Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Breast Tumor Centre, Department of Medical Oncology, Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Imaging Diagnostic and Interventional Center, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
JAMA Netw Open. 2020 Dec 1;3(12):e2028086. doi: 10.1001/jamanetworkopen.2020.28086.
Axillary lymph node metastasis (ALNM) status, typically estimated using an invasive procedure with a high false-negative rate, strongly affects the prognosis of recurrence in breast cancer. However, preoperative noninvasive tools to accurately predict ALNM status and disease-free survival (DFS) are lacking.
To develop and validate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic signatures for preoperative identification of ALNM and to assess individual DFS in patients with early-stage breast cancer.
DESIGN, SETTING, AND PARTICIPANTS: This retrospective prognostic study included patients with histologically confirmed early-stage breast cancer diagnosed at 4 hospitals in China from July 3, 2007, to September 21, 2019, randomly divided (7:3) into development and vaidation cohorts. All patients underwent preoperative MRI scans, were treated with surgery and sentinel lymph node biopsy or ALN dissection, and were pathologically examined to determine the ALNM status. Data analysis was conducted from February 15, 2019, to March 20, 2020.
Clinical and DCE-MRI radiomic signatures.
The primary end points were ALNM and DFS.
This study included 1214 women (median [IQR] age, 47 [42-55] years), split into development (849 [69.9%]) and validation (365 [30.1%]) cohorts. The radiomic signature identified ALNM in the development and validation cohorts with areas under the curve (AUCs) of 0.88 and 0.85, respectively, and the clinical-radiomic nomogram accurately predicted ALNM in the development and validation cohorts (AUC, 0.92 and 0.90, respectively) based on a least absolute shrinkage and selection operator (LASSO)-logistic regression model. The radiomic signature predicted 3-year DFS in the development and validation cohorts (AUC, 0.81 and 0.73, respectively), and the clinical-radiomic nomogram could discriminate high-risk from low-risk patients in the development cohort (hazard ratio [HR], 0.04; 95% CI, 0.01-0.11; P < .001) and the validation cohort (HR, 0.04; 95% CI, 0.004-0.32; P < .001) based on a random forest-Cox regression model. The clinical-radiomic nomogram was associated with 3-year DFS in the development and validation cohorts (AUC, 0.89 and 0.90, respectively). The decision curve analysis demonstrated that the clinical-radiomic nomogram displayed better clinical predictive usefulness than the clinical or radiomic signature alone.
This study described the application of MRI-based machine learning in patients with breast cancer, presenting novel individualized clinical decision nomograms that could be used to predict ALNM status and DFS. The clinical-radiomic nomograms were useful in clinical decision-making associated with personalized selection of surgical interventions and therapeutic regimens for patients with early-stage breast cancer.
重要性：腋窝淋巴结转移（ALNM）状态通常通过具有高假阴性率的侵入性程序来估计，强烈影响乳腺癌的复发预后。然而，缺乏术前非侵入性工具来准确预测 ALNM 状态和无病生存（DFS）。
目的：开发和验证动态对比增强磁共振成像（DCE-MRI）的放射组学特征，用于术前识别 ALNM，并评估早期乳腺癌患者的个体 DFS。
设计、设置和参与者：这是一项回顾性预后研究，纳入了 2007 年 7 月 3 日至 2019 年 9 月 21 日在中国 4 家医院确诊的组织学证实的早期乳腺癌患者，随机（7:3）分为开发和验证队列。所有患者均接受术前 MRI 扫描，接受手术和前哨淋巴结活检或 ALN 解剖，并进行病理检查以确定 ALNM 状态。数据分析于 2019 年 2 月 15 日至 2020 年 3 月 20 日进行。
暴露：临床和 DCE-MRI 放射组学特征。
主要结果和措施：主要终点是 ALNM 和 DFS。
结果：这项研究纳入了 1214 名女性（中位数[IQR]年龄，47[42-55]岁），分为开发（849[69.9%]）和验证（365[30.1%]）队列。放射组学特征在开发和验证队列中识别 ALNM 的曲线下面积（AUC）分别为 0.88 和 0.85，而临床-放射组学列线图基于最小绝对收缩和选择算子（LASSO）-逻辑回归模型准确预测了开发和验证队列中的 ALNM（AUC，分别为 0.92 和 0.90）。放射组学特征预测了开发和验证队列的 3 年 DFS（AUC，分别为 0.81 和 0.73），临床-放射组学列线图可在开发队列中区分高危和低危患者（风险比[HR]，0.04；95%CI，0.01-0.11；P<.001）和验证队列（HR，0.04；95%CI，0.004-0.32；P<.001）基于随机森林-Cox 回归模型。临床-放射组学列线图与开发和验证队列的 3 年 DFS 相关（AUC，分别为 0.89 和 0.90）。决策曲线分析表明，临床-放射组学列线图比临床或放射组学特征单独具有更好的临床预测有用性。
结论和相关性：本研究描述了基于 MRI 的机器学习在乳腺癌患者中的应用，提出了新的个体化临床决策列线图，可用于预测 ALNM 状态和 DFS。临床-放射组学列线图在与早期乳腺癌患者的手术干预和治疗方案的个体化选择相关的临床决策中具有实用价值。
