Zhang Hongxiao, Niu Shuxian, Chen Huanhuan, Wang Lihua, Wang Xiaoyu, Wu Yujiao, Shi Jiaxin, Li Zhuoning, Hu Yanjun, Yang Zhiguang, Jiang Xiran
School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China.
Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
Front Cell Dev Biol. 2024 Jan 8;11:1220320. doi: 10.3389/fcell.2023.1220320. eCollection 2023.
This study explores the potential of radiomics to predict the proliferation marker protein Ki-67 levels and human epidermal growth factor receptor 2 (HER-2) status based on MRI images of patients with spinal metastasis from primary breast cancer. A total of 110 patients with pathologically confirmed spinal metastases from primary breast cancer were enrolled between Dec. 2017 and Dec. 2021. All patients underwent T1-weighted contrast-enhanced MRI scans. The PyRadiomics package was used to extract features from the MRI images based on the intraclass correlation coefficient and least absolute shrinkage and selection operator. The most predictive features were used to develop the radiomics signature. The Chi-Square test, Fisher's exact test, Student's -test, and Mann-Whitney U test were used to evaluate the clinical and pathological characteristics between the high- and low-level Ki-67 groups and the HER-2 positive/negative groups. The radiomics models were compared using receiver operating characteristic curve analysis. The area under the receiver operating characteristic curve (AUC), sensitivity (SEN), and specificity (SPE) were generated as comparison metrics. From the spinal MRI scans, five and two features were identified as the most predictive for the Ki-67 level and HER-2 status, respectively. The developed radiomics signatures generated good prediction performance for the Ki-67 level in the training (AUC = 0.812, 95% CI: 0.710-0.914, SEN = 0.667, SPE = 0.846) and validation (AUC = 0.799, 95% CI: 0.652-0.947, SEN = 0.722, SPE = 0.833) cohorts. Good prediction performance for the HER-2 status was also achieved in the training (AUC = 0.796, 95% CI: 0.686-0.906, SEN = 0.720, SPE = 0.776) and validation (AUC = 0.705, 95% CI: 0.506-0.904, SEN = 0.733, SPE = 0.762) cohorts. The results of this study provide a better understanding of the potential clinical implications of spinal MRI-based radiomics on the prediction of Ki-67 levels and HER-2 status in breast cancer.
本研究基于原发性乳腺癌脊柱转移患者的MRI图像，探讨放射组学预测增殖标志物蛋白Ki-67水平和人表皮生长因子受体2（HER-2）状态的潜力。2017年12月至2021年12月期间，共纳入110例经病理证实为原发性乳腺癌脊柱转移的患者。所有患者均接受了T1加权对比增强MRI扫描。使用PyRadiomics软件包基于组内相关系数和最小绝对收缩和选择算子从MRI图像中提取特征。使用最具预测性的特征构建放射组学特征。采用卡方检验、Fisher精确检验、Student's检验和Mann-Whitney U检验评估高、低水平Ki-67组和HER-2阳性/阴性组之间的临床和病理特征。使用受试者工作特征曲线分析比较放射组学模型。生成受试者工作特征曲线下面积（AUC）、敏感性（SEN）和特异性（SPE）作为比较指标。从脊柱MRI扫描中，分别确定了5个和2个对Ki-67水平和HER-2状态最具预测性的特征。所构建的放射组学特征在训练队列（AUC = 0.812，95%CI：0.710-0.914，SEN = 0.667，SPE = 0.846）和验证队列（AUC = 0.799，95%CI：0.652-0.947，SEN = 0.722，SPE = 0.833）中对Ki-67水平产生了良好的预测性能。在训练队列（AUC = 0.796，95%CI：0.686-0.906，SEN = 0.720，SPE = 0.776）和验证队列（AUC = 0.705，95%CI：0.506-0.904，SEN = 0.733，SPE = 0.762）中对HER-2状态也取得了良好的预测性能。本研究结果有助于更好地理解基于脊柱MRI的放射组学在预测乳腺癌Ki-67水平和HER-2状态方面的潜在临床意义。
