Abstract
BACKGROUND
Timely identification of local failure after Stereotactic Radiosurgery (SRS) offers the opportunity for appropriate treatment modifications that may result in improved treatment outcomes, patient survival, and quality of life. Previous studies showed that the addition of either radiomics or deep learning features to clinical features increased the accuracy of the models in predicting local control (LC) of brain metastases after SRS. To date, however, no study combined both radiomics and deep learning features together with clinical features to develop machine learning algorithms to predict LC of brain metastases. In this study, we examined whether a model trained with a combination of all these features could predict LC better than models trained with only a subset of these features.
MATERIAL AND METHODS
Pre-treatment brain MRIs and clinical data were collected retrospectively for 129 patients at the Gamma Knife Center of Elisabeth-TweeSteden Hospital (ETZ), Tilburg, The Netherlands. The patients were split into 103 patients for training and 26 patients for testing. The segment-based radiomics features were extracted using the radiomics feature extractor of the python radiomics package. The deep learning features were extracted using a fine-tuned 3D ResNet model and then combined with the clinical and radiomics features. A Random Forest classifier was trained with the training data set and then tested with the test data set. The performance was compared across 4 different models trained with clinical features only, clinical and radiomics features, clinical and deep learning features, and clinical, radiomics and deep learning features.
RESULTS
The prediction model with only clinical variables demonstrated an area under the receiver operating characteristic curve (AUC) of 0.82 and an accuracy of 75.6%. The prediction model with the combination of clinical and radiomics features demonstrated an AUC of 0.880 and an accuracy of 83.3% whereas the prediction model with the combination of clinical and deep learning features demonstrated an AUC of 0.863 and an accuracy of 78.3%. The best prediction performance was associated with the model that combined the clinical, radiomics and deep learning features with an AUC of 0.886 and 87% accuracy.
CONCLUSION
Machine learning models built on radiomics features and deep learning features combined with patient characteristics show potential to predict LC after SRS with high accuracy. The promising findings from this study demonstrate the potential for early prediction of SRS outcome for brain metastasis prior to treatment initiation and might offer the opportunity for appropriate treatment modifications that may result in improved treatment outcomes, patient survival, and quality of life.
Timely identification of local failure after Stereotactic Radiosurgery (SRS) offers the opportunity for appropriate treatment modifications that may result in improved treatment outcomes, patient survival, and quality of life. Previous studies showed that the addition of either radiomics or deep learning features to clinical features increased the accuracy of the models in predicting local control (LC) of brain metastases after SRS. To date, however, no study combined both radiomics and deep learning features together with clinical features to develop machine learning algorithms to predict LC of brain metastases. In this study, we examined whether a model trained with a combination of all these features could predict LC better than models trained with only a subset of these features.
MATERIAL AND METHODS
Pre-treatment brain MRIs and clinical data were collected retrospectively for 129 patients at the Gamma Knife Center of Elisabeth-TweeSteden Hospital (ETZ), Tilburg, The Netherlands. The patients were split into 103 patients for training and 26 patients for testing. The segment-based radiomics features were extracted using the radiomics feature extractor of the python radiomics package. The deep learning features were extracted using a fine-tuned 3D ResNet model and then combined with the clinical and radiomics features. A Random Forest classifier was trained with the training data set and then tested with the test data set. The performance was compared across 4 different models trained with clinical features only, clinical and radiomics features, clinical and deep learning features, and clinical, radiomics and deep learning features.
RESULTS
The prediction model with only clinical variables demonstrated an area under the receiver operating characteristic curve (AUC) of 0.82 and an accuracy of 75.6%. The prediction model with the combination of clinical and radiomics features demonstrated an AUC of 0.880 and an accuracy of 83.3% whereas the prediction model with the combination of clinical and deep learning features demonstrated an AUC of 0.863 and an accuracy of 78.3%. The best prediction performance was associated with the model that combined the clinical, radiomics and deep learning features with an AUC of 0.886 and 87% accuracy.
CONCLUSION
Machine learning models built on radiomics features and deep learning features combined with patient characteristics show potential to predict LC after SRS with high accuracy. The promising findings from this study demonstrate the potential for early prediction of SRS outcome for brain metastasis prior to treatment initiation and might offer the opportunity for appropriate treatment modifications that may result in improved treatment outcomes, patient survival, and quality of life.
Original language | English |
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Pages (from-to) | v60-v61 |
Journal | Neuro-Oncology |
Volume | 26 |
Issue number | Supplement_5 |
DOIs | |
Publication status | Published - 2024 |