School of Medicine, South China University of Technology, Guangzhou, China.
Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
J Magn Reson Imaging. 2023 Nov;58(5):1580-1589. doi: 10.1002/jmri.28647. Epub 2023 Feb 16.
BACKGROUND: Preoperative assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) is of high clinical relevance for treatment decision-making and prognosis. PURPOSE: To investigate the associations of preoperative clinical and magnetic resonance imaging (MRI) characteristics with LVI and disease-free survival (DFS) by using machine learning methods in patients with IBC. STUDY TYPE: Retrospective. POPULATION: Five hundred and seventy-five women (range: 24-79 years) with IBC who underwent preoperative MRI examinations at two hospitals, divided into the training (N = 386) and validation datasets (N = 189). FIELD STRENGTH/SEQUENCE: Axial fat-suppressed T2-weighted turbo spin-echo sequence and dynamic contrast-enhanced with fat-suppressed T1-weighted three-dimensional gradient echo imaging. ASSESSMENT: MRI characteristics (clinical T stage, breast edema score, MRI axillary lymph node status, multicentricity or multifocality, enhancement pattern, adjacent vessel sign, and increased ipsilateral vascularity) were reviewed independently by three radiologists. Logistic regression (LR), eXtreme Gradient Boosting (XGBoost), k-Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms were used to establish the models by combing preoperative clinical and MRI characteristics for assessing LVI status in the training dataset, and the methods were further applied in the validation dataset. The LVI score was calculated using the best-performing of the four models to analyze the association with DFS. STATISTICAL TESTS: Chi-squared tests, variance inflation factors, receiver operating characteristics (ROC), Kaplan-Meier curve, log-rank, Cox regression, and intraclass correlation coefficient were performed. The area under the ROC curve (AUC) and hazard ratios (HR) were calculated. A P-value <0.05 was considered statistically significant. RESULTS: The model established by the XGBoost algorithm had better performance than LR, SVM, and KNN models, achieving an AUC of 0.832 (95% confidence interval [CI]: 0.789, 0.876) in the training dataset and 0.838 (95% CI: 0.775, 0.901) in the validation dataset. The LVI score established by the XGBoost model was an independent indicator of DFS (adjusted HR: 2.66, 95% CI: 1.22-5.80). DATA CONCLUSION: The XGBoost model based on preoperative clinical and MRI characteristics may help to investigate the LVI status and survival in patients with IBC. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.
背景：术前评估浸润性乳腺癌（IBC）中的淋巴血管侵犯（LVI）对于治疗决策和预后具有重要的临床意义。 目的：使用机器学习方法，研究 IBC 患者术前临床和磁共振成像（MRI）特征与 LVI 和无病生存（DFS）之间的关系。 研究类型：回顾性。 研究人群：575 名（年龄范围：24-79 岁）接受了两家医院术前 MRI 检查的 IBC 女性患者，分为训练集（N=386）和验证集（N=189）。 磁场强度/序列：轴位脂肪抑制 T2 加权涡轮自旋回波序列和动态对比增强脂肪抑制三维梯度回波成像。 评估：三位放射科医生独立评估 MRI 特征（临床 T 分期、乳腺水肿评分、MRI 腋窝淋巴结状态、多中心或多灶性、增强模式、毗邻血管征和同侧血管增多）。通过结合术前临床和 MRI 特征，使用逻辑回归（LR）、极端梯度提升（XGBoost）、k-最近邻（KNN）和支持向量机（SVM）算法，在训练数据集中建立模型，并将方法进一步应用于验证数据集。使用四种模型中表现最佳的模型计算 LVI 评分，以分析与 DFS 的关系。 统计检验：卡方检验、方差膨胀因子、受试者工作特征曲线（ROC）、Kaplan-Meier 曲线、对数秩检验、Cox 回归和组内相关系数。计算 ROC 曲线下面积（AUC）和危险比（HR）。P 值<0.05 被认为具有统计学意义。 结果：XGBoost 算法建立的模型在训练数据集中的表现优于 LR、SVM 和 KNN 模型，AUC 为 0.832（95%置信区间[CI]：0.789，0.876），在验证数据集中为 0.838（95%CI：0.775，0.901）。XGBoost 模型建立的 LVI 评分是 DFS 的独立预后指标（调整后的 HR：2.66，95%CI：1.22-5.80）。 数据结论：基于术前临床和 MRI 特征的 XGBoost 模型可以帮助研究 IBC 患者的 LVI 状态和生存情况。 证据水平：3 级，技术功效：2 级。
BMC Med Imaging. 2024-10-16
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