Whole-tumor histogram models based on quantitative maps from synthetic MRI for predicting axillary lymph node status in invasive ductal breast cancer

医学 乳腺癌 直方图 接收机工作特性 淋巴结 逻辑回归 乳房磁振造影 核医学 放射科 癌症 内科学 人工智能 乳腺摄影术 计算机科学 图像(数学)
作者
Fang Zeng,Zheting Yang,Xiaoxue Tang,Lin Lin,Hailong Lin,Yue Wu,Zongmeng Wang,Minyan Chen,Lili Chen,Lihong Chen,Pu‐Yeh Wu,Chuang Wang,Yunjing Xue
出处
期刊:European Journal of Radiology [Elsevier BV]
卷期号:172: 111325-111325
标识
DOI:10.1016/j.ejrad.2024.111325
摘要

Abstract

Purpose

To investigate the potential of using histogram analysis of synthetic MRI (SyMRI) images before and after contrast enhancement to predict axillary lymph node (ALN) status in patients with invasive ductal carcinoma (IDC).

Methods

From January 2022 to October 2022, a total of 212 patients with IDC underwent breast MRI examination including SyMRI. Standard T2 weight images, DCE-MRI and quantitative maps of SyMRI were obtained. 13 features of the entire tumor were extracted from these quantitative maps, standard T2 weight images and DCE-MRI. Statistical analyses, including Student's t-test, Mann-Whiney U test, logistic regression, and receiver operating characteristic (ROC) curves, were used to evaluate the data. The mean values of SyMRI quantitative parameters derived from the conventional 2D region of interest (ROI) were also evaluated.

Results

The combined model based on T1-Gd quantitative map (energy, minimum, and variance) and clinical features (age and multifocality) achieved the best diagnostic performance in the prediction of ALN between N0 (with non-metastatic ALN) and N+ group (metastatic ALN ≥ 1) with the AUC of 0.879. Among individual quantitative maps and standard sequence-derived models, the synthetic T1-Gd model showed the best performance for the prediction of ALN between N0 and N+ groups (AUC = 0.823). Synthetic T2_entropy and PD-Gd_energy were useful for distinguishing N1 group (metastatic ALN ≥ 1 and ≤ 3) from the N2-3 group (metastatic ALN > 3) with an AUC of 0.722.

Conclusions

Whole-tumor histogram features derived from quantitative parameters of SyMRI can serve as a complementary noninvasive method for preoperatively predicting ALN metastases.
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