Evaluating Large Language Models for Automated Reporting and Data Systems Categorization: Cross-Sectional Study.

BACKGROUND: Large language models show promise for improving radiology workflows, but their performance on structured radiological tasks such as Reporting and Data Systems (RADS) categorization remains unexplored. OBJECTIVE: This study aims to evaluate 3 large language model chatbots-Claude-2, GPT-3.5, and GPT-4-on assigning RADS categories to radiology reports and assess the impact of different prompting strategies. METHODS: This cross-sectional study compared 3 chatbots using 30 radiology reports (10 per RADS criteria), using a 3-level prompting strategy: zero-shot, few-shot, and guideline PDF-informed prompts. The cases were grounded in Liver Imaging Reporting & Data System (LI-RADS) version 2018, Lung CT (computed tomography) Screening Reporting & Data System (Lung-RADS) version 2022, and Ovarian-Adnexal Reporting & Data System (O-RADS) magnetic resonance imaging, meticulously prepared by board-certified radiologists. Each report underwent 6 assessments. Two blinded reviewers assessed the chatbots' response at patient-level RADS categorization and overall ratings. The agreement across repetitions was assessed using Fleiss κ. RESULTS: Claude-2 achieved the highest accuracy in overall ratings with few-shot prompts and guideline PDFs (prompt-2), attaining 57% (17/30) average accuracy over 6 runs and 50% (15/30) accuracy with k-pass voting. Without prompt engineering, all chatbots performed poorly. The introduction of a structured exemplar prompt (prompt-1) increased the accuracy of overall ratings for all chatbots. Providing prompt-2 further improved Claude-2's performance, an enhancement not replicated by GPT-4. The interrun agreement was substantial for Claude-2 (k=0.66 for overall rating and k=0.69 for RADS categorization), fair for GPT-4 (k=0.39 for both), and fair for GPT-3.5 (k=0.21 for overall rating and k=0.39 for RADS categorization). All chatbots showed significantly higher accuracy with LI-RADS version 2018 than with Lung-RADS version 2022 and O-RADS (P<.05); with prompt-2, Claude-2 achieved the highest overall rating accuracy of 75% (45/60) in LI-RADS version 2018. CONCLUSIONS: When equipped with structured prompts and guideline PDFs, Claude-2 demonstrated potential in assigning RADS categories to radiology cases according to established criteria such as LI-RADS version 2018. However, the current generation of chatbots lags in accurately categorizing cases based on more recent RADS criteria.

Study on breast cancerization and isolated diagnosis in situ by HOF-ATR-MIR spectroscopy with deep learning.

Mid-infrared (MIR) spectroscopy can characterize the content and structural changes of macromolecular components in different breast tissues, which can be used for feature extraction and model training by machine learning to achieve accurate classification and recognition of different breast tissues. In parallel, the one-dimensional convolutional neural network (1D-CNN) stands out in the field of deep learning for its ability to efficiently process sequential data, such as spectroscopic signals. In this study, MIR spectra of breast tissue were collected in situ by coupling the self-developed MIR hollow optical fiber attenuated total reflection (HOF-ATR) probe with a Fourier transform infrared spectroscopy (FTIR) spectrometer. Staging analysis was conducted on the changes in macromolecular content and structure in breast cancer tissues. For the first time, a trinary classification model was established based on 1D-CNN for recognizing normal, paracancerous and cancerous tissues. The final predication results reveal that the 1D-CNN model based on baseline correction (BC) and data augmentation yields more precise classification results, with a total accuracy of 95.09%, exhibiting superior discrimination ability than machine learning models of SVM-DA (90.00%), SVR (88.89%), PCA-FDA (67.78%) and PCA-KNN (70.00%). The experimental results suggest that the application of 1D-CNN enables accurate classification and recognition of different breast tissues, which can be considered as a precise, efficient and intelligent novel method for breast cancer diagnosis.

Radiomics analysis for prediction of lymph node metastasis after neoadjuvant chemotherapy based on pretreatment MRI in patients with locally advanced cervical cancer.

