UASSIGNED:分子亚型在乳腺癌中起重要作用,是指导治疗的主要参考,与预后密切相关。这项研究的目的是探索基于非对比增强胸部CT的影像组学非侵入性预测乳腺癌分子亚型的潜力。
UNASSIGNED:共有300例接受常规胸部CT检查的乳腺癌患者(153例管腔类型和147例非管腔类型)被纳入研究,其中220例属于训练集,80例属于时间无关测试集。分子亚型的鉴定基于术后组织样品的免疫组织化学染色。在CT图像的连续切片上描绘了乳腺肿块的感兴趣区域(ROI)。通过6种特征筛选方法和7种机器学习分类器的组合,建立了42种预测乳腺癌管腔类型的模型;5倍交叉验证(cv)用于内部验证。最后,选择最优模型对独立测试集进行外部验证.此外,我们还利用了Shapley加法扩张(SHAP)值对机器学习模型进行了解释。
UNASSIGNED:在内部验证期间,不同模型的曲线下面积(AUC)值范围为0.599~0.842,准确度范围为0.540~0.775.最终,选择LASSO_SVM组合作为最终模型,其中包括9个影像组学特征。AUC,准确度,灵敏度,模型区分管腔与非管腔类型的特异性为0.842[95%CI:0.728-0.957],训练集中的0.773、0.818和0.773和0.757[95%CI:0.640-0.866],测试集中的0.713、0.767和0.676。
UNASSIGNED:基于胸部CT的影像组学可能为识别乳腺癌分子亚型提供了新思路。
UNASSIGNED: The molecular subtype plays an important role in breast cancer, which is the main reference to guide treatment and is closely related to prognosis. The objective of this
study was to explore the potential of the non-contrast-enhanced chest CT-based radiomics to predict breast cancer molecular subtypes non-invasively.
UNASSIGNED: A total of 300 breast cancer patients (153
luminal types and 147 non-
luminal types) who underwent routine chest CT examination were included in the
study, of which 220 cases belonged to the training set and 80 cases to the time-independent test set. Identification of the molecular subtypes is based on immunohistochemical staining of postoperative tissue samples. The region of interest (ROI) of breast masses was delineated on the continuous slices of CT images. Forty-two models to predict the
luminal type of breast cancer were established by the combination of six feature screening methods and seven machine learning classifiers; 5-fold cross-validation (cv) was used for internal validation. Finally, the optimal model was selected for external validation on the independent test set. In addition, we also took advantage of SHapley Additive exPlanations (SHAP) values to make explanations of the machine learning model.
UNASSIGNED: During internal validation, the area under the curve (AUC) values for different models ranged from 0.599 to 0.842, and the accuracy ranged from 0.540 to 0.775. Eventually, the LASSO_SVM combination was selected as the final model, which included 9 radiomics features. The AUC, accuracy, sensitivity, and specificity of the model to distinguish
luminal from the non-
luminal type were 0.842 [95% CI: 0.728-0.957], 0.773, 0.818, and 0.773 in the training set and 0.757 [95% CI: 0.640-0.866], 0.713, 0.767, and 0.676 in the test set.
UNASSIGNED: The radiomics based on chest CT may provide a new idea for the identification of breast cancer molecular subtypes.