The contrast-free diffusion MRI multiple index for the early prediction of pathological response to neoadjuvant chemotherapy in breast cancer.

Early tumor response prediction can help avoid overtreatment with unnecessary chemotherapy sessions. It is important to determine whether multiple apparent diffusion coefficient indices (S index, ADC-diff) are effective in the early prediction of pathological response to neoadjuvant chemotherapy (NAC) in breast cancer (BC). Patients with stage II and III BCs who underwent T1WI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI using a 3 T system were included. They were divided into two groups: major histological responders (MHRs, Miller-Payne G4/5) and nonmajor histological responders (nMHRs, Miller-Payne G1-3). Three b values were used for DWI to derive the S index; ADC-diff values were obtained using b = 0 and 1000 s/mm2. The different interquartile ranges of percentile S-index and ADC-diff values after treatment were calculated and compared. The assessment was performed at baseline and after two and four NAC cycles. A total of 59 patients were evaluated. There are some correlations of interquartile ranges of S-index parameters and ADC-diff values with histopathological prognostic factors (such as estrogen receptor and human epidermal growth factor receptor 2 expression, all p < 0.05), but no significant differences were found in some other interquartile ranges of S-index parameters or ADC-diff values between progesterone receptor positive and negative or for Ki-67 tumors (all P > 0.05). No differences were found in the dynamic contrast-enhanced MRI characteristics between the two groups. HER-2 expression and kurtosis of the S-index distribution were screened out as independent risk factors for predicting MHR group (p < 0.05, area under the curve (AUC) = 0.811) before NAC. After early NAC (two cycles), only the 10th percentile S index was statistically significant between the two groups (p < 0.05, AUC = 0.714). No significant differences were found in ADC-diff value at any time point of NAC between the two groups (P > 0.1). These findings demonstrate that the S-index value may be used as an early predictor of pathological response to NAC in BC; the value of ADC-diff as an imaging biomarker of NAC needs to be further confirmed by ongoing multicenter prospective trials.

Association of peritumoral region features assessed on breast MRI and prognosis of breast cancer: a systematic review and meta-analysis.

BACKGROUND: Increasing attention has been given to the peritumoral region. However, conflicting findings have been reported regarding the relationship between peritumoral region features on MRI and the prognosis of breast cancer. PURPOSE: To evaluate the relationship between peritumoral region features on MRI and prognosis of breast cancer. MATERIALS AND METHODS: A retrospective meta-analysis of observational studies comparing either qualitative or quantitative assessments of peritumoral MRI features on breast cancer with poor prognosis and control subjects was performed for studies published till October 2022. Pooled odds ratios (ORs) or standardized mean differences and 95% confidence intervals (CIs) were estimated by using random-effects models. The heterogeneity across the studies was measured using the statistic I2. Sensitivity analyses were conducted to test this association according to different study characteristics. RESULTS: Twenty-four studies comprising 1853 breast cancers of poor prognosis and 2590 control participants were included in the analysis. Peritumoral edema was associated with non-luminal breast cancers (OR=3.56; 95%CI: 2.17, 5.83; p=.000), high expression of the Ki-67 index (OR=3.70; 95%CI: 2.41, 5.70; p =.000), high histological grade (OR=5.85; 95%CI: 3.89, 8.80; p=.000), lymph node metastasis (OR=2.83; 95%CI: 1.71, 4.67; p=.000), negative expression of HR (OR=3.15; 95%CI: 2.03, 4.88; p=.000), and lymphovascular invasion (OR=1.72; 95%CI: 1.28, 2.30; p=.000). The adjacent vessel sign was associated with greater odds of breast cancer with poor prognosis (OR=2.02; 95%CI: 1.68, 2.44; p=.000). Additionally, breast cancers with poor prognosis had higher peritumor-tumor ADC ratio (SMD=0.67; 95%CI: 0.54, 0.79; p=.000) and peritumoral ADCmean (SMD=0.29; 95%CI: 0.15, 0.42; p=.000). A peritumoral region of 2-20 mm away from the margin of the tumor is recommended. CONCLUSION: The presence of peritumoral edema and adjacent vessel signs, higher peritumor-tumor ADC ratio, and peritumoral ADCmean were significantly correlated with poor prognosis of breast cancer. CLINICAL RELEVANCE STATEMENT: MRI features of the peritumoral region can be used as a non-invasive index for the prognostic evaluation of invasive breast cancer. KEY POINTS: • Peritumoral edema was positively associated with non-luminal breast cancer, high expression of the Ki-67 index, high histological grade, lymph node metastasis, negative expression of HR, and lymphovascular invasion. • The adjacent vessel sign was associated with greater odds of breast cancers with poor prognosis. • Breast cancers with poor prognosis had higher peritumor-tumor ADC ratio and peritumoral ADCmean.

