Acceleration of uterine 3D T2 weighted imaging by compressed SENSE--a multicenter study.

OBJECTIVES: To find the optimal acceleration factor (AF) of the compressed SENSE (CS) technique for uterine isotropic high-resolution 3D T2-weighted imaging (3D-ISO-T2WI). METHODS: A total of 91 female volunteers from the XXX, XXX and XXX were recruited. A total of 44 volunteers received uterus sagittal 3D-ISO-T2WI scans on 3.0 T MRI device with different CS AFs (including SENSE3, CS3, CS4, CS5, CS6, and CS7), 51 received 3D-ISO-T2WI scans with different degrees of fat suppression (none, light, moderate, and severe), while 4 volunteers received both series of scans. Image quality was subjectively evaluated with a three-point scoring system. Junction zone signal-to-noise ratio (SNR) and contrast to noise ratio (CNR) and myometrial SNR were also calculated. Intraclass correlation coefficients (ICC) were used to analyze the consistency of the measurement results by two observers. ANOVA test or Friedman rank sum test was used to compare the differences in subjective scores, SNR and CNR under different AFs/different degrees of fat suppression. RESULTS: Images by AFs of CS3, CS4, and CS5 had the highest SNR and CNR. Among them, CS5 had the shortest scan time. CS5 also had one of the highest subjective scores. There was no significant difference in SNR and CNR among images acquired with different degrees of fat suppression. Also, images with moderate fat suppression had the highest subjective scores. CONCLUSION: The CS5 combined with moderate fat suppression is recommended for routine female pelvic 3D-ISO-T2WI scan. ADVANCES IN KNOWLEDGE: The CS5 has the highest images quality and has the shortest scan time, which is the best AF. Moderate fat suppression has the highest subjective scores. The CS5 and moderate fat suppression are the best combination for female pelvic 3D-ISO-T2WI scan.

Multimodal MRI for Estimating Her-2 Gene Expression in Endometrial Cancer.

PURPOSE: To assess the value of multimodal MRI, including amide proton transfer-weighted imaging (APT), diffusion kurtosis imaging (DKI), and T2 mapping sequences for estimating human epidermal growth factor receptor-2 (Her-2) expression in patients with endometrial cancer (EC). METHODS: A total of 54 patients with EC who underwent multimodal pelvic MRI followed by biopsy were retrospectively selected and divided into the Her-2 positive (n = 24) and Her-2 negative (n = 30) groups. Her-2 expression was confirmed by immunohistochemistry (IHC). Two observers measured APT, mean kurtosis (MK), mean diffusivity (MD), and T2 values for EC lesions. RESULTS: The Her-2 (+) group showed higher APT values and lower MD and T2 values than the Her-2 (-) group (all p < 0.05); there was no significant difference in MK values (p > 0.05). The area under the receiver operating characteristic curve (AUC) of APT, MD, T2, APT + T2, APT + MD, T2 + MD, and APT + MD + T2 models to identify the two groups of cases were 0.824, 0.695, 0.721, 0.824, 0.858, 0.782, and 0.860, respectively, and the diagnostic efficacy after combined APT + MD + T2 value was significantly higher than those of MD and T2 values individually (p = 0.018, 0.028); the diagnostic efficacy of the combination of APT + T2 values was significantly higher than that of T2 values separately (p = 0.028). Weak negative correlations were observed between APT and T2 values (r = -0.365, p = 0.007), moderate negative correlations between APT and MD values (r = -0.560, p < 0.001), and weak positive correlations between MD and T2 values (r = 0.336, p = 0.013). The APT values were independent predictors for assessing Her-2 expression in EC patients. CONCLUSION: The APT, DKI, and T2 mapping sequences can be used to preoperatively assess the Her-2 expression in EC, which can contribute to more precise treatment for clinical preoperative.

APTw combined with mDixon-Quant imaging to distinguish the differentiation degree of cervical squamous carcinoma.

