Improved Differential Diagnosis Based on BI-RADS Descriptors and Apparent Diffusion Coefficient for Breast Lesions: A Multiparametric MRI Analysis as Compared to Kaiser Score.

RATIONALE AND OBJECTIVES: To develop the nomogram utilizing the American College of Radiology BI-RADS descriptors, clinical features, and apparent diffusion coefficient (ADC) to differentiate benign from malignant breast lesions. MATERIALS AND METHODS: A total of 341 lesions (161 malignant and 180 benign) were included. Clinical data and imaging features were reviewed. Univariable and multivariable logistic regression analyses were performed to determine the independent variables. ADC as a continuous or classified into binary form with a cutoff value of 1.30 × 10-3 mm2/s, incorporated other independent predictors to construct two nomograms, respectively. Receiver operating curve and calibration plot was employed to test the models' discriminative ability. The diagnostic performance between the developed model and the Kaiser score (KS) was also compared. RESULTS: In both models, high patient age, the presence of root sign, time-intensity curves (TICs) types (plateau and washout), heterogenous internal enhancement, the presence of peritumoral edema, and ADC were independently associated with malignancy. The AUCs of two multivariable models (AUC, 0.957; 95% CI: 0.929-0.976 and AUC, 0.958; 95% CI: 0.931-0.976) were significantly higher than that of the KS (AUC, 0.919, 95% CI: 0.885-0.946; both P < 0.001). At the same sensitivity of 95.7%, our models showed an increase in specificity by 5.56% (P = 0.076) and 6.11% (P = 0.035), respectively, as compared to the KS. CONCLUSION: The models incorporating MRI features (root sign, TIC, margins, internal enhancement, and presence of edema), quantitative ADC value, and patient age showed improved diagnostic performance and might have avoided more unnecessary biopsies in comparison with the KS, although further external validation is required.

Evaluation of the differentiation of benign and malignant breast lesions using synthetic relaxometry and the Kaiser score.

Objective: To investigate whether there is added value of quantitative parameters from synthetic magnetic resonance imaging (SyMRI) as a complement to the Kaiser score (KS) to differentiate benign and malignant breast lesions. Materials and methods: In this single-institution study, 122 patients who underwent breast MRI from March 2020 to May 2021 were retrospectively analyzed. SyMRI and dynamic contrast-enhanced MRI were performed using a 3.0-T system. Two experienced radiologists independently assigned the KS and measured the quantitative values of T1 relaxation time (T1), T2 relaxation time (T2), and proton density (PD) from SyMRI. Pathology was regarded as the gold standard. The diagnostic values were compared using the appropriate statistical tests. Results: There were 122 lesions (86 malignant and 36 benign) in 122 women. The T1 value was identified as the only independent factor for the differentiation of malignant and benign lesions. The diagnostic accuracy of incorporating the T1 into the KS protocol (T1+KS) was 95.1% and 92.1% for all lesions (ALL) and The American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions, respectively, which was significantly higher than that of either T1 (ALL: 82.8%, P = 0.0001; BI-RADS 4: 78.9%, P = 0.002) or KS (ALL: 90.2%, P = 0.031; BI-RADS 4: 84.2%, P = 0.031) alone. The sensitivity and specificity of T1+KS were also higher than those of the T1 or KS alone. The combined diagnosis could have avoided another 15.6% biopsies compared with using KS alone. Conclusions: Incorporating T1 into the KS protocol improved both the sensitivity and specificity to differentiate benign and malignant breast lesions, thus avoiding unnecessary invasive procedures.

A Comparative Assessment of MR BI-RADS 4 Breast Lesions With Kaiser Score and Apparent Diffusion Coefficient Value.

