Osteoid Metaplasia in Femoral Artery Plaques Is Associated With the Clinical Severity of Lower Extremity Artery Disease in Men

Mirjami Laivuori; Johanna Tolva; A Inkeri Lokki; Nina Linder; Johan Lundin; Riitta Paakkanen; Anders Albäck; Maarit Venermo; Mikko I Mäyränpää; Marja-Liisa Lokki; Juha Sinisalo

doi:10.3389/fcvm.2020.594192

Osteoid Metaplasia in Femoral Artery Plaques Is Associated With the Clinical Severity of Lower Extremity Artery Disease in Men

Front Cardiovasc Med. 2020 Dec 10:7:594192. doi: 10.3389/fcvm.2020.594192. eCollection 2020.

Authors

Mirjami Laivuori¹, Johanna Tolva², A Inkeri Lokki^{2

3

4}, Nina Linder⁵, Johan Lundin^{5

6}, Riitta Paakkanen^{2

3}, Anders Albäck¹, Maarit Venermo¹, Mikko I Mäyränpää⁷, Marja-Liisa Lokki², Juha Sinisalo³

Affiliations

¹ Department of Vascular Surgery, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
² Transplantation Laboratory, Department of Pathology, University of Helsinki, Helsinki, Finland.
³ Department of Cardiology, Heart and Lung Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
⁴ Translational Immunology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland.
⁵ Institute for Molecular Medicine Finland, HILIFE, University of Helsinki, Helsinki, Finland.
⁶ Department of Global Public Health, Global Health/IHCAR, Karolinska Institutet, Stockholm, Sweden.
⁷ Department of Pathology, HUSLAB, Meilahti Central Laboratory of Pathology, University of Helsinki, Helsinki, Finland.

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.

Keywords: artificial intelligence; atherosclerosis; digital pathology; histology; lower extremity artery disease (LEAD); machine learning; osteoid metaplasia.