Longitudinal assessment of carotid plaque texture in three-dimensional ultrasound images based on semi-supervised graph-based dimensionality reduction and feature selection

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Author(s)

  • He Cui
  • Weifu Chen
  • Arna van Engelen
  • Marleen de Bruijne
  • M. Reza Azarpazhooh
  • Seyed Mojtaba Sohrevardi
  • J. David Spence

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number103586
Journal / PublicationComputers in Biology and Medicine
Volume116
Online published14 Dec 2019
Publication statusPublished - Jan 2020

Abstract

With continuous development of therapeutic options for atherosclerosis, image-based biomarkers sensitive to the effect of new interventions are required to be developed for cost-effective clinical evaluation. Although 3D ultrasound measurement of total plaque volume (TPV) showed the efficacy of high-dose statin, more sensitive biomarkers are needed to establish the efficacy of dietary supplements expected to confer a smaller beneficial effect. This study involved 171 subjects who participated in a one-year placebo-controlled trial evaluating the effect of pomegranate. A framework involving a feature selection technique known as discriminative feature selection (DFS) and a semi-supervised graph-based regression (SSGBR) technique was proposed for sensitive detection of plaque textural changes over the trial. 376 textual features of plaques were extracted from 3D ultrasound images acquired at baseline and a follow-up session. A scalar biomarker for each subject were generated by SSGBR based on prominent textural features selected by DFS. The ability of this biomarker for discriminating pomegranate from placebo subjects was quantified by the p-values obtained in Mann–Whitney U test. The discriminative power of SSGBR was compared with global and local dimensionality reduction techniques, including linear discriminant analysis (LDA), maximum margin criterion (MMC) and Laplacian Eigenmap (LE). Only SSGBR (p=4.12×10−6) and normalized LE (p=0.002) detected a difference between the two groups at the 5% significance level. As compared with ΔT  PV, SSGBR reduced the sample size required to establish a significant difference by a factor of 60. The application of this framework will substantially reduce the cost incurred in clinical trials.

Research Area(s)

  • 3D ultrasound imaging, Carotid atherosclerosis, Discriminative feature selection (DFS), Plaque texture, Pomegranate therapy, Semi-supervised graph-based regression (SSGBR)

Citation Format(s)

Longitudinal assessment of carotid plaque texture in three-dimensional ultrasound images based on semi-supervised graph-based dimensionality reduction and feature selection. / Lin, Mingquan; Cui, He; Chen, Weifu; van Engelen, Arna; de Bruijne, Marleen; Azarpazhooh, M. Reza; Sohrevardi, Seyed Mojtaba; Spence, J. David; Chiu, Bernard.

In: Computers in Biology and Medicine, Vol. 116, 103586, 01.2020.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal