Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan province 411000, PR China (W.G., Y.Z., X.Y., L.Z.).
School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, Hunan province, PR China (X.F., Z.Z.).
Acad Radiol. 2024 Nov;31(11):4317-4328. doi: 10.1016/j.acra.2024.05.043. Epub 2024 Jun 13.
Accurate assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) plays a pivotal role in tailoring personalized treatment plans. This study aimed to investigate habitats-based spatial distributions to quantitatively measure tumor heterogeneity on multiparametric magnetic resonance imaging (MRI) scans and assess their predictive capability for LVI in patients with IBC.
In this retrospective cohort study, we consecutively enrolled 241 women diagnosed with IBC between July 2020 and July 2023 and who had 1.5 T/T1-weighted images, fat-suppressed T2-weighted images, and dynamic contrast-enhanced MRI. Habitats-based spatial distributions were derived from the gross tumor volume (GTV) and gross tumor volume plus peritumoral volume (GPTV). GTV_habitats and GPTV_habitats were generated through sub-region segmentation, and their performances were compared. Subsequently, a combined nomogram was developed by integrating relevant spatial distributions with the identified MR morphological characteristics. Diagnostic performance was compared using receiver operating characteristic curve analysis and decision curve analysis. Statistical significance was set at p < 0.05.
GPTV_habitats exhibited superior performance compared to GTV_habitats. Consequently, the GPTV_habitats, diffusion-weighted imaging rim signs, and peritumoral edema were integrated to formulate the combined nomogram. This combined nomogram outperformed individual MR morphological characteristics and the GPTV_habitats index, achieving area under the curve values of 0.903 (0.847 -0.959), 0.770 (0.689 -0.852), and 0.843 (0.776 -0.910) in the training set and 0.931 (0.863 -0.999), 0.747 (0.613 -0.880), and 0.849 (0.759 -0.938) in the validation set.
The combined nomogram incorporating the GPTV_habitats and identified MR morphological characteristics can effectively predict LVI in patients with IBC.
准确评估浸润性乳腺癌（IBC）中的脉管侵犯（LVI）对于制定个体化治疗计划至关重要。本研究旨在通过基于生境的空间分布来定量测量多参数磁共振成像（MRI）扫描中肿瘤异质性，并评估其对 IBC 患者 LVI 的预测能力。
本回顾性队列研究纳入了 2020 年 7 月至 2023 年 7 月期间连续诊断为 IBC 的 241 名女性患者，所有患者均接受了 1.5T/T1 加权成像、脂肪抑制 T2 加权成像和动态对比增强 MRI。基于生境的空间分布由大体肿瘤体积（GTV）和大体肿瘤体积加肿瘤周围体积（GPTV）衍生而来。通过亚区分割生成 GTV_habitats 和 GPTV_habitats，并比较其性能。随后，通过整合相关空间分布与识别的 MR 形态学特征，构建了一个联合列线图。采用接受者操作特征曲线分析和决策曲线分析比较诊断性能。统计显著性水平设定为 p<0.05。
GPTV_habitats 的性能优于 GTV_habitats。因此，将 GPTV_habitats、弥散加权成像边缘征象和肿瘤周围水肿纳入联合列线图。该联合列线图优于单独的 MR 形态学特征和 GPTV_habitats 指数，在训练集和验证集中的曲线下面积分别为 0.903（0.847-0.959）、0.770（0.689-0.852）和 0.843（0.776-0.910）、0.931（0.863-0.999）、0.747（0.613-0.880）和 0.849（0.759-0.938）。
纳入 GPTV_habitats 和识别的 MR 形态学特征的联合列线图可以有效地预测 IBC 患者的 LVI。