The adaptive evolution of Euryale ferox to the aquatic environment through paleo-hexaploidization.

Occupation of living space is one of the main driving forces of adaptive evolution, especially for aquatic plants whose leaves float on the water surface and thus have limited living space. Euryale ferox, from the angiosperm basal family Nymphaeaceae, develops large, rapidly expanding leaves to compete for space on the water surface. Microscopic observation found that the cell proliferation of leaves is almost completed underwater, while the cell expansion occurs rapidly after they grow above water. To explore the mechanism underlying the specific development of leaves, we performed sequences assembly and analyzed the genome and transcriptome dynamics of E. ferox. Through reconstruction of the three sub-genomes generated from the paleo-hexaploidization event in E. ferox, we revealed that one sub-genome was phylogenetically closer to Victoria cruziana, which also exhibits gigantic floating leaves. Further analysis revealed that while all three sub-genomes promoted the evolution of the specific leaf development in E. ferox, the genes from the sub-genome closer to V. cruziana contributed more to this adaptive evolution. Moreover, we found that genes involved in cell proliferation and expansion, photosynthesis, and energy transportation were over-retained and showed strong expression association with the leaf development stages, such as the expression divergence of SWEET orthologs as energy uploaders and unloaders in the sink and source leaf organs of E. ferox. These findings provide novel insights into the genome evolution through polyploidization, as well as the adaptive evolution regarding the leaf development accomplished through biased gene retention and expression sub-functionalization of multi-copy genes in E. ferox.

Intratumoral and peritumoral radiomics based on contrast-enhanced MRI for preoperatively predicting treatment response of transarterial chemoembolization in hepatocellular carcinoma.

BACKGROUND: Noninvasive and precise methods to estimate treatment response and identify hepatocellular carcinoma (HCC) patients who could benefit from transarterial chemoembolization (TACE) are urgently required. The present study aimed to investigate the ability of intratumoral and peritumoral radiomics based on contrast-enhanced magnetic resonance imaging (CE-MRI) to preoperatively predict tumor response to TACE in HCC patients. METHODS: A total of 138 patients with HCC who received TACE were retrospectively included and randomly divided into training and validation cohorts at a ratio of 7:3. Total 1206 radiomics features were extracted from arterial, venous, and delayed phases images. The inter- and intraclass correlation coefficients, the spearman's rank correlation test, and the gradient boosting decision tree algorithm were used for radiomics feature selection. Radiomics models on intratumoral region (TR) and peritumoral region (PTR) (3 mm, 5 mm, and 10 mm) were established using logistic regression. Three integrated radiomics models, including intratumoral and peritumoral region (T-PTR) (3 mm), T-PTR (5 mm), and T-PTR (10 mm) models, were constructed using TR and PTR radiomics scores. A clinical-radiological model and a combined model incorporating the optimal radiomics score and selected clinical-radiological predictors were constructed, and the combined model was presented as a nomogram. The discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively. RESULTS: The T-PTR radiomics models performed better than the TR and PTR models, and the T-PTR (3 mm) radiomics model demonstrated preferable performance with the AUCs of 0.884 (95%CI, 0.821-0.936) and 0.911 (95%CI, 0.825-0.975) in both training and validation cohorts. The T-PTR (3 mm) radiomics score, alkaline phosphatase, tumor size, and satellite nodule were fused to construct a combined nomogram. The combined nomogram [AUC: 0.910 (95%CI, 0.854-0.958) and 0.918 (95%CI, 0.831-0.986)] outperformed the clinical-radiological model [AUC: 0.789 (95%CI, 0.709-0.863) and 0.782 (95%CI, 0.660-0.902)] in the both cohorts and achieved good calibration capability and clinical utility. CONCLUSIONS: CE-MRI-based intratumoral and peritumoral radiomics approach can provide an effective tool for the precise and individualized estimation of treatment response for HCC patients treated with TACE.

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.

Intravoxel incoherent motion predicts positive surgical margins and Gleason score upgrading after radical prostatectomy for prostate cancer.

BACKGROUND: Whether Intravoxel incoherent motion (IVIM) can be used as a predictive tool of positive surgical margins (PSMs) and Gleason score (GS) upgrading in prostate cancer (PCa) patients after radical prostatectomy (RP) still remains unclear. The aim of this study is to explore the ability of IVIM and clinical characteristics to predict PSMs and GS upgrading. METHODS: A total of 106 PCa patients after RP who underwent pelvic mpMRI (multiparametric Magnetic Resonance Imaging) between January 2016 and December 2021 and met the requirements were retrospectively included in our study. IVIM parameters were obtained using GE Functool post-processing software. Logistic regression models were fitted to confirm the predictive risk factor of PSMs and GS upgrading. The area under the curve and fourfold contingency table were used to evaluate the diagnostic efficacy of IVIM and clinical parameters. RESULTS: Multivariate logistic regression analyses revealed that percent of positive cores, apparent diffusion coefficient and molecular diffusion coefficient (D) were independent predictors of PSMs (Odds Ratio (OR) were 6.07, 3.62 and 3.16, respectively), Biopsy GS and pseudodiffusion coefficient (D*) were independent predictors of GS upgrading (OR were 0.563 and 7.15, respectively). The fourfold contingency table suggested that combined diagnosis increased the ability of predicting PSMs but had no advantage in predicting GS upgrading except the sensitivity from 57.14 to 91.43%. CONCLUSIONS: IVIM showed good performance in predicting PSMs and GS upgrading. Combining IVIM and clinical factors enhanced the performance of predicting PSMs, which may contribute to clinical diagnosis and treatment.

[A multimodal medical image contrastive learning algorithm with domain adaptive denormalization].

Recently, deep learning has achieved impressive results in medical image tasks. However, this method usually requires large-scale annotated data, and medical images are expensive to annotate, so it is a challenge to learn efficiently from the limited annotated data. Currently, the two commonly used methods are transfer learning and self-supervised learning. However, these two methods have been little studied in multimodal medical images, so this study proposes a contrastive learning method for multimodal medical images. The method takes images of different modalities of the same patient as positive samples, which effectively increases the number of positive samples in the training process and helps the model to fully learn the similarities and differences of lesions on images of different modalities, thus improving the model's understanding of medical images and diagnostic accuracy. The commonly used data augmentation methods are not suitable for multimodal images, so this paper proposes a domain adaptive denormalization method to transform the source domain images with the help of statistical information of the target domain. In this study, the method is validated with two different multimodal medical image classification tasks: in the microvascular infiltration recognition task, the method achieves an accuracy of (74.79 ± 0.74)% and an F1 score of (78.37 ± 1.94)%, which are improved as compared with other conventional learning methods; for the brain tumor pathology grading task, the method also achieves significant improvements. The results show that the method achieves good results on multimodal medical images and can provide a reference solution for pre-training multimodal medical images.

[Hepatocellular carcinoma segmentation and pathological differentiation degree prediction method based on multi-task learning].

