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Spectral CT for preoperative prediction of lymphovascular invasion in resectable gastric cancer: With external prospective validation

Authors :
Jing Li
Yi Wang
Rui Wang
Jian-bo Gao
Jin-rong Qu
Source :
Frontiers in oncology. 12
Publication Year :
2022

Abstract

ObjectivesTo develop and externally validate a spectral CT based nomogram for the preoperative prediction of LVI in patients with resectable GC.MethodsThe two centered study contained a retrospective primary dataset of 224 pathologically confirmed gastric adenocarcinomas (161 males, 63 females; mean age: 60.57 ± 10.81 years, range: 20-86 years) and an external prospective validation dataset from the second hospital (77 males and 35 females; mean age, 61.05 ± 10.51 years, range, 31 to 86 years). Triple-phase enhanced CT scans with gemstone spectral imaging mode were performed within one week before surgery. The clinicopathological characteristics were collected, the iodine concentration (IC) of the primary tumours at arterial phase (AP), venous phase (VP), and delayed phase (DP) were measured and then normalized to aorta (nICs). Univariable analysis was used to compare the differences of clinicopathological and IC values between LVI positive and negative groups. Independent predictors for LVI were screened by multivariable logistic regression analysis in primary dataset and used to develop a nomogram, and its performance was evaluated by using ROC analysis and tested in validation dataset. Its clinical use was evaluated by decision curve analysis (DCA).ResultsTumor thickness, Borrmann classification, CT reported lymph node (LN) status and nICDP were independent predictors for LVI, and the nomogram based on these indicators was significantly associated with LVI (PPConclusionThis study presented a dual energy CT quantification based nomogram, which enables preferable preoperative individualized prediction of LVI in patients with GC.

Subjects

Subjects :
Cancer Research
Oncology

Details

ISSN :
2234943X
Volume :
12
Database :
OpenAIRE
Journal :
Frontiers in oncology
Accession number :
edsair.doi.dedup.....dda84c5950323d42ec369569042e8f4e