Background: To investigate the value of amide proton transfer weighted (APTw) imaging combined with modified Dixon fat quantification (mDixon-Quant) imaging in determining the degree of differentiation of cervical squamous carcinoma (CSC) against histopathologic. Methods: Magnetic resonance imaging (MRI) data were collected from 52 CSC patients. According to histopathologic results, patients were divided into the poorly differentiated group (37 cases) and the well/moderately differentiated group (15 cases). The APTw value by APTw imaging and the fat fraction (FF) and transverse relaxation rate R 2 * values by mDixon-Quant were independently measured by two radiologists. Intra-class correlation coefficients (ICCs) were used to test the consistency of APTw, FF, and R 2 * values measured by the two observers. The Mann-Whitney U test was used to analyze the difference in each parameter between the two groups. Logistic regression analysis was used to assess the association between the degree of differentiation on histopathology and imaging parameters by APTw and mDixon Quant. The ROC curve was used to evaluate the diagnostic efficacy of various parameters and their combination in distinguishing the degree of CSC differentiation on histopathology. The DeLong test was used to access the differences among the area under the ROC curves (AUCs). The Pearson correlation coefficient was used to evaluate the correlation between APTw and mDixon-Quant imaging parameters. Results: The APTw means were 2.95 ± 0.78% and 2.05 (1.85, 2.65)% in the poorly and well/moderately differentiated groups, respectively. The R 2 * values were 26.62 (21.99, 33.31)/s and 22.93 ± 6.09/s in the poorly and well/moderately differentiated groups, respectively (P < 0.05). The AUCs of APTw, R 2 * , and their combination were 0.762, 0.686, and 0.843, respectively. The Delong test suggested statistical significance between R 2 * and the combination of APTw and R 2 * . R 2 * values showed a significant correlation with APTw values in the poorly differentiated group. Conclusions: APTw combined with mDixon-Quant can be used to efficiently distinguish the differention degrees of CSC diagnosed on histopathology.

The combined application of amide proton transfer imaging and diffusion kurtosis imaging for differentiating stage Ia endometrial carcinoma and endometrial polyps.

OBJECTIVE: This study aims to explore the diagnostic performance of amide proton transfer (APT) imaging combined with diffusion kurtosis imaging (DKI) in differentiating stage Ia endometrial carcinoma (EC) and endometrial polyps (EP). METHODS: All patients were scanned with APT and DKI sequences with 3.0 T MRI before surgery. The MRI data of 32 patients with histopathologically confirmed stage Ia EC and 17 patients with EP were retrospectively analyzed. Amide proton transfer, mean kurtosis (MK) and mean diffusivity (MD) values were measured. ROC curves were employed to evaluate the efficiency of differentiating and diagnosing stage Ia EC and EP, followed by the Delong test to compare the differences between the areas under the curve (AUCs) of each parameter. Additionally, the Pearson correlation analysis was performed to assess the correlation between APT values and MK and MD. RESULTS: The measured APT, MK, and MD values of the patients with stage Ia EC were 2.609 ± 0.504%,0.641 ± 0.113 and 0.904(0.816, 1.108) µm2/ms, while those of patients with EP were1.909 ± 0.418%, 0.495 ± 0.069, and 1.650 (1.458, 1.815) µm2/ms. The AUCs of APT, MK, MD, MK + MD, and APT + MK + MD in differentiating stage Ia EC and EP were.850, 0.879, 0.893, 0.930 and 0.976, respectively. The AUCs of APT + MK + MD were significantly higher than the AUCs of APT or MK (P < 0.05). The APT value was weakly and positively correlated with the MK value (r = 0.299, P = 0.037), while the APT value was moderately and negatively correlated with the MD value (r = -0.520, P < 0.001). CONCLUSION: Both APT and DKI effectively differentiated stage Ia EC and EP; however, when combined, APT and DKI improved the ability to differentiate these diseases, boosting the value of using a combination of these modalities in clinical applications.

