Kim Soo-Yeon, Woo Jungwoo, Lee Sewon, Hong Hyunsook
Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
Seoul National University College of Medicine, Seoul, Republic of Korea.
Magn Reson Imaging. 2025 Feb;116:110292. doi: 10.1016/j.mri.2024.110292. Epub 2024 Dec 3.
To investigate whether radiomic features obtained from the intratumoral and peritumoral regions of pretreatment magnetic resonance imaging (MRI) can predict progression in patients with triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy (NAC) in comparison with the previously determined clinical score.
This single-center retrospective study evaluated 224 women with TNBC who underwent NAC between 2010 and 2019. Women were randomly allocated to the training set (n = 169) for model development and the test set (n = 55) for model validation. The clinical score consisted of the histologic type, Ki-67 index, and degree of edema on T2-weighted imaging. Intratumoral and peritumoral radiomic features were extracted from T2-weighted images and the first- and last-phase images of dynamic contrast-enhanced MRI. The radiomics model was built using only radiomic features, whereas the combined model incorporated the clinical score along with radiomic features. The area under the receiver operating characteristic curve (AUC) was used to assess performance.
Progression occurred in 18 and five patients in the training and test sets, respectively. The radiomics model selected three radiomic features (two peritumoral and one intratumoral), while the combined model selected the clinical score and five radiomic features (four peritumoral and one intratumoral). Among the total radiomic features, Inverse Difference Normalized of the peritumoral region of the T2-weighted images, reflective of peritumoral heterogeneity, demonstrated the highest level of association with tumor progression. In the test set, the AUC values of the radiomics-only model, the combined model, and the clinical score were 0.592, 0.764, and 0.720, respectively. Compared to the clinical score, the radiomics-only model (0.720 vs. 0.592, p = 0.468) and the combined model (0.720 vs. 0.764, p = 0.553) did not show superior performance.
The radiomics features were not superior in predicting the progression of TNBC compared to the clinical score, although the peritumoral heterogeneity on T2-weighted images showed a potential.
研究从新辅助化疗(NAC)前的磁共振成像(MRI)肿瘤内及瘤周区域获得的影像组学特征,与先前确定的临床评分相比,能否预测三阴性乳腺癌(TNBC)患者的病情进展。
这项单中心回顾性研究评估了2010年至2019年间接受NAC的224例TNBC女性患者。将患者随机分配至用于模型开发的训练集(n = 169)和用于模型验证的测试集(n = 55)。临床评分由组织学类型、Ki-67指数和T2加权成像上的水肿程度组成。从T2加权图像以及动态对比增强MRI的第一期和最后一期图像中提取肿瘤内和瘤周的影像组学特征。仅使用影像组学特征构建影像组学模型,而联合模型则将临床评分与影像组学特征相结合。采用受试者操作特征曲线(ROC)下面积(AUC)评估模型性能。
训练集和测试集分别有18例和5例患者出现病情进展。影像组学模型选择了三个影像组学特征(两个瘤周和一个肿瘤内),而联合模型选择了临床评分和五个影像组学特征(四个瘤周和一个肿瘤内)。在所有影像组学特征中,反映瘤周异质性的T2加权图像瘤周区域的逆差归一化值与肿瘤进展的关联程度最高。在测试集中,仅影像组学模型、联合模型和临床评分的AUC值分别为0.592、0.764和0.720。与临床评分相比,仅影像组学模型(0.720对0.592,p = 0.468)和联合模型(0.720对0.764,p = 0.553)均未表现出更优的性能。
与临床评分相比,影像组学特征在预测TNBC病情进展方面并不更优,尽管T2加权图像上的瘤周异质性显示出一定潜力。