Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent

Orkhan Mammadov, Burak Han Akkurt, Manfred Musigmann, Asena Petek Ari, David A Blömer, Dilek N G Kasap, Dylan J H A Henssen, Nabila Gala Nacul, Elisabeth Sartoretti, Thomas Sartoretti, Philipp Backhaus, Christian Thomas, Walter Stummer, Walter Heindel, Manoj Mannil*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Objective: Our aim is to define the capabilities of radiomics in predicting pseudoprogression from pre-treatment MR images in patients diagnosed with high-grade gliomas using T1 non-contrast-enhanced and contrast-enhanced images.

Material & methods: In this retrospective IRB-approved study, image segmentation of high-grade gliomas was semi-automatically performed using 3D Slicer. Non-contrast-enhanced T1-weighted images and contrast-enhanced T1-weighted images were used prior to surgical therapy or radio-chemotherapy. Imaging data was split into a training sample and an independent test sample at random. We extracted 107 radiomic features by use of PyRadiomics. Feature selection and model construction were performed using Generalized Boosted Regression Models (GBM).

Results: Our cohort included 124 patients (female: n = 53), diagnosed with progressive (n = 61) and pseudoprogressive disease (n = 63) of primary high-grade gliomas. Based on non-contrast-enhanced T1-weighted images of the independent test sample, the mean area under the curve (AUC), mean sensitivity, mean specificity and mean accuracy of our model were 0.651 [0.576, 0.761], 0.616 [0.417, 0.833], 0.578 [0.417, 0.750] and 0.597 [0.500, 0.708] to predict the development of pseudoprogression. In comparison, the independent test data of contrast-enhanced T1-weighted images yielded significantly higher values of AUC = 0.819 [0.760, 0.872], sensitivity = 0.817 [0.750, 0.833], specificity = 0.723 [0.583, 0.833] and accuracy = 0.770 [0.687, 0.833].

Conclusion: Our findings show that it is possible to predict pseudoprogression of high-grade gliomas with a Radiomics model using contrast-enhanced T1-weighted images with comparatively good discriminatory power. The use of a contrast agent results in a clear added value.

Original languageEnglish
Article numbere10023
Number of pages9
Issue number8
Publication statusPublished - Aug 2022


  • Artificial intelligence
  • Glioma
  • Patient outcome assessment

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