Amide Proton Transfer-Weighted Imaging Combined With Intravoxel Incoherent Motion for Evaluating Microsatellite Instability in Endometrial Cancer.

BACKGROUND: Microsatellite instability (MSI), caused by mismatch repair (MMR) protein defects that lead to uncorrectable mismatch bases, results in the accumulation of gene mutations and ultimately to tumors. Preoperative prediction of MSI can provide a basis for personalized and precise treatment of endometrial cancer (EC) patients. PURPOSE: To investigate amide proton transfer weighting (APTw) imaging combined with intravoxel incoherent motion (IVIM) in the assessment of MSI in EC. STUDY TYPE: Retrospective. POPULATION: A total of 71 patients with EC (12 classified as the MSI group and 22 as the microsatellite stabilization [MSS] group after entering and leaving the group standard). FIELD STRENGTH/SEQUENCE: A 3.0 T/IVIM, diffusion-weighted imaging (DWI) and APTw. ASSESSMENT: Amide proton transfer (APT) value, apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo diffusion coefficient (D*), and perfusion fraction (f) were calculated and compared between MSI and MSS groups. STATISTICAL TESTS: The Kendall's W test; Mann-Whitney U-test; Chi-square test or Fisher's exact test; logistic regression analysis; Area under the receiver operating characteristic (ROC) curve (AUC); The Delong test; Pearson or Spearman correlation coefficients. The significance threshold was set at P < 0.05. RESULTS: APT and D* values of the MSI group were significantly higher than those of the MSS group. While ADC, D, and f values in the MSI group were significantly lower than those in the MSS group. The multivariate analysis revealed that only APT and D* values were independent predictors to evaluate the MSI status. And the ROC curves indicated that the combination of APT and D* values could distinguish the MSI status of EC with the highest diagnostic efficacy (AUC = 0.973), even without significant difference to those by APT (AUC = 0.894) or D* (AUC = 0.920) value separately (P = 0.149 and 0.078, respectively). CONCLUSION: Combination of APTw and IVIM imaging may serve as an effective noninvasive method for clinical assessment of MSI in EC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Multi-parametric MRI-based radiomics for preoperative prediction of multiple biological characteristics in endometrial cancer.

Purpose: To develop and validate multi-parametric MRI (MP-MRI)-based radiomics models for the prediction of biological characteristics in endometrial cancer (EC). Methods: A total of 292 patients with EC were divided into LVSI (n = 208), DMI (n = 292), MSI (n = 95), and Her-2 (n = 198) subsets. Total 2316 radiomics features were extracted from MP-MRI (T2WI, DWI, and ADC) images, and clinical factors (age, FIGO stage, differentiation degree, pathological type, menopausal state, and irregular vaginal bleeding) were included. Intra-class correlation coefficient (ICC), spearman's rank correlation test, univariate logistic regression, and least absolute shrinkage and selection operator (LASSO) were used to select radiomics features; univariate and multivariate logistic regression were used to identify clinical independent risk factors. Five classifiers were applied (logistic regression, random forest, decision tree, K-nearest neighbor, and Bayes) to construct radiomics models for predicting biological characteristics. The clinical model was built based on the clinical independent risk factors. The combined model incorporating the radiomics score (radscore) and the clinical independent risk factors was constructed. The model was evaluated by ROC curve, calibration curve (H-L test), and decision curve analysis (DCA). Results: In the training cohort, the RF radiomics model performed best among the five classifiers for the three subsets (MSI, LVSI, and DMI) according to AUC values (AUCMSI: 0.844; AUCLVSI: 0.952; AUCDMI: 0.840) except for Her-2 subset (Decision tree: AUC=0.714), and the combined model had higher AUC than the clinical model in each subset (MSI: AUCcombined =0.907, AUCclinical =0.755; LVSI: AUCcombined =0.959, AUCclinical =0.835; DMI: AUCcombined = 0.883, AUCclinical =0.796; Her-2: AUCcombined =0.812, AUCclinical =0.717; all P<0.05). Nevertheless, in the validation cohort, significant differences between the two models (combined vs. clinical model) were found only in the DMI and LVSI subsets (DMI: AUCcombined =0.803, AUCclinical =0.698; LVSI: AUCcombined =0.926, AUCclinical =0.796; all P<0.05). Conclusion: The radiomics analysis based on MP-MRI and clinical independent risk factors can potentially predict multiple biological features of EC, including DMI, LVSI, MSI, and Her-2, and provide valuable guidance for clinical decision-making.

