Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Br J Radiol. 2022 May 1;95(1133):20210598. doi: 10.1259/bjr.20210598. Epub 2022 Feb 9.
This study aimed to develop a radiomics nomogram that incorporates radiomics, conventional ultrasound (US) and clinical features in order to differentiate triple-negative breast cancer (TNBC) from fibroadenoma.
A total of 182 pathology-proven fibroadenomas and 178 pathology-proven TNBCs, which underwent preoperative US examination, were involved and randomly divided into training ( = 253) and validation cohorts ( = 107). The radiomics features were extracted from the regions of interest of all lesions, which were delineated on the basis of preoperative US examination. The least absolute shrinkage and selection operator model and the maximum relevance minimum redundancy algorithm were established for the selection of tumor status-related features and construction of radiomics signature (Rad-score). Then, multivariate logistic regression analyses were utilized to develop a radiomics model by incorporating the radiomics signature and clinical findings. Finally, the usefulness of the combined nomogram was assessed by using the receiver operator characteristic curve, calibration curve, and decision curve analysis (DCA).
The radiomics signature, composed of 12 selected features, achieved good diagnostic performance. The nomogram incorporated with radiomics signature and clinical data showed favorable diagnostic efficacy in the training cohort (AUC 0.986, 95% CI, 0.975-0.997) and validation cohort (AUC 0.977, 95% CI, 0.953-1.000). The radiomics nomogram outperformed the Rad-score and clinical models ( < 0.05). The calibration curve and DCA demonstrated the good clinical utility of the combined radiomics nomogram.
The radiomics signature is a potential predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models.
Recent advances in radiomics-based US are increasingly showing potential for improved diagnosis, assessment of therapeutic response and disease prediction in oncology. Rad-score is an independent predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models.
本研究旨在开发一种放射组学列线图，将放射组学、常规超声（US）和临床特征相结合，以区分三阴性乳腺癌（TNBC）和纤维腺瘤。
共纳入 182 例经病理证实的纤维腺瘤和 178 例经病理证实的 TNBC，均行术前 US 检查，并随机分为训练集（n = 253）和验证集（n = 107）。从所有病变的感兴趣区域提取放射组学特征，这些特征是基于术前 US 检查进行描绘的。采用最小绝对收缩和选择算子模型和最大相关性最小冗余算法选择与肿瘤状态相关的特征，并构建放射组学特征（Rad-score）。然后，利用多变量逻辑回归分析，结合放射组学特征和临床发现，建立放射组学模型。最后，采用受试者工作特征曲线、校准曲线和决策曲线分析（DCA）评估联合列线图的实用性。
由 12 个选定特征组成的放射组学特征具有良好的诊断性能。纳入放射组学特征和临床数据的列线图在训练集（AUC 0.986，95%CI，0.975-0.997）和验证集（AUC 0.977，95%CI，0.953-1.000）中表现出良好的诊断效能。放射组学列线图优于 Rad-score 和临床模型（<0.05）。校准曲线和 DCA 表明联合放射组学列线图具有良好的临床实用性。
放射组学特征是区分 TNBC 和纤维腺瘤的潜在预测指标。联合 Rad-score、US 常规特征和临床数据的放射组学列线图优于 Rad-score 和临床模型。
基于放射组学的 US 新技术的进展越来越多地显示出在肿瘤学中改善诊断、评估治疗反应和疾病预测的潜力。Rad-score 是区分 TNBC 和纤维腺瘤的独立预测指标。联合 Rad-score、US 常规特征和临床数据的放射组学列线图优于 Rad-score 和临床模型。
