Intratumoral and peritumoral radiomics for preoperative prediction of neoadjuvant chemotherapy effect in breast cancer based on 18F-FDG PET/CT.

IF 2.7 3区 医学 Q3 ONCOLOGY Journal of Cancer Research and Clinical Oncology Pub Date : 2024-11-02 DOI:10.1007/s00432-024-05987-w
Xuefeng Hou, Kun Chen, Xing Wan, Huiwen Luo, Xiaofeng Li, Wengui Xu
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Abstract

Objective: To investigate the value of 18F-FDG PET/CT-based intratumoral and peritumoral radiomics in predicting the efficacy of neoadjuvant chemotherapy (NAC) for breast cancer.

Methods: 190 patients who met the inclusion and exclusion criteria from 2017 to 2022 were studied. Features were extracted from the PET/CT intratumoral and peritumoral regions, feature selection was performed through the correlation analysis, t-tests, and least absolute shrinkage and selection operator regression (LASSO). Four classifiers, support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR), and naive bayes (NB) were used to build the prediction models. The receiver operating characteristic (ROC) curves were plotted to measure the predictive performance of the models. Concurrent stratified analysis was conducted to establish subtype-specific features for each molecular subtype.

Results: Compared to intratumoral features alone, intratumoral + peritumoral features achieved higher AUC values in each classifier. The SVM model constructed with intratumoral + peritumoral features achieved the highest AUC values in both the train and test set (train set: 0.95 and test set: 0.83). Subtype-specific features improve performance in predicting the efficacy of NAC (luminal group: 0.90; HER2 + group: 0.86; triple negative group: 0.92).

Conclusion: Intratumoral and peritumoral radiomics models based on 18F-FDG PET/CT can reliably forecast the efficacy of NAC, thereby assisting clinical decision-making.

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基于18F-FDG PET/CT的瘤内和瘤周放射组学用于乳腺癌新辅助化疗效果的术前预测
目的研究基于18F-FDG PET/CT的瘤内和瘤周放射组学在预测乳腺癌新辅助化疗(NAC)疗效方面的价值。方法:研究对象为2017年至2022年符合纳入和排除标准的190例患者。从 PET/CT 瘤内和瘤周区域提取特征,通过相关性分析、t 检验和最小绝对收缩和选择算子回归(LASSO)进行特征选择。支持向量机(SVM)、k-近邻(KNN)、逻辑回归(LR)和天真贝叶斯(NB)四种分类器被用来建立预测模型。绘制接收者操作特征曲线(ROC)来衡量模型的预测性能。同时还进行了分层分析,以确定每个分子亚型的亚型特异性特征:与单独的瘤内特征相比,瘤内+瘤周特征在每个分类器中都获得了更高的AUC值。利用瘤内+瘤周特征构建的 SVM 模型在训练集和测试集中都获得了最高的 AUC 值(训练集:0.95,测试集:0.83)。亚型特异性特征提高了预测 NAC 疗效的性能(管腔组:0.90;HER2 + 组:0.86;三阴组:0.92):基于18F-FDG PET/CT的瘤内和瘤周放射组学模型可以可靠地预测NAC的疗效,从而帮助临床决策。
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来源期刊
CiteScore
4.00
自引率
2.80%
发文量
577
审稿时长
2 months
期刊介绍: The "Journal of Cancer Research and Clinical Oncology" publishes significant and up-to-date articles within the fields of experimental and clinical oncology. The journal, which is chiefly devoted to Original papers, also includes Reviews as well as Editorials and Guest editorials on current, controversial topics. The section Letters to the editors provides a forum for a rapid exchange of comments and information concerning previously published papers and topics of current interest. Meeting reports provide current information on the latest results presented at important congresses. The following fields are covered: carcinogenesis - etiology, mechanisms; molecular biology; recent developments in tumor therapy; general diagnosis; laboratory diagnosis; diagnostic and experimental pathology; oncologic surgery; and epidemiology.
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