Literature DB >> 33363220

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

Mirjami Laivuori1, Johanna Tolva2, A Inkeri Lokki2,3,4, Nina Linder5, Johan Lundin5,6, Riitta Paakkanen2,3, Anders Albäck1, Maarit Venermo1, Mikko I Mäyränpää7, Marja-Liisa Lokki2, Juha Sinisalo3.   

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.
Copyright © 2020 Laivuori, Tolva, Lokki, Linder, Lundin, Paakkanen, Albäck, Venermo, Mäyränpää, Lokki and Sinisalo.

Entities:  

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

Year:  2020        PMID: 33363220      PMCID: PMC7758249          DOI: 10.3389/fcvm.2020.594192

Source DB:  PubMed          Journal:  Front Cardiovasc Med        ISSN: 2297-055X


  38 in total

1.  Bone Like Arterial Calcification in Femoral Atherosclerotic Lesions: Prevalence and Role of Osteoprotegerin and Pericytes.

Authors:  J-M Davaine; T Quillard; M Chatelais; F Guilbaud; R Brion; B Guyomarch; M Á Brennan; D Heymann; M-F Heymann; Y Gouëffic
Journal:  Eur J Vasc Endovasc Surg       Date:  2015-11-30       Impact factor: 7.069

2.  Comparison of Different Decalcification Methods Using Rat Mandibles as a Model.

Authors:  Flavia M Savi; Gary I Brierly; Jeremy Baldwin; Christina Theodoropoulos; Maria A Woodruff
Journal:  J Histochem Cytochem       Date:  2017-09-29       Impact factor: 2.479

3.  Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

Authors:  Babak Ehteshami Bejnordi; Mitko Veta; Paul Johannes van Diest; Bram van Ginneken; Nico Karssemeijer; Geert Litjens; Jeroen A W M van der Laak; Meyke Hermsen; Quirine F Manson; Maschenka Balkenhol; Oscar Geessink; Nikolaos Stathonikos; Marcory Crf van Dijk; Peter Bult; Francisco Beca; Andrew H Beck; Dayong Wang; Aditya Khosla; Rishab Gargeya; Humayun Irshad; Aoxiao Zhong; Qi Dou; Quanzheng Li; Hao Chen; Huang-Jing Lin; Pheng-Ann Heng; Christian Haß; Elia Bruni; Quincy Wong; Ugur Halici; Mustafa Ümit Öner; Rengul Cetin-Atalay; Matt Berseth; Vitali Khvatkov; Alexei Vylegzhanin; Oren Kraus; Muhammad Shaban; Nasir Rajpoot; Ruqayya Awan; Korsuk Sirinukunwattana; Talha Qaiser; Yee-Wah Tsang; David Tellez; Jonas Annuscheit; Peter Hufnagl; Mira Valkonen; Kimmo Kartasalo; Leena Latonen; Pekka Ruusuvuori; Kaisa Liimatainen; Shadi Albarqouni; Bharti Mungal; Ami George; Stefanie Demirci; Nassir Navab; Seiryo Watanabe; Shigeto Seno; Yoichi Takenaka; Hideo Matsuda; Hady Ahmady Phoulady; Vassili Kovalev; Alexander Kalinovsky; Vitali Liauchuk; Gloria Bueno; M Milagro Fernandez-Carrobles; Ismael Serrano; Oscar Deniz; Daniel Racoceanu; Rui Venâncio
Journal:  JAMA       Date:  2017-12-12       Impact factor: 56.272

4.  Bone formation in carotid plaques: a clinicopathological study.

Authors:  Jennifer L Hunt; Ronald Fairman; Marc E Mitchell; Jeffrey P Carpenter; Michael Golden; Tigran Khalapyan; Megan Wolfe; David Neschis; Ross Milner; Benjamin Scoll; Anita Cusack; Emile R Mohler
Journal:  Stroke       Date:  2002-05       Impact factor: 7.914

Review 5.  Mechanisms of plaque formation and rupture.

Authors:  Jacob Fog Bentzon; Fumiyuki Otsuka; Renu Virmani; Erling Falk
Journal:  Circ Res       Date:  2014-06-06       Impact factor: 17.367

6.  Determinants of peripheral arterial disease in the elderly: the Rotterdam study.

Authors:  W T Meijer; D E Grobbee; M G Hunink; A Hofman; A W Hoes
Journal:  Arch Intern Med       Date:  2000-10-23

7.  New Quantitative Digital Image Analysis Method of Histological Features of Carotid Atherosclerotic Plaques.

Authors:  Huaien Zheng; Karina Gasbarrino; John P Veinot; Chi Lai; Stella S Daskalopoulou
Journal:  Eur J Vasc Endovasc Surg       Date:  2019-09-19       Impact factor: 7.069

8.  Sex differences in peripheral arterial disease: leg symptoms and physical functioning.

Authors:  Mary McGrae McDermott; Philip Greenland; Kiang Liu; Michael H Criqui; Jack M Guralnik; Lillian Celic; Cheeling Chan
Journal:  J Am Geriatr Soc       Date:  2003-02       Impact factor: 5.562

9.  Osteoprotegerin, pericytes and bone-like vascular calcification are associated with carotid plaque stability.

Authors:  Jean-Michel Davaine; Thibaut Quillard; Régis Brion; Olivier Lapérine; Béatrice Guyomarch; Thierry Merlini; Mathias Chatelais; Florian Guilbaud; Meadhbh Áine Brennan; Céline Charrier; Dominique Heymann; Yann Gouëffic; Marie-Françoise Heymann
Journal:  PLoS One       Date:  2014-09-26       Impact factor: 3.240

Review 10.  Deep Learning for Whole Slide Image Analysis: An Overview.

Authors:  Neofytos Dimitriou; Ognjen Arandjelović; Peter D Caie
Journal:  Front Med (Lausanne)       Date:  2019-11-22
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