Hepatocellular carcinoma (HCC) is the most common liver malignancy, where HCC segmentation and prediction of the degree of pathological differentiation are two important tasks in surgical treatment and prognosis evaluation. Existing methods usually solve these two problems independently without considering the correlation of the two tasks. In this paper, we propose a multi-task learning model that aims to accomplish the segmentation task and classification task simultaneously. The model consists of a segmentation subnet and a classification subnet. A multi-scale feature fusion method is proposed in the classification subnet to improve the classification accuracy, and a boundary-aware attention is designed in the segmentation subnet to solve the problem of tumor over-segmentation. A dynamic weighted average multi-task loss is used to make the model achieve optimal performance in both tasks simultaneously. The experimental results of this method on 295 HCC patients are superior to other multi-task learning methods, with a Dice similarity coefficient (Dice) of (83.9 ± 0.88)% on the segmentation task, while the average recall is (86.08 ± 0.83)% and an F1 score is (80.05 ± 1.7)% on the classification task. The results show that the multi-task learning method proposed in this paper can perform the classification task and segmentation task well at the same time, which can provide theoretical reference for clinical diagnosis and treatment of HCC patients.

Evaluation of brown adipose tissue with intermolecular double-quantum coherence magnetic resonance spectroscopy at 3.0 T.

In the current study, we propose a single-voxel (SV) magnetic resonance spectroscopy (MRS) pulse sequence, based on intermolecular double-quantum coherence (iDQC), for in vivo specific assessment of brown adipose tissue (BAT) at 3 T. The multilocular adipocyte, present in BAT, typically contains a large number of small lipid droplets surrounded by abundant intracellular water, while the monolocular adipocyte, present in white adipose tissue (WAT), accommodates only a single large lipid droplet with much less water content. The SV-iDQC sequence probes the spatial correlation between water and fat spins at a distance of about the size of an adipocyte, thus can be used for assessment of BAT, even when mixed with WAT and/or muscle tissues. This sequence for measurement of water-to-fat (water-fat) iDQC signals was tested on phantoms and mouse BAT and WAT tissues. It was then used to differentiate adipose tissues in the supraclavicular and subcutaneous regions of healthy youth human volunteers (n = 6). Phantom results with water-fat emulsions demonstrated enhanced water-fat iDQC signal with increased voxel size, increased energy level of emulsification, or increased distribution balance of water and fat spins. The animal tissue experiments resulted in obvious water-fat iDQC signal in mouse BAT, while this signal was almost absent in the WAT spectrum. The optimal choice of the dipolar coupling distance for the observation was approximately 100 µm, as tested on both emulsion phantom and animal tissue. The water-fat iDQC signals observed in the supraclavicular adipose tissues were higher than in the subcutaneous adipose tissues in healthy young volunteers (0.43 ± 0.36 vs. 0.10 ± 0.06, p = 0.06). It was concluded that the iDQC-based sequence has potential for assessment of mouse and human BAT at 3 T, which is of interest for clinical research and the diagnosis of obesity and associated diseases.

Imaging features of biliary adenofibroma of the liver with malignant transformation: a case report with literature review.

BACKGROUND: Biliary adenofibroma (BAF) is a rare primary hepatic tumor with the potential risk of malignant transformation. Given the extreme rarity of the disease, the imaging features of BAF are unclear. We presented a case of malignant BAF and conducted a systematic literature review. We highlighted the key imaging features in the diagnosis and aggressiveness assessment of BAF, as well as the role of various imaging modalities in evaluating BAF. CASE PRESENTATION: We reported a 64-year-old woman with a 5-months history of pain in the right upper quadrant abdomen. US of the liver showed a hypoechoic subcapsular nodule. CT scan revealed a subcapsular solid-cystic mass in segment V of the liver. The mass showed a marked enhancement in the arterial phase followed by wash-out in the venous phase. The patient underwent partial resection of liver's right lobe. The mass was diagnosed as BAF with malignant transformation by postoperative pathology. CONCLUSIONS: CT and MRI are helpful in recognizing and characterizing BAF. The imaging features of BAF include a solitary, large solid-cystic mass with a well-defined margin, lobulated shape, and internal septa; subcapsular location; no intrahepatic bile duct communication; the presence of von Meyenberg complexes in background liver. The enhancement patterns may have the potential to assess the aggressiveness of BAF, and that marked enhancement in the arterial phase followed by wash-out in the venous phase is suggestive of malignant BAF.

