AUTHOR=Qin Yanjin , Wu Feng , Hu Qilan , He Litong , Huo Min , Tang Caili , Yi Jingru , Zhang Huiting , Yin Ting , Ai Tao 

TITLE=Histogram analysis of multi-model high-resolution diffusion-weighted MRI in breast cancer: correlations with molecular prognostic factors and subtypes

JOURNAL=Frontiers in Oncology

VOLUME=13

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1139189

DOI=10.3389/fonc.2023.1139189

ISSN=2234-943X

ABSTRACT=<sec><title>Objective</title><p>To investigate the correlations between quantitative diffusion parameters and prognostic factors and molecular subtypes of breast cancer, based on a single fast high-resolution diffusion-weighted imaging (DWI) sequence with mono-exponential (Mono), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) models.</p></sec><sec><title>Materials and Methods</title><p>A total of 143 patients with histopathologically verified breast cancer were included in this retrospective study. The multi-model DWI-derived parameters were quantitatively measured, including Mono-ADC, IVIM-<italic>D</italic>, IVIM-<italic>D*</italic>, IVIM-<italic>f</italic>, DKI-Dapp, and DKI-Kapp. In addition, the morphologic characteristics of the lesions (shape, margin, and internal signal characteristics) were visually assessed on DWI images. Next, Kolmogorov–Smirnov test, Mann-Whitney <italic>U</italic> test, Spearman’s rank correlation, logistic regression, receiver operating characteristic (ROC) curve, and Chi-squared test were utilized for statistical evaluations.</p></sec><sec><title>Results</title><p>The histogram metrics of Mono-ADC, IVIM-<italic>D</italic>, DKI-Dapp, and DKI-Kapp were significantly different between estrogen receptor (ER)-positive <italic>vs</italic>. ER-negative groups, progesterone receptor (PR)-positive <italic>vs</italic>. PR-negative groups, Luminal <italic>vs</italic>. non-Luminal subtypes, and human epidermal receptor factor-2 (HER2)-positive <italic>vs</italic>. non-HER2-positive subtypes. The histogram metrics of Mono-ADC, DKI-Dapp, and DKI-Kapp were also significantly different between triple-negative (TN) <italic>vs</italic>. non-TN subtypes. The ROC analysis revealed that the area under the curve considerably improved when the three diffusion models were combined compared with every single model, except for distinguishing lymph node metastasis (LNM) status. For the morphologic characteristics of the tumor, the margin showed substantial differences between ER-positive and ER-negative groups.</p></sec><sec><title>Conclusions</title><p>Quantitative multi-model analysis of DWI showed improved diagnostic performance for determining the prognostic factors and molecular subtypes of breast lesions. The morphologic characteristics obtained from high-resolution DWI can be identifying ER statuses of breast cancer.</p></sec>