Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
GE Healthcare, Beijing, China.
Br J Radiol. 2022 Oct 1;95(1139):20220186. doi: 10.1259/bjr.20220186. Epub 2022 Oct 17.
To establish and validate a radiomics nomogram based on contrast-enhanced (CE)-MRI for predicting the efficacy of neoadjuvant chemotherapy (NAC) in human epidermal growth factor receptor 2 (HER2)-positive breast cancer with non-mass enhancement (NME).
A cohort comprising 117 HER2-positive breast cancer patients showing NME on CE-MRI between January 2012 and December 2019 were retrospectively analysed in our study. Patients were classified as pathological complete respone (pCR) according to surgical specimens and axillary lymph nodes without invasive tumour cells. Clinicopathological data were recorded, and images were assessed by two radiologists. A total of 1130 radiomics features were extracted from the primary tumour and six radiomics features were selected by the maximal relevance and minimal redundancy and least absolute shrinkage and selection operator algorithms. Univariate logistic regression was used to screen meaningful clinical and imaging features. The rad-score and independent risk factors were incorporated to build a nomogram model. Calibration and receiver operator characteristic curves were used to confirm the performance of the nomogram in the training and testing cohorts. The clinical usefulness of the nomogram was evaluated by decision curve analysis.
The difference in the rad-score between the pCR and non-pCR groups was significant in the training and testing cohorts ( < 0.01). The nomogram model showed good calibration and discrimination, with AUCs of 0.900 and 0.810 in the training and testing cohorts. Decision curve analysis indicated that the radiomics-based model was superior in terms of patient clinical benefit.
The MRI-based radiomics nomogram model could be used to pre-operatively predict the efficacy of NAC in HER2-positive breast cancer patients showing NME.
HER2-positive breast cancer showing segmental enhancement on CE-MRI was more likely to achieve pCR after NAC than regional enhancement and diffuse enhancement.The MRI-based radiomics nomogram model could be used to pre-operatively predict the efficacy of NAC in HER2-positive breast cancer that showed NME.
建立并验证基于对比增强磁共振成像（CE-MRI）的放射组学列线图，用于预测人表皮生长因子受体 2（HER2）阳性乳腺癌伴非肿块样强化（NME）患者新辅助化疗（NAC）疗效。
回顾性分析 2012 年 1 月至 2019 年 12 月期间在我院行 CE-MRI 检查显示 NME 的 117 例 HER2 阳性乳腺癌患者的资料。根据手术标本和腋窝淋巴结中无浸润性肿瘤细胞的情况将患者分为病理完全缓解（pCR）组。记录临床病理资料，由 2 位放射科医生评估图像。从原发肿瘤中提取 1130 个放射组学特征，通过最大相关性和最小冗余以及最小绝对值收缩和选择算子算法选择 6 个放射组学特征。采用单因素逻辑回归筛选有意义的临床和影像学特征。将 rad-score 和独立危险因素纳入构建列线图模型。校准和受试者工作特征曲线用于验证训练集和测试集列线图的性能。通过决策曲线分析评估列线图的临床实用性。
在训练集和测试集中，pCR 组和非 pCR 组的 rad-score 差异均有统计学意义（<0.01）。列线图模型具有良好的校准度和区分度，在训练集和测试集中的 AUC 分别为 0.900 和 0.810。决策曲线分析表明，基于放射组学的模型在患者临床获益方面具有优势。
基于 MRI 的放射组学列线图模型可用于术前预测 HER2 阳性乳腺癌伴 NME 患者 NAC 的疗效。
CE-MRI 显示乳腺癌呈节段样强化的 HER2 阳性患者比区域性强化和弥漫性强化患者更有可能在 NAC 后获得 pCR。基于 MRI 的放射组学列线图模型可用于术前预测 HER2 阳性乳腺癌伴 NME 患者 NAC 的疗效。
