The role of three-dimensional MRI in the differentiation between angular pregnancy and interstitial pregnancy.

BACKGROUND: In clinical practice it is an ongoing challenge to distinguish between angular pregnancy and interstitial pregnancy. With the three-dimensional (3D) magnetic resonance imaging (MRI) being increasingly used, it is worth exploring its role in differentiating angular pregnancy from interstitial pregnancy. This study aims to investigate how 3D MRI can help reveal the differences between these two special pregnancies in the early diagnosis. METHODS: We reviewed and analyzed the 3D MRI images of 50 patients with interstitial pregnancy and 55 patients with angular pregnancy retrospectively. Imaging features were identified to compare these two special pregnancies, and the ROC (Receiver Operating Characteristic) analysis was conducted to assess the diagnostic performance. RESULTS: The significant differences of the 3D MRI imaging features between interstitial pregnancy and angular pregnancy were found in the outline of uterus cavity (p < 0.001), involvement of junctional zone (p < 0.001), the signal of surroundings (p = 0.005), the relationship with round ligament (p = 0.042), and the overlying myometrial thickness (p = 0.041). Furthermore, the multivariate logistic regression analysis identified a series of significant indicators for angular pregnancy, including the junctional zone involvement, being-surrounded by hyper/iso-intensity on 3D images, and the asymmetric outline of uterus cavity. Combining these three imaging features, the AUC (Area under the Curve) of ROC curve was 0.87 in distinguishing interstitial pregnancy from angular pregnancy. CONCLUSIONS: This study suggests that 3D MRI can help distinguish angular pregnancy from interstitial pregnancy in clinical practice, with the advantages that conventional MRI or ultrasound does not have. Through the significant image features, 3D MRI plays an important role in improving the timing of diagnosis, avoiding unnecessary interventions, and preventing hemorrhage in clinical practice.

Effect of different reconstruction algorithms on coronary artery calcium scores using the reduced radiation dose protocol: a clinical and phantom study.

BACKGROUND: This study aimed to evaluate the effects of different iterative reconstruction (IR) algorithms on coronary artery calcium (CAC) score quantification using the reduced radiation dose (RRD) protocol in an anthropomorphic phantom and in patients. METHODS: A thorax phantom, containing 9 calcification inserts with varying hydroxyapatite (HA) densities, was scanned with the reference protocol [120 kv, 80 mAs, filtered back projection (FBP)] and RRD protocol (120 kV, 20-80 mAs, 5 mAs interval) using a 256-slice computed tomography (CT) scanner. Raw data were reconstructed with different reconstruction algorithms [iDose4 levels 1-7 and iterative model reconstruction (IMR) levels 1-3]. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and Agatston score (AS) were calculated for each image series. The correction factor was derived from linear regression analysis between the reference image series and other image series with different parameters. Additionally, 40 patients were scanned with the RRD protocol (50 mAs) and reconstructed with FBP, iDose4 level 4, and IMR level 2. AS was calculated for the 3-group image series, and was corrected by applying a correction factor for the IMR group. The agreement of risk stratification with different reconstruction algorithms was also analyzed. RESULTS: For the phantom study, the iDose4 and IMR groups had significantly higher SNR and CNR than the FBP group (all P<0.05). There were no significant differences in the total AS after comparing image series reconstructed with iDose4 (level 1-7) and FBP (all P>0.05), while AS from the IMR (level 1-3) image series were lower than the FBP group (all P<0.05). The tube current of 50 mAs was determined for the clinical study, and the correction factor was 1.14. For the clinical study, the median AS from the iDose4 and IMR groups were both significantly lower compared to the FBP image series [(112.89 (63.01, 314.09), 113.22 (64.78, 364.95) vs. 118.59 (65.05, 374.48), both P<0.05]. After applying the correction factor, the adjusted AS from the IMR group was not significantly different from that of the FBP group [126.48 (69.62, 355.85) vs. 118.59 (65.05, 374.48), P=0.145]. Moreover, the agreement in risk stratification between FBP and IMR improved from 0.81 to 0.85. CONCLUSIONS: The RRD CAC scoring scan using the IMR reconstruction algorithm is clinically feasible, and a correction factor can help reduce the AS underestimation effect.

Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study.

