TY - JOUR

T1 - Longitudinal assessment of carotid plaque texture in three-dimensional ultrasound images based on semi-supervised graph-based dimensionality reduction and feature selection

AU - Lin, Mingquan

AU - Cui, He

AU - Chen, Weifu

AU - van Engelen, Arna

AU - de Bruijne, Marleen

AU - Azarpazhooh, M. Reza

AU - Sohrevardi, Seyed Mojtaba

AU - Spence, J. David

AU - Chiu, Bernard

PY - 2020/1

Y1 - 2020/1

N2 - 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 ΔTPV, 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.

AB - 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 ΔTPV, 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.

KW - 3D ultrasound imaging

KW - Carotid atherosclerosis

KW - Discriminative feature selection (DFS)

KW - Plaque texture

KW - Pomegranate therapy

KW - Semi-supervised graph-based regression (SSGBR)

UR - http://www.scopus.com/inward/record.url?scp=85076635390&partnerID=8YFLogxK

U2 - 10.1016/j.compbiomed.2019.103586

DO - 10.1016/j.compbiomed.2019.103586

M3 - Journal article

C2 - 32425160

AN - SCOPUS:85076635390

VL - 116

JO - Computers in Biology and Medicine

JF - Computers in Biology and Medicine

SN - 0010-4825

M1 - 103586

ER -