Advanced Computing Core, Center of Advanced Studies and Technology (CAST), "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy.
Department of Innovative Technologies in Medicine and Odonoiatry, "G. d'Annunzio" University, Chieti, Italy.
J Digit Imaging. 2023 Jun;36(3):1071-1080. doi: 10.1007/s10278-023-00781-5. Epub 2023 Jan 25.
Oncotype Dx Recurrence Score (RS) has been validated in patients with ER + /HER2 - invasive breast carcinoma to estimate patient risk of recurrence and guide the use of adjuvant chemotherapy. We investigated the role of MRI-based radiomics features extracted from the tumor and the peritumoral tissues to predict the risk of tumor recurrence. A total of 62 patients with biopsy-proved ER + /HER2 - breast cancer who underwent pre-treatment MRI and Oncotype Dx were included. An RS > 25 was considered discriminant between low-intermediate and high risk of tumor recurrence. Two readers segmented each tumor. Radiomics features were extracted from the tumor and the peritumoral tissues. Partial least square (PLS) regression was used as the multivariate machine learning algorithm. PLS β-weights of radiomics features included the 5% features with the largest β-weights in magnitude (top 5%). Leave-one-out nested cross-validation (nCV) was used to achieve hyperparameter optimization and evaluate the generalizable performance of the procedure. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The exploratory analysis for the complete dataset revealed an average absolute correlation among features of 0.51. The nCV framework delivered an AUC of 0.76 (p = 1.1∙10). When combining "early" and "peak" DCE images of only T or TST, a tendency toward statistical significance was obtained for TST with an AUC of 0.61 (p = 0.05). The 47 features included in the top 5% were balanced between T and TST (23 and 24, respectively). Moreover, 33/47 (70%) were texture-related, and 25/47 (53%) were derived from high-resolution images (1 mm). A radiomics-based machine learning approach shows the potential to accurately predict the recurrence risk in early ER + /HER2 - breast cancer patients.
Oncotype DX 复发评分（RS）已在 ER+/HER2-浸润性乳腺癌患者中得到验证，可用于估计患者的复发风险并指导辅助化疗的应用。我们研究了从肿瘤和肿瘤周围组织中提取的基于 MRI 的放射组学特征在预测肿瘤复发风险方面的作用。共纳入 62 例经活检证实为 ER+/HER2-乳腺癌且接受术前 MRI 和 Oncotype DX 检查的患者。RS＞25 被认为是肿瘤复发低-中危和高危的区分因素。两位读者分别对每个肿瘤进行分割。从肿瘤和肿瘤周围组织中提取放射组学特征。采用偏最小二乘法（PLS）回归作为多元机器学习算法。PLS β-权重包括幅度最大的 5%特征（前 5%）。采用留一法嵌套交叉验证（nCV）实现超参数优化，并评估该方法的泛化性能。通过接受者操作特征（ROC）分析评估放射组学模型的诊断性能。选择 5%的零假设概率阈值（p＜0.05）。对完整数据集的探索性分析显示特征之间的平均绝对相关性为 0.51。nCV 框架得到的 AUC 为 0.76（p=1.1×10-6）。当仅结合 T 或 TST 的“早期”和“峰值”DCE 图像时，TST 的 AUC 获得了统计意义上的趋势，为 0.61（p=0.05）。前 5%包括的 47 个特征在 T 和 TST 之间平衡（分别为 23 和 24 个）。此外，33/47（70%）与纹理相关，25/47（53%）来自高分辨率图像（1mm）。基于放射组学的机器学习方法显示出准确预测早期 ER+/HER2-乳腺癌患者复发风险的潜力。
