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Brain metastases from NSCLC treated with stereotactic radiotherapy: prediction mismatch between two different radiomic platforms

Published:November 23, 2022DOI:https://doi.org/10.1016/j.radonc.2022.11.013

      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.

      Graphical abstract

      Keywords

      Abbreviations:

      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)
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