Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

Abstract

This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient's prognoses.

Keywords: Artificial intelligence; Deep learning; Glioma; Machine learning; Radiomics.

Fig. 1

General framework showing the main steps of the radiomics

Fig. 2

Pre-processing step on glioma tumor subject (T2-FLAIR imaging modality)

Fig. 3

Manual segmentation for a glioma tumor in two different imaging modalities

Fig. 4

Differences between three groups of features (texture, shape, and histogram) extracted from two glioma subjects with different grades (HGG and LGG)