Background: This study aims to develop and validate a pretreatment MRI-based radiomics model to predict lymph node metastasis (LNM) following neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC). Methods: Patients with LACC who underwent NACT from two centers between 2013 and 2022 were enrolled retrospectively. Based on the lymph node (LN) status determined in the pathology reports after radical hysterectomy, patients were categorized as LN positive or negative. The patients from center 1 were assigned as the training set while those from center 2 formed the validation set. Radiomics features were extracted from pretreatment sagittal T2-weighted imaging (Sag-T2WI), axial diffusion-weighted imaging (Ax-DWI), and the delayed phase of dynamic contrast-enhanced sagittal T1-weighted imaging (Sag-T1C) for each patient. The K-best and least absolute shrinkage and selection operator (LASSO) methods were employed to reduce dimensionality, and the radiomics features strongly associated with LNM were selected and used to construct three single-sequence models. Furthermore, clinical variables were incorporated through multivariate regression analysis and fused with the selected radiomics features to construct the clinical-radiomics combined model. The diagnostic performance of the models was assessed using receiver operating characteristic (ROC) curve analysis. The clinical utility of the models was evaluated by the area under the ROC curve (AUC) and decision curve analysis (DCA). Results: A total of 282 patients were included, comprising 171 patients in the training set, and 111 patients in the validation set. Compared to the Sag-T2WI model (AUC, 95%CI, training set, 0.797, 0.722-0.782; validation set, 0.648, 0.521-0.776) and the Sag-T1C model (AUC, 95%CI, training set, 0.802, 0.723-0.882; validation set, 0.630, 0.505-0.756), the Ax-DWI model exhibited the highest diagnostic performance with AUCs of 0.855 (95%CI, 0.791-0.919) in training set, and 0.753 (95%CI, 0.638-0.867) in validation set, respectively. The combined model, integrating selected features from three sequences and FIGO stage, surpassed predictive ability compared to the single-sequence models, with AUC of 0.889 (95%CI, 0.833-0.945) and 0.859 (95%CI, 0.781-0.936) in the training and validation sets, respectively. Conclusions: The pretreatment MRI-based radiomics model, integrating radiomics features from three sequences and clinical variables, exhibited superior performance in predicting LNM following NACT in patients with LACC.

Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer.

Objective: To investigate the value of predicting axillary lymph node (ALN) metastasis based on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinico-radiological characteristics in breast cancer. Methods: A total of 473 breast cancer patients who underwent preoperative DCE-MRI from Jan 2017 to Dec 2020 were enrolled. These patients were randomly divided into training (n=378) and testing sets (n=95) at 8:2 ratio. Intratumoral regions (ITRs) of interest were manually delineated, and peritumoral regions of 3 mm (3 mmPTRs) were automatically obtained by morphologically dilating the ITR. Radiomics features were extracted, and ALN metastasis-related radiomics features were selected by the Mann-Whitney U test, Z score normalization, variance thresholding, K-best algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. Clinico-radiological risk factors were selected by logistic regression and were also used to construct predictive models combined with radiomics features. Then, 5 models were constructed, including ITR, 3 mmPTR, ITR+3 mmPTR, clinico-radiological and combined (ITR+3 mmPTR+ clinico-radiological) models. The performance of models was assessed by sensitivity, specificity, accuracy, F1 score and area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves and decision curve analysis (DCA). Results: A total of 2264 radiomics features were extracted from each region of interest (ROI), 3 and 10 radiomics features were selected for the ITR and 3 mmPTR, respectively. 5 clinico-radiological risk factors were selected, including lesion size, human epidermal growth factor receptor 2 (HER2) expression, vascular cancer thrombus status, MR-reported ALN status, and time-signal intensity curve (TIC) type. In the testing set, the combined model showed the highest AUC (0.839), specificity (74.2%), accuracy (75.8%) and F1 Score (69.3%) among the 5 models. DCA showed that it had the greatest net clinical benefit compared to the other models. Conclusion: The intra- and peritumoral radiomics models based on DCE-MRI could be used to predict ALN metastasis in breast cancer, especially for the combined model with clinico-radiological characteristics showing promising clinical application value.

Radiomics analysis of intratumoral and different peritumoral regions from multiparametric MRI for evaluating HER2 status of breast cancer: A comparative study.