Assessment of late gadolinium enhancement-negative chronic total occlusion by longitudinal strain analysis using cardiac magnetic resonance imaging.

BACKGROUND: Strain analysis has become commonly used in clinical practice in various heart diseases. PURPOSE: To explore whether late gadolinium enhancement (LGE)-negative areas with coronary artery chronic total occlusion (CTO) appear normal when analyzed for longitudinal strain using cardiac magnetic resonance (CMR) imaging. MATERIAL AND METHODS: A total of 16 patients and 31 healthy controls who underwent 1.5-T MR at our hospital between January 2015 and July 2017 were included in the study. The LGE-CMR of patients with CTO was negative. Left ventricular functional parameters, segmental longitudinal strain/strain rate, and perfusion parameters were measured using CVI42 software. RESULTS: For myocardial segments supplied by CTO vessels, systolic longitudinal strain rate (SLSR)was significantly lower than that of healthy controls, and diastolic longitudinal strain rate (DLSR) was significantly higher (1.19 1/s vs. 1.02 1/s; P = 0.018). Moreover, longitudinal strain (LS), SLSR, and DLSR did not differ between good and poor collateral circulation. Perfusion index of CTO territory segments was lower than non-CTO territory segments (0.20 vs. 0.22; P = 0.027). No correlation was found between longitudinal strain parameters and perfusion parameters. CONCLUSION: Although LGE-CMR was negative in patients with CTO, the myocardial SLSR of CTO territory segments was significantly lower than that of healthy controls.

Myocardium microcirculation study in a healthy Chinese population using 3.0-T cardiac magnetic resonance intravoxel incoherent motion imaging.

BACKGROUND: Intravoxel incoherent motion imaging (IVIM) can non-invasively evaluate diffusion and microvascular perfusion. PURPOSE: To explore the myocardium microcirculation of a healthy Chinese population by using cardiac magnetic resonance (CMR) IVIM. MATERIAL AND METHODS: A total of 80 healthy volunteers (44 men, 36 women) who underwent 3.0-T CMR examination were enrolled. All participants had cardiac cine imaging and short-axis CMR-IVIM of the left ventricle (LV) using multiple b-values. The consistency of the IVIM parameters was assessed by intraclass correlation coefficient (ICC) and the Bland-Altman test. Spearman correlation analysis was performed between IVIM parameters and age, and body mass index (BMI). The differences of IVIM parameters were analyzed between gender and different ages. RESULTS: LV end-diastolic volume (EDV), end-systolic volume (ESV), LVmass, cardiac output (CO), and BMI in the male group were higher than those in the female group (P<0.05). IVIM parameters had good intra-observer and inter-observer consistency (≥0.75). Bland-Altman analysis also showed good intra-observer and inter-observer consistency. ADCfast decreased with increasing female age (rs = -0.37; P = 0.01), while IVIM parameters had no correlation with BMI regardless of sex. ADCfast in the female group had a statistical difference between different age groups. The ADCslow and f in the male group were lower than those in the female group (P<0.05); however, there was no statistical difference in ADCfast between genders. CONCLUSION: IVIM parameters in healthy Chinese volunteers provided good consistency. There was a negative correlation between ADCfast and age in the female group.

EfABI4 Transcription Factor Is Involved in the Regulation of Starch Biosynthesis in Euryale ferox Salisb Seeds.

