Abstract：
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: The combined nomogram incorporating the GPTV_habitats and identified MR morphological characteristics can effectively predict LVI in patients with IBC.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: The combined nomogram incorporating the GPTV_habitats and identified MR morphological characteristics can effectively predict LVI in patients with IBC.
摘要：
目的：准确评估浸润性乳腺癌（IBC）的淋巴管浸润（LVI）在定制个性化治疗计划中起着关键作用。这项研究旨在研究基于栖息地的空间分布，以定量测量多参数磁共振成像（MRI）扫描中的肿瘤异质性，并评估其对IBC患者LVI的预测能力。
方法：在这项回顾性队列研究中，我们连续招募了241名在2020年7月至2023年7月之间诊断为IBC的女性，他们有1.5T/T1加权图像，脂肪抑制的T2加权图像，和动态对比增强MRI。基于生境的空间分布来自大体肿瘤体积（GTV）和大体肿瘤体积加上肿瘤周围体积（GPTV）。通过子区域分割生成GTV_栖息地和GPTV_栖息地，并对他们的表现进行了比较。随后，通过将相关的空间分布与已识别的MR形态学特征相结合，形成了组合列线图。使用接收器工作特性曲线分析和决策曲线分析比较了诊断性能。统计学显著性设定为p<0.05。
结果：与GTV_生境相比，GPTV_生境表现出优异的性能。因此,GPTV_栖息地，弥散加权成像边缘征象，和瘤周水肿被整合以制定组合列线图。此组合列线图优于单个MR形态特征和GPTV_栖息地指数，曲线下面积值为0.903(0.847-0.959)，0.770（0.689-0.852），和0.843（0.776-0.910）的训练集和0.931（0.863-0.999），0.747（0.613-0.880），和0.849(0.759-0.938)在验证集中。
结论：结合GPTV_生境和确定的MR形态学特征的组合列线图可以有效预测IBC患者的LVI。
方法：在这项回顾性队列研究中，我们连续招募了241名在2020年7月至2023年7月之间诊断为IBC的女性，他们有1.5T/T1加权图像，脂肪抑制的T2加权图像，和动态对比增强MRI。基于生境的空间分布来自大体肿瘤体积（GTV）和大体肿瘤体积加上肿瘤周围体积（GPTV）。通过子区域分割生成GTV_栖息地和GPTV_栖息地，并对他们的表现进行了比较。随后，通过将相关的空间分布与已识别的MR形态学特征相结合，形成了组合列线图。使用接收器工作特性曲线分析和决策曲线分析比较了诊断性能。统计学显著性设定为p<0.05。
结果：与GTV_生境相比，GPTV_生境表现出优异的性能。因此,GPTV_栖息地，弥散加权成像边缘征象，和瘤周水肿被整合以制定组合列线图。此组合列线图优于单个MR形态特征和GPTV_栖息地指数，曲线下面积值为0.903(0.847-0.959)，0.770（0.689-0.852），和0.843（0.776-0.910）的训练集和0.931（0.863-0.999），0.747（0.613-0.880），和0.849(0.759-0.938)在验证集中。
结论：结合GPTV_生境和确定的MR形态学特征的组合列线图可以有效预测IBC患者的LVI。