Purpose: To investigate the potential of radiomics signatures (RSs) from intratumoral and peritumoral regions on multiparametric magnetic resonance imaging (MRI) to noninvasively evaluate HER2 status in breast cancer. Method: In this retrospective study, 992 patients with pathologically confirmed breast cancers who underwent preoperative MRI were enrolled. The breast cancer lesions were segmented manually, and the intratumor region of interest (ROIIntra) was dilated by 2, 4, 6 and 8 mm (ROIPeri2mm, ROIPeri4mm, ROIPeri6mm, and ROIPeri8mm, respectively). Quantitative radiomics features were extracted from dynamic contrast-enhanced T1-weighted imaging (DCE-T1), fat-saturated T2-weighted imaging (T2) and diffusion-weighted imaging (DWI). A three-step procedure was performed for feature selection, and RSs were constructed using a support vector machine (SVM) to predict HER2 status. Result: The best single-area RSs for predicting HER2 status were DCE_Peri4mm-RS, T2_Peri4mm-RS, and DWI_Peri4mm-RS, yielding areas under the curve (AUCs) of 0.716 (95% confidence interval (CI), 0.648-0.778), 0.706 (95% CI, 0.637-0.768), and 0.719 (95% CI, 0.651-0.780), respectively, in the test set. The optimal RSs combining intratumoral and peritumoral regions for evaluating HER2 status were DCE-T1_Intra + DCE_Peri4mm-RS, T2_Intra + T2_Peri6mm-RS and DWI_Intra + DWI_Peri4mm-RS, with AUCs of 0.752 (95% CI, 0.686-0.810), 0.754 (95% CI, 0.688-0.812) and 0.725 (95% CI, 0.657-0.786), respectively, in the test set. Combining three sequences in the ROIIntra, ROIPeri2mm, ROIPeri4mm, ROIPeri6mm and ROIPeri8mm areas, the optimal RS was DCE-T1_Peri4mm + T2_Peri4mm + DWI_Peri4mm-RS, achieving an AUC of 0.795 (95% CI, 0.733-0.849) in the test set. Conclusion: This study systematically explored the influence of the intratumoral region, different peritumoral sizes and their combination in radiomics analysis for predicting HER2 status in breast cancer based on multiparametric MRI and found the optimal RS.

Synovial fluid research based on SERS and SERRS for enhanced detection of biomarkers in staged osteoarthritis.

Surface-enhanced (resonance) Raman scattering (SER(R)S) can extremely enhance Raman intensity of samples, which is helpful for detecting synovial fluid (SF) that does not show Raman activity under normal conditions. In this study, SER(R)S spectra of SF from three different osteoarthritis (OA) stages were collected and analyzed for OA progress, finding that the content of collagen increased throughout the disease, while non-collagen proteins and polysaccharides decreased sharply at advanced OA stage accompanied by the increase of phospholipid. The spectral features and differences were enhanced by salting-out and centrifugation. Much more information on biomolecules at different OA stages was disclosed by using SERRS for the first time, these main trace components (ß-carotene, collagen, hyaluronic acid, nucleotide, and phospholipid) can be used as potential biomarkers. It indicates that SERRS has a more comprehensive ability to assist SERS in seeking micro(trace) biomolecules as biomarkers and facilitating accurate and efficient diagnosis and mechanism research of OA.

Microbial interventions in yak colibacillosis: Lactobacillus-mediated regulation of intestinal barrier.

Introduction: The etiology of Escherichia coli in yaks, along with its drug resistance, results in economic losses within the yak breeding industry. The utilization of lactic acid bacteria treatment has emerged as a viable alternative to antibiotics in managing colibacillosis. Methods: To elucidate the therapeutic mechanisms of Lactobacillus against Escherichia coli-induced intestinal barrier damage in yaks, we employed yak epithelial cells as the experimental model and established a monolayer epithelial barrier using Transwell. The study encompassed four groups: a control group, a model group (exposed to E. coli O78), a low-dose Lactobacillus group (E. coli O78 + 1 × 105CFU LAB), and a high-dose Lactobacillus group (E. coli O78 + 1 × 107CFU LAB). Various techniques, including transmembrane resistance measurement, CFU counting, RT-qPCR, and Western Blot, were employed to assess indicators related to cell barrier permeability and tight junction integrity. Results: In the Model group, Escherichia coli O78 significantly compromised the permeability and tight junction integrity of the yak epithelial barrier. It resulted in decreased transmembrane resistance, elevated FD4 flux, and bacterial translocation. Furthermore, it downregulated the mRNA and protein expression of MUC2, Occludin, and ZO-1, while upregulating the mRNA expression and protein expression of FABP2 and Zonulin, thereby impairing intestinal barrier function. Contrastingly, Lactobacillus exhibited a remarkable protective effect. It substantially increased transmembrane resistance, mitigated FD4 flux, and reduced bacterial translocation. Moreover, it significantly upregulated the mRNA and protein expression of MUC2, Occludin, and ZO-1, while downregulating the mRNA and protein expression of FABP2 and Zonulin. Notably, high-dose LAB demonstrated superior regulatory effects compared to the low-dose LAB group. Discussion: In conclusion, our findings suggest that Lactobacillus holds promise in treating yak colibacillosis by enhancing mucin and tight junction protein expression. Furthermore, we propose that Lactobacillus achieves these effects through the regulation of Zonulin.

