Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
J Magn Reson Imaging. 2023 Aug;58(2):444-453. doi: 10.1002/jmri.28547. Epub 2022 Nov 28.
While the Oncotype DX 21-gene recurrence score (RS) has been recommended for guiding ER+/HER2- breast cancer treatment decisions, it is limited by cost and availability.
To develop a multiparametric MRI-based radiomics model for assessing ER+/HER2- breast cancer patients' 21-gene RS.
Retrospective.
A total of 151 patients with pathologically confirmed ER+/HER2- breast cancers, who underwent preoperative breast MR examinations and 21-gene expression assays, divided into training (n = 106) and validation (n = 45) cohorts.
FIELD STRENGTH/SEQUENCE: T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhancement (DCE) sequence at 1.5 T or 3 T.
A total of 1046 radiomics features were extracted from each MRI sequence with a manual lesion segmentation method. After feature dimension reduction by the recursive feature elimination method and dataset balance by the synthetic minority oversampling technique, linear support vector machine classifier models were built to distinguish high RS (RS ≥ 26) from low RS (RS < 26) from T2WI, DWI apparent diffusion coefficient (ADC) maps, DCE and their combination (multiparametric). A model based on clinical characteristics and a fusion model combining clinical characteristics and multiparametric MRI were also built.
Receiver operating characteristic (ROC) curve analysis and De Long's test with Bonferroni correction were used. A P value <0.01 was considered statistically significant.
The area under the ROC curve (AUC) value of multiparametric radiomics model was 0.92, significantly higher than DCE (0.83), T2WI (0.78), and ADC (0.77) models in the training cohort. The radiomics model also achieved good performance in the validation cohort (AUC = 0.77). The fusion model had significantly higher performance than the clinical model in both the training (AUC = 0.92 and 0.64, respectively) and validation cohorts (AUC = 0.78 and 0.62, respectively).
The proposed multiparametric MRI-based radiomics models may have potential to help distinguish ER+/HER2- breast cancer patients' recurrence risk.
Stage 2.
虽然 Oncotype DX 21 基因复发评分（RS）已被推荐用于指导 ER+/HER2-乳腺癌的治疗决策，但它受到成本和可用性的限制。
开发一种基于多参数 MRI 的放射组学模型，用于评估 ER+/HER2-乳腺癌患者的 21 基因 RS。
回顾性。
共 151 例经病理证实的 ER+/HER2-乳腺癌患者，均行术前乳腺 MRI 检查和 21 基因表达检测，分为训练集（n=106）和验证集（n=45）。
磁场强度/序列：1.5T 或 3T 下的 T2 加权成像（T2WI）、弥散加权成像（DWI）和动态对比增强（DCE）序列。
采用手动病变分割方法从每个 MRI 序列中提取 1046 个放射组学特征。通过递归特征消除方法进行特征降维和合成少数过采样技术进行数据集平衡后，构建线性支持向量机分类器模型，以区分高 RS（RS≥26）和低 RS（RS<26），并分别来自 T2WI、DWI 表观扩散系数（ADC）图、DCE 及其组合（多参数）。还建立了基于临床特征的模型和结合临床特征和多参数 MRI 的融合模型。
使用接收器工作特征（ROC）曲线分析和带有 Bonferroni 校正的 De Long 检验。P 值<0.01 被认为具有统计学意义。
多参数放射组学模型的 ROC 曲线下面积（AUC）值为 0.92，在训练队列中明显高于 DCE（0.83）、T2WI（0.78）和 ADC（0.77）模型。该放射组学模型在验证队列中也表现出良好的性能（AUC=0.77）。融合模型在训练（AUC=0.92 和 0.64）和验证（AUC=0.78 和 0.62）队列中的性能均明显高于临床模型。
所提出的基于多参数 MRI 的放射组学模型可能有助于区分 ER+/HER2-乳腺癌患者的复发风险。
3。
阶段 2。
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