Adjuvant radiosurgery to the cavities of surgically resected brain metastases provides excellent local tumor control while reducing the risk of deleterious cognitive decline associated with whole brain radiotherapy. A subset of these patients, however, will develop disease recurrence following radiosurgery. In this study, we sought to assess the predictive capability of radiomic-based models, as compared with standard clinical features, in predicting local tumor control. Methods: We performed a retrospective chart review of patients treated with adjuvant radiosurgery for resected brain metastases at the ''Institution" from 2009 to 2019. Shape, intensity and texture based radiomics features of the cavities were extracted from the pre-radiosurgery treatment planning MRI scans and trained using a gradient boosting technique with K-fold cross validation. Results: In total, 71 cavities from 67 treated patients were included for analysis. The 6 and 12 month local control estimates were 86% and 76%, respectively. The 6 and 12 month overall survival was 78% and 55%, respectively. Thirty-six patients developed intracranial failures outside of the surgical cavity. The predictive model for local control trained on imaging features from the whole cavity achieved an area-underthe-curve (AUC) of 0.73 on the validation set versus an AUC of 0.40 for the clinical features. Conclusions: Here we report a single institutional experience using radiomic-based predictive modeling of local tumor control following adjuvant Gamma Knife radiosurgery for resected brain metastases. We found the radiomics features to provide more robust predictive models of local control rates versus clinical features alone. Such techniques could potentially prove useful in the clinical setting and warrant further investigation.
Characterizing the motile properties of glioblastoma tumor cells could provide a useful way to predict the spread of tumors and to tailor the therapeutic approach. Radiomics has emerged as a diagnostic tool in the classification of tumor grade, stage, and prognosis. The purpose of this work is to examine the potential of radiomics to predict the motility of glioblastoma cells. Tissue specimens were obtained from 31 patients undergoing surgical resection of glioblastoma. Mean tumor cell motility was calculated from time-lapse videos of specimen cells. Manual segmentation was used to define the border of the enhancing tumor T1-weighted MR images, and 107 radiomics features were extracted from the normalized image volumes. Model parameter coefficients were estimated using the adaptive lasso technique validated with leave-one-out cross validation (LOOCV) and permutation tests. The R-squared value for the predictive model was 0.60 with p-values for each individual parameter estimate less than 0.0001. Permutation test models trained with scrambled motility failed to produce a model that out-performed the model trained on the true data. The results of this work suggest that it is possible for a quantitative MRI feature-based regression model to non-invasively predict the cellular motility of glioblastomas.
OBJECTIVES/GOALS: Trigeminal Neuralgia (TN) is a debilitating neuropathic condition characterized by electric-shock-like pain attacks. TN is considered a clinical diagnosis, and few proposed objective markers exist. This work studies the ability of advanced MRI techniques to diagnose and classify TN. METHODS/STUDY POPULATION: Anatomical MRI data from patients undergoing radiosurgery to treat TN was collected. A custom deep-learning UNet algorithm was trained to segment trigeminal nerves from the pons to the anterior wall of Meckels cave using segments drawn by an expert in neuroanatomy. 108 radiomics features related to nerve shape, voxel intensity, and image texture were extracted from the segmented nerves. A 2 layer neural network was trained to distinguish TN affected nerves from the pain-free contralateral nerves. Feature selection was performed within a cross-validation scheme to prevent model overfitting. Mean model performance over the validation sets was used to estimate model generalizability. RESULTS/ANTICIPATED RESULTS: 134 patients and 268 nerves were included. The average number of years with TN was 8. The average validation set accuracy was 78% [range: 75-80%]. The average validation set sensitivity and specificity were 0.82 [range: 0.79-0.84] and 0.76 [range: 0.70-0.79]. 34% of patients had undergone a prior invasive procedure to treat their TN. To evaluate whether the model detected signal changes relating to the previous treatment, those patients were excluded and the model was retrained on the surgically naive patients. Model performance in a reduced cohort of patients was similar to the model trained on all the patients, with accuracy of 77% [range: 73-82%]. DISCUSSION/SIGNIFICANCE: This study suggests that radiomics features calculated from MRIs of trigeminal nerves correlate with anatomical changes in TN affected nerves. This technique will need to be verified in a larger, more heterogeneous cohort of TN patients with a range of MRI acquisition parameters.
PURPOSE Radiomics has shown considerable success as a diagnostic tool in the classification of tumor grade, stage, and prognosis. The purpose of this work is to examine the potential for radiomics techniques to accurately predict the motility properties of glioblastoma cells. METHODS Tissues specimens were obtained from a total of 31 patients undergoing surgical resection of glioblastoma. Mean tumor cell motility was calculated from time-lapse videos using previously described methods (Bangasser et al. Nat Comm 2017). Preoperative, post-contrast T1-weighted MR images with 1 mm voxels were obtained from the medical records for each patient. Manual segmentation was used to define the border of the enhancing tumor and 108 radiomics features were extracted from the normalized image volumes using the PyRadiomics software package. Imaging features that correlated strongly with cellular motility were selected in a stepwise manner by p-value. The four features most strongly correlated with cellular motility were included in a regression model using the adaptive lasso technique with leave-one-out cross validation (LOOCV). RESULTS Two first order and two gray-level run length matrix features were selected for the model. The R-squared value for the predictive model was 0.60 with p-values for each individual parameter estimate less than 0.0001. An analysis of the residual values of the predicted model did not show any evidence of bias in the estimate. The average root mean squared error between the predicted and actual motility from the LOOCV for the model was 0.75. CONCLUSION The results of this work suggest that it is possible for a quantitative image feature-based model to predict the cellular motility of glioblastomas. Further work will prospectively test the model and explore the role of cellular motility in clinical outcomes such as time to recurrence and patterns of failure.
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