Starch is the final product of photosynthesis and the main storage form in plants. Studies have shown that there is a close synergistic regulatory relationship between ABA signal transduction and starch biosynthesis. In this study, we employed RNA sequencing (RNA-Seq) to investigate transcriptomic changes of the Euryale ferox seeds treated by exogenous ABA. The differentially expressed genes engaged in the "Starch and sucrose" and "TCA cycle" pathway. Furthermore, the key transcription factor EfABI4 in ABA signaling pathway and the key genes of starch biosynthesis (EfDBE1, EfSBE2, EfSS1, EfSS2, EfSS3, EfSS4 and EfGBSS1) were significantly up-regulated. Further, the Euryale ferox plant was treated with ABA, it was found that the total starch content of Euryale ferox seeds at different development stages was significantly higher than that of the control, and the key genes of starch synthesis in Euryale ferox seeds were also significantly up-regulated. Finally, yeast one-hybrid and dual luciferase assay proved that EfABI4 can promote the expression of EfSS1 by directly binding to its promoter. Subcellular localization results showed that EfABI4 protein was located at the nucleus and EfSS1 protein was located in the cytomembrane. These findings revealed that ABA promotes starch synthesis and accumulation by mediating EfABI4 to directly promote EfSS1 gene expression, which is helpful for understanding starch synthesis in seeds.

Metabolomics and transcriptome analysis of the biosynthesis mechanism of flavonoids in the seeds of Euryale ferox Salisb at different developmental stages.

Flavonoids belong to polyphenolic compounds, which are widely distributed in plants and have rich functions. Euryale ferox Salisb is an important medicinal and edible homologous plant, and flavonoids are its main functional substances. However, the biosynthesis mechanism of flavonoids in E. ferox is still poorly understood. To explore the dynamic changes of flavonoid biosynthesis during the development of E. ferox seeds, the targeted flavonoid metabolome was determined. A total of 129 kinds of flavonoid metabolites were characterized in the seeds of E. ferox, including 11 flavanones, 8 dihydroflavanols, 16 flavanols, 29 flavones, 3 isoflavones, 12 anthocyanins, 29 flavonols, 6 flavonoid carbonosides, 3 chalcones and 13 proanthocyanidins. The relative content of flavonoid metabolites accumulated continuously during the development of E. ferox seeds, and reached the highest at T30. In transcriptome, the expression of key genes in the flavonoid pathway, such as PAL, CHS, F3H, FLS, ANS, was highest in T30, which was consistent with the trend of metabolites. Six candidate transcription factors (R2R3MYBs and bHLHs) may affect the biosynthesis of flavonoids by regulating the expression of structural genes. Furthermore, transcriptome analysis and exogenous ABA and SA treatment demonstrated that ABA (PYR1, PP2Cs, SnRK2s) and SA (NPR1) are involved in the positive regulation of flavonoid biosynthesis. This study clarified the differential changes of flavonoid metabolites during the development of E. ferox seeds, confirmed that ABA and SA promote the synthesis of flavonoids, and found key candidate genes that are involved in the regulation of ABA and SA in the positive regulation of flavonoid biosynthesis.

Multi-task convolutional neural network-based design of radio frequency pulse and the accompanying gradients for magnetic resonance imaging.

Modern MRI systems usually load the predesigned RFs and the accompanying gradients during clinical scans, with minimal adaption to the specific requirements of each scan. Here, we describe a neural network-based method for real-time design of excitation RF pulses and the accompanying gradients' waveforms to achieve spatially two-dimensional selectivity. Nine thousand sets of radio frequency (RF) and gradient waveforms with two-dimensional spatial selectivity were generated as the training dataset using the Shinnar-Le Roux (SLR) method. Neural networks were created and trained with five strategies (TS-1 to TS-5). The neural network-designed RF and gradients were compared with their SLR-designed counterparts and underwent Bloch simulation and phantom imaging to investigate their performances in spin manipulations. We demonstrate a convolutional neural network (TS-5) with multi-task learning to yield both the RF pulses and the accompanying two channels of gradient waveforms that comply with the SLR design, and these design results also provide excitation spatial profiles comparable with SLR pulses in both simulation (normalized root mean square error [NRMSE] of 0.0075 ± 0.0038 over the 400 sets of testing data between TS-5 and SLR) and phantom imaging. The output RF and gradient waveforms between the neural network and SLR methods were also compared, and the joint NRMSE, with both RF and the two channels of gradient waveforms considered, was 0.0098 ± 0.0024 between TS-5 and SLR. The RF and gradients were generated on a commercially available workstation, which took ~130 ms for TS-5. In conclusion, we present a convolutional neural network with multi-task learning, trained with SLR transformation pairs, that is capable of simultaneously generating RF and two channels of gradient waveforms, given the desired spatially two-dimensional excitation profiles.

