Preoperative prediction of lymphovascular and perineural invasion in gastric cancer using spectral computed tomography imaging and machine learning  

作　　者：Hui-Ting Ge Jian-Wu Chen Li-Li Wang Tian-Xiu Zou Bin Zheng Yuan-Fen Liu Yun-Jing Xue Wei-Wen Lin 

机构地区：[1]Department of Radiology,Fujian Medical University Union Hospital,Fuzhou 350001,Fujian Province,China [2]Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors,Fujian Medical University,Fuzhou 350001,Fujian Province,China [3]Digestive,Hematological and Breast Malignancies,Clinical Research Center for Radiology and Radiotherapy of Fujian Province,Fuzhou 350001,Fujian Province,China [4]Department of Radiation Oncology,Fujian Medical University Union Hospital,Fuzhou 350001,Fujian Province,China [5]Department of Diagnostic Radiology,Fujian Medical University Union Hospital,Fuzhou 350001,Fujian Province,China [6]School of Electrical and Computer Engineering,University of Oklahoma,Oklahoma,OK 73019,United States

出　　处：《World Journal of Gastroenterology》2024年第6期542-555,共14页世界胃肠病学杂志（英文版）

基　　金：Supported by Science and Technology Project of Fujian Province,No.2022Y0025.

摘　　要：BACKGROUND Lymphovascular invasion(LVI)and perineural invasion(PNI)are important prognostic factors for gastric cancer(GC)that indicate an increased risk of metastasis and poor outcomes.Accurate preoperative prediction of LVI/PNI status could help clinicians identify high-risk patients and guide treatment deci-sions.However,prior models using conventional computed tomography(CT)images to predict LVI or PNI separately have had limited accuracy.Spectral CT provides quantitative enhancement parameters that may better capture tumor invasion.We hypothesized that a predictive model combining clinical and spectral CT parameters would accurately preoperatively predict LVI/PNI status in GC patients.AIM To develop and test a machine learning model that fuses spectral CT parameters and clinical indicators to predict LVI/PNI status accurately.METHODS This study used a retrospective dataset involving 257 GC patients(training cohort,n=172;validation cohort,n=85).First,several clinical indicators,including serum tumor markers,CT-TN stages and CT-detected extramural vein invasion(CT-EMVI),were extracted,as were quantitative spectral CT parameters from the delineated tumor regions.Next,a two-step feature selection approach using correlation-based methods and information gain ranking inside a 10-fold cross-validation loop was utilized to select informative clinical and spectral CT parameters.A logistic regression(LR)-based nomogram model was subsequently constructed to predict LVI/PNI status,and its performance was evaluated using the area under the receiver operating characteristic curve(AUC).RESULTS In both the training and validation cohorts,CT T3-4 stage,CT-N positive status,and CT-EMVI positive status are more prevalent in the LVI/PNI-positive group and these differences are statistically significant(P<0.05).LR analysis of the training group showed preoperative CT-T stage,CT-EMVI,single-energy CT values of 70 keV of venous phase(VP-70 keV),and the ratio of standardized iodine concentration of equilibrium phase(EP-NIC)were ind

关 键 词：Spectral computed tomography Gastric cancer Lymphovascular invasion Perineural invasion 

分 类 号：R735.37[医药卫生—肿瘤]