.J.; de Marinis F.; Piperno G.; Vigorito S.; Gandini S.; Cremonesi M.; Positano V.; Jereczek-Fossa B.A.
Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy and CNR-ISTI and Department of Information Engineering, University of Pisa, Pisa, Italy; Unit of Radiation Research, IEO, European Institute of Oncology, IRCCS, Milan, Italy; Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS and Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy; Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS and Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy; Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy; Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy; Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS and Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy; Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS and Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy; Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy; Department of Experimental Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy; Department of Experimental Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology and Department of Thoracic Surgery, IEO, European Institute of Oncology, Milan, Italy; Division of Thoracic Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy; Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy; Unit of Medical Physics, IEO, European Institute of Oncology, IRCCS, Milan, Italy; Department of Experimental Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy; Unit of Radiation Research, IEO, European Institute of Oncology, IRCCS, Milan, Italy; Department of Information Engineering, University of Pisa and Gabriele Monasterio Foundation, Pisa, Italy; Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS and Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
Background and purpose. Radiomics enables the mining of quantitative features from medical images. The influence of the radiomic feature extraction software on the final performance of models is still a poorly understood topic. This study aimed to investigate the ability of radiomic features extracted by two different radiomic platforms to predict clinical outcomes in patients treated with radiosurgery for brain metastases from non-small cell lung cancer. We developed models integrating pre-treatment magnetic resonance imaging (MRI)-derived radiomic features and clinical data. Materials and methods. Pre-radiotherapy gadolinium enhanced axial T1-weighted MRI scans were used. MRI images were re-sampled, intensity-shifted, and histogram-matched before radiomic extraction by means of two different platforms (PyRadiomics and SOPHiA Radiomics). We adopted LASSO Cox regression models for multivariable analyses by creating radiomic, clinical, and combined models using three survival clinical endpoints (local control, distant progression, and overall survival). The statistical analysis was repeated 50 times with different random seeds and the median concordance index was used as performance metric of the models. Results. We analysed 276 metastases from 148 patients. The use of the two platforms resulted in differences in both the quality and the number of extractable features. That led to mismatches in terms of end-to-end performance, statistical significance of radiomic scores, and clinical covariates found significant in combined models. Conclusion. This study shed new light on how extracting radiomic features from the same images using two different platforms could yield several discrepancies. That may lead to acute consequences on drawing conclusions, comparing results across the literature, and translating radiomics into clinical practice.
Radiotherapy and oncology 178
Radiomics, Non-small cell lung cancer, Brain metastases, Radiosurgery, Radiomic platform, Performance variability
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 474421
Year: 2023
Type: Articolo in rivista
Creation: 2022-12-01 15:24:55.000
Last update: 2023-01-16 09:28:06.000
CNR authors
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1016/j.radonc.2022.11.013
URL: https://www.sciencedirect.com/science/article/pii/S0167814022045613
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:474421
DOI: 10.1016/j.radonc.2022.11.013
Scopus: 2-s2.0-85145558922