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目的：基于对比增强CT开发用于预测乳腺癌(BC)患者肿瘤浸润淋巴细胞(TIL)水平的新型影像组学模型。方法：回顾性纳入210名非特殊浸润性BC患者(训练集：147名，验证集：63名),使用Pyradiomics软件包提取患者对比增强CT图像中的高维影像组学特征，然后利用Mann-Whitney U检验、斯皮尔曼相关系数和最小绝对值收敛和选择算子(LASSO)算法进行特征逐步筛选。筛选后的最优特征通过非线性支持向量机(NLSVM)方法在训练集中开发了影像组学模型，并在验证集中对模型的判别能力进行验证。之后利用沙普利加和解释(SHAP)算法对模型进行全局解释与特征重要性排序。结果：模型训练组和验证组的曲线下面积(AUC)分别为0.824(95%CI:0.762～0.886)和0.766(95%CI:0.624～0.909),影像组学模型有3个正向影响特征与4个负向影响特征，其中log-sigma-5-0-mm-3D＿firstorder＿Maximum对模型的影响力最大，其值越大时，模型输出SHAP值越小。结论：基于对比增强CT的影像组学模型可以帮助临床医生在治疗前准确预测BC患者的肿瘤浸润淋巴细胞水平，促进BC患者的个性化治疗。
Abstract：Objective: To develop a novel radiomics model based on contrast-enhanced CT for predicting tumor-infiltrating lymphocytes(TIL) levels in breast cancer(BC) patients. Methods: 210 patients with non-specific invasive BC were retrospectively enrolled(training group: 147, validation group: 63), and high-dimensional radiomcis features in the contrast-enhanced CT images of the patients were extracted using the Pyradiomics software package, followed by the use of the Mann-Whitney U-test, Spearman's correlation coefficient, and the least absolute shrinkage and selection operator(LASSO) algorithm for stepwise feature screening. The filtered optimal features were developed in the training set and validated in the validation set by non-linear support vector machine(NLSVM) method and the discriminative ability of the model was verified in the validation set. Afterwards, the global interpretation of the model with feature importance ranking is performed using the Shapley additive and exPlanatory(SHAP)algorithm.Results:The area under curve(AUC)of training and validation groups were 0.824(95%CI:0.762-0.886)and 0.766(95%CI:0.624-0.909),respectively,and there were three positively influencing features features four negatively influencing features for the radiomcis model,of which log-sigma-5-0-mm-3D＿firstorder＿Maximum has the greatest influence on the model,and the larger its value,the smaller the model output SHAP value.Conclusion:Contrast-enhanced CT-based radiomcis model can help clinicians accurately predict the level of tumor infiltrating lymphocyte in BC patients before treatment and facilitate personalised treatment for BC patients.
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基本信息:
中图分类号:R737.9;R730.44
引用信息:
[1]李永,崔书君,杨飞,等.基于对比增强CT的影像组学模型可预测乳腺癌患者的肿瘤浸润淋巴细胞水平[J].影像科学与光化学,2024,42(02):148-154.
基金信息:
河北省医学重点课题（20210091）
2024-03-15
2024-03-15