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- W2275040620 abstract "Conventional MRI is no longer sufficient to accurately identify tumor presence considering the widely documented infiltrative nature of gliomas. Diffusion Weighted Imaging (DWI) has lent further interpretation to available imaging, but data on the optimal combination of DWI sequences to be employed remains elusive. Our intent was to use a convolutional neural network approach to machine learning employing DWI sequences (apparent diffusion coefficient (ADC), relative cerebral blood volume (rCBV)) to identify an imaging biomarker for GBM. Ten histologically documented GBM cases with available detailed operative reports and gross tumor present on DWI prior to the administration of radiation therapy were selected. T1 post-gad images were used to manually delineate tumor which were then coregistered with DWI series. These 10 manually delineated tumors were used to train a convolutional neural network classifier (CNN). In testing, the trained CNN is employed to assign each pixel in the image a probability of belonging to tumor. Receiver Operating Characteristic (ROC) analysis was performed on the probability map to determine optimum thresholds for tumor grading and to obtain the sensitivity, specificity, and positive and negative predictive values for identifying high-grade gliomas. Backtesting of the 10 GBM cases used for machine training achieved almost 100% probability concordance with the T1-gad manually delineated tumors. The trained CNN was then tested on 5 GBM patient datasets with T1-gad, ADC, rCBV images. A sensitivity of 75% and a specificity of 80% with positive and negative predictive values of 79% and 76% respectively were achieved. Optimum threshold for tumor was 0.47. The convolutional neural network approach to DWI analysis may be very useful in identifying a high grade glioma imaging biomarker. Further training with patient data will further improve the accuracy of this approach, enabling its use for recurrence pattern analysis in setting of radiation therapy and systemic treatment, with possible future applicability in tumor grading as well as radiation treatment field design." @default.
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- W2275040620 date "2015-11-01" @default.
- W2275040620 modified "2023-10-16" @default.
- W2275040620 title "A Novel Approach to Diffusion Weighted Imaging Analysis in Newly Diagnosed GBM" @default.
- W2275040620 doi "https://doi.org/10.1016/j.ijrobp.2015.07.775" @default.
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