
==== Front
Neurooncol Adv
Neurooncol Adv
noa
Neuro-Oncology Advances
2632-2498
Oxford University Press US

10.1093/noajnl/vdae090.075
vdae090.075
Final Category: Neuroimaging/Radiologic Advances
AcademicSubjects/MED00300
AcademicSubjects/MED00310
NIRL-14 RADIOGRAPHIC STUDIES ON THE THRESHOLD OF HUMAN CAPACITY IN EARLY DETECTION OF SMALL BRAIN METASTASES ESTABLISH A BASIS FOR DEVELOPING ARTIFICIAL INTELLIGENCE RECOGNITION TECHNOLOGY IN BRAIN MRI
Zhang Isabella University of Nebraska Medical Center, Omaha/NE, USA

Burr Justin University of Nebraska Medical Center, Omaha/NE, USA

Wang Shuo University of Nebraska Medical Center, Omaha/NE, USA

8 2024
02 8 2024
02 8 2024
6 Suppl 1 2024 SNO/ASCO CNS Metastases Conference i23i23
© The Author(s) 2024. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Abstract

BACKGROUND

Brain Metastases are the most common cancers in central nervous system and carry poor prognosis in cancer patients with their rapidly growing nature. Early identification of these tumors is crucial in improving patient survival. This investigation aims to study the rate of human error in missing early brain metastases and factors associated with the threshold of sensitivity of human eyes at the professional level.

METHODS

The ­database from a single institution was used which included patients with new brain metastasis diagnosed based on brain magnetic resonance imaging (MRI) who also had previous MRI scan(s) 1-6 months before diagnosis and no exposure to whole brain radiotherapy. The brain MRI used for diagnosis of brain metastasis and the MRI performed 1-6 months prior were reviewed. Based on the location of the newly diagnosed tumor, the corresponding location in the previous MRI was assessed for a missed incidence of a preexisting tumor. The sizes of the missed tumors were then measured to assess the threshold of human eyes in detecting metastases.

RESULTS

The percentage of missed metastases was 44% (56/126). The mean size of missed metastases was 3.0 millimeters (range 1.2 mm to 7.7 mm). No clinical factors were significantly associated with higher rate of missed diagnosis. The most likely reason for the missed diagnosis is the tiny size although visual distraction seems to play a role including adjacent contrast-enhancing structures such as blood vessels.

CONCLUSIONS

The results show a high rate of human error for missing small metastases even with high level of expertise indicating the limits of human capacity to reliably detect brain metastases at 1-2 mm in size. These results justify the development of artificial intelligence-based recognition to assist neuroradiologists in diagnosis. Our data support current standard practice of surveillance brain MRI every 3-6 months for patients with history of brain metastasis.
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