Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Mammadov O

PROVIDER: S-EPMC9364026 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

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

Mammadov Orkhan O Akkurt Burak Han BH Musigmann Manfred M Ari Asena Petek AP Blömer David A DA Kasap Dilek N G DNG Henssen Dylan J H A DJHA Nacul Nabila Gala NG Sartoretti Elisabeth E Sartoretti Thomas T Backhaus Philipp P Thomas Christian C Stummer Walter W Heindel Walter W Mannil Manoj M

Heliyon 20220802 8

<h4>Objective</h4>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.<h4>Material & methods</h4>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 sur ...[more]

PMID: 35965975

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Dataset Information

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

Objective

Material & methods

Results

Conclusion

Publications

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

Similar Datasets

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