Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China.
Department of Ultrasound, Affiliated Hospital of Jiangsu University, 438 Jiefang Road, Zhenjiang, 212050, China.
Eur Radiol. 2024 Jan;34(1):136-148. doi: 10.1007/s00330-023-09995-1. Epub 2023 Jul 31.
To develop and validate an ultrasound (US) radiomics-based nomogram for the preoperative prediction of the lymphovascular invasion (LVI) status in patients with invasive breast cancer (IBC).
In this multicentre, retrospective study, 456 consecutive women were enrolled from three institutions. Institutions 1 and 2 were used to train (n = 320) and test (n = 136), and 130 patients from institution 3 were used for external validation. Radiomics features that reflected tumour information were derived from grey-scale US images. The least absolute shrinkage and selection operator and the maximum relevance minimum redundancy (mRMR) algorithm were used for feature selection and radiomics signature (RS) building. US radiomics-based nomogram was constructed by using multivariable logistic regression analysis. Predictive performance was assessed with the receiving operating characteristic curve, discrimination, and calibration.
The nomogram based on clinico-ultrasonic features (menopausal status, US-reported lymph node status, posterior echo features) and RS yielded an optimal AUC of 0.88 (95% confidence interval [CI], 0.84-0.91), 0.89 (95% CI, 0.84-0.94) and 0.95 (95% CI, 0.92-0.99) in the training, internal and external validation cohort. The nomogram outperformed the clinico-ultrasonic and RS model (p < 0.05). The nomogram performed favourable discrimination (C-index, 0.88; 95% CI: 0.84-0.91) and was confirmed in the validation (0.88 for internal, 0.95 for external) cohorts. The calibration and decision curve demonstrated the nomogram showed good calibration and was clinically useful.
The radiomics nomogram incorporated in the RS and US and the clinical findings exhibited favourable preoperative individualised prediction of LVI.
The US radiomics-based nomogram incorporating menopausal status, posterior echo features, US reported-ALN status, and radiomics signature has the potential to predict lymphovascular invasion in patients with invasive breast cancer.
• The clinico-ultrsonic model of menopausal status, posterior echo features, and US-reported ALN status achieved a better predictive efficacy for LVI than either of them alone. • The radiomics nomogram showed optimal prediction in predicting LVI from patients with IBC (ROC, 0.88 and 0.89 in the training and validation sets). • A nomogram demonstrated favourable performance (area under the receiver operating characteristic curve, 0.95) and well calibration (C-index, 0.95) in an independent validation cohort (n = 130).
开发和验证一种基于超声（US）放射组学的列线图，用于术前预测浸润性乳腺癌（IBC）患者的脉管侵犯（LVI）状态。
本研究为多中心回顾性研究，纳入了来自三家机构的 456 名连续女性患者。机构 1 和 2 用于训练（n=320）和测试（n=136），机构 3 的 130 名患者用于外部验证。从灰阶 US 图像中提取反映肿瘤信息的放射组学特征。最小绝对收缩和选择算子（LASSO）和最大相关性最小冗余（mRMR）算法用于特征选择和放射组学特征（RS）构建。使用多变量逻辑回归分析构建基于 US 放射组学的列线图。通过接受者操作特征曲线、判别和校准评估预测性能。
基于临床超声特征（绝经状态、US 报告的淋巴结状态、后向回声特征）和 RS 的列线图在训练、内部和外部验证队列中获得了最佳 AUC 值分别为 0.88（95%置信区间[CI]，0.84-0.91）、0.89（95%CI，0.84-0.94）和 0.95（95%CI，0.92-0.99）。该列线图优于临床超声和 RS 模型（p<0.05）。该列线图在验证队列中表现出良好的判别能力（C 指数，0.88；95%CI：0.84-0.91），并在验证（内部 0.88，外部 0.95）队列中得到证实。校准和决策曲线表明该列线图具有良好的校准效果和临床应用价值。
放射组学列线图纳入 RS 和 US 以及临床特征，可对 LVI 进行术前个体化预测。
基于 US 的放射组学列线图纳入绝经状态、后向回声特征、US 报告的 ALN 状态和放射组学特征，有可能预测浸润性乳腺癌患者的脉管侵犯。
临床超声模型的绝经状态、后向回声特征和 US 报告的 ALN 状态在预测 LVI 方面比单独使用任何一种方法都有更好的预测效果。
放射组学列线图在预测 IBC 患者的 LVI 方面表现出最佳预测效果（ROC 曲线在训练集和验证集的 AUC 值分别为 0.88 和 0.89）。
列线图在独立验证队列（n=130）中表现出良好的性能（受试者工作特征曲线下面积 0.95）和良好的校准（C 指数 0.95）。
