随机森林
无线电技术
逻辑回归
医学
Lasso(编程语言)
胰腺癌
人工智能
特征选择
接收机工作特性
计算机科学
支持向量机
磁共振成像
人口
放射科
模式识别(心理学)
核医学
癌症
机器学习
内科学
万维网
环境卫生
作者
Ning Xie,Xuhui Fan,Desheng Chen,Jingwen Chen,Hongwei Yu,Meijuan He,Hao Líu,Xunrui Yin,Baiwen Li,Han Wang
摘要
Background Radiomics‐based preoperative evaluation of lymph node metastasis (LNM) and histological grade (HG) might facilitate the decision‐making for pancreatic cancer and further efforts are needed to develop effective models. Purpose To develop multiparametric MRI (MP‐MRI)‐based radiomics models to evaluate LNM and HG. Study Type Retrospective. Population The pancreatic cancer patients from the main center ( n = 126) were assigned to the training and validation sets at a 4:1 ratio. The patients from the other center ( n = 40) served as external test sets. Field Strength/Sequence A 3.0 T and 1.5 T / T2 ‐weighted imaging, diffusion‐weighted imaging, and dynamic contrast enhancement T1 ‐weighted imaging. Assessment A total of 10,686 peritumoral and intratumoral radiomics features were extracted which contained first‐order, shape‐based, and texture features. The following three‐step method was applied to reduce the feature dimensionality: SelectKBest (a function from scikit‐learn package), least absolute shrinkage and selection operator (LASSO), and recursive feature elimination based on random forest (RFE‐RF). Six classifiers (random forest, logistic regression, support vector machine, K‐nearest neighbor, decision tree, and XGBOOST) were trained and selected based on their performance to construct the clinical, radiomics, and combination models. Statistical Tests Delong's test was used to compare the models' performance. P value less than 0.05 was considered significant. Results Twelve significant features for LNM and 11 features for HG were obtained. Random forest and logistic regression performed better than the other classifiers in evaluating LNM and HG, respectively, according to the surgical pathological results. The best performance was obtained with the models that combined peritumoral and intratumoral features with area under curve (AUC) values of 0.944 and 0.892 in the validation and external test sets for HG and 0.924 and 0.875 for LNM. Data Conclusion Radiomics holds the potential to evaluate LNM and HG of pancreatic cancer. The combination of peritumoral and intratumoral features will make models more accurate. Evidence Level 4. Technical Efficacy Stage 2.
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