Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Shushan District, Hefei, 230022, Anhui, China.
Department of Ultrasound, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University Nan Chong), Sichuan, China.
Radiol Med. 2023 Oct;128(10):1206-1216. doi: 10.1007/s11547-023-01694-7. Epub 2023 Aug 19.
To construct a nomogram based on sonogram features and radiomics features to differentiate granulomatous lobular mastitis (GLM) from invasive breast cancer (IBC).
A retrospective collection of 213 GLMs and 472 IBCs from three centers was divided into a training set, an internal validation set, and an external validation set. A radiomics model was built based on radiomics features, and the RAD score of the lesion was calculated. The sonogram radiomics model was constructed using ultrasound features and RAD scores. Finally, the diagnostic efficacy of the three sonographers with different levels of experience before and after combining the RAD score was assessed in the external validation set.
The RAD score, lesion diameter, orientation, echogenicity, and tubular extension showed significant differences in GLM and IBC (p < 0.05). The sonogram radiomics model based on these factors achieved optimal performance, and its area under the curve (AUC) was 0.907, 0.872, and 0.888 in the training, internal, and external validation sets, respectively. The AUCs before and after combining the RAD scores were 0.714, 0.750, and 0.830 and 0.834, 0.853, and 0.878, respectively, for sonographers with different levels of experience. The diagnostic efficacy was comparable for all sonographers when combined with the RAD score (p > 0.05).
Radiomics features effectively enhance the ability of sonographers to discriminate between GLM and IBC and reduce interobserver variation. The nomogram combining ultrasound features and radiomics features show promising diagnostic efficacy and can be used to identify GLM and IBC in a noninvasive approach.
构建基于超声特征和放射组学特征的列线图，以区分肉芽肿性小叶乳腺炎（GLM）和浸润性乳腺癌（IBC）。
回顾性收集来自三个中心的 213 例 GLM 和 472 例 IBC，分为训练集、内部验证集和外部验证集。基于放射组学特征构建放射组学模型，并计算病变的 RAD 评分。利用超声特征和 RAD 评分构建超声放射组学模型。最后，在外部验证集中评估不同经验水平的三位超声医师在结合 RAD 评分前后的诊断效能。
GLM 和 IBC 之间的 RAD 评分、病变直径、方位、回声特征和管状延伸存在显著差异（p<0.05）。基于这些因素的超声放射组学模型表现最佳，其在训练集、内部验证集和外部验证集中的曲线下面积（AUC）分别为 0.907、0.872 和 0.888。结合 RAD 评分前后的 AUC 分别为 0.714、0.750 和 0.830，以及 0.834、0.853 和 0.878，对于不同经验水平的超声医师而言。当结合 RAD 评分后，所有超声医师的诊断效能相当（p>0.05）。
放射组学特征可有效增强超声医师区分 GLM 和 IBC 的能力，减少观察者间差异。结合超声特征和放射组学特征的列线图具有良好的诊断效能，可用于非侵入性方法识别 GLM 和 IBC。
