Liao Tingting, Yang Yuting, Lin Xiaohui, Ouyang Rushan, Deng Yaohong, Ma Jie
Department of Radiology, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, Shenzhen, China.
Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.
Front Oncol. 2024 Dec 18;14:1479565. doi: 10.3389/fonc.2024.1479565. eCollection 2024.
This study aimed to develop a nomogram that combines intratumoral and peritumoral radiomics based on multi-parametric MRI for predicting the postoperative pathological upgrade of high-risk breast lesions and sparing unnecessary surgeries.
In this retrospective study, 138 patients with high-risk breast lesions (January 1, 2019, to January 1, 2023) were randomly divided into a training set (n=96) and a validation set (n=42) at a 7:3 ratio. The best-performing MRI sequence for intratumoral radiomics was selected to develop individual and combined radiomics scores (Rad-Scores). The best Rad-Score was integrated with independent clinical and radiological risk factors by a nomogram. The diagnostic performance of the nomogram was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve, along with accuracy, specificity, and sensitivity analysis.
The nomogram based on the combined intratumoral and peritumoral Rad-Score of the dynamic contrast-enhanced MRI and clinical-radiological features achieved superior diagnostic efficacy in the training (AUC=0.914) and validation set (AUC=0.867) compared to other models. It also achieved a specificity and accuracy of 85.1% and 82.3% during training and 66.7% and 76.2% during validation.
The nomogram encapsulating the combined intratumoral and peritumoral radiomics demonstrated superior diagnostic efficacy in postoperative pathological upgrades of high-risk breast lesions, enabling clinicians to make more informed decisions about interventions and follow-up strategies.
本研究旨在基于多参数磁共振成像(MRI)开发一种结合瘤内和瘤周放射组学的列线图,以预测高危乳腺病变的术后病理升级情况,并避免不必要的手术。
在这项回顾性研究中,138例高危乳腺病变患者(2019年1月1日至2023年1月1日)按7:3的比例随机分为训练集(n = 96)和验证集(n = 42)。选择瘤内放射组学表现最佳的MRI序列来制定个体和联合放射组学评分(Rad-Scores)。通过列线图将最佳Rad-Score与独立的临床和放射学危险因素相结合。使用受试者操作特征曲线的曲线下面积(AUC)以及准确性、特异性和敏感性分析来评估列线图的诊断性能。
与其他模型相比,基于动态对比增强MRI的瘤内和瘤周联合Rad-Score以及临床放射学特征的列线图在训练集(AUC = 0.914)和验证集(AUC = 0.867)中具有更高的诊断效能。在训练期间,其特异性和准确性分别达到85.1%和82.3%,在验证期间分别为66.7%和76.2%。
包含瘤内和瘤周联合放射组学的列线图在高危乳腺病变的术后病理升级中显示出卓越的诊断效能,使临床医生能够在干预措施和随访策略方面做出更明智的决策。