A Radiogenomic Deep Ensemble Learning Model for Identifying Radionecrosis Following Brain Metastases (BM) Stereotactic Radiosurgery in Patients With Non-small Cell Lung Cancer BM
Purpose: Stereotactic radiosurgery (SRS) is widely used for brain metastases (BM), but the risk of radionecrosis poses a challenge in post-SRS management. Given the lack of noninvasive imaging methods for distinguishing radionecrosis from recurrence, we aimed to design a deep ensemble learning model...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | Advances in Radiation Oncology |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2452109425001137 |
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| Summary: | Purpose: Stereotactic radiosurgery (SRS) is widely used for brain metastases (BM), but the risk of radionecrosis poses a challenge in post-SRS management. Given the lack of noninvasive imaging methods for distinguishing radionecrosis from recurrence, we aimed to design a deep ensemble learning model that integrates patient clinical features and genomic profiles to identify radionecrosis in patients with BM with post-SRS radiographic progression. Methods and Materials: We studied 90 BMs from 62 patients with non-small cell lung cancer, with 27 biopsy-confirmed post-SRS local recurrences. Clinical features and molecular features were collected. A deep neural network (DNN) was trained for radionecrosis/recurrence prediction using the 3-month post-SRS T1+c magnetic resonance imaging. Preceding the binary prediction output, latent variables were extracted as 1024 deep features. An ensemble learning model was then developed, comprising 2 submodels that fused deep features with clinical (“D+C”) or genomic (“D+G”) features. We employed our positional encoding method to optimally fuse the low-dimensional clinical/genomic features with the high-dimensional image features. The postfusion feature in each submodel yielded a logit result after traversing fully connected layers. The ensemble's final output was the synthesized result of these 2 submodels’ logits via logistic regression. Model training employed an 8:2 train/test split, and 10 model versions were developed for robustness evaluation. Performance metrics were compared against image-only DNN model and “D+C” and “D+G” submodels. Results: The deep ensemble model showed satisfactory performance on the test set, with the area under the receiver operating characteristic curve (ROCAUC) = 0.91 ± 0.04, sensitivity = 0.87 ± 0.16, specificity = 0.86 ± 0.08, and accuracy = 0.87 ± 0.04. This significantly outperformed the image-only DNN result (ROCAUC = 0.71 ± 0.05, sensitivity = 0.66 ± 0.32). Higher average performance was also observed compared to the “D+C” result (ROCAUC = 0.82 ± 0.03, sensitivity = 0.67 ± 0.17) and “D+G” result (ROCAUC = 0.83 ± 0.02, sensitivity = 0.76 ± 0.22). Conclusions: The deep ensemble model achieved the best performance among the models evaluated in this study for distinguishing BM radionecrosis from recurrence using 3-month post-SRS T1+c MR images, clinical features, and genomic features. This highlights the potential of artificial intelligence in clinical decision-making for BM management, warranting further investigation into its clinical applications. |
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| ISSN: | 2452-1094 |