AI-Powered Radiomics Algorithm Based on Slice Pooling for the Glioma Grading

• Published in: IEEE transactions on industrial informatics Vol. 18; no. 8; pp. 5383 - 5393
• Main Authors: Zhao, Guohua, Man, Panpan, Bai, Jie, Li, Longfei, Wang, Peipei, Yang, Guan, Shi, Lei, Tao, Yongcai, Lin, Yusong, Cheng, Jingliang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial intelligence; Computer-aided diagnosis (CAD); Feature extraction; glioma; Image coding; Image compression; Image segmentation; Informatics; Magnetic resonance imaging; magnetic resonance imaging (MRI); Medical imaging; Radiomics; slice-pooling; Tumors
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2021.3105665

In this article, glioma segmentation in the glioma grading computer-aided diagnosis (CAD) system requires manual delineation from radiologists, adding substantially to their workload. Although automatic segmentation is powerful, it cannot fully delegate power to artificial intelligence. We propose an AI-powered radiomics algorithm based on slice pooling (AI-RASP). AI-RASP generated compress images by compressing the gray value of each magnetic resonance imaging slice for radiologists to segment manually. In addition, AI-RASP integrated radiomics models to verify the glioma grading effect and the availability of compressed images. AI-RASP significantly reduce the time of manual segmentation. Results reported on multicenter datasets reveal that our architecture is better than the traditional manual segmentation while being over five times faster. The radiomics model with slice pooling mechanism achieves an area under the curve values of 0.86, 086, and 0.83 in the validation cohorts. Radiologists and patients can benefit from a CAD system integrated with AI-RASP.