Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Department of Geratology, Hubei Provincial Hospital of Integrated Chinese and Western medicine, 11 Lingjiaohu Avenue, Wuhan, 430015, China.
Eur J Radiol. 2021 Aug;141:109781. doi: 10.1016/j.ejrad.2021.109781. Epub 2021 May 18.
To develop a nomogram incorporating B-mode ultrasound (BMUS) and shear-wave elastography (SWE) radiomics to predict malignant status of breast lesions seen on US non-invasively.
Data on 278 consecutive patients from Hospital #1 (training cohort) and 123 cases from Hospital #2 (external validation cohort) referred for breast US with subsequent histopathologic analysis between May 2017 and October 2019 were retrospectively collected. Using their BMUS and SWE images, we built a radiomics nomogram to improve radiology workflow for management of breast lesions. The performance of the algorithm was compared with a consensus of three ACR BI-RADS committee experts and four individual radiologists, all of whom interpreted breast US images in clinical practice.
Twelve features from BMUS and three from SWE were selected finally to construct the respective radiomic signature. The nomogram based on the dual-modal US radiomics achieved good diagnostic performance in the training (AUC 0.96; 95% confidence intervals [CI], 0.94-0.98) and the validation set (AUC 0.92; 95% CI, 0.87-0.97). For the 123 test lesions, the algorithm achieved 105 of 123 (85%) accuracy, comparable to the expert consensus (104 of 123 [85%], P = 0.86) and four individual radiologists (93, 99, 95 and 97 of 123, with P value of 0.05, 0.31, 0.10 and 0.18 respectively). Furthermore, the model also performed well in the BI-RADS 4 and 5 categories.
Performance of a dual-model US radiomics nomogram based on SWE for breast lesion classification may comparable to that of expert radiologists who used ACR BI-RADS guideline.
开发一个纳入 B 型超声（BMUS）和剪切波弹性成像（SWE）放射组学的列线图，以无创性预测 US 所见乳腺病变的恶性状态。
回顾性收集了 2017 年 5 月至 2019 年 10 月期间因乳腺超声检查后进行组织病理学分析而转至 1 号医院（训练队列）的 278 例连续患者和 123 例 2 号医院（外部验证队列）患者的数据。使用他们的 BMUS 和 SWE 图像，我们构建了一个放射组学列线图，以改善乳腺病变管理的放射科工作流程。该算法的性能与 ACR BI-RADS 委员会三位专家和四位放射科医生的共识进行了比较，所有这些专家和医生在临床实践中都对乳腺超声图像进行了解释。
最终从 BMUS 中选择了 12 个特征，从 SWE 中选择了 3 个特征来构建各自的放射组学特征。基于双模态 US 放射组学的列线图在训练集（AUC 0.96；95%置信区间 [CI]，0.94-0.98）和验证集（AUC 0.92；95%CI，0.87-0.97）中均具有良好的诊断性能。对于 123 个测试病变，该算法的准确率为 123 个中的 105 个（85%），与专家共识（123 个中的 104 个 [85%]，P=0.86）和四位个别放射科医生（93、99、95 和 97 个中的 123 个，P 值分别为 0.05、0.31、0.10 和 0.18）相当。此外，该模型在 BI-RADS 4 和 5 类别中也表现良好。
基于 SWE 的乳腺病变分类的双模式 US 放射组学列线图的性能可与使用 ACR BI-RADS 指南的专家放射科医生相媲美。
