Ting Chen

Background The optimal acceleration strategy for deep learning reconstruction (DLR) in cervical cancer T2-weighted imaging (T2WI) remains unestablished. Methods Forty-eight patients with primary cervical cancer prospectively underwent 3.0T pelvic MRI. Sagittal T2WI was acquired using conventional parallel imaging (Conv) and DLR at acceleration factors (AF) 2 and 3. Two radiologists evaluated quantitative metrics and six qualitative metrics. Results Compared to conventional imaging at the same acceleration, DLR significantly improved signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) (DL_AF2 vs. Conv_AF2, adjusted p = 0.005; DL_AF3 vs. Conv_AF3, adjusted p < 0.001). No significant quantitative differences were found between AF2 and AF3 for either reconstruction method. Despite statistically comparable objective metrics between DL_AF2 and DL_AF3, DL_AF2 received significantly higher subjective scores across all six qualitative dimensions (all adjusted p < 0.001), with median scores of 5 for all image quality metrics. DL_AF3 demonstrated significantly lower diagnostic confidence scores, suggesting perceptual over-smoothing at higher acceleration. Conclusion At equivalent scan times, DLR with moderate acceleration (AF 2) achieves the optimal balance between objective image quality and subjective anatomical fidelity for sagittal T2WI in cervical cancer evaluation.

Tumor-derived extracellular vesicles (TEVs) are promising autologous cancer vaccines due to their intrinsic tumor-associated antigens. However, their trans

Rationale and objectives Pregnancy-induced hypertension (PIH) is a common and serious pregnancy complication that threatens maternal safety and perinatal outcomes. We aimed to develop a nomogram model integrating intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) microstructure and microcirculation and multisequence MR texture features for predicting adverse outcomes in PIH. Materials and methods This retrospective study enrolled 63 PIH patients (37 with, 26 without adverse neonatal outcomes). Texture features were extracted from T1-, T2-weighted, and IVIM-DWI images. IVIM-DWI placental parameters included one DWI-based virtual magnetic resonance elastography (vMRE) parameter and six IVIM-based diffusion and perfusion parameters. Three predictive models were constructed: model 1 (texture score [T-score]) utilized multisequence texture features, model 2 incorporated IVIM-DWI parameters and model 3 (a nomogram model) combined T-score and IVIM-DWI parameters. Model performances were assessed via receiver operating characteristic (ROC) curves, area under the curve (AUC), decision curve analysis (DCA), and calibration. Results Texture analysis identified 12 features from three sequences for model 1, which demonstrated an AUC of 0.796. The placental shear modulus (μ_diff) from vMRE and perfusion fraction (f) from IVIM were identified as predictors of adverse neonatal outcomes in PIH patients; model 2 using these two parameters displayed an AUC of 0.785. Model 3, the nomogram model that included T-score, μ_diff, and f, exhibited enhanced predictive accuracy with an AUC of 0.844, sensitivity of 83.8%, and specificity of 73.1%. Conclusion The nomogram model constructed using placental IVIM-DWI-derived microstructural, microcirculatory, and multisequence texture features may facilitate predicting adverse neonatal outcomes in patients with PIH.

Objectives This study aimed to identify the magnetic resonance imaging (MRI)-based radiomics phenotypes of intermediate-to-high-risk endometrial cancers (ECs), explore their association with histopathologic features, and compare their prognostic ability with the International Federation of Gynecology and Obstetrics (FIGO) stage. Methods This study retrospectively recruited 355 patients with pathologically confirmed EC from 01/2016 to 06/2023. 166(46.8%) were classified as intermediate-to-high-risk ECs according to the European Society for Medical Oncology guidelines. Radiomics clustering analysis was performed on preoperative MRI to identify the radiomics phenotype of intermediate-to-high-risk ECs. The association between the radiomics phenotypes and the clinicopathologic information was explored, and the added value in predicting the recurrence was also evaluated using concordance index (C-index). Results Of the included 166 patients (average age 56.83 ± 9.25 years), 23 were recurrent patients. The corresponding tumors in various clusters were assigned to phenotypes 1 and 2. Larger tumor diameter (P < .01), cervical mucosa invasion [30(36.15%) vs 15(18.07%), P = .01], deep myometrial infiltration [51(61.45%) vs 31(37.35%), P = .00], and histologic subtype [17(20.48%) vs 5(6.02%), P = .01] were associated with subtype 1. The risk of recurrence (P = .01) was higher in phenotype 1, and the FIGO stage could further differentiate higher recurrence risk in phenotype 1 (P < .01). The C-index was 0.66 for the radiomics phenotype model, 0.69 for the FIGO stage model, and 0.72 for the combined model. Conclusions MRI-based radiomics consensus clustering enabled the identification of associations between radiomics features and histopathologic features in intermediate-to-high-risk EC. The FIGO stage could further elevate the prediction ability of recurrence risk.

Objective: It is critical to early monitor and manage small-for-gestational age (SGA) infants with truly adverse outcomes not detected by conventional methods. We aimed to explore the value of diffusion-weighted imaging (DWI)-based virtual magnetic resonance elastography (vMRE) and intravoxel incoherent motion (IVIM)-based biexponential and stretched exponential parameters in predicting adverse outcomes of SGA infants. Methods: Twenty SGA infants with adverse outcomes and forty without adverse outcomes were included in this prospective study. One DWI-based vMRE parameter [the stiffness value (μ_diff)], five IVIM–based parameters [true diffusion coefficient (D), pseudo-diffusion coefficient (D^*), perfusion fraction (f), diffusion distribution coefficient (DDC), and diffusion heterogeneity index (Alpha)] and apparent diffusion coefficient (ADC) were calculated and compared between groups. The predictive efficiency was compared by the logistic regression analysis and receiver operating characteristic curve analysis. The relationship between the μ_diff value with gestational age was also evaluated. Results: The placental μ_diff value was remarkably higher, and the f, DDC, and ADC values were considerably lower in the SGA infants with adverse outcomes compared with those without adverse outcomes. The μ_diff and f value were predictive risk factors for SGA infants with adverse outcomes. A combined predictive model (μ_diff and f) improved the predictive efficacy. Moreover, there was no statistically significant correlation between the placental stiffness value and gestational age. Conclusions: Functional MRI parameters to quantify placenta elastography and microcirculation in SGA patients. This might be a useful tool to assess placental function and a vital non-invasive supplement for predicting adverse outcomes of SGA infants. Critical relevance statement: This prospective study shows DWI-based virtual magnetic resonance elastography and intravoxel incoherent motion-based functional parameters to quantify placenta elastography and microcirculation in small-for-gestational-age patients, which could complement existing non-invasive methods for monitoring and predicting neonatal perinatal adverse outcome. Key points: • vMRE is an emerging non-invasive imaging technique for evaluating placenta stiffness. • SGA infants with adverse outcome have stiffer placental elasticity and lower microcirculation. • Risk factors combination displayed better efficacy in predicting adverse outcomes of SGA. Graphical