  Efforts to utilize growing volumes of clinical imaging data to generate tumor
evaluations continue to require significant manual data wrangling owing to the
data heterogeneity. Here, we propose an artificial intelligence-based solution
for the aggregation and processing of multisequence neuro-oncology MRI data to
extract quantitative tumor measurements. Our end-to-end framework i) classifies
MRI sequences using an ensemble classifier, ii) preprocesses the data in a
reproducible manner, iii) delineates tumor tissue subtypes using convolutional
neural networks, and iv) extracts diverse radiomic features. Moreover, it is
robust to missing sequences and adopts an expert-in-the-loop approach, where
the segmentation results may be manually refined by radiologists. Following the
implementation of the framework in Docker containers, it was applied to two
retrospective glioma datasets collected from the Washington University School
of Medicine (WUSM; n = 384) and the M.D. Anderson Cancer Center (MDA; n = 30)
comprising preoperative MRI scans from patients with pathologically confirmed
gliomas. The scan-type classifier yielded an accuracy of over 99%, correctly
identifying sequences from 380/384 and 30/30 sessions from the WUSM and MDA
datasets, respectively. Segmentation performance was quantified using the Dice
Similarity Coefficient between the predicted and expert-refined tumor masks.
Mean Dice scores were 0.882 ($\pm$0.244) and 0.977 ($\pm$0.04) for whole tumor
segmentation for WUSM and MDA, respectively. This streamlined framework
automatically curated, processed, and segmented raw MRI data of patients with
varying grades of gliomas, enabling the curation of large-scale neuro-oncology
datasets and demonstrating a high potential for integration as an assistive
tool in clinical practice.
