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DCE-MRI radiomics models predicting the expression of radioresistant-related factors of LRP-1 and survivin in locally advanced rectal cancer.

Authors :
Zhiheng Li
Huizhen Huang
Chuchu Wang
Zhenhua Zhao
Weili Ma
Dandan Wang
Haijia Mao
Fang Liu
Ye Yang
Weihuo Pan
Zengxin Lu
Source :
Frontiers in Oncology; 8/29/2022, Vol. 12, p1-15, 15p
Publication Year :
2022

Abstract

Objective: Low-density lipoprotein receptor-related protein-1 (LRP-1) and survivin are associated with radiotherapy resistance in patients with locally advanced rectal cancer (LARC). This study aimed to evaluate the value of a radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the preoperative assessment of LRP-1 and survivin expressions in these patients. Methods: One hundred patients with pathologically confirmed LARC who underwent DCE-MRI before surgery between February 2017 and September 2021 were included in this retrospective study. DCE-MRI perfusion histogram parameters were calculated for the entire lesion using post-processing software (Omni Kinetics, G.E. Healthcare, China), with three quantitative parameter maps. LRP-1 and survivin expressions were assessed by immunohistochemical methods and patients were classified into low- and high-expression groups. Results: Four radiomics features were selected to construct the LRP-1 discrimination model. The LRP-1 predictive model achieved excellent diagnostic performance, with areas under the receiver operating curve (AUCs) of 0.853 and 0.747 in the training and validation cohorts, respectively. The other four radiomics characteristics were screened to construct the survivin predictive model, with AUCs of 0.780 and 0.800 in the training and validation cohorts, respectively. Decision curve analysis confirmed the clinical usefulness of the radiomics models. Conclusion: DCE-MRI radiomics models are particularly useful for evaluating LRP-1 and survivin expressions in patients with LARC. Our model has significant potential for the preoperative identification of patients with radiotherapy resistance and can serve as an essential reference for treatment planning. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
2234943X
Volume :
12
Database :
Complementary Index
Journal :
Frontiers in Oncology
Publication Type :
Academic Journal
Accession number :
159243105
Full Text :
https://doi.org/10.3389/fonc.2022.881341