Deriving stable multi-parametric MRI radiomic signatures in the presence of inter-scanner variations: survival prediction of glioblastoma via imaging pattern analysis and machine learning techniques

Saima Rathore; Spyridon Bakas; Hamed Akbari; Gaurav Shukla; Martin Rozycki; Christos Davatzikos

doi:10.1117/12.2293661

27 February 2018 Deriving stable multi-parametric MRI radiomic signatures in the presence of inter-scanner variations: survival prediction of glioblastoma via imaging pattern analysis and machine learning techniques

Saima Rathore, Spyridon Bakas, Hamed Akbari, Gaurav Shukla, Martin Rozycki, Christos Davatzikos

Author Affiliations +

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 1057509 (2018) https://doi.org/10.1117/12.2293661
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

There is mounting evidence that assessment of multi-parametric magnetic resonance imaging (mpMRI) profiles can noninvasively predict survival in many cancers, including glioblastoma. The clinical adoption of mpMRI as a prognostic biomarker, however, depends on its applicability in a multicenter setting, which is hampered by inter-scanner variations. This concept has not been addressed in existing studies. We developed a comprehensive set of within-patient normalized tumor features such as intensity profile, shape, volume, and tumor location, extracted from multicenter mpMRI of two large (n_patients=353) cohorts, comprising the Hospital of the University of Pennsylvania (HUP, n_patients=252, n_scanners=3) and The Cancer Imaging Archive (TCIA, n_patients=101, n_scanners=8). Inter-scanner harmonization was conducted by normalizing the tumor intensity profile, with that of the contralateral healthy tissue. The extracted features were integrated by support vector machines to derive survival predictors. The predictors’ generalizability was evaluated within each cohort, by two cross-validation configurations: i) pooled/scanner-agnostic, and ii) across scanners (training in multiple scanners and testing in one). The median survival in each configuration was used as a cut-off to divide patients in long- and short-survivors. Accuracy (ACC) for predicting long- versus short-survivors, for these configurations was ACC_pooled=79.06% and ACC_pooled=84.7%, ACC_across=73.55% and ACCacross=74.76%, in HUP and TCIA datasets, respectively. The hazard ratio at 95% confidence interval was 3.87 (2.87–5.20, P<0.001) and 6.65 (3.57-12.36, P<0.001) for HUP and TCIA datasets, respectively. Our findings suggest that adequate data normalization coupled with machine learning classification allows robust prediction of survival estimates on mpMRI acquired by multiple scanners.

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Saima Rathore, Spyridon Bakas, Hamed Akbari, Gaurav Shukla, Martin Rozycki, and Christos Davatzikos "Deriving stable multi-parametric MRI radiomic signatures in the presence of inter-scanner variations: survival prediction of glioblastoma via imaging pattern analysis and machine learning techniques", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057509 (27 February 2018); https://doi.org/10.1117/12.2293661

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