赣南医学院学报2024，Vol.44Issue(3)：260-265,6.DOI:10.3969/j.issn.1001-5779.2024.03.009
基于超声影像组学及临床特征构建乳腺癌列线图的预测模型
A prediction model of breast cancer nomogram based on ultrasound radiomics and clinical features
摘要
Abstract
Objective:To integrating clinical risk factors and preoperative echotomic scores,to develop an echotomic-based nomogram to predict breast cancer.Methods:Ultrasound images of 525 breast masses(241 benign and 284 malignant)from 525 patients with definite pathological results from October 2020 to February 2023 were retrospectively collected.They were randomly divided into training group(368 cases)and verification group(157 cases)according to the ratio of 7∶3.The region of interest(ROI)was delineated according to the outline of the tumor,and the radiomics features were obtained.After dimension reduction analysis using the least absolute shrinkage and selection operator(LASSO),and the logistic regression classifier was selected to convert the resulting output to the radiomics score(Rad-Score)as the Rad-Score model.In addition,logistic regression was used to integrate radiomics scores with clinical risk factors to construct a combined diagnostic model and draw a nomogram.ROC curves and calibration curves were drawn to evaluate the model performance.Results:Of the 851 radiomics features extracted,13 non-zero features were selected for building the Rad-Score model.In multivariate analysis,age was the independent risk factor for breast cancer patients.A combined model was constructed based on age and Rad-Score.In the training group and validation group,the AUC values of clinical model were 0.772 and 0.847;the AUC values of radiomics model were 0.790 and 0.820;the AUC values of combined model were 0.846,0.909.The DeLong test showed that in the training set and validation set,the combined model was better than the clinical model(P＜0.05),and there was no significant difference between the combined model and the radiomics model,as well as between the radiomics model and the clinical model(P＞0.05).The calibration curve of the combined model in the training set and the validation set showed that the probability of predicting breast cancer risk was closed to the actual incidence,which could better guide clinical decision-making.Conclusion:The nomogram constructed based on clinical risk factors and ultrasound radiomics score has high value for predicting breast cancer.关键词
乳腺肿瘤/超声检查,乳房/危险因素/人工智能/影像组学/列线图Key words
Breast cancer/Ultrasonography,Breast/Risk factors/Artificial intelligence/Radiomics/Nomogram分类
医药卫生引用本文复制引用
张盼盼,孙医学,李阳,李林,杜欢,路丽丽,乔佳业..基于超声影像组学及临床特征构建乳腺癌列线图的预测模型[J].赣南医学院学报,2024,44(3):260-265,6.基金项目
蚌埠医学院自然科学重点科技项目(2021byzd066) （2021byzd066）
