Xiong Lang, Chen Haolin, Tang Xiaofeng, Chen Biyun, Jiang Xinhua, Liu Lizhi, Feng Yanqiu, Liu Longzhong, Li Li
Department of Medical Imaging, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Guangzhou, China.
School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Front Oncol. 2021 Apr 29;11:621993. doi: 10.3389/fonc.2021.621993. eCollection 2021.
Accurate prediction of recurrence is crucial for personalized treatment in breast cancer, and whether the radiomics features of ultrasound (US) could be used to predict recurrence of breast cancer is still uncertain. Here, we developed a radiomics signature based on preoperative US to predict disease-free survival (DFS) in patients with invasive breast cancer and assess its additional value to the clinicopathological predictors for individualized DFS prediction.
We identified 620 patients with invasive breast cancer and randomly divided them into the training (n = 372) and validation (n = 248) cohorts. A radiomics signature was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression in the training cohort and validated in the validation cohort. Univariate and multivariate Cox proportional hazards model and Kaplan-Meier survival analysis were used to determine the association of the radiomics signature and clinicopathological variables with DFS. To evaluate the additional value of the radiomics signature for DFS prediction, a radiomics nomogram combining the radiomics signature and clinicopathological predictors was constructed and assessed in terms of discrimination, calibration, reclassification, and clinical usefulness.
The radiomics signature was significantly associated with DFS, independent of the clinicopathological predictors. The radiomics nomogram performed better than the clinicopathological nomogram (C-index, 0.796 0.761) and provided better calibration and positive net reclassification improvement (0.147, = 0.035) in the validation cohort. Decision curve analysis also demonstrated that the radiomics nomogram was clinically useful.
US radiomics signature is a potential imaging biomarker for risk stratification of DFS in invasive breast cancer, and US-based radiomics nomogram improved accuracy of DFS prediction.
准确预测复发对于乳腺癌的个性化治疗至关重要，而超声（US）的放射组学特征是否可用于预测乳腺癌复发仍不确定。在此，我们基于术前超声开发了一种放射组学特征，以预测浸润性乳腺癌患者的无病生存期（DFS），并评估其对临床病理预测指标在个性化DFS预测中的附加价值。
我们纳入了620例浸润性乳腺癌患者，并将他们随机分为训练组（n = 372）和验证组（n = 248）。在训练组中使用最小绝对收缩和选择算子（LASSO）Cox回归构建放射组学特征，并在验证组中进行验证。使用单因素和多因素Cox比例风险模型以及Kaplan-Meier生存分析来确定放射组学特征和临床病理变量与DFS的关联。为了评估放射组学特征对DFS预测的附加价值，构建了一个结合放射组学特征和临床病理预测指标的放射组学列线图，并从区分度、校准度、重新分类和临床实用性方面进行评估。
放射组学特征与DFS显著相关，独立于临床病理预测指标。放射组学列线图在验证组中的表现优于临床病理列线图（C指数，0.796对0.761），并提供了更好的校准度和正净重新分类改善（0.147，P = 0.035）。决策曲线分析也表明放射组学列线图具有临床实用性。
超声放射组学特征是浸润性乳腺癌DFS风险分层的潜在影像学生物标志物，基于超声的放射组学列线图提高了DFS预测的准确性。
