Artificial Intelligence for Survival Prediction in Brain Tumors on Neuroimaging

Abstract

Survival prediction of patients affected by brain tumors provides essential information to guide surgical planning, adjuvant treatment selection, and patient counseling. Current reliance on clinical factors, such as Karnofsky Performance Status Scale, and simplistic radiological characteristics are, however, inadequate for survival prediction in tumors such as glioma that demonstrate molecular and clinical heterogeneity with variable survival outcomes. Advances in the domain of artificial intelligence have afforded powerful tools to capture a large number of hidden high-dimensional imaging features that reflect abundant information about tumor structure and physiology. Here, we provide an overview of current literature that apply computational analysis tools such as radiomics and machine learning methods to the pipeline of image preprocessing, tumor segmentation, feature extraction, and construction of classifiers to establish survival prediction models based on neuroimaging. We also discuss challenges relating to the development and evaluation of such models and explore ethical issues surrounding the future use of machine learning predictions.

Association of prognostic nutritional index with prognostic outcomes in patients with glioma: a meta-analysis and systematic review

“…Accurate prediction of glioma prognosis is critical for guiding treatment decisions (e.g., surgical planning and adjuvant treatment selection), counseling patients and their families, and optimizing healthcare resource allocation ( 5 , 31 , 32 ). Several predictors of prognosis for glioma have been proposed.…”

Advancements in Image-Based Models for High-Grade Gliomas Might Be Accelerated

“…Similar to the above-discussed contributions to image-based modelling for the diagnosis of HGG, advances in AI may offer progress in detailing the structure and pathology of those tumors, previously beyond the scope of human experience alone; ultimately this contributes to improving patient prognosis. These possibilities pass through a machine learning process involving the steps of image preprocessing, tumor segmentation, feature extraction and the construction of classifiers which may finally lead to the development of prognostic models [126]. Wang et al described the characteristics of adult HGG with the H3.3 G34 mutation (Table 2).…”

Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review

“…Machine learning has proven to be effective in the identification and extraction of essential characteristics of diseases, leading to a wide range of clinical uses [16]. In neuro-oncology, machine learning has yielded promising outcomes, with encouraging findings and novel opportunities for the improved care of patients affected by brain tumors [17], such as automated detection [18], differential diagnosis, grading [19] and mutation status [20], and the evaluation of the aggressiveness of tumors, as well as the prediction of treatment response, recurrence [21], and survival [22]. The application of machine learning to radiomics provides automatic, objective, and quantitative data with high efficiency, which is an improvement over the traditional radiology practice of manual annotation, which relies on trained physicians to deal with large amounts of information.…”