Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics - Keek, Simon A.; Beuque, Manon; Primakov, Sergey; Woodruff, Henry C.; Chatterjee, Avishek; van Timmeren, Janita E.; Vallières, Martin; Hendriks, Lizza E. L.; Kraft, Johannes; Andratschke, Nicolaus; Braunstein, Steve E.; Morin, Olivier; et al. (Frontiers in Oncology, 2022)
Introduction There is a cumulative risk of 20-40% of developing brain metastases (BM) in solid cancers. Stereotactic radiotherapy (SRT) enables application of high focal doses of radiation to a volume, and is often used for BM treatment. However, SRT can cause adverse radiation effects (ARE) such as radiation necrosis, sometimes causing irreversible damage to the brain. It is therefore of clinical interest to identify patients at high-risk of developing ARE. We hypothesized that models trained with radiomics features, deep learning (DL) features, or patient characteristics, or a combination, can predict ARE risk in patients with BM before SRT. Methods Gadolinium-enhanced T1-weighted MRIs and characteristics from patients treated with SRT for BM were collected for a training and testing cohort (N=1404), and a validation cohort (N=237) from a separate institute. From each lesion in the training set, radiomics features were extracted and used to train an extreme gradient boosting (XGBoost) model. A DL model was trained on the same cohort to make a separate prediction, and to extract the last layer of features. Different models using XGBoost were built using only radiomics features, DL features and patient characteristics, or a combination of them. Evaluation was performed using the area under the curve (AUC) of the receiver operating characteristic curve on the external dataset. Predictions for individual lesions and per-patient developing ARE were investigated. Results The best performing XGBoost model on a lesion-level was trained on a combination of radiomics features and DL features (AUC of 0.71, recall of 0.80). On a patient-level, a combination of radiomics features, DL features, and patient characteristics obtained the best performance (AUC of 0.72, recall of 0.84). The DL model achieved an AUC of 0.64 and recall of 0.85 per-lesion, and an AUC of 0.70 and recall of 0.60 per-patient. Conclusion Machine learning models built on radiomics features and DL features extracted from BM combined with patient characteristics show potential to predict ARE at the patient and lesion level. These models could be used in clinical decision making, informing patients on their risk of ARE, and allowing physicians to opt for different therapies.