دورية أكاديمية
Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data
| العنوان: | Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data |
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| المؤلفون: | Se Jin Cho, Wonwoo Cho, Dongmin Choi, Gyuhyeon Sim, So Yeong Jeong, Sung Hyun Baik, Yun Jung Bae, Byung Se Choi, Jae Hyoung Kim, Sooyoung Yoo, Jung Ho Han, Chae-Yong Kim, Jaegul Choo, Leonard Sunwoo |
| المصدر: | Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024) |
| بيانات النشر: | Nature Portfolio, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Brain metastasis, Stereotactic radiosurgery, Deep learning, Radiomics, Longitudinal analysis, Treatment response, Medicine, Science |
| الوصف: | Abstract We developed artificial intelligence models to predict the brain metastasis (BM) treatment response after stereotactic radiosurgery (SRS) using longitudinal magnetic resonance imaging (MRI) data and evaluated prediction accuracy changes according to the number of sequential MRI scans. We included four sequential MRI scans for 194 patients with BM and 369 target lesions for the Developmental dataset. The data were randomly split (8:2 ratio) for training and testing. For external validation, 172 MRI scans from 43 patients with BM and 62 target lesions were additionally enrolled. The maximum axial diameter (Dmax), radiomics, and deep learning (DL) models were generated for comparison. We evaluated the simple convolutional neural network (CNN) model and a gated recurrent unit (Conv-GRU)-based CNN model in the DL arm. The Conv-GRU model performed superior to the simple CNN models. For both datasets, the area under the curve (AUC) was significantly higher for the two-dimensional (2D) Conv-GRU model than for the 3D Conv-GRU, Dmax, and radiomics models. The accuracy of the 2D Conv-GRU model increased with the number of follow-up studies. In conclusion, using longitudinal MRI data, the 2D Conv-GRU model outperformed all other models in predicting the treatment response after SRS of BM. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-024-60781-5 |
| URL الوصول: | https://doaj.org/article/7ed62ff7c1444cf4a285baf44cc594e8 |
| رقم الأكسشن: | edsdoj.7ed62ff7c1444cf4a285baf44cc594e8 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 20452322 |
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| DOI: | 10.1038/s41598-024-60781-5 |