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.

Advancements in Oncology with Artificial Intelligence—A Review Article

“…The feature-based radiomics algorithms evaluate subsets of specific features from segmented regions and volumes of interest (VOI) into mathematical representations. This multistep process includes image pre-processing (noise reduction, spatial resampling, and intensity modification), precise tumor segmentation (manual vs. DL-based techniques), feature extraction (histogram-based, textural, and higher-order statistics features), feature selection (filter methods, wrapper approaches, and embedded techniques), and model generation and evaluation (neural networks, SVM, decision trees/ random forests, linear regression, and logistic regression models) [ 95 , 98 ]. DL radiomics use CNNs, in which the model learns in a cascading fashion without any prior description of features and requires a large amount of data in the learning process.…”

AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models

“…Rathore et al employed high and low-grade tumors from The Cancer Imaging Archive (original images acquired 1983–2008) to extract an extensive set of engineered features (intensity, histogram, and texture) from delineated tumor regions on MRI and histopathologic images and used Cox proportional hazard regression and SVM models to MRI features only, histopathologic features only and combined MRI and histopathologic features and found that the combined model had higher accuracy in predicting OS as compared to either model in isolation (AUC 0.86) [ 91 ]. Ultimately, traditional ML-based methods do depend on several aspects including segmentation which does introduce both a component of workload as well as bias since the segmentation itself and the methods involved do dictate the signal that is eventually measured and interpreted [ 19 , 30 , 38 , 48 ]. It should also be noted that in the context of central nervous system tumors and other cancers treated with radiation therapy, the tumor volumes themselves are manually delineated to allow for targeting of the tumor with radiation therapy.…”

“…In a separate study, a similar analysis was carried out for IDH mutation status reporting a sensitivity of 0.97, specificity of 0.98 and an AUC of 0.98[ 100 ]. DL-based methods are growing in scope and importance in imaging analysis for central nervous system tumors in particular with respect to diagnosis and classification reflecting the complexity and evolving understanding of molecular characterization of central nervous system tumors and the inability to label large-scale molecular data by human experts [ 19 , 22 , 25 ].…”

“…AI-driven methods have gained prominence in diagnosis (as exemplified by Computer-Aided Detection (CADe) systems and Computer-Aided Diagnosis (CADx) systems) for tuberculosis [ 5 , 6 ], lung cancer [ 7 , 8 ] and metastatic disease to the brain[ 9 ] and also been applied to multiple other areas of clinical need [ 4 ] including notably infectious diseases as described in the context of COVID-19 [ 10 – 12 ], internal medicine[ 13 ], diabetic retinopathy [ 14 ]. In oncology, AI methods are being explored in most cancer types including prominently in lung [ 8 , 15 ], breast [ 16 , 17 ], prostate [ 18 ], central nervous system cancers [ 13 , 19 – 26 ], and other malignancies [ 27 ]. There is ongoing progress in employing AI methods towards imaging for oncology treatment e.g., generating radiation therapy volumes [ 28 ], assessing treatment response [ 29 ], and understanding and communicating prognosis [ 19 , 30 ].…”

“…In oncology, AI methods are being explored in most cancer types including prominently in lung [ 8 , 15 ], breast [ 16 , 17 ], prostate [ 18 ], central nervous system cancers [ 13 , 19 – 26 ], and other malignancies [ 27 ]. There is ongoing progress in employing AI methods towards imaging for oncology treatment e.g., generating radiation therapy volumes [ 28 ], assessing treatment response [ 29 ], and understanding and communicating prognosis [ 19 , 30 ]. Challenges relate to limited available data sets, variability in imaging changes over time present even within any one disease entity contingent on patient and disease-related factors, further augmented by a growing variability in analysis approaches [ 30 – 33 ].…”