Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, AH, P R China.
Department of Ultrasound, The Second People's Hospital of WuHu, Wuhu, AH P R China.
Acad Radiol. 2023 Aug;30(8):1628-1637. doi: 10.1016/j.acra.2022.11.002. Epub 2022 Nov 28.
To develop and validate a nomogram for predicting the risk of malignancy of breast imaging reporting and data system (BI-RADS) 4A lesions to reduce unnecessary invasive examinations.
From January 2017 to July 2021, 190 cases of 4A lesions included in this study were divided into training and validation sets in a ratio of 8:2. Radiomics features were extracted from sonograms by Automatic Breast Volume Scanner (ABVS) and B-ultrasound. We constructed the radiomics model and calculated the rad-scores. Univariate and multivariate logistic regressions were used to assess demographics and lesion elastography values (virtual touch tissue image, shear wave velocity) and to develop clinical model. A clinical radiomics model was developed using rad-score and independent clinical factors, and a nomogram was plotted. Nomogram performance was evaluated using discrimination, calibration, and clinical utility.
The nomogram included rad-score, age, and elastography, and showed good calibration. In the training set, the area under the receiver operating characteristic curve (AUC) of the clinical radiomics model (0.900, 95% confidence interval (CI): 0.843-0.958) was superior to that of the radiomics model (0.860, 95% CI: 0.799-0.921) and clinical model (0.816, 95% CI: 0.735-0.958) (p = 0.024 and 0.008, respectively). The decision curve analysis showed that the clinical radiomics model had the highest net benefit in most threshold probability ranges.
ABVS and B-ultrasound-based radiomics nomograms have satisfactory performance in differentiating benign and malignant 4A lesions. This can help clinicians make an accurate diagnosis of 4A lesions and reduce unnecessary biopsy.
开发并验证一种列线图模型，用于预测乳腺影像报告和数据系统（BI-RADS）4A 级病变的恶性风险，以减少不必要的有创检查。
本研究纳入了 2017 年 1 月至 2021 年 7 月的 190 例 4A 级病变患者，按 8:2 的比例分为训练集和验证集。通过自动乳腺容积扫描（ABVS）和 B 超提取声像图的放射组学特征。构建放射组学模型并计算 rad-score。采用单因素和多因素逻辑回归评估人口统计学和病变的超声弹性成像值（虚拟触诊组织成像、剪切波速度），并建立临床模型。使用 rad-score 和独立的临床因素构建临床放射组学模型，并绘制列线图。通过判别、校准和临床实用性评估列线图的性能。
该列线图包括 rad-score、年龄和弹性成像，且具有良好的校准度。在训练集中，临床放射组学模型的受试者工作特征曲线下面积（AUC）（0.900，95%置信区间（CI）：0.843-0.958）优于放射组学模型（0.860，95% CI：0.799-0.921）和临床模型（0.816，95% CI：0.735-0.958）（p=0.024 和 0.008）。决策曲线分析表明，在大多数阈值概率范围内，临床放射组学模型具有最高的净获益。
ABVS 和 B 超联合的放射组学列线图在区分良性和恶性 4A 级病变方面具有良好的性能。这有助于临床医生对 4A 级病变做出准确的诊断，减少不必要的活检。
