Abstract：
OBJECTIVE: The purpose of this study was to assess the predictive performance of multiparametric magnetic resonance imaging (MRI) for molecular subtypes and interpret features using SHapley Additive exPlanations (SHAP) analysis.
METHODS: Patients with breast cancer who underwent pre-treatment MRI (including ultrafast dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, diffusion kurtosis imaging and intravoxel incoherent motion) were recruited between February 2019 and January 2022. Thirteen semantic and thirteen multiparametric features were collected and the key features were selected to develop machine-learning models for predicting molecular subtypes of breast cancers (luminal A, luminal B, triple-negative and HER2-enriched) by using stepwise logistic regression. Semantic model and multiparametric model were built and compared based on five machine-learning classifiers. Model decision-making was interpreted using SHAP analysis.
RESULTS: A total of 188 women (mean age, 53 ± 11 [standard deviation] years; age range: 25-75 years) were enrolled and further divided into training cohort (131 women) and validation cohort (57 women). XGBoost demonstrated good predictive performance among five machine-learning classifiers. Within the validation cohort, the areas under the receiver operating characteristic curves (AUCs) for the semantic models ranged from 0.693 (95% confidence interval [CI]: 0.478-0.839) for HER2-enriched subtype to 0.764 (95% CI: 0.681-0.908) for luminal A subtype, inferior to multiparametric models that yielded AUCs ranging from 0.771 (95% CI: 0.630-0.888) for HER2-enriched subtype to 0.857 (95% CI: 0.717-0.957) for triple-negative subtype. The AUCs between the semantic and the multiparametric models did not show significant differences (P range: 0.217-0.640). SHAP analysis revealed that lower iAUC, higher kurtosis, lower D*, and lower kurtosis were distinctive features for luminal A, luminal B, triple-negative breast cancer, and HER2-enriched subtypes, respectively.
CONCLUSIONS: Multiparametric MRI is superior to semantic models to effectively predict the molecular subtypes of breast cancer.
METHODS: Patients with breast cancer who underwent pre-treatment MRI (including ultrafast dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, diffusion kurtosis imaging and intravoxel incoherent motion) were recruited between February 2019 and January 2022. Thirteen semantic and thirteen multiparametric features were collected and the key features were selected to develop machine-learning models for predicting molecular subtypes of breast cancers (luminal A, luminal B, triple-negative and HER2-enriched) by using stepwise logistic regression. Semantic model and multiparametric model were built and compared based on five machine-learning classifiers. Model decision-making was interpreted using SHAP analysis.
RESULTS: A total of 188 women (mean age, 53 ± 11 [standard deviation] years; age range: 25-75 years) were enrolled and further divided into training cohort (131 women) and validation cohort (57 women). XGBoost demonstrated good predictive performance among five machine-learning classifiers. Within the validation cohort, the areas under the receiver operating characteristic curves (AUCs) for the semantic models ranged from 0.693 (95% confidence interval [CI]: 0.478-0.839) for HER2-enriched subtype to 0.764 (95% CI: 0.681-0.908) for luminal A subtype, inferior to multiparametric models that yielded AUCs ranging from 0.771 (95% CI: 0.630-0.888) for HER2-enriched subtype to 0.857 (95% CI: 0.717-0.957) for triple-negative subtype. The AUCs between the semantic and the multiparametric models did not show significant differences (P range: 0.217-0.640). SHAP analysis revealed that lower iAUC, higher kurtosis, lower D*, and lower kurtosis were distinctive features for luminal A, luminal B, triple-negative breast cancer, and HER2-enriched subtypes, respectively.
CONCLUSIONS: Multiparametric MRI is superior to semantic models to effectively predict the molecular subtypes of breast cancer.
摘要：
目的：本研究的目的是评估多参数磁共振成像（MRI）对分子亚型的预测性能，并使用SHapley加法移植（SHAP）分析解释特征。
方法：接受治疗前MRI（包括超快动态对比增强MRI，磁共振波谱,在2019年2月至2022年1月之间招募了扩散峰度成像和体素内不相干运动）。收集了13个语义和13个多参数特征，并选择了关键特征来开发用于预测乳腺癌分子亚型的机器学习模型（luminalA，管腔B,三阴性和HER2富集)，采用逐步逻辑回归。建立了基于5种机器学习分类器的语义模型和多参数模型并进行了比较。使用SHAP分析解释模型决策。
结果：共有188名女性（平均年龄，53±11[标准偏差]岁；年龄范围：25-75岁）被纳入，并进一步分为培训队列（131名女性）和验证队列（57名女性）。XGBoost在五个机器学习分类器中表现出良好的预测性能。在验证队列中，语义模型的受试者工作特征曲线（AUC）下的面积范围从HER2富集亚型的0.693（95％置信区间[CI]：0.478-0.839）到腔内A亚型的0.764（95％CI：0.681-0.908），劣于多参数模型,这些模型产生的AUC范围从HER2富集亚型的0.771(95%CI:0.630-0.888)到三阴性亚型的0.857(95%CI:0.717-0.957).语义模型和多参数模型之间的AUC没有显示显着差异（P范围：0.217-0.640）。SHAP分析显示，较低的iAUC，更高的峰度，较低的D*，较低的峰度是腔A的独特特征，管腔B,三阴性乳腺癌,和HER2富集亚型，分别。
结论：多参数MRI在有效预测乳腺癌分子亚型方面优于语义模型。
方法：接受治疗前MRI（包括超快动态对比增强MRI，磁共振波谱,在2019年2月至2022年1月之间招募了扩散峰度成像和体素内不相干运动）。收集了13个语义和13个多参数特征，并选择了关键特征来开发用于预测乳腺癌分子亚型的机器学习模型（luminalA，管腔B,三阴性和HER2富集)，采用逐步逻辑回归。建立了基于5种机器学习分类器的语义模型和多参数模型并进行了比较。使用SHAP分析解释模型决策。
结果：共有188名女性（平均年龄，53±11[标准偏差]岁；年龄范围：25-75岁）被纳入，并进一步分为培训队列（131名女性）和验证队列（57名女性）。XGBoost在五个机器学习分类器中表现出良好的预测性能。在验证队列中，语义模型的受试者工作特征曲线（AUC）下的面积范围从HER2富集亚型的0.693（95％置信区间[CI]：0.478-0.839）到腔内A亚型的0.764（95％CI：0.681-0.908），劣于多参数模型,这些模型产生的AUC范围从HER2富集亚型的0.771(95%CI:0.630-0.888)到三阴性亚型的0.857(95%CI:0.717-0.957).语义模型和多参数模型之间的AUC没有显示显着差异（P范围：0.217-0.640）。SHAP分析显示，较低的iAUC，更高的峰度，较低的D*，较低的峰度是腔A的独特特征，管腔B,三阴性乳腺癌,和HER2富集亚型，分别。
结论：多参数MRI在有效预测乳腺癌分子亚型方面优于语义模型。
