Liu Jinjin, Wang Xuchao, Hu Mengshang, Zheng Yan, Zhu Lin, Wang Wei, Hu Jisu, Zhou Zhiyong, Dai Yakang, Dong Fenglin
Department of Ultrasound, The First Affiliated Hospital of Soochow University, Suzhou, China.
Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China.
Front Oncol. 2022 Aug 15;12:963925. doi: 10.3389/fonc.2022.963925. eCollection 2022.
To develop and validate a radiomics nomogram that could incorporate clinicopathological characteristics and ultrasound (US)-based radiomics signature to non-invasively predict Ki-67 expression level in patients with breast cancer (BC) preoperatively.
A total of 328 breast lesions from 324 patients with BC who were pathologically confirmed in our hospital from June 2019 to October 2020 were included, and they were divided into high Ki-67 expression level group and low Ki-67 expression level group. Routine US and shear wave elastography (SWE) were performed for each lesion, and the ipsilateral axillary lymph nodes (ALNs) were scanned for abnormal changes. The datasets were randomly divided into training and validation cohorts with a ratio of 7:3. Correlation analysis and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomics features obtained from gray-scale US images of BC patients, and each radiomics score (Rad-score) was calculated. Afterwards, multivariate logistic regression analysis was used to establish a radiomics nomogram based on the radiomics signature and clinicopathological characteristics. The prediction performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC), the calibration curve, and decision curve analysis (DCA) using the results of immunohistochemistry as the gold standard.
The radiomics signature, consisted of eight selected radiomics features, achieved a nearly moderate prediction efficacy with AUC of 0.821 (95% CI:0.764-0.880) and 0.713 (95% CI:0.612-0.814) in the training and validation cohorts, respectively. The radiomics nomogram, incorporating maximum diameter of lesions, stiff rim sign, US-reported ALN status, and radiomics signature showed a promising performance for prediction of Ki-67 expression level, with AUC of 0.904 (95% CI:0.860-0.948) and 0.890 (95% CI:0.817-0.964) in the training and validation cohorts, respectively. The calibration curve and DCA indicated promising consistency and clinical applicability.
The proposed US-based radiomics nomogram could be used to non-invasively predict Ki-67 expression level in BC patients preoperatively, and to assist clinicians in making reliable clinical decisions.
开发并验证一种放射组学列线图，该列线图可纳入临床病理特征和基于超声（US）的放射组学特征，以术前无创预测乳腺癌（BC）患者的Ki-67表达水平。
纳入2019年6月至2020年10月在我院经病理确诊的324例BC患者的328个乳腺病灶，将其分为Ki-67高表达水平组和Ki-67低表达水平组。对每个病灶进行常规超声和剪切波弹性成像（SWE）检查，并对同侧腋窝淋巴结（ALN）进行扫描以观察有无异常变化。将数据集按7:3的比例随机分为训练队列和验证队列。采用相关性分析和最小绝对收缩和选择算子（LASSO）从BC患者的灰阶超声图像中选择放射组学特征，并计算每个放射组学评分（Rad-score）。之后，采用多因素逻辑回归分析，基于放射组学特征和临床病理特征建立放射组学列线图。以免疫组化结果为金标准，通过受试者操作特征曲线（AUC）下面积、校准曲线和决策曲线分析（DCA）评估列线图的预测性能。
由8个选定放射组学特征组成的放射组学特征在训练队列和验证队列中的预测效能接近中等，AUC分别为0.821（95%CI：0.764-0.880）和0.713（95%CI：0.612-0.814）。纳入病灶最大直径、硬边征、超声报告的ALN状态和放射组学特征的放射组学列线图在预测Ki-67表达水平方面表现出良好性能，在训练队列和验证队列中的AUC分别为0.904（95%CI：0.860-0.948）和0.890（95%CI：0.817-0.964）。校准曲线和DCA表明具有良好的一致性和临床适用性。
所提出的基于超声的放射组学列线图可用于术前无创预测BC患者的Ki-67表达水平，并协助临床医生做出可靠的临床决策。
