Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Department of Breast surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Br J Radiol. 2022 Dec 1;95(1140):20220626. doi: 10.1259/bjr.20220626. Epub 2022 Nov 15.
To construct a combined radiomics model based on pre-treatment ultrasound for predicting of advanced breast cancers sensitive to neoadjuvant chemotherapy (NAC).
A total of 288 eligible breast cancer patients who underwent NAC before surgery were enrolled in the retrospective study cohort. Radiomics features reflecting the phenotype of the pre-NAC tumors were extracted. With features selected using the least absolute shrinkage and selection operator (LASSO) regression, radiomics signature (Rad-score) was established based on the pre-NAC ultrasound. Then, radiomics nomogram of ultrasound (RU) was established on the basis of the best radiomic signature incorporating independent clinical features. The performance of RU was evaluated in terms of calibration curve, area under the curve (AUC), and decision curve analysis (DCA).
Nine features were selected to construct the radiomics signature in the training cohort. Combined with independent clinical characteristics, the performance of RU for identifying Grade 4-5 patients was significantly superior than the clinical model and Rad-score alone ( < 0.05, as per the Delong test), which achieved an AUC of 0.863 (95% CI, 0.814-0.963) in the training group and 0.854 (95% CI, 0.776-0.931) in the validation group. DCA showed that this model satisfactory clinical utility, suggesting its robustness as a response predictor.
This study demonstrated that RU has a potential role in predicting drug-sensitive breast cancers.
Aiming at early detection of Grade 4-5 breast cancer patients, the radiomics nomogram based on ultrasound has been approved as a promising indicator with high clinical utility. It is the first application of ultrasound-based radiomics nomogram to distinguish drug-sensitive breast cancers.
构建基于治疗前超声的联合放射组学模型，预测新辅助化疗（NAC）敏感的晚期乳腺癌。
回顾性研究共纳入 288 例接受 NAC 治疗前手术的乳腺癌患者。提取反映治疗前肿瘤表型的放射组学特征。采用最小绝对收缩和选择算子（LASSO）回归选择特征，基于治疗前超声建立放射组学特征（Rad-score）。然后，在最佳放射组学特征的基础上结合独立的临床特征，建立超声放射组学列线图（RU）。RU 的性能通过校准曲线、曲线下面积（AUC）和决策曲线分析（DCA）进行评估。
在训练队列中选择了 9 个特征来构建放射组学特征。RU 结合独立的临床特征，在识别 4-5 级患者方面的性能明显优于临床模型和 Rad-score 单独使用（<0.05，根据 Delong 检验），在训练组中的 AUC 为 0.863（95%CI，0.814-0.963），在验证组中的 AUC 为 0.854（95%CI，0.776-0.931）。DCA 表明该模型具有令人满意的临床应用价值，提示其作为反应预测因子的稳健性。
本研究表明，RU 有可能预测药物敏感型乳腺癌。
为了早期发现 4-5 级乳腺癌患者，超声基放射组学列线图已被证明是一种很有前途的高临床实用指标。这是首次应用基于超声的放射组学列线图来区分药物敏感型乳腺癌。
