Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review

Abstract

Glioma and brain metastasis can be difficult to distinguish on conventional magnetic resonance imaging (MRI) due to the similarity of imaging features in specific clinical circumstances. Multiple studies have investigated the use of machine learning (ML) models for non-invasive differentiation of glioma from brain metastasis. Many of the studies report promising classification results, however, to date, none have been implemented into clinical practice. After a screening of 12,470 studies, we included 29 eligible studies in our systematic review. From each study, we aggregated data on model design, development, and best classifiers, as well as quality of reporting according to the TRIPOD statement. In a subset of eligible studies, we conducted a meta-analysis of the reported AUC. It was found that data predominantly originated from single-center institutions (n = 25/29) and only two studies performed external validation. The median TRIPOD adherence was 0.48, indicating insufficient quality of reporting among surveyed studies. Our findings illustrate that despite promising classification results, reliable model assessment is limited by poor reporting of study design and lack of algorithm validation and generalizability. Therefore, adherence to quality guidelines and validation on outside datasets is critical for the clinical translation of ML for the differentiation of glioma and brain metastasis.

Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction

“…Multiple works demonstrated high accuracy with use of ML for tumor segmentation, identification of images with brain tumors from other pathologies, glioma grade and molecular subtype prediction, differentiation of gliomas from lymphoma or brain metastases. These results suggest that clinical implementation of these algorithms is imminent and will be seen in the clinical practice in the next few years (Subramanian et al, 2021;Afridi et al, 2022;Avery et al, 2022;Bahar et al, 2022;Cassinelli Petersen et al, 2022;Jekel et al, 2022;Tillmanns et al, 2022). The next frontier in neuro-oncology imaging is identification of clinical applications of ML algorithms in clinical practice and determining the aspects of clinical care that can be improved with predictions that can be generated by these algorithms.…”

“…Future work will include implementation of advanced algorithms, such as nnUNET which show higher DSC scores. Currently machine learning in medical imaging is a hot topic with a large spike in publications starting in 2017 (Subramanian et al, 2021;Afridi et al, 2022;Avery et al, 2022;Bahar et al, 2022;Cassinelli Petersen et al, 2022;Jekel et al, 2022). Multiple works demonstrated high accuracy with use of ML for tumor segmentation, identification of images with brain tumors from other pathologies, glioma grade and molecular subtype prediction, differentiation of gliomas from lymphoma or brain metastases.…”

A Systematic Review of the Current Status and Quality of Radiomics for Glioma Differential Diagnosis

“…As a result, there is a continued need for more accurate pre-operative glioma differential diagnosis (DDx), which may be conducted non-invasively with more advanced imaging techniques or through artificial intelligence methods [ 13 , 14 ].…”

Standardizing Machine Learning Manuscript Reporting in Otolaryngology‐Head & Neck Surgery

“…Nevertheless, since its publication in 2015, the TRIPOD Statement has been widely adopted in systematic reviews examining biomedical applications of ML predictive models and their reporting quality. 9,10 The Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) was developed in 2013 as a framework for the minimum reporting content in a clinical-trial protocol. 11 Recognizing an increase in interest and integration of AI/ML models in clinical trial protocols, the SPIRIT investigator consortium amended the original framework with 15 additional items specific to clinical trials for interventions involving AI.…”

“…As of the writing of this manuscript, a ML‐focused version of the TRIPOD is under development. Nevertheless, since its publication in 2015, the TRIPOD Statement has been widely adopted in systematic reviews examining biomedical applications of ML predictive models and their reporting quality 9,10 …”