VBrain

VBrain is an artificial intelligence-driven software that utilizes deep learning neural networks to automatically generate initial contours of specific, pre-diagnosed brain tumors (metastases, meningiomas, acoustic neuromas) from axial T1 contrast-enhanced MRI images. This tool aims to assist medical professionals in radiation therapy treatment planning by providing a starting point for tumor segmentation, which requires subsequent review and modification by the clinician.

VBrain is a software device designed to assist trained medical professionals in radiation therapy treatment planning for adult patients with known brain tumors. Its primary function is to provide initial object contours of diagnosed brain metastases, meningiomas, and acoustic neuromas on axial T1 contrast-enhanced MRI images. The system utilizes deep learning neural networks for image segmentation, involving preprocessing of MRI data, application of the AI algorithm to generate tumor contours, and postprocessing to export these contours as DICOM-RT objects. VBrain operates within a PACS network, allowing users to either pull patient scans or send specific MR images for analysis. It is explicitly stated that VBrain is for informational purposes, not for diagnosis or replacing manual contouring, and users must confirm or modify the generated contours using external DICOM-RT compatible software. The core technology of VBrain relies on artificial intelligence, specifically deep learning neural networks, to automatically segment brain tumors. The software does not alter the original MRI images and is intended only for specific tumor types (brain metastases, meningiomas, acoustic neuromas), requiring the user to pre-identify the tumor type. Performance was evaluated in a retrospective, blinded, multicenter, multinational study involving 116 cases and 238 tumors from four institutions. Key performance metrics included a lesion-wise sensitivity of 90.3%, a false-positive rate of 0.681 tumors/case, a lesion-wise Dice similarity coefficient of 0.793, an average Hausdorff distance (relative to lesion size) of 5.0%, and an average centroid distance (relative to lesion size) of 5.6%, with all metrics meeting predefined performance goals.

AI Regulatory Concerns

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• Q1. How does VBrain's deep learning model differentiate between brain metastases, meningiomas, and acoustic neuromas, given the user must pre-specify the tumor type?; What was the variability in performance metrics (e.g., Dice coefficient, Hausdorff distance) across the different institutions and imaging equipment used in the multinational study?; How does the performance of VBrain compare to manual contouring by experienced radiation oncologists in terms of time efficiency and inter-observer variability for the specified tumor types?; What specific preprocessing steps are applied to the axial T1 contrast-enhanced MRI images before they are fed into the deep learning network, and how sensitive is the algorithm to variations in image acquisition parameters?; Are there plans to expand VBrain's capabilities to include other brain tumor types or different imaging modalities, and what challenges are anticipated in such expansions?