Liang Yue, Li Qing-Yu, Li Jia-Hao, Zhang Lan, Wang Ying, Wang Bin-Jie, Wang Chang-Fu
Background: Huai-he Hospital of Henan University, Kaifeng, China.
Background: Huai-he Hospital of Henan University, Kaifeng, China.
Magn Reson Imaging. 2025 Apr;117:110325. doi: 10.1016/j.mri.2025.110325. Epub 2025 Jan 7.
To explore the application value of MRI-based imaging histology and deep learning model in the identification and classification of breast phyllodes tumors.
Seventy-seven patients diagnosed as breast phyllodes tumors and fibroadenomas by pathological examination were retrospectively analyzed, and traditional radiomics features, subregion radiomics features, and deep learning features were extracted from MRI images, respectively. The features were screened and modeled using variance selection method, statistical test, random forest importance ranking method, Spearman correlation analysis, least absolute shrinkage and selection operator (LASSO). The efficacy of each model was assessed using the subject operating characteristic (ROC) curve, The DeLong test was used to assess the differences in the AUC values of the different models, and the clinical benefit of each model was assessed using the decision curve (DCA), and the predictive accuracy of the model was assessed using the calibration curve (CCA).
Among the constructed models for classification of breast phyllodes tumors, the fusion model (AUC: 0.97) had the best diagnostic efficacy and highest clinical benefit. The traditional radiomics model (AUC: 0.81) had better diagnostic efficacy compared with subregion radiomics model (AUC: 0.70). De-Long test, there is a statistical difference between the fusion model traditional radiomics model, and subregion radiomics model in the training group. Among the models constructed to distinguish phyllodes tumors from fibroadenomas in the breast, the TDT_CIDL model (AUC: 0.974) had the best predictive efficacy and the highest clinical benefit. De-Long test, the TDT_CI combination model was statistically different from the remaining five models in the training group.
Traditional radiomics models, subregion radiomics models and deep learning models based on MRI sequences can help to differentiate benign from junctional phyllodes tumors, phyllodes tumors from fibroadenomas, and provide personalized treatment for patients.
探讨基于MRI的影像组织学及深度学习模型在乳腺叶状肿瘤鉴别及分类中的应用价值。
回顾性分析77例经病理检查确诊为乳腺叶状肿瘤及纤维腺瘤的患者，分别从MRI图像中提取传统影像组学特征、亚区域影像组学特征及深度学习特征。采用方差选择法、统计检验、随机森林重要性排序法、Spearman相关分析、最小绝对收缩和选择算子（LASSO）对特征进行筛选和建模。采用受试者工作特征（ROC）曲线评估各模型的效能，用DeLong检验评估不同模型AUC值的差异，用决策曲线（DCA）评估各模型的临床获益，用校准曲线（CCA）评估模型的预测准确性。
在构建的乳腺叶状肿瘤分类模型中，融合模型（AUC：0.97）诊断效能最佳，临床获益最高。传统影像组学模型（AUC：0.81）较亚区域影像组学模型（AUC：0.70）诊断效能更好。De-Long检验显示，训练组中融合模型、传统影像组学模型和亚区域影像组学模型之间存在统计学差异。在构建的鉴别乳腺叶状肿瘤与纤维腺瘤的模型中，TDT_CIDL模型（AUC：0.974）预测效能最佳，临床获益最高。De-Long检验显示，训练组中TDT_CI组合模型与其余5个模型存在统计学差异。
基于MRI序列的传统影像组学模型、亚区域影像组学模型及深度学习模型有助于鉴别乳腺叶状肿瘤的良恶性、叶状肿瘤与纤维腺瘤，并为患者提供个性化治疗。
