Machine Learning Optimization of Spectral CT for Predicting Invasiveness in Gastric Adenocarcinoma
Objective It is to investigate the potential value of machine learning(ML)algorithms combined with quantitative parameters of spectral CT and clinical models in predicting lymphovascular invasion(LVI)and perineural invasion(PNI)in patients with gastric adenocarcinoma(GAC).Methods A total of 114 patients with GAC confirmed by pathology from December 2017 to May 2022 were collected.The study parameters involved serum tumor markers,CT-TN staging,extramural venous invasion assessed by CT(CT-EMVI),and quantitative parameters of spectral CT.Feature screening was conduc-ted using the Best-First algorithm in WEKA software,and models were built using Bayesian network(BN)and support vector machine(SVM)algorithms.Results Compared with the LVI/PNI(-)group,CT-T3-4 stage,CT-N positive,CT-EMVI positive,and serum tumor markers(CA72-4,CA19-9)were more common in LVI/PNI(+)group,and the spectral CT parameters were higher in LVI/PNI(+)group,with statistically significant differences observed(P＜0.05).After feature screening,key varia-bles included CT-T staging,CT-EMVI,VP-NIC,and EP-70 keV CT values.Based on these variables,six models were constructed using BN and SVM,including clinical parameter models,spectral CT parame-ter models,and combined models.All the six models demonstrated high predictive performance without overfitting.The combined model using BN showed the best predictive performance with an AUC range of 0.890 to 0.933,and Delong test showed that it was statistically significant(P＜0.05).In contrast,the SVM combined model did not show a statistical difference between the model and the other two models(P＞0.05).Conclusion Machine learning models combining clinical and spectral CT parameters can effi-ciently evaluate the LVI and PNI status of GAC patients,with the BN combined model achieving the high-est accuracy.
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