OBJECTIVES: To investigate the diagnostic performance of the Kaiser score and apparent diffusion coefficient (ADC) to differentiate Breast Imaging Reporting and Data System (BI-RADS) Category 4 lesions at dynamic contrast-enhanced (DCE) MRI. METHODS: This was a single-institution retrospective study of patients who underwent breast MRI from March 2020 to June 2021. All image data were acquired with a 3-T MRI system. Kaiser score of each lesion was assigned by an experienced breast radiologist. Kaiser score+ was determined by combining ADC and Kaiser score. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of Kaiser score+, Kaiser score, and ADC. The area under the curve (AUC) values were calculated and compared by using the Delong test. The differences in sensitivity and specificity between different indicators were determined by the McNemar test. RESULTS: The study involved 243 women (mean age, 43.1 years; age range, 18-67 years) with 268 MR BI-RADS 4 lesions. Overall diagnostic performance for Kaiser score (AUC, 0.902) was significantly higher than for ADC (AUC, 0.81; p = 0.004). There were no significant differences in AUCs between Kaiser score and Kaiser score+ (p = 0.134). The Kaiser score was superior to ADC in avoiding unnecessary biopsies (p < 0.001). Compared with the Kaiser score alone, the specificity of Kaiser score+ increased by 7.82%, however, at the price of a lower sensitivity. CONCLUSION: For MR BI-RADS category 4 breast lesions, the Kaiser score was superior to ADC mapping regarding the potential to avoid unnecessary biopsies. However, the combination of both indicators did not significantly contribute to breast cancer diagnosis of this subgroup.

Diagnostic Value of Diffusion Tensor Imaging for Infants' Brain Development Retardation Caused by Pre-Eclampsia.

Objective: Pre-eclampsia (PE) can cause brain development delay in infants. This work aims to characterize the pattern differences of brain white matter development in premature infants under PE conditions and those without. Methods: Eighty preterm infants delivered by women with PE were selected as the PE group, and ninety-six preterm infants of the same period born to women without high-risk perinatal factors were used as control. All infants underwent diffusion tensor imaging (DTI) examination. The fractional anisotropy (FA) was measured in five regions of interests (ROIs), including posterior limbs of internal capsule (PLIC), splenium of the corpus callosum (SCC), superior frontal gyrus (SFG), superior parietal lobule (SPL), and superior occipital gyrus (SOG). The relationship between the FA values and postmenstrual age (PMA) was analyzed. Results: After adjusting for the birth weight and gestational ages, in the SCC and PLIC, the PMA and FA values showed a low-to-medium intensity positive correlation in the control group (r = 0.30, p=0.003; r = 0.53, p < 0.0001), while no positive relevance was detected in the PE group (r = 0.08, p=0.47; r = 0.19, p < 0.08). In the PE and control groups, in the SPL and SOG, the PMA and FA values showed a near-consistent positive correlation (r = 0.57, r = 0.55 vs. r = 0.31, r = 0.55; all p < 0.05). In the control group, in SFG, the PMA and FA values had a medium intensity positive correlation (r = 0.47, p < 0.0001), but there was no statistical difference in correlation in PE (r = 0.10, p=0.39). Conclusion: PE may cause lagging brain development in the SCC, PLIC, and SFG during infancy. DTI may be an effective and sensitive detection tool.

Exploration and validation of hub genes and pathways in the progression of hypoplastic left heart syndrome via weighted gene co-expression network analysis.

BACKGROUND: Despite significant progress in surgical treatment of hypoplastic left heart syndrome (HLHS), its mortality and morbidity are still high. Little is known about the molecular abnormalities of the syndrome. In this study, we aimed to probe into hub genes and key pathways in the progression of the syndrome. METHODS: Differentially expressed genes (DEGs) were identified in left ventricle (LV) or right ventricle (RV) tissues between HLHS and controls using the GSE77798 dataset. Then, weighted gene co-expression network analysis (WGCNA) was performed and key modules were constructed for HLHS. Based on the genes in the key modules, protein-protein interaction networks were conducted, and hub genes and key pathways were screened. Finally, the GSE23959 dataset was used to validate hub genes between HLHS and controls. RESULTS: We identified 88 and 41 DEGs in LV and RV tissues between HLHS and controls, respectively. DEGs in LV tissues of HLHS were distinctly involved in heart development, apoptotic signaling pathway and ECM receptor interaction. DEGs in RV tissues of HLHS were mainly enriched in BMP signaling pathway, regulation of cell development and regulation of blood pressure. A total of 16 co-expression network were constructed. Among them, black module (r = 0.79 and p value = 2e-04) and pink module (r = 0.84 and p value = 4e-05) had the most significant correlation with HLHS, indicating that the two modules could be the most relevant for HLHS progression. We identified five hub genes in the black module (including Fbn1, Itga8, Itga11, Itgb5 and Thbs2), and five hub genes (including Cblb, Ccl2, Edn1, Itgb3 and Map2k1) in the pink module for HLHS. Their abnormal expression was verified in the GSE23959 dataset. CONCLUSIONS: Our findings revealed hub genes and key pathways for HLHS through WGCNA, which could play key roles in the molecular mechanism of HLHS.

