Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis.

PURPOSE: To systematically review the literature regarding the application of machine learning (ML) of magnetic resonance imaging (MRI) radiomics in common sellar tumors. To identify future directions for application of ML in sellar tumor MRI. METHODS: PubMed, Medline, Embase, Google Scholar, Scopus, ArxIV, and bioRxiv were searched to identify relevant studies published between 2010 and September 2021. Studies were included if they specifically involved ML of MRI radiomics in the analysis of sellar masses. Risk of bias assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) Tool. RESULTS: Fifty-eight articles were identified for review. All papers utilized retrospective data, and a quantitative systematic review was performed for thirty-one studies utilizing a public dataset which compared pituitary adenomas, meningiomas, and gliomas. One of the analyzed architectures yielded the highest classification accuracy of 0.996. The remaining twenty-seven articles were qualitatively reviewed and showed promising findings in predicting specific tumor characteristics such as tumor consistency, Ki-67 proliferative index, and post-surgical recurrence. CONCLUSION: This review highlights the potential clinical application of ML using MRI radiomic data of the sellar region in diagnosis and predicting treatment outcomes. We describe future directions for practical application in the clinical care of patients with pituitary neuroendocrine and other sellar tumors.

Spatial and time domain analysis of eye-tracking data during screening of brain magnetic resonance images.

INTRODUCTION: Eye-tracking research has been widely used in radiology applications. Prior studies exclusively analysed either temporal or spatial eye-tracking features, both of which alone do not completely characterise the spatiotemporal dynamics of radiologists' gaze features. PURPOSE: Our research aims to quantify human visual search dynamics in both domains during brain stimuli screening to explore the relationship between reader characteristics and stimuli complexity. The methodology can be used to discover strategies to aid trainee radiologists in identifying pathology, and to select regions of interest for machine vision applications. METHOD: The study was performed using eye-tracking data 5 seconds in duration from 57 readers (15 Brain-experts, 11 Other-experts, 5 Registrars and 26 Naïves) for 40 neuroradiological images as stimuli (i.e., 20 normal and 20 pathological brain MRIs). The visual scanning patterns were analysed by calculating the fractal dimension (FD) and Hurst exponent (HE) using re-scaled range (R/S) and detrended fluctuation analysis (DFA) methods. The FD was used to measure the spatial geometrical complexity of the gaze patterns, and the HE analysis was used to measure participants' focusing skill. The focusing skill is referred to persistence/anti-persistence of the participants' gaze on the stimulus over time. Pathological and normal stimuli were analysed separately both at the "First Second" and full "Five Seconds" viewing duration. RESULTS: All experts were more focused and a had higher visual search complexity compared to Registrars and Naïves. This was seen in both the pathological and normal stimuli in the first and five second analyses. The Brain-experts subgroup was shown to achieve better focusing skill than Other-experts due to their domain specific expertise. Indeed, the FDs found when viewing pathological stimuli were higher than those in normal ones. Viewing normal stimuli resulted in an increase of FD found in five second data, unlike pathological stimuli, which did not change. In contrast to the FDs, the scanpath HEs of pathological and normal stimuli were similar. However, participants' gaze was more focused for "Five Seconds" than "First Second" data. CONCLUSIONS: The HE analysis of the scanpaths belonging to all experts showed that they have greater focus than Registrars and Naïves. This may be related to their higher visual search complexity than non-experts due to their training and expertise.