Radiomics Analysis Based on Multiparametric MRI for Predicting Early Recurrence in Hepatocellular Carcinoma After Partial Hepatectomy.

BACKGROUND: Preoperative prediction of early recurrence (ER) of hepatocellular carcinoma (HCC) plays a critical role in individualized risk stratification and further treatment guidance. PURPOSE: To investigate the role of radiomics analysis based on multiparametric MRI (mpMRI) for predicting ER in HCC after partial hepatectomy. STUDY TYPE: Retrospective. POPULATION: In all, 113 HCC patients (ER, n = 58 vs. non-ER, n = 55), divided into training (n = 78) and validation (n = 35) cohorts. FIELD STRENGTH/SEQUENCE: 1.5T or 3.0T, gradient-recalled-echo in-phase T1 -weighted imaging (I-T1 WI) and opposed-phase T1 WI (O-T1 WI), fast spin-echo T2 -weighted imaging (T2 WI), spin-echo planar diffusion-weighted imaging (DWI), and gradient-recalled-echo contrast-enhanced MRI (CE-MRI). ASSESSMENT: In all, 1146 radiomics features were extracted from each image sequence, and radiomics models based on each sequence and their combination were established via multivariate logistic regression analysis. The clinicopathologic-radiologic (CPR) model and the combined model integrating the radiomics score with the CPR risk factors were constructed. A nomogram based on the combined model was established. STATISTICAL TESTS: Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminative performance of each model. The potential clinical usefulness was evaluated by decision curve analysis (DCA). RESULTS: The radiomics model based on I-T1 WI, O-T1 WI, T2 WI, and CE-MRI sequences presented the best performance among all radiomics models with an area under the ROC curve (AUC) of 0.771 (95% confidence interval (CI): 0.598-0.894) in the validation cohort. The combined nomogram (AUC: 0.873; 95% CI: 0.756-0.989) outperformed the radiomics model and the CPR model (AUC: 0.742; 95% CI: 0.577-0.907). DCA demonstrated that the combined nomogram was clinically useful. DATA CONCLUSION: The mpMRI-based radiomics analysis has potential to predict ER of HCC patients after hepatectomy, which could enhance risk stratification and provide support for individualized treatment planning. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 4.

Quantification of Hepatic Fat Fraction in Patients With Nonalcoholic Fatty Liver Disease: Comparison of Multimaterial Decomposition Algorithm and Fat (Water)-Based Material Decomposition Algorithm Using Single-Source Dual-Energy Computed Tomography.

METHODS: Hepatic fat fractions were quantified by noncontrast (HFFnon-CE) and contrast-enhanced single-source dual-energy computed tomography in arterial phase (HFFAP), portal venous phase (HFFPVP) and equilibrium phase (HFFEP) using MMD in 19 nonalcoholic fatty liver disease patients. The fat concentration was measured on fat (water)-based images. As the standard of reference, magnetic resonance iterative decomposition of water and fat with echo asymmetry and least-squares estimation-iron quantification images were reconstructed to obtain HFF (HFFIDEAL-IQ). RESULTS: There was a strong correlation between HFFnon-CE, HFFAP, HFFPVP, HFFEP, fat concentration and HFFIDEAL-IQ (r = 0.943, 0.923, 0.942, 0.952, and 0.726) with HFFs having better correlation with HFFIDEAL-IQ. Hepatic fat fractions did not significantly differ across scanning phases. The HFFs of 3-phase contrast-enhanced computed tomography had a good consistency with HFFnon-CE. CONCLUSIONS: Hepatic fat fraction using MMD has excellent correlation with that of magnetic resonance imaging, is independent of the computed tomography scanning phases, and may be used as a routine technique for quantitative assessment of HFF.

An Individualized Contrast-Enhanced Liver Computed Tomography Imaging Protocol Based on Body Mass Index in 126 Patients Seen for Liver Cirrhosis.