The prognostic value of radiological and pathological lymph node status in patients with cervical cancer who underwent neoadjuvant chemotherapy and followed hysterectomy.

To investigate the prognostic value of lymph node status in patients with cervical cancer (CC) patients who underwent neoadjuvant chemotherapy (NACT) and followed hysterectomy. Patients in two referral centers were retrospectively analyzed. The baseline tumor size and radiological lymph node status (LNr) were evaluated on pre-NACT MRI. Tumor histology, differentiation and pathological lymph node status (LNp) were obtained from post-operative specimen. The log-rank test was used to compare survival between patient groups. Cox proportional hazards regression models were employed to estimate the hazard ratio (HR) of various factors with progression-free survival (PFS) and overall survival (OS). A total of 266 patients were included. Patients with 2018 FIGO IIIC showed worse PFS compared to those with FIGO IB-IIB (p < 0.001). The response rate in patients with LNp(-) was 64.1% (134/209), significantly higher than that of 45.6% (26/57) in patients with LNp( +) (p = 0.011). Multivariate Cox analysis identified the main independent predictors of PFS as LNp( +) (HR = 3.777; 95% CI 1.715-8.319), non-SCC (HR = 2.956; 95% CI 1.297-6.736), poor differentiation (HR = 2.370; 95% CI 1.130-4.970) and adjuvant radiation (HR = 3.266; 95% CI 1.183-9.019). The interaction between LNr and LNp regarding PFS were significant both for univariate and multivariate (P = 0.000171 and 1.5357e-7 respectively). In patients with LNr( +), a significant difference in PFS was observed between patients with LNp(-) and LNp( +) (p = 0.0027). CC patients with FIGO 2018 stage IIIC who underwent NACT and followed hysterectomy had worse PFS compared to those with IB-IIB. LNp( +), non-SCC, poor differentiation and adjuvant radiation were independent risk factors for PFS. The adverse prognostic value of LNp( +) was more significant in patients with LNr( +).

Prognostic Value of 18F-FDG PET/CT Radiomics in Extranodal Nasal-Type NK/T Cell Lymphoma.

OBJECTIVE: To investigate the prognostic utility of radiomics features extracted from 18F-fluorodeoxyglucose (FDG) PET/CT combined with clinical factors and metabolic parameters in predicting progression-free survival (PFS) and overall survival (OS) in individuals diagnosed with extranodal nasal-type NK/T cell lymphoma (ENKTCL). MATERIALS AND METHODS: A total of 126 adults with ENKTCL who underwent 18F-FDG PET/CT examination before treatment were retrospectively included and randomly divided into training (n = 88) and validation cohorts (n = 38) at a ratio of 7:3. Least absolute shrinkage and selection operation Cox regression analysis was used to select the best radiomics features and calculate each patient's radiomics scores (RadPFS and RadOS). Kaplan-Meier curve and Log-rank test were used to compare survival between patient groups risk-stratified by the radiomics scores. Various models to predict PFS and OS were constructed, including clinical, metabolic, clinical + metabolic, and clinical + metabolic + radiomics models. The discriminative ability of each model was evaluated using Harrell's C index. The performance of each model in predicting PFS and OS for 1-, 3-, and 5-years was evaluated using the time-dependent receiver operating characteristic (ROC) curve. RESULTS: Kaplan-Meier curve analysis demonstrated that the radiomics scores effectively identified high- and low-risk patients (all P < 0.05). Multivariable Cox analysis showed that the Ann Arbor stage, maximum standardized uptake value (SUVmax), and RadPFS were independent risk factors associated with PFS. Further, ß2-microglobulin, Eastern Cooperative Oncology Group performance status score, SUVmax, and RadOS were independent risk factors for OS. The clinical + metabolic + radiomics model exhibited the greatest discriminative ability for both PFS (Harrell's C-index: 0.805 in the validation cohort) and OS (Harrell's C-index: 0.833 in the validation cohort). The time-dependent ROC analysis indicated that the clinical + metabolic + radiomics model had the best predictive performance. CONCLUSION: The PET/CT-based clinical + metabolic + radiomics model can enhance prognostication among patients with ENKTCL and may be a non-invasive and efficient risk stratification tool for clinical practice.

