TO THE EDITOR
Despite advancements in neuroimaging techniques, the false positive diagnoses of intracranial aneurysms remain a rare yet significant concern. Yang and Mai[1] reported a case where chronic middle cerebral artery (MCA) occlusion mimicked the appearance of an aneurysm on both computer tomography angiogram (CTA) and digital subtraction angiogram (DSA). False positive diagnoses of aneurysms often occur due to conditions that can mimic their appearance on imaging, such as arterial occlusion with vascular stump formation, infundibular widening, fenestration, arterial dissection, and venous varix. Accurate differentiation between unruptured aneurysms and other conditions is crucial to avoid unnecessary surgical interventions. Most studies on this topic are limited to case reports or small case series, making it difficult to establish the exact frequency of false positive diagnoses[2]. The incidence varies depending on imaging modality and the expertise of the interpreting professionals[3]. False-positive aneurysms are predominantly associated with the anterior circulation, notably at the bifurcation of the MCA, constituting 85% of reported cases. Within this category, 40% of instances specifically occur at the MCA bifurcation. Conversely, in the posterior circulation, false-positive aneurysms have been observed primarily at the basilar artery or the vertebrobasilar junction[2]. Arterial occlusion with vascular stump formation, often due to progressive atherosclerotic changes (36.7%), is the leading cause of aneurysm mimics, particularly at the origin of M2 from the MCA. Infundibular widening of the arterial orifice accounts for 20% of cases, making it the second most common cause[2].
Imaging modalities commonly used for detecting intracranial aneurysms include CTA, magnetic resonance imaging (MRI)/magnetic resonance angiography (MRA), and the gold standard, DSA. CTA and MRA provide numerous advantages over DSA, such as lower cost, reduced risks of arterial injury and stroke, faster data acquisition, and the capability for retrospective manipulation of imaging data. Despite these advantages, DSA continues to be regarded as the gold standard for diagnosing intracranial aneurysms[4]. MRA has been reported to have a sensitivity of 95% and a specificity of 85%[5], with some studies suggesting equivalent detection rates to DSA (a sensitivity of 87% and a specificity of 92%)[6]. MRI offers the advantage of evaluating vessel wall enhancement, aiding in the assessment of aneurysm evolution and rupture risk. However, MRI may produce false-negative results in small aneurysms (< 5 mm) or in certain locations, such as the internal carotid artery or anterior communicating artery. DSA, with its superior spatial resolution, remains more reliable[7]. Additional techniques, such as MRI three-dimensional constructive interference in steady state and three-dimensional fast imaging employing steady state acquisition fused with MRA[8], have been proposed to enhance vascular detail at occlusion points, aiding in differential diagnosis[6,7]. High-resolution vessel wall imaging also shows promise in detecting various intracranial vascular pathologies, including atherosclerosis and vasculitis[9]. DSA is widely recognized as the gold standard for diagnosing intracranial aneurysms. However, the potential for false positives remains. In clinical practice, it is crucial for clinicians to distinguish aneurysms from other anatomical structures or pathological conditions that may mimic aneurysms in imaging studies, in order to avoid false positive diagnoses[10]. The use of deep neural networks for automated detection and segmentation of aneurysms in DSA sequences has shown considerable promise in mitigating false positives. These networks leverage both spatial and temporal data, leading to notable improvements in detection accuracy and efficiency[11]. A two-stage convolutional neural network has been developed for automatic detection of intracranial aneurysms on DSA images, offering greater accuracy and faster detection times compared to traditional digital image processing methods[12].
In conclusion, while DSA remains the gold standard for diagnosing intracranial aneurysms, the challenge of false positive diagnoses persists, primarily due to conditions that mimic aneurysms. Accurate differentiation is vital to prevent unnecessary interventions, and advancements in imaging techniques and deep learning methods hold promise for improving diagnostic accuracy. Continued research and the development of enhanced imaging modalities are essential to further minimize false positive rates and optimize patient outcomes in the assessment of intracranial aneurysms.
