Magnetic resonance fingerprinting for preoperative differentiation between gonadotroph and non-gonadotroph pituitary macroadenomas.

OBJECTIVES: To use magnetic resonance fingerprinting (MRF)-derived T1 and T2 values to differentiate gonadotroph from non-gonadotroph pituitary macroadenomas based on the 2017 World Health Organization classification of pituitary adenomas. METHODS: A total of 57 patients with suspected pituitary macroadenomas were enrolled for analyses in this study between May 2018 and January 2020. Conventional magnetic resonance imaging (MRI) and MRF were performed in all patients before surgery using a 3-T MRI scanner. MRF-derived T1 and T2 values were compared between the gonadotroph and non-gonadotroph pituitary macroadenomas using a Mann-Whitney U test. The Knosp classification was used to evaluate cavernous sinus invasion by the adenomas. Receiver operating characteristic analyses were used to determine the diagnostic performance of T1 and T2 values. RESULTS: Quantitative T1 and T2 values yielded from MRF of gonadotroph pituitary macroadenomas were significantly higher than those of the non-gonadotroph pituitary macroadenomas (p < 0.001 and = 0.002, respectively). The AUC for the T2 value (0.888) was significantly greater than that for the T1 value (0.742) (p = 0.034). The AUC for combined T1 and T2 values was 0.885. Non-gonadotroph pituitary macroadenomas were more likely to invade the cavernous sinus than gonadotroph pituitary macroadenomas (55% vs 26%, p = 0.026). CONCLUSIONS: MRF may help to preoperatively differentiate between gonadotroph and non-gonadotroph pituitary macroadenomas and may be useful in guiding the treatment of these adenomas. KEY POINTS: • Somatostatin receptor type 3 is the most abundant receptor subtype in gonadotroph pituitary adenomas. • Magnetic resonance fingerprinting may help to preoperatively differentiate between gonadotroph and non-gonadotroph pituitary macroadenomas. • Magnetic resonance fingerprinting shows potential for guiding the treatment of pituitary macroadenomas.

Evaluation of Glypican-3 Expression in Hepatocellular Carcinoma by Using IDEAL IQ Magnetic Resonance Imaging.

RATIONALE AND OBJECTIVES: To investigate the value of iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL IQ) and gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for evaluating Glypican-3 (GPC3) expression in hepatocellular carcinoma (HCC). MATERIALS AND METHODS: Seventy-six patients with histopathologic diagnosis of HCC were retrospectively included in this study. In all patients IDEAL IQ and Gd-EOB-DTPA-enhanced MRI were performed preoperatively using a 3 T MRI system. For an identical slice through the liver of each patient a region of interest was drawn on the tumor in the hepatobiliary phase image and copied to the R2* map and fat fraction map produced by IDEAL IQ. A Mann-Whitney U test was used to compare the region of interest values of R2*, fat fraction and uptake of Gd-EOB-DTPA values between patients with positive and negative GPC3 expression HCC. Receiver operating characteristic analysis was used to determine the diagnostic performances of each of the MRI parameters in evaluating GPC3 expression and histological grade in HCC. RESULTS: R2* value was significantly higher in cases of positive than negative GPC3 expression HCCs (p < 0.001), whereas there were no significant differences in fat fraction and uptake of Gd-EOB-DTPA between the 2 groups (both p > 0.05). R2* value had higher areas under receiver operating characteristic (0.881), sensitivity (85.96%), and specificity (84.21%) compared to the fat fraction and uptake of Gd-EOB-DTPA. CONCLUSION: R2* value yielded from IDEAL IQ could reliably predict GPC3 expression in HCC prior to surgery.

Evaluating the HER-2 status of breast cancer using mammography radiomics features.

PURPOSE: The aim of our study was to evaluate the HER-2 status in breast cancer patients using mammography (MG) radiomics features. METHODS: A total of 306 Chinese female patients with invasive ductal carcinoma of no special type (IDC-NST) enrolled from January 2013 to July 2018 were divided into a training set (n = 244) and a testing set (n = 62). One hundred and eighty-six radiomics features were extracted from digital MG images based on the training set. The least absolute shrinkage and selection operator (LASSO) method was used to select the optimal predictive features for HER-2 status from the training set. Both support vector machine (SVM) and logistic regression models were employed based on the selected features. The area under the receiver operating characteristic (ROC) curves (AUCs) of the training set and testing set were used to evaluate the predictive performance of the models. RESULTS: Compared with the SVM model, the performance of the logistic regression model using a combination of cranial caudal (CC) and mediolateral oblique (MLO) MG views was optimal. In the training set, the sensitivity, specificity, accuracy and area under the curve (AUC) values of the logistic regression model for evaluating HER-2 status based on quantitative radiomics features were 87.29%, 58.73%, 80.00% and 0.846 (95% confidence interval (CI), 0.800-0.887), respectively, and in the testing set, the values were 73.91%, 68.75%, 77.00% and 0.787 (95% CI, 0.673-0.885), respectively. CONCLUSIONS: Radiomics features could be an efficient tool for the preoperative evaluation of HER-2 status in patients with breast cancer.

SIRT1 promotes proliferation, migration, and invasion of breast cancer cell line MCF-7 by upregulating DNA polymerase delta1 (POLD1).

Sirtuin 1 (SIRT1), class III histone deacetylase, plays an important character in cell proliferation, cell cycle, apoptosis, energy metabolism and DNA repair. In recent years, researchers have attached increasing attention on the role of SIRT1 in tumorigenesis, development and drug resistance. The effect of SIRT1 on breast cancer is still controversial and its exact role remains to be elucidated. In the present study, we investigated the significant role of SIRT1 in breast cancer by exploring the effect of SIRT1 on DNA polymerase delta1 (POLD1), the gene coding for DNA polymerase δ catalytic subunit p125. Immunohistochemistry showed that the protein expression level of SIRT1 was higher in breast cancer tissues relative to adjacent normal tissues. Knockdown of SIRT1 by shRNA decreased the proliferation, migration, and invasion of human breast cancer cell line MCF-7, while the overexpression of SIRT1 promoted the proliferation, migration, and invasion of MCF-7 cells. Clinically, the immunohistochemistry results revealed that the expression of SIRT1 was positively correlated with p125. Further analysis demonstrated that silencing of SIRT1 increased the expression of p53, while the expression level of POLD1/p125 decreased, and the result by overexpressing SIRT1 was opposite. Collectively, these data suggest that SIRT1 is an oncogenic factor in breast cancer cells and can be involved in the progression of breast cancer by inhibiting p53 and activating POLD1. Our finding provides new insights into the mechanisms of breast cancer.