A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology

TL;DR

This paper reviews recent advancements in AI-enabled radiomics for neuro-oncology, highlighting the shift towards deep learning and CNNs, and discusses future trends in the field.

Contribution

It provides a comprehensive survey of current best practices and future directions for AI radiomics in neuro-oncology, emphasizing deep learning methods.

Findings

CNN-based radiomics achieves state-of-the-art results

Deep learning outperforms traditional machine learning in neuro-oncology radiomics

Future trends include advanced deep learning techniques and integration with clinical workflows

Abstract

Artificial intelligence (AI) enabled radiomics has evolved immensely especially in the field of oncology. Radiomics provide assistancein diagnosis of cancer, planning of treatment strategy, and predictionof survival. Radiomics in neuro-oncology has progressed significantly inthe recent past. Deep learning has outperformed conventional machinelearning methods in most image-based applications. Convolutional neu-ral networks (CNNs) have seen some popularity in radiomics, since theydo not require hand-crafted features and can automatically extract fea-tures during the learning process. In this regard, it is observed that CNNbased radiomics could provide state-of-the-art results in neuro-oncology,similar to the recent success of such methods in a wide spectrum ofmedical image analysis applications. Herein we present a review of the most recent best practices and establish the future trends for AI enabled radiomics in neuro-oncology.