Back to Search Start Over

The role of computed tomography features in assessing response to neoadjuvant chemotherapy in locally advanced gastric cancer

Authors :
Chengzhi Wei
Yun He
Ma Luo
Guoming Chen
Runcong Nie
Xiaojiang Chen
Zhiwei Zhou
Yongming Chen
Source :
BMC Cancer, Vol 23, Iss 1, Pp 1-8 (2023)
Publication Year :
2023
Publisher :
BMC, 2023.

Abstract

Abstract Objective To compare the computed tomography (CT) images of patients with locally advanced gastric cancer (GC) before and after neoadjuvant chemotherapy (NAC) in order to identify CT features that could predict pathological response to NAC. Methods We included patients with locally advanced GC who underwent gastrectomy after NAC from September 2016 to September 2021. We retrieved and collected the patients’ clinicopathological characteristics and CT images before and after NAC. We analyzed CT features that could differentiate responders from non-responders and established a logistic regression equation based on these features. Results We included 97 patients (69 [71.1%] men; median [range] age, 60 [26–75] years) in this study, including 66 (68.0%) responders and 31 (32.0%) non-responders. No clinicopathological variable prior to treatment was significantly associated with pathological response. Out of 16 features, three features (ratio of tumor thickness reduction, ratio of reduction of primary tumor attenuation in arterial phase, and ratio of reduction of largest lymph node attenuation in venous phase) on logistic regression analysis were used to establish a regression equation that demonstrated good discrimination performance in predicting pathological response (area under receiver operating characteristic curve 0.955; 95% CI, 0.911–0.998). Conclusion Logistic regression equation based on three CT features can help predict the pathological response of patients with locally advanced GC to NAC.

Details

Language :
English
ISSN :
14712407
Volume :
23
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BMC Cancer
Publication Type :
Academic Journal
Accession number :
edsdoj.2c042c4345f454d93f794f0334538cc
Document Type :
article
Full Text :
https://doi.org/10.1186/s12885-023-11619-2