OBJECTIVE: To construct a MRI radiomics model and help radiologists to improve the assessments of pelvic lymph node metastasis (PLNM) in endometrial cancer (EC) preoperatively. METHODS: During January 2014 and May 2019, 622 EC patients (age 56.6 ± 8.8 years; range 27-85 years) from five different centers (A to E) were divided into training set, validation set 1 (351 cases from center A), and validation set 2 (271 cases from centers B-E). The radiomics features were extracted basing on T2WI, DWI, ADC, and CE-T1WI images, and most related radiomics features were selected using the random forest classifier to build a radiomics model. The ROC curve was used to evaluate the performance of training set and validation sets, radiologists based on MRI findings alone, and with the aid of the radiomics model. The clinical decisive curve (CDC), net reclassification index (NRI), and total integrated discrimination index (IDI) were used to assess the clinical benefit of using the radiomics model. RESULTS: The AUC values were 0.935 for the training set, 0.909 and 0.885 for validation sets 1 and 2, 0.623 and 0.643 for the radiologists 1 and 2 alone, and 0.814 and 0.842 for the radiomics-aided radiologists 1 and 2, respectively. The AUC, CDC, NRI, and IDI showed higher diagnostic performance and clinical net benefits for the radiomics-aided radiologists than for the radiologists alone. CONCLUSIONS: The MRI-based radiomics model could be used to assess the status of pelvic lymph node and help radiologists improve their performance in predicting PLNM in EC. KEY POINTS: • A total of 358 radiomics features were extracted. The 37 most important features were selected using the random forest classifier. • The reclassification measures of discrimination confirmed that the radiomics-aided radiologists performed better than the radiologists alone, with an NRI of 1.26 and an IDI of 0.21 for radiologist 1 and an NRI of 1.37 and an IDI of 0.24 for radiologist 2.

Preoperative Assessment for High-Risk Endometrial Cancer by Developing an MRI- and Clinical-Based Radiomics Nomogram: A Multicenter Study.

BACKGROUND: High- and low-risk endometrial cancer (EC) differ in whether lymphadenectomy is performed. Assessment of high-risk EC is essential for planning surgery appropriately. PURPOSE: To develop a radiomics nomogram for high-risk EC prediction preoperatively. STUDY TYPE: Retrospective. POPULATION: In all, 717 histopathologically confirmed EC patients (mean age, 56 years ± 9) divided into a primary group (394 patients from Center A), validation groups 1 and 2 (146 patients from Center B and 177 patients from Centers C-E). FIELD STRENGTH/SEQUENCE: 1.5/3T scanners; T2 -weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, and contrast enhancement sequences. ASSESSMENT: A radiomics nomogram was generated by combining the selected radiomics features and clinical parameters (metabolic syndrome, cancer antigen 125, age, tumor grade following curettage, and tumor size). The area under the curve (AUC) of the receiver operator characteristic was used to evaluate the predictive performance of the radiomics nomogram for high-risk EC. The surgical procedure suggested by the nomogram was compared with the actual procedure performed for the patients. Net benefit of the radiomics nomogram was evaluated by a clinical decision curve (CDC), net reclassification index (NRI), and integrated discrimination improvement (IDI). STATISTICAL TESTS: Binary least absolute shrinkage and selection operator (LASSO) logistic regression, linear regression, and multivariate binary logistic regression were used to select radiomics features and clinical parameters. RESULTS: The AUC for prediction of high-risk EC for the radiomics nomogram in the primary group, validation groups 1 and 2 were 0.896 (95% confidence interval [CI]: 0.866-0.926), 0.877 (95% CI: 0.825-0.930), and 0.919 (95% CI: 0.879-0.960), respectively. The nomogram achieved good net benefit by CDC analysis for high-risk EC. NRIs were 1.17, 1.28, and 1.51, and IDIs were 0.41, 0.60, and 0.61 in the primary group, validation groups 1 and 2, respectively. DATA CONCLUSION: The radiomics nomogram exhibited good performance in the individual prediction of high-risk EC, and might be used for surgical management of EC. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1872-1882.

Single nucleotide polymorphisms in the CD40 gene associate with the disease susceptibility and severity in knee osteoarthritis in the Chinese Han population: a case-control study.

BACKGROUND: This study explored the association between single nucleotide polymorphisms (SNPs) in the CD40 gene, rs4810485 G > T and rs1883832 C > T, as well as disease susceptibility and severity in knee osteoarthritis (KOA) in the Chinese Han population. METHOD: Peripheral venous blood was collected from 133 KOA patients (KOA group) and 143 healthy people (control group) from December 2012 to November 2013. The patients in the KOA group were classified into mild, moderate and severe groups according to disease severity. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was used to test the genotypes of all subjects. Binary logistic regression analyses were performed to analyze the risk factors for KOA. RESULTS: The KOA group was significantly different from the control group in living environment (P < 0.05). The KOA group had a lower frequency of TT genotype and T allele distribution of rs4810485 G > T compared with the control group, and rs4810485 G > T TT genotype and T allele may associate with low incidence of KOA (all P < 0.05). Besides, T allele and mutant homozygous TT genotype of rs1883832 C > T increased the susceptibility to KOA. Genotype and allele distribution of rs4810485 G > T and rs1883832 C > T were significantly different among the mild, moderate and severe groups (P < 0.05). There were more patients with rs4810485 G > T GG genotype and rs1883832 C > T TT genotype in the severe group than other genotypes of these two SNPs. According to binary logistic regression analysis, rs4810485 G > T TT genotype could alleviate disease severity in KOA, rs1883832 C > T TT genotype increase the severity of KOA and living environment is an important external factor that affects KOA severity. CONCLUSIONS: These data provide evidences that rs4810485 G > T and rs1883832 C > T in the CD40 gene may be associated with disease susceptibility and severity in KOA.