术前影像智能预测早期乳腺癌患者结局 / 开普饭

　　术前对腋窝淋巴结转移状态进行评估，有助于预测乳腺癌复发风险、选择手术方案和术后治疗方案。目前常用的有创方法假阴性率较高，可严重影响预后的准确性。目前，仍然缺乏术前准确预测腋窝淋巴结转移和复发风险的无创方法。

　　2020年12月8日，《美国医学会杂志》网络开放版在线发表中山大学孙逸仙纪念医院、中山大学肿瘤防治中心、南方医科大学顺德医院、中山大学附属东华医院、中山大学附属第三医院、广东医科大学、中国科学院院士宋尔卫等学者的研究报告，开发并验证了用于术前确定腋窝淋巴结转移并预测早期乳腺癌患者无病生存的动态对比增强磁共振影像特征人工智能分析方法。

　　该多中心预后回顾研究将2007年7月3日～2019年9月21日中国4家医院经组织学确诊早期乳腺癌女性患者1214例（中位年龄47岁，四分位42～55岁）按7∶3随机分为开发组849例（69.9%）和验证组365例（30.1%）。全部患者都完成术前动态对比增强磁共振扫描、手术和前哨淋巴结活检或腋窝淋巴结清扫，并进行病理检查以确定腋窝淋巴结转移状态。2019年2月15日～2020年3月20日对临床数据和磁共振扫描数据进行分析。主要研究终点为腋窝淋巴结转移和无病生存。根据受试者操作特征（真假阳性率）曲线下面积，判断预测方法的准确性。

　　结果，根据影像特征，预测开发组和验证组的腋窝淋巴结转移，真假阳性率曲线下面积分别为0.88和0.85。

　　根据临床数据＋影像特征，采用最小绝对值收敛选择算法、逻辑回归模型建立列线图，预测开发组和验证组的腋窝淋巴结转移，真假阳性率曲线下面积分别提高至0.92和0.90。

　　根据影像特征，预测开发组和验证组的三年无病生存，真假阳性率曲线下面积分别为0.81和0.73。

　　根据临床数据＋影像特征，采用随机森林算法、多因素比例风险回归模型建立列线图，预测开发组和验证组的三年无病生存，真假阳性率曲线下面积分别提高至0.89和0.90，并可区分患者的复发风险高低：

开发组：复发风险相差96%（风险比：0.04，95%置信区间：0.01～0.11，P<0.001）
验证组：复发风险相差96%（风险比：0.04，95%置信区间：0.004～0.32，P<0.001）

　　决策曲线分析表明，根据临床数据＋影像特征建立列线图，与仅仅根据临床数据或影像特征相比，临床预测准确性显著较高。

　　因此，该研究结果表明，根据临床数据＋磁共振影像特征人工智能机器学习个体化临床决策列线图，可准确预测乳腺癌患者的腋窝淋巴结转移状态和无病生存，该临床数据＋影像特征列线图有助于早期乳腺癌手术干预和治疗方案个体化选择的临床决策，故有必要进一步开展多中心大样本前瞻研究和长期随访进行验证，并进考虑纳入基因组学、转录组学、肿瘤突变、放疗化疗等影响因素，以提高该模型的准确性和可解释性。

　　对此，美国凯斯西储大学生物医学工程系纳撒尼尔·布拉曼博士发表特邀评论：用于手术计划和预后的影像组学。

JAMA Netw Open. 2020 Dec 8;3(12):e2028086.

Development and Validation of a Preoperative Magnetic Resonance Imaging Radiomics-Based Signature to Predict Axillary Lymph Node Metastasis and Disease-Free Survival in Patients With Early-Stage Breast Cancer.

Yu Y, Tan Y, Xie C, Hu Q, Ouyang J, Chen Y, Gu Y, Li A, Lu N, He Z, Yang Y, Chen K, Ma J, Li C, Ma M, Li X, Zhang R, Zhong H, Ou Q, Zhang Y, He Y, Li G, Wu Z, Su F, Song E, Yao H.

Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China; Sun Yat-sen University Cancer Center, Guangzhou, China; Shunde Hospital, Southern Medical University, Foshan, China; Tungwah Hospital, Sun Yat-sen University, Dongguan, China; The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Guangdong Medical University, Zhanjiang, China; Fountain-Valley Institute for Life Sciences, Guangzhou Institute of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.

This prognostic study develops and validates dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic signatures for preoperative identification of axillary lymph node metastasis and to assess individual disease-free survival in patients with early-stage breast cancer.

QUESTION: Can multiparametric magnetic resonance imaging (MRI) radiomic profiles be used to predict axillary lymph node metastasis (ALNM) and disease-free survival (DFS) in patients with early-stage breast cancer?

FINDINGS: In this prognostic study that included 1214 patients, 2 clinical-radiomic nomograms were developed that accurately predicted ALNM and stratified patients into low-risk and high-risk groups for DFS.

MEANING: In this study, 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.

IMPORTANCE: 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.

OBJECTIVE: 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.

EXPOSURE: Clinical and DCE-MRI radiomic signatures.

MAIN OUTCOMES AND MEASURES: The primary end points were ALNM and DFS.

RESULTS: 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.

CONCLUSIONS AND RELEVANCE: 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.

PMID: 33289845

DOI: 10.1001/jamanetworkopen.2020.28086

JAMA Netw Open. 2020 Dec 1;3(12):e2028608.

Radiomics For Surgical Planning and Prognostication.

Braman N.

Case Western Reserve University, Cleveland, Ohio.

PMID: 33289841

DOI: 10.1001/jamanetworkopen.2020.28608