Abstract
Lamellar metaplastic bone, osteoid metaplasia (OM), is found in atherosclerotic plaques, especially in the femoral arteries. In the carotid arteries, OM has been documented to be associated with plaque stability. This study investigated the clinical impact of OM load in femoral artery plaques of patients with lower extremity artery disease (LEAD) by using a deep learning-based image analysis algorithm. Plaques from 90 patients undergoing endarterectomy of the common femoral artery were collected and analyzed. After decalcification and fixation, 4-μm-thick longitudinal sections were stained with hematoxylin and eosin, digitized, and uploaded as whole-slide images on a cloud-based platform. A deep learning-based image analysis algorithm was trained to analyze the area percentage of OM in whole-slide images. Clinical data were extracted from electronic patient records, and the association with OM was analyzed. Fifty-one (56.7%) sections had OM. Females with diabetes had a higher area percentage of OM than females without diabetes. In male patients, the area percentage of OM inversely correlated with toe pressure and was significantly associated with severe symptoms of LEAD including rest pain, ulcer, or gangrene. According to our results, OM is a typical feature of femoral artery plaques and can be quantified using a deep learning-based image analysis method. The association of OM load with clinical features of LEAD appears to differ between male and female patients, highlighting the need for a gender-specific approach in the study of the mechanisms of atherosclerotic disease. In addition, the role of plaque characteristics in the treatment of atherosclerotic lesions warrants further consideration in the future.
Highlights
Atherosclerosis is a chronic multifactorial disease characterized by progressing calcification of the vasculature [1]
Previous research of atherosclerosis has been dominated by studies on coronary artery disease (CAD) with less attention given to lower extremity artery disease (LEAD) [2] caused by atherosclerosis of the lower limb arteries
Our study presents a novel approach to atherosclerotic plaque research by applying a deep learning (DL) algorithm
Summary
Atherosclerosis is a chronic multifactorial disease characterized by progressing calcification of the vasculature [1]. The prevalence of LEAD increased by nearly 25% between 2000 and 2010 [4]. The globally increasing incidence of LEAD warrants further understanding of the mechanisms and etiology of the disease. Artificial intelligence (AI) algorithms and deep learning (DL) have enabled unprecedented progress in histological analysis. DL-based methods provide an accurate method of characterizing and quantifying tissue entities in digitized histopathological samples [5, 6]. The technological advances enabling the quantitative study of tissue samples have grown rapidly, and there is a call for translational research bridging the way to the clinical setting and patient care [7]
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