Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent
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,...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-08-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844022013111 |
| _version_ | 1818009965019791360 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Orkhan Mammadov |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T05:49:29Z |
| format | Article |
| id | doaj.art-6f121726d452463daf172ebc1b1765e3 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-14T05:49:29Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-6f121726d452463daf172ebc1b1765e32022-12-22T02:09:10ZengElsevierHeliyon2405-84402022-08-0188e10023Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agentOrkhan Mammadov0Burak Han Akkurt1Manfred Musigmann2Asena Petek Ari3David A. Blömer4Dilek N.G. Kasap5Dylan J.H.A. Henssen6Nabila Gala Nacul7Elisabeth Sartoretti8Thomas Sartoretti9Philipp Backhaus10Christian Thomas11Walter Stummer12Walter Heindel13Manoj Mannil14University Clinic for Radiology, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, GermanyUniversity Clinic for Radiology, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, GermanyUniversity Clinic for Radiology, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, GermanyUniversity Clinic for Radiology, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, GermanyUniversity Clinic for Radiology, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, GermanyUniversity Clinic for Radiology, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, GermanyDepartment of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, the NetherlandsUniversity Clinic for Radiology, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, GermanyFaculty of Medicine, University of Zurich, Zurich, SwitzerlandFaculty of Medicine, University of Zurich, Zurich, Switzerland; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht University, Maastricht, the NetherlandsDepartment of Nuclear Medicine, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, Germany; European Institute for Molecular Imaging, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, GermanyInstitute of Neuropathology, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, GermanyDepartment of Neurosurgery, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, GermanyUniversity Clinic for Radiology, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, GermanyUniversity Clinic for Radiology, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, DE-48149 Muenster, Germany; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2405844022013111Artificial intelligenceGliomaPatient outcome assessment |
| spellingShingle | 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 Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent Heliyon Artificial intelligence Glioma Patient outcome assessment |
| title | Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent |
| title_full | Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent |
| title_fullStr | Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent |
| title_full_unstemmed | Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent |
| title_short | Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent |
| title_sort | radiomics for pseudoprogression prediction in high grade gliomas added value of mr contrast agent |
| topic | Artificial intelligence Glioma Patient outcome assessment |
| url | http://www.sciencedirect.com/science/article/pii/S2405844022013111 |
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