Department of Ultrasonography, Peking University Shenzhen Hospital, Shenzhen, China.
Department of Ultrasonography, Peking University Shenzhen Hospital, Shenzhen, China.
Ultrasound Med Biol. 2023 Dec;49(12):2459-2468. doi: 10.1016/j.ultrasmedbio.2023.08.005. Epub 2023 Sep 11.
Ultrasonography (US) is the primary imaging method for soft tissue tumors (STTs), the diagnostic performance of which still requires improvement. To achieve an accurate evaluation of STTs, we built the diagnostic nomogram for STTs using the clinical and US features of patients with STTs.
A total of 613 patients with 195 malignant and 418 benign STTs were retrospectively recruited. We used a blend of clinical and ultrasonic features, as well as exclusively US features, to develop two distinct diagnostic models for STTs: the clinical-US model and the US-only model, respectively. The two models were evaluated and compared by measuring their areas under the receiver operating characteristic curve (AUC), calibration, integrated discrimination improvement (IDI) and decision curve analysis. The performance of the clinical-US model was also compared with that of two radiologists.
The clinical-US model had better diagnostic performance than the model based on US imaging features alone (AUCs of the clinical-US and US-only models: 0.95 [0.93-0.97] vs. 0.89 [0.87-0.92], p < 0.001; IDI of the two models: 0.15 ± 0.03, p < 0.001). The clinical-US model was also superior to the two radiologists in diagnosing STTs (AUCs of clinical-US model and two radiologists: 0.95 [0.93-0.97] vs. 0.79 [0.75-0.82] and 0.83 [0.80-0.85], p < 0.001).
The diagnostic model based on clinical and US imaging features had high diagnostic performance in STTs, which could help identify malignant STTs for radiologists.
超声检查(US)是软组织肿瘤(STT)的主要影像学检查方法,但其诊断性能仍需提高。为了准确评估 STT,我们使用 STT 患者的临床和 US 特征构建了 STT 的诊断列线图。
回顾性纳入了 613 例 195 例恶性和 418 例良性 STT 患者。我们使用临床和超声特征以及仅超声特征来分别建立两种不同的 STT 诊断模型:临床-US 模型和仅 US 模型。通过测量受试者工作特征曲线(ROC)下面积(AUC)、校准、综合判别改善(IDI)和决策曲线分析来评估和比较这两个模型。还将临床-US 模型与两名放射科医生的表现进行了比较。
临床-US 模型的诊断性能优于仅基于 US 成像特征的模型(临床-US 和仅 US 模型的 AUC:0.95[0.93-0.97]比 0.89[0.87-0.92],p<0.001;两个模型的 IDI:0.15±0.03,p<0.001)。在诊断 STT 方面,临床-US 模型也优于两名放射科医生(临床-US 模型和两名放射科医生的 AUC:0.95[0.93-0.97]比 0.79[0.75-0.82]和 0.83[0.80-0.85],p<0.001)。
基于临床和 US 成像特征的诊断模型在 STT 中有较高的诊断性能,可帮助放射科医生识别恶性 STT。