Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features
Abstract Background and purpose Timely identification of local failure after stereotactic radiotherapy for brain metastases allows for treatment modifications, potentially improving outcomes. While previous studies showed that adding radiomics or Deep Learning (DL) features to clinical features incr...
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| Format: | Article |
| Language: | English |
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BMC
2024-12-01
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| Series: | Radiation Oncology |
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| Online Access: | https://doi.org/10.1186/s13014-024-02573-9 |
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| author | Hemalatha Kanakarajan Wouter De Baene Patrick Hanssens Margriet Sitskoorn |
| author_facet | Hemalatha Kanakarajan Wouter De Baene Patrick Hanssens Margriet Sitskoorn |
| author_sort | Hemalatha Kanakarajan |
| collection | DOAJ |
| description | Abstract Background and purpose Timely identification of local failure after stereotactic radiotherapy for brain metastases allows for treatment modifications, potentially improving outcomes. While previous studies showed that adding radiomics or Deep Learning (DL) features to clinical features increased Local Control (LC) prediction accuracy, their combined potential to predict LC remains unexplored. We examined whether a model using a combination of radiomics, DL and clinical features achieves better accuracy than models using only a subset of these features. Materials and methods We collected pre-treatment brain MRIs (TR/TE: 25/1.86 ms, FOV: 210 × 210 × 150, flip angle: 30°, transverse slice orientation, voxel size: 0.82 × 0.82 × 1.5 mm) and clinical data for 129 patients at the Gamma Knife Center of the Elisabeth-TweeSteden Hospital. Radiomics features were extracted using the Python radiomics feature extractor and DL features were obtained using a 3D ResNet model. A Random Forest machine learning algorithm was employed to train four models using: (1) clinical features only; (2) clinical and radiomics features; (3) clinical and DL features; and (4) clinical, radiomics, and DL features. The average accuracy and other metrics were derived using K-fold cross validation. Results The prediction model utilizing only clinical variables provided an Area Under the receiver operating characteristic Curve (AUC) of 0.85 and an accuracy of 75.0%. Adding radiomics features increased the AUC to 0.86 and accuracy to 79.33%, while adding DL features resulted in an AUC of 0.82 and accuracy of 78.0%. The best performance came from combining clinical, radiomics, and DL features, achieving an AUC of 0.88 and accuracy of 81.66%. This model’s prediction improvement was statistically significant compared to models trained with clinical features alone or with the combination of clinical and DL features. However, the improvement was not statistically significant when compared to the model trained with clinical and radiomics features. Conclusion Integrating radiomics and DL features with clinical characteristics improves prediction of local control after stereotactic radiotherapy for brain metastases. Models incorporating radiomics features consistently outperformed those utilizing clinical features alone or clinical and DL features. The increased prediction accuracy of our integrated model demonstrates the potential for early outcome prediction, enabling timely treatment modifications to improve patient management. |
| format | Article |
| id | doaj-art-52f4d90cf33246babd0fef06fca95d06 |
| institution | Kabale University |
| issn | 1748-717X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Radiation Oncology |
| spelling | doaj-art-52f4d90cf33246babd0fef06fca95d062025-01-05T12:42:17ZengBMCRadiation Oncology1748-717X2024-12-0119111510.1186/s13014-024-02573-9Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning featuresHemalatha Kanakarajan0Wouter De Baene1Patrick Hanssens2Margriet Sitskoorn3Department of Cognitive Neuropsychology, Tilburg UniversityDepartment of Cognitive Neuropsychology, Tilburg UniversityGamma Knife Center, Elisabeth-TweeSteden HospitalDepartment of Cognitive Neuropsychology, Tilburg UniversityAbstract Background and purpose Timely identification of local failure after stereotactic radiotherapy for brain metastases allows for treatment modifications, potentially improving outcomes. While previous studies showed that adding radiomics or Deep Learning (DL) features to clinical features increased Local Control (LC) prediction accuracy, their combined potential to predict LC remains unexplored. We examined whether a model using a combination of radiomics, DL and clinical features achieves better accuracy than models using only a subset of these features. Materials and methods We collected pre-treatment brain MRIs (TR/TE: 25/1.86 ms, FOV: 210 × 210 × 150, flip angle: 30°, transverse slice orientation, voxel size: 0.82 × 0.82 × 1.5 mm) and clinical data for 129 patients at the Gamma Knife Center of the Elisabeth-TweeSteden Hospital. Radiomics features were extracted using the Python radiomics feature extractor and DL features were obtained using a 3D ResNet model. A Random Forest machine learning algorithm was employed to train four models using: (1) clinical features only; (2) clinical and radiomics features; (3) clinical and DL features; and (4) clinical, radiomics, and DL features. The average accuracy and other metrics were derived using K-fold cross validation. Results The prediction model utilizing only clinical variables provided an Area Under the receiver operating characteristic Curve (AUC) of 0.85 and an accuracy of 75.0%. Adding radiomics features increased the AUC to 0.86 and accuracy to 79.33%, while adding DL features resulted in an AUC of 0.82 and accuracy of 78.0%. The best performance came from combining clinical, radiomics, and DL features, achieving an AUC of 0.88 and accuracy of 81.66%. This model’s prediction improvement was statistically significant compared to models trained with clinical features alone or with the combination of clinical and DL features. However, the improvement was not statistically significant when compared to the model trained with clinical and radiomics features. Conclusion Integrating radiomics and DL features with clinical characteristics improves prediction of local control after stereotactic radiotherapy for brain metastases. Models incorporating radiomics features consistently outperformed those utilizing clinical features alone or clinical and DL features. The increased prediction accuracy of our integrated model demonstrates the potential for early outcome prediction, enabling timely treatment modifications to improve patient management.https://doi.org/10.1186/s13014-024-02573-9Brain metastasesDeep learningLocal controlRadiomicsStereotactic radiotherapy |
| spellingShingle | Hemalatha Kanakarajan Wouter De Baene Patrick Hanssens Margriet Sitskoorn Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features Radiation Oncology Brain metastases Deep learning Local control Radiomics Stereotactic radiotherapy |
| title | Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features |
| title_full | Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features |
| title_fullStr | Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features |
| title_full_unstemmed | Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features |
| title_short | Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features |
| title_sort | predicting local control of brain metastases after stereotactic radiotherapy with clinical radiomics and deep learning features |
| topic | Brain metastases Deep learning Local control Radiomics Stereotactic radiotherapy |
| url | https://doi.org/10.1186/s13014-024-02573-9 |
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