Quantitative Parameters of Cell Size Imaging: Correlation With Histopathological Features in Patients With Breast Cancer

Patients with breast masses and suspected breast malignancies by ultrasound / mammography were prospectively included. After routine MRI scanning, all patients underwent average cell size imaging sequence scanning, and finally underwent breast MRI enhanced scanning. Inclusion criteria of breast cancer patients: (1) breast cancer confirmed by surgery or biopsy; (2) The status of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor-2 (HER-2), Ki-67 and lymphatic vessel invasion (LVI) in breast cancer were clearly diagnosed by pathology; (3) Routine MRI, PGSE and OGSE scans were performed within 1 week before pathological examination. Exclusion criteria: (1) breast tumor patients who had received treatment before PGSE and OGSE sequence scanning; (2) Patients who underwent breast tumor puncture within 2 weeks before PGSE and OGSE sequence scanning; (3) Patients with breast masses without surgery or biopsy after PGSE and OGSE sequence scanning; (4) The breast mass was confirmed to be other diseases except breast cancer by pathological examination; (5) Due to poor image quality caused by motion artifacts or other reasons, PGSE and OGSE sequence post-processing cannot be carried out. All subjects were required to sign written informed consent.

Breast MRI data were collected using Philips ingenia DNA 3T MR scanner in the Netherlands. All subjects used standardized breast MRI scanning schemes, including T2 weighted imaging (T2WI), T1 weighted imaging (T1WI), diffusion weighted imaging (DWI), PGSE, OGSE and contrast dynamic enhancement (DCE). Three quantitative parameters of VIN, DEX and D were derived from MATLAB software. The correlation between the quantitative parameters of mean cell size imaging and pathological indexes Er, PR, HER-2, Ki-67 and LVI was evaluated by Spearman correlation analysis. The predictive factors of the quantitative parameters of mean cell size model for different pathological characteristics of breast cancer were determined by logistic regression model, The diagnostic efficacy of quantitative parameters of mean cell size model for pathological classification indexes was evaluated by subject operating characteristic (ROC) curve and area under curve (AUC).