Co-Expression of Pig IL-2 and Fusion Bovine Cathelicidin Gene by Recombinant Plasmids in Yeast and Their Promotion of Mouse Antibacterial Defense.

In order to develop an effective and safe immunomodulator to enhance the antimicrobial bioactivity and immunity of animals against infectious bacterial diseases, a recombinant plasmid pGAPZαA-IL2-B co-expressing pig interleukin-2 (PIL-2) and fused bovine cathelicidin (FBC) genes were constructed using the 2A self-cleavage technique. After being expressed in Pichia pastoris strain SMD1168, the recombinant yeast was administered orally to 5-week-old female ICR mice. The control mice were similarly dosed with P. pastoris with a blank plasmid or FBC recombinant plasmid alone. At 28 days post-treatment, the mice were challenged intraperitoneally with virulent strains of either E. coli or S. aureus. Compared with the control groups, the mice that received recombinant yeast co-expressing PIL-2/FBC manifested significant increases in the number of leukocytes, CD4+ and CD8+ T cells, IgG, and the gene expressions of TLRs(TLR1,4,6,9), antimicrobial peptides(CRP4 and CRAMP) and cytokines (IL-2, 4, 6, 7, 12, 15, 23, IFN-γ, and TNF-α) in the blood. Furthermore, the treated mice displayed significantly higher survival than the other two control groups after the challenge. These results suggest that the antimicrobial activity and immunity of animals can be effectively enhanced by the in vivo co-expression of IL-2 and the FBS gene, which can facilitate the development of new immunopotentiation molecules to overcome the infection of antibiotic-resistant bacteria.

Amide proton transfer weighted imaging combined with dynamic contrast-enhanced MRI in predicting lymphovascular space invasion and deep stromal invasion of IB1-IIA1 cervical cancer.

Objectives: To investigate the value of amide proton transfer weighted (APTw) imaging combined with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting intermediate-risk factors of deep stromal invasion (DSI) and lymphovascular vascular space invasion (LVSI) in cervical cancer. Methods: Seventy patients with cervical cancer who underwent MRI before operation from July 2019 to February 2022 were retrospectively included in this study. Clinical information including age, histologic subtype etc. were recorded for patients. ATPw imaging parameter APTmean and DCE-MRI parameters Ktrans, Kep and Ve were measured and analyzed. The independent-sample t-test, Mann-Whitney U test, or Chi-square test was used to compare the differences of parameters between DSI/LVSI positive and negative groups. Logistic analysis was used to develop a combined predictive model. The receiver operating characteristic curve was for predictive performance. ANOVA and Kruskal-Wallis test were used to compare the differences of consecutive parameters among multiple groups. Results: Ktrans and SCC-Ag were independent factors in predicting DSI; Ktrans+SCC-Ag had the highest AUC 0.819 with sensitivity and specificity of 71.74% and 91.67%, respectively. APTmean and Ktrans were independent factors in predicting LVSI; APTmean+Ktrans had the highest AUC 0.874 with sensitivity and specificity of 92.86% and 75.00%, respectively. Ktrans and Ve could discriminate coexistence of DSI and LVSI from presence of single one, APTmean could discriminate the presence of DSI or LVSI from no risk factor presence. Conclusion: The combination of APTw and DCE-MRI is valuable in predicting intermediate-risk factors of DSI and LVSI in cervical cancer.