BACKGROUND Computed tomography (CT) imaging using iodinated contrast medium is associated with the radiation dose to the patient, which may require reduction in individual circumstances. This study aimed to evaluate an individualized liver CT protocol based on body mass index (BMI) in 126 patients investigated for liver cirrhosis. MATERIAL AND METHODS From November 2017 to December 2020, in this prospective study, 126 patients with known or suspected liver cirrhosis were recruited. Patients underwent liver CT using individualized protocols based on BMI, as follows. BMI ≤24.0 kg/m²: 80 kV, 352 mg I/kg; BMI 24.1-28.0 kg/m²: 100 kV, 440 mg I/kg; BMI ≥28.1 kg/m²: 120 kV, 550 mg I/kg. Figure of merit (FOM) and size-specific dose estimates (SSDEs) were calculated and compared using the Mann-Whitney U test. Subjective image quality and timing adequacy of the late arterial phase were evaluated with Likert scales. RESULTS The SSDE was significantly lower in the 80 kV protocol, corresponding to a dose reduction of 36% and 50% compared with the others (all P<0.001). In the comparison of 80-, 100-, and 120-kV protocols, no statistically significant differences were found in FOMs (P=0.108~0.620). Of all the examinations, 95.2% (120 of 126) were considered as appropriate timing for the late arterial phase. In addition, overall image quality, hepatocellular carcinoma conspicuity, and detection rate did not differ significantly among the 3 protocols (P=0.383~0.737). CONCLUSIONS This study demonstrated the feasibility of using an individualized liver CT protocol based on BMI, and showed that patients with lower BMI should receive lower doses of iodinated contrast medium and significantly reduced radiation dose.

NnABI4-Mediated ABA Regulation of Starch Biosynthesis in Lotus (Nelumbo nucifera Gaertn).

Starch is an important component in lotus. ABA is an important plant hormone, which plays a very crucial role in regulating plant starch synthesis. Using 'MRH' as experimental materials, the leaves were sprayed with exogenous ABA before the rhizome expansion. The results showed that stomatal conductance and transpiration rate decreased while net photosynthetic rate increased. The total starch content of the underground rhizome of lotus increased significantly. Meanwhile, qPCR results showed that the relative expression levels of NnSS1, NnSBE1 and NnABI4 were all upregulated after ABA treatment. Then, yeast one-hybrid and dual luciferase assay suggested that NnABI4 protein can promote the expression of NnSS1 by directly binding to its promoter. In addition, subcellular localization results showed that NnABI4 encodes a nuclear protein, and NnSS1 protein was located in the chloroplast. Finally, these results indicate that ABA induced the upregulated expression of NnABI4, and NnABI4 promoted the expression of NnSS1 and thus enhanced starch accumulation in lotus rhizomes. This will provide a theoretical basis for studying the molecular mechanism of ABA regulating starch synthesis in plant.

Leukemia inhibitory factor inhibits T helper 17 cell differentiation and confers treatment effects of neural progenitor cell therapy in autoimmune disease.

Neural progenitor cell (NPC) therapy is considered a promising treatment modality for multiple sclerosis (MS), potentially acting through neural repair. Here, we showed that intravenous administration of NPCs ameliorated experimental autoimmune encephalomyelitis (EAE) by selectively inhibiting pathogenic T helper 17 (Th17) cell differentiation. Leukemia inhibitory factor (LIF) produced by NPCs was responsible for the observed EAE suppression. Through the inducible LIF receptor expression, LIF inhibited the differentiation of Th17 cells in EAE mice and that from MS subjects. At the molecular level, LIF exerted an opposing effect on interleukin 6 (IL-6)-induced signal transducer and activator of transcription 3 (STAT3) phosphorylation required for Th17 cell differentiation by triggering a signaling cascade that activated extracellular signal-regulated MAP kinase (ERK) and upregulated suppressor of cytokine signaling 3 (SOCS3) expression. This study reveals a critical role for LIF in regulating Th17 cell differentiation and provides insights into the mechanisms of action of NPC therapy in MS.