NS-HGlio: A Generalizable and Repeatable HGG Segmentation and Volumetric Measurement AI Algorithm
In December, 2022, Neosoma published an article in Neuro-Oncology Advances detailing its core deep learning technology architecture, training, validation, and performance. This patented technology is currently applied to high-grade glioma tumor segmentation for longitudinal assessment, and will be applied to other neuro-oncology indications.
The authors summarized the importance of the study as follows:
The success of clinical trials and clinical management of gliomas is dependent on many factors. The accurate, repeatable, and rapid quantification of HGG Enh. volumes on MRIs are important to inform mRANO and the accurate and repeatable quantification of HGG Ed. volume is of value in objectifying FLAIR assessment for RANO. Volumetric measurement of HGG Enh. and Ed. volumes likely correlate better with Progression Free Survival (PFS) and Overall Survival (OS) when compared to standard bi-dimensional measures. However, the clinical translation of an automated AI technology to perform this task has not been successful given the complexity of the postoperative and post-treatment imaging in gliomas and the poor quality of the ground truth (GT) of the publicly available datasets. We describe the development and validation of a Deep Learning (DL) device to perform segmentation, volumetric measurement, and visualization of the Enh. and Ed. components of HGG at accuracy levels comparable to those of experts while eliminating inter-rater and intra-rater variability and potentially the need for adjudications in clinical trials. This would additionally allow for standardized tumor growth and regression analysis and quick bulk re-analysis of historical clinical trials data in need of re-categorization based on the 2021 WHO classification of CNS tumors fifth edition (WHOCNS5). Furthermore, given that this AI is currently FDA cleared, this would allow for routine implementation of response assessment in clinical care allowing for better adherence of institutions to RANO and mRANO criteria.
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