Performance of node reporting and data system (node-RADS): a preliminary study in cervical cancer.

BACKGROUND: Node Reporting and Data System (Node-RADS) was proposed and can be applied to lymph nodes (LNs) across all anatomical sites. This study aimed to investigate the diagnostic performance of Node-RADS in cervical cancer patients. METHODS: A total of 81 cervical cancer patients treated with radical hysterectomy and LN dissection were retrospectively enrolled. Node-RADS evaluations were performed by two radiologists on preoperative MRI scans for all patients, both at the LN level and patient level. Chi-square and Fisher's exact tests were employed to evaluate the distribution differences in size and configuration between patients with and without LN metastasis (LNM) in various regions. The receiver operating characteristic (ROC) and the area under the curve (AUC) were used to explore the diagnostic performance of the Node-RADS score for LNM. RESULTS: The rates of LNM in the para-aortic, common iliac, internal iliac, external iliac, and inguinal regions were 7.4%, 9.3%, 19.8%, 21.0%, and 2.5%, respectively. At the patient level, as the NODE-RADS score increased, the rate of LNM also increased, with rates of 26.1%, 29.2%, 42.9%, 80.0%, and 90.9% for Node-RADS scores 1, 2, 3, 4, and 5, respectively. At the patient level, the AUCs for Node-RADS scores > 1, >2, > 3, and > 4 were 0.632, 0.752, 0.763, and 0.726, respectively. Both at the patient level and LN level, a Node-RADS score > 3 could be considered the optimal cut-off value with the best AUC and accuracy. CONCLUSIONS: Node-RADS is effective in predicting LNM for scores 4 to 5. However, the proportions of LNM were more than 25% at the patient level for scores 1 and 2, which does not align with the expected very low and low probability of LNM for these scores.

Metagenomic analysis for exploring the potential of Lactobacillus yoelii FYL1 to mitigate bacterial diarrhea and changes in the gut microbiota of juvenile yaks.

Diarrhea in calves is a common disease that results in poor nutrient absorption, poor growth and early death which leads to productivity and economic losses. Therefore, it is important to explore the methods to reduce diarrhea in yak's calves. Efficacy of lactic acid bacteria (LAB) for improvement of bacterial diarrhea is well recognized. For this purpose, two different doses (107 CFU, 1011 CFU) of Lactobacillus yoelii FYL1 isolated from yaks were fed to juvenile yaks exposed to E. coli O78. After a trial period of ten days fresh feces and intestinal contents of the experimental yaks were collected and metagenomics sequencing was performed. It was found that feeding a high dose of Lactobacillus yoelii FYL1 decreased abundance of phylum Firmicutes in the E. coli O78 infected group whereas, it was high in animals fed low dose of Lactobacillu yoelii FYL1. Results also revealed that counts of bacteria from the family Oscillospiraceae, genus Synergistes and Megasphaera were higher in control group whereas, order Bifidobacteriales and family Bifidobacteriaceae were higher in infected group. It was observed that bacterial counts for Pseudoruminococcus were significantly (P < 0.05) higher in animals of group that were given high dose of Lactobacillus yoelii FYL1 (HLAB). Compared to infected group multiple beneficial bacterial genera such as Deinococus and Clostridium were found higher in the animals that were given a low dose of Lactobacillus yoelii FYL1 (LLAB). The abundance of pathogenic bacterial genera that included Parascardovia, Bacteroides and Methanobrevibacter was decreased (P < 0.05) in the lower dose treated group. The results of functional analysis revealed that animals of LLAB had a higher metabolism of terpenoids and polyketides compared to animals of infected group. Virus annotation also presented a significant inhibitory effect of LLAB on some viruses (P < 0.05). It was concluded that L. yoelii FYL1 had an improved effect on gut microbiota of young yaks infected with E. coli O78. This experiment contributes to establish the positive effects of LAB supplementation while treating diarrhea.

Exploring the induction of endometrial epithelial cell apoptosis in clinical-type endometritis in yaks through the cyt-c/caspase-3 signaling axis.

