Brain metastases from NSCLC treated with stereotactic radiotherapy: prediction mismatch between two different radiomic platforms. (January 2023)

• Record Type: Journal Article
• Authors: Carloni, Gianluca; Garibaldi, Cristina; Marvaso, Giulia; Volpe, Stefania; Zaffaroni, Mattia; Pepa, Matteo; Isaksson, Lars Johannes; Colombo, Francesca; Durante, Stefano; Lo Presti, Giuliana; Raimondi, Sara; Spaggiari, Lorenzo; de Marinis, Filippo; Piperno, Gaia; Vigorito, Sabrina; Gandini, Sara; Cremonesi, Marta; Positano, Vincenzo; Jereczek-Fossa, Barbara Alicja
• Is Part Of: Radiotherapy and oncology. Volume 178(2023)
• Publication Date: 2023-01
• Subjects: Radiomics -- Non-small cell lung cancer -- Brain metastases -- Radiosurgery -- Radiomic platform -- Performance variability; ALK Anaplastic lymphoma kinase -- BED Biologically Effective Dose -- BM Brain Metastasis -- C-index Concordance index -- CR Complete Response -- CT Computed Tomography -- DP Distant Progression -- EGFR Epidermal growth factor receptor -- EQD2 Equivalent dose in 2Gy fractions -- GLCM Gray Level Co-Occurrence Matrix -- GLDZM Gray Level Distance Zone Matrix -- GLRLM Gray Level Run Length Matrix -- GLSZM Gray Level Size Zone Matrix -- HR Hazard Ratio -- IBSI Imaging Biomarker Standardization Initiative -- IEO Istituto Europeo di Oncologia (European Institute of Oncology) IRCCS, Milan, Italy -- KM Kaplan-Meier -- KPS Karnofsky Performance Status -- LASSO Least Absolute Shrinkage and Selection Operator -- LC Local Control -- LoG Laplacian of Gaussian -- MRI Magnetic Resonance Imaging -- NGLDM Neighbourhood Gray Level Dependence Matrix -- NGTDM Neighbouring Gray Tone Difference Matrix -- NSCLC Non-Small Cell Lung Cancer -- OS Overall Survival -- PD Progression Disease -- PR Partial Response -- PyR PyRadiomics -- RS Radiomic Score -- RT Radiotherapy -- RTSS Radiation Therapy Structure Sets -- SD Stable Disease -- SR SOPHiA Radiomics -- SRS Stereotactic Radiosurgery -- T1-w T1-weighted; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2022.11.013
• Languages: English
• ISSNs: 0167-8140

Abstract

Graphical abstract: Highlights: Using different platforms for radiomic extraction affects models' performance. Variables' relevance is inconsistent among platforms. MRI features are correlated to radiosurgery response in brain metastases from NSCLC. Higher number of radiomic features does not necessarily imply better performance. Abstract: 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.