Cargando…
Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study
BACKGROUND: To determine whether radiomics features based on contrast-enhanced CT (CECT) can preoperatively predict lymphovascular invasion (LVI) and clinical outcome in gastric cancer (GC) patients. METHODS: In total, 160 surgically resected patients were retrospectively analyzed, and seven predict...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7132895/ https://www.ncbi.nlm.nih.gov/pubmed/32248822 http://dx.doi.org/10.1186/s40644-020-00302-5 |
_version_ | 1783517527063658496 |
---|---|
author | Chen, Xiaofeng Yang, Zhiqi Yang, Jiada Liao, Yuting Pang, Peipei Fan, Weixiong Chen, Xiangguang |
author_facet | Chen, Xiaofeng Yang, Zhiqi Yang, Jiada Liao, Yuting Pang, Peipei Fan, Weixiong Chen, Xiangguang |
author_sort | Chen, Xiaofeng |
collection | PubMed |
description | BACKGROUND: To determine whether radiomics features based on contrast-enhanced CT (CECT) can preoperatively predict lymphovascular invasion (LVI) and clinical outcome in gastric cancer (GC) patients. METHODS: In total, 160 surgically resected patients were retrospectively analyzed, and seven predictive models were constructed. Three radiomics predictive models were built from radiomics features based on arterial (A), venous (V) and combination of two phase (A + V) images. Then, three Radscores (A-Radscore, V-Radscore and A + V-Radscore) were obtained. Another four predictive models were constructed by the three Radscores and clinical risk factors through multivariate logistic regression. A nomogram was developed to predict LVI by incorporating A + V-Radscore and clinical risk factors. Kaplan-Meier curve and log-rank test were utilized to analyze the outcome of LVI. RESULTS: Radiomics related to tumor size and intratumoral inhomogeneity were the top-ranked LVI predicting features. The related Radscores showed significant differences according to LVI status (P < 0.01). Univariate logistic analysis identified three clinical features (T stage, N stage and AJCC stage) and three Radscores as LVI predictive factors. The Clinical-Radscore (namely, A + V + C) model that used all these factors showed a higher performance (AUC = 0.856) than the clinical (namely, C, including T stage, N stage and AJCC stage) model (AUC = 0.810) and the A + V-Radscore model (AUC = 0.795) in the train cohort. For patients without LVI and with LVI, the median progression-free survival (PFS) was 11.5 and 8.0 months (P < 0.001),and the median OS was 20.2 and 17.0 months (P = 0.3), respectively. In the Clinical-Radscore-predicted LVI absent and LVI present groups, the median PFS was 11.0 and 8.0 months (P = 0.03), and the median OS was 20.0 and 18.0 months (P = 0.05), respectively. N stage, LVI status and Clinical-Radscore-predicted LVI status were associated with disease-specific recurrence or mortality. CONCLUSIONS: Radiomics features based on CECT may serve as potential markers to successfully predict LVI and PFS, but no evidence was found that these features were related to OS. Considering that it is a single central study, multi-center validation studies will be required in the future to verify its clinical feasibility. |
format | Online Article Text |
id | pubmed-7132895 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-71328952020-04-11 Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study Chen, Xiaofeng Yang, Zhiqi Yang, Jiada Liao, Yuting Pang, Peipei Fan, Weixiong Chen, Xiangguang Cancer Imaging Research Article BACKGROUND: To determine whether radiomics features based on contrast-enhanced CT (CECT) can preoperatively predict lymphovascular invasion (LVI) and clinical outcome in gastric cancer (GC) patients. METHODS: In total, 160 surgically resected patients were retrospectively analyzed, and seven predictive models were constructed. Three radiomics predictive models were built from radiomics features based on arterial (A), venous (V) and combination of two phase (A + V) images. Then, three Radscores (A-Radscore, V-Radscore and A + V-Radscore) were obtained. Another four predictive models were constructed by the three Radscores and clinical risk factors through multivariate logistic regression. A nomogram was developed to predict LVI by incorporating A + V-Radscore and clinical risk factors. Kaplan-Meier curve and log-rank test were utilized to analyze the outcome of LVI. RESULTS: Radiomics related to tumor size and intratumoral inhomogeneity were the top-ranked LVI predicting features. The related Radscores showed significant differences according to LVI status (P < 0.01). Univariate logistic analysis identified three clinical features (T stage, N stage and AJCC stage) and three Radscores as LVI predictive factors. The Clinical-Radscore (namely, A + V + C) model that used all these factors showed a higher performance (AUC = 0.856) than the clinical (namely, C, including T stage, N stage and AJCC stage) model (AUC = 0.810) and the A + V-Radscore model (AUC = 0.795) in the train cohort. For patients without LVI and with LVI, the median progression-free survival (PFS) was 11.5 and 8.0 months (P < 0.001),and the median OS was 20.2 and 17.0 months (P = 0.3), respectively. In the Clinical-Radscore-predicted LVI absent and LVI present groups, the median PFS was 11.0 and 8.0 months (P = 0.03), and the median OS was 20.0 and 18.0 months (P = 0.05), respectively. N stage, LVI status and Clinical-Radscore-predicted LVI status were associated with disease-specific recurrence or mortality. CONCLUSIONS: Radiomics features based on CECT may serve as potential markers to successfully predict LVI and PFS, but no evidence was found that these features were related to OS. Considering that it is a single central study, multi-center validation studies will be required in the future to verify its clinical feasibility. BioMed Central 2020-04-05 /pmc/articles/PMC7132895/ /pubmed/32248822 http://dx.doi.org/10.1186/s40644-020-00302-5 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Chen, Xiaofeng Yang, Zhiqi Yang, Jiada Liao, Yuting Pang, Peipei Fan, Weixiong Chen, Xiangguang Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study |
title | Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study |
title_full | Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study |
title_fullStr | Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study |
title_full_unstemmed | Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study |
title_short | Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study |
title_sort | radiomics analysis of contrast-enhanced ct predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7132895/ https://www.ncbi.nlm.nih.gov/pubmed/32248822 http://dx.doi.org/10.1186/s40644-020-00302-5 |
work_keys_str_mv | AT chenxiaofeng radiomicsanalysisofcontrastenhancedctpredictslymphovascularinvasionanddiseaseoutcomeingastriccancerapreliminarystudy AT yangzhiqi radiomicsanalysisofcontrastenhancedctpredictslymphovascularinvasionanddiseaseoutcomeingastriccancerapreliminarystudy AT yangjiada radiomicsanalysisofcontrastenhancedctpredictslymphovascularinvasionanddiseaseoutcomeingastriccancerapreliminarystudy AT liaoyuting radiomicsanalysisofcontrastenhancedctpredictslymphovascularinvasionanddiseaseoutcomeingastriccancerapreliminarystudy AT pangpeipei radiomicsanalysisofcontrastenhancedctpredictslymphovascularinvasionanddiseaseoutcomeingastriccancerapreliminarystudy AT fanweixiong radiomicsanalysisofcontrastenhancedctpredictslymphovascularinvasionanddiseaseoutcomeingastriccancerapreliminarystudy AT chenxiangguang radiomicsanalysisofcontrastenhancedctpredictslymphovascularinvasionanddiseaseoutcomeingastriccancerapreliminarystudy |