Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma
Background: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict pro...
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| Format: | Article |
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MDPI AG
2024-01-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/16/3/589 |
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| author | Keon Mahmoudi Daniel H. Kim Elham Tavakkol Shingo Kihira Adam Bauer Nadejda Tsankova Fahad Khan Adilia Hormigo Vivek Yedavalli Kambiz Nael |
| author_facet | Keon Mahmoudi Daniel H. Kim Elham Tavakkol Shingo Kihira Adam Bauer Nadejda Tsankova Fahad Khan Adilia Hormigo Vivek Yedavalli Kambiz Nael |
| author_sort | Keon Mahmoudi |
| collection | DOAJ |
| description | Background: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. Methods: In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (<18 months vs. ≥18 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic model in combination with clinical and histopathological data was inserted into a backward stepwise logistic regression model to assess survival. The diagnostic performance of this model was reported for the training and external validation sets. Results: A total of 116 patients were included for model development and 40 patients for external testing validation. The diagnostic performance (AUC/sensitivity/specificity) of the radiomic model generated from seven texture features in determination of ≥18 months survival was 0.71/69.0/70.3. Three variables remained as independent predictors of survival, including radiomics (<i>p</i> = 0.004), age (<i>p</i> = 0.039), and <i>MGMT</i> status (<i>p</i> = 0.025). This model yielded diagnostic performance (AUC/sensitivity/specificity) of 0.77/81.0/66.0 (training) and 0.89/100/78.6 (testing) in determination of survival ≥ 18 months. Conclusions: Results show that our radiogenomic model generated from radiomic features at baseline MRI, age, and <i>MGMT</i> status can predict survival ≥ 18 months in patients with GBM. |
| first_indexed | 2024-03-08T03:59:54Z |
| format | Article |
| id | doaj.art-ad38a364e6134f29926a13afa483ca35 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-08T03:59:54Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-ad38a364e6134f29926a13afa483ca352024-02-09T15:09:12ZengMDPI AGCancers2072-66942024-01-0116358910.3390/cancers16030589Multiparametric Radiogenomic Model to Predict Survival in Patients with GlioblastomaKeon Mahmoudi0Daniel H. Kim1Elham Tavakkol2Shingo Kihira3Adam Bauer4Nadejda Tsankova5Fahad Khan6Adilia Hormigo7Vivek Yedavalli8Kambiz Nael9Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USADepartment of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USADepartment of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USADepartment of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USADepartment of Radiology, Kaiser Permanente Fontana Medical Center, Fontana, CA 92335, USADepartment of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USADepartment of Pathology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USADepartment of Oncology, Montefiore Einstein Cancer Center, Albert Einstein College of Medicine, Bronx, NY 10461, USADepartment of Radiology and Radiological Science, Johns Hopkins Bayview Medical Center, Baltimore, MD 21224, USADepartment of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USABackground: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. Methods: In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (<18 months vs. ≥18 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic model in combination with clinical and histopathological data was inserted into a backward stepwise logistic regression model to assess survival. The diagnostic performance of this model was reported for the training and external validation sets. Results: A total of 116 patients were included for model development and 40 patients for external testing validation. The diagnostic performance (AUC/sensitivity/specificity) of the radiomic model generated from seven texture features in determination of ≥18 months survival was 0.71/69.0/70.3. Three variables remained as independent predictors of survival, including radiomics (<i>p</i> = 0.004), age (<i>p</i> = 0.039), and <i>MGMT</i> status (<i>p</i> = 0.025). This model yielded diagnostic performance (AUC/sensitivity/specificity) of 0.77/81.0/66.0 (training) and 0.89/100/78.6 (testing) in determination of survival ≥ 18 months. Conclusions: Results show that our radiogenomic model generated from radiomic features at baseline MRI, age, and <i>MGMT</i> status can predict survival ≥ 18 months in patients with GBM.https://www.mdpi.com/2072-6694/16/3/589multiparametricradiogenomicstexture analysisgliomatumor segmentationMRI |
| spellingShingle | Keon Mahmoudi Daniel H. Kim Elham Tavakkol Shingo Kihira Adam Bauer Nadejda Tsankova Fahad Khan Adilia Hormigo Vivek Yedavalli Kambiz Nael Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma Cancers multiparametric radiogenomics texture analysis glioma tumor segmentation MRI |
| title | Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma |
| title_full | Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma |
| title_fullStr | Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma |
| title_full_unstemmed | Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma |
| title_short | Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma |
| title_sort | multiparametric radiogenomic model to predict survival in patients with glioblastoma |
| topic | multiparametric radiogenomics texture analysis glioma tumor segmentation MRI |
| url | https://www.mdpi.com/2072-6694/16/3/589 |
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