Brain Development in Infants of Mothers With Gestational Diabetes Mellitus: A Diffusion Tensor Imaging Study.

OBJECTIVE: The objective of this study was to investigate clinical neurocognitive performance and microstructural white matter (WM) alterations in infants of mothers with gestational diabetes mellitus (GDM) using diffusion tensor imaging. MATERIALS AND METHODS: Infants (corrected gestational age, 33.42-36.00 weeks) of mothers with GDM (n = 31) and gestational age- and sex-matched unexposed controls (n = 31) accomplished 3-T diffusion tensor imaging scans and neurocognitive tests. Diffusion tensor imaging measures, mainly referring to fractional anisotropy (FA) values, were compared between 2 groups, and within-group analysis of correlation between FA values and neurocognitive testing outcomes in GDM-exposed infants was conducted subsequently. RESULTS: Fractional anisotropy was significantly decreased in the splenium of corpus callosum, posterior limb of internal capsule, thalamus in infants of mothers with GDM when compared with controls (P < 0.05), reflecting microstructural WM abnormalities in the GDM group. Decreased FA was associated with worse neurocognitive performance in the exposed group (P < 0.05). CONCLUSIONS: Individuals of mothers with GDM showed microstructural WM abnormalities in different brain regions, which were significantly related to worse neurocognitive performance. This might reveal that GDM directly insults the brain development of the offspring.

Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study.

BACKGROUND: Identification of pregnancies with postpartum haemorrhage (PPH) antenatally rather than intrapartum would aid delivery planning, facilitate transfusion requirements and decrease maternal complications. MRI has been increasingly used for placenta evaluation. Here, we aim to build a nomogram incorporating both clinical and radiomic features of placenta to predict the risk for PPH in pregnancies during caesarian delivery (CD). METHODS: A total of 298 pregnant women were retrospectively enrolled from Henan Provincial People's Hospital (training cohort: n = 207) and from The Third Affiliated Hospital of Zhengzhou University (external validation cohort: n = 91). These women were suspected with placenta accreta spectrum (PAS) disorders and underwent MRI for placenta evaluation. All of them underwent CD and were singleton. PPH was defined as more than 1000 mL estimated blood loss (EBL) during CD. Radiomic features were selected based on their correlations with EBL. Radiomic, clinical, radiological, clinicoradiological and clinicoradiomic models were built to predict the risk of PPH for each patient. The model with the best prediction performance was validated with its discrimination ability, calibration curve and clinical application. FINDINGS: Thirty-five radiomic features showed strong correlation with EBL. The clinicoradiomic model resulted in the best discrimination ability for risk prediction of PPH, with AUC of 0.888 (95% CI, 0.844-0.933) and 0.832 (95% CI, 0.746-0.913), sensitivity of 91.2% (95% CI, 85.8%-96.7%) and 97.6% (95% CI, 92.7%-100%) in the training and validation cohort respectively. For patients with severe PPH (EBL more than 2000 mL), 53 out of 55 pregnancies (96.4%) in the training cohort and 18 out of 18 (100%) pregnancies in the validation cohort were identified by the clinicoradiomic model. The model performed better in patients without placenta previa (PP) than in patients with PP, with AUC of 0.983 compared with 0.867, sensitivity of 100% compared with 90.8% in the training cohort, AUC of 0.832 compared with 0.815, sensitivity of 97.6% compared with 97.2% in the validation cohort. INTERPRETATION: The clinicoradiomic model incorporating both prenatal clinical factors and radiomic signature of placenta on T2WI showed good performance for risk prediction of PPH. The predictive model can identify severe PPH with high sensitivity and can be applied in patients with and without PP.