Nomogram based on CT radiomics for predicting pathological types of gastric cancer:Difference between endoscopic biopsy and postoperative pathology
10.13929/j.issn.1672-8475.2024.06.007
- VernacularTitle:CT影像组学列线图预测胃癌内镜活检与术后病理学分型差异
- Author:
Shuai ZHAO
1
;
Yiyang LIU
;
Siteng LIU
;
Xingzhi CHEN
;
Mengchen YUAN
;
Yaru YOU
;
Chencui HUANG
;
Jianbo GAO
Author Information
1. 郑州大学第一附属医院放射科,河南 郑州 450052
- Keywords:
stomach neoplasms;
tomography,X-ray computed;
pathology;
radiomics
- From:
Chinese Journal of Interventional Imaging and Therapy
2024;21(6):343-348
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of CT radiomics-based nomogram for predicting difference of Lauren types of gastric cancers between endoscopic biopsy and postoperative pathology.Methods Totally 126 patients with gastric cancer diagnosed by surgical pathology were retrospectively analyzed.The patients were divided into concordant group(n=77)and inconsistent group(n=49)according to the concordance between endoscopic biopsy and postoperative pathology results or not,also divided into training set and validation set at the ratio of 2∶1.Clinical predictors were screened,then a clinical prediction model was constructed.Radiomics features were extracted based on venous-phase CT images and screened using L1 regularization.Radiomics models were constructed using 3 machine learning(ML)algorithms,i.e.decision trees,random forests and logistic regression.The nomogram based on clinical and the best ML radiomics model was constructed,and the efficacy and clinical utility of the above models and nomogram for predicting inconsistency of Lauren types of gastric cancers between endoscopic biopsy and postoperative pathology were evaluated.Results Patients'age,platelet count,and arterial-phase CT values of tumors were all independent predictors of inconsistency between endoscopic biopsy and postoperative pathology of Lauren types of gastric cancer.CT radiomics model using random forests algorithm showed better predictive efficacy among 3 ML models,with the area under the curve(AUC)of 0.835 in training set and 0.724 in validation set,respectively.The AUC of clinical model,radiomics model and the nomogram in training set was 0.764,0.835 and 0.884,while was 0.760,0.724 and 0.841 in validation set,respectively.In both training set and validation set,the nomogram showed a good fit and considerable clinical utility.Conclusion CT radiomics-based nomogram had potential clinical application value for predicting inconsistency of Lauren types of gastric cancers between endoscopic biopsy and postoperative pathology.