Endometritis is a significant contributor to reduced productivity in yaks in Tibet, China. The Cyt-c/Caspase-3 signaling axis plays a crucial role in the mitochondrial pathway that triggers cell apoptosis due to endogenous factors. In this study, we examined the endometrial epithelial tissue of yaks with endometritis using pathological examination, immunohistochemical analysis, TUNEL staining, qRT-PCR, and Western blot. The results indicated significant changes in the apoptotic factors of the Cyt-c/Caspase-3 signaling axis. The expression levels of Bak1, Bax, Cyt-c, Apaf-1, Caspase-9, and Caspase-3 were significantly increased (P < 0.05), while the expression level of Bcl-2 was significantly decreased. Immunohistochemistry results revealed significant increase in Bak1, Bax, Cyt-c, Apaf-1, Caspase-9, and Caspase-3 expression in the cytoplasm compared to the healthy group, except for Bcl-2, which showed a significant decrease. Pathological section analysis demonstrated that clinical endometritis in yaks led to structural damage, bleeding, congestion, and inflammatory cell infiltration in the endometrial epithelium. Our study findings indicated that clinical endometritis in yaks can modulate apoptosis of endometrial epithelial cells via the Cyt-c/Caspase-3 signaling pathway, resulting in different levels of damage. This research is pioneering in exploring cell apoptosis induced by clinical endometritis in yaks, offering novel insights and potential strategies for the future prevention and treatment of endometritis in yaks.

Radial magnetic resonance image reconstruction with a deep unrolled projected fast iterative soft-thresholding network.

Radially sampling of magnetic resonance imaging (MRI) is an effective way to accelerate the imaging. How to preserve the image details in reconstruction is always challenging. In this work, a deep unrolled neural network is designed to emulate the iterative sparse image reconstruction process of a projected fast soft-threshold algorithm (pFISTA). The proposed method, an unrolled pFISTA network for Deep Radial MRI (pFISTA-DR), include the preprocessing module to refine coil sensitivity maps and initial reconstructed image, the learnable convolution filters to extract image feature maps, and adaptive threshold to robustly remove image artifacts. Experimental results show that, among the compared methods, pFISTA-DR provides the best reconstruction and achieved the highest PSNR, the highest SSIM and the lowest reconstruction errors.

Outcome prediction for symptomatic patients with fibroids who underwent uterine artery embolization.

PURPOSE: To predict the clinical outcome of symptomatic patients with uterine leiomyomas who underwent uterine artery embolization (UAE), based on clinical and radiological features. METHODS: Patients with symptomatic uterine leiomyomas who underwent UAE from March 2010 to September 2019 were consecutively included in this retrospective study. Patients with persistent or recurrent symptoms and those who needed repeated UAE, myomectomy, or hysterectomy following the initial treatment were considered to have a poor outcome after UAE. The total and enhancing volume of the dominant leiomyoma in each location and uterine volume were obtained before and after UAE. Univariate and multivariate Cox proportional hazard analyses were used to evaluate the parameters that could predict poor outcome. RESULTS: A total of 116 patients (mean age, 45 ± 5 years) were included. Forty-six patients (46/116, 39.7%) showed poor outcome. Cox regression analysis showed higher hazard of poor outcome for younger patients vs. older patients (HR: 0.92, p-value: 0.01), patients with adenomyosis vs. patients without adenomyosis (HR: 2.47, p-value < 0.01), patients with adenomyosis thickness > 2.5 cm before UAE vs. patients without adenomyosis (HR: 4.2, p-value < 0.01) and for patients with intramural fibroid enhancement volume > 440 cm3 compared to patients with no intramural fibroids (p-value: 0.06). Multivariate Cox regression analysis including age, the thickness of adenomyosis, and intramural leiomyoma volume of enhancement before UAE was chosen as the best model to predict the outcome. CONCLUSIONS: Pretreatment clinical and MRI features could identify patients with a higher risk for poor outcome after UAE.

Differentiation between small hepatocellular carcinoma (<3 cm) and benign hepatocellular lesions in patients with Budd-Chiari syndrome: the role of multiparametric MR imaging.

Objective: To investigate the value of multiparametric MR imaging to differentiate between small hepatocellular carcinoma (s-HCC) versus benign liver lesions in patients with Budd-Chiari syndrome. Methods: 12 patients with benign hepatocellular lesions and 32 patients with small (<3 cm) HCCs were assessed. MRI images were reviewed by two radiologists blinded to the patient background information; lesion T1 and T2 signal intensities and ADC values were compared with the background liver. Enhancement of lesion relative to hepatic parenchyma [(T1Enh-T1liver)/T1liver] in the arterial, venous, and delayed phases was also compared between the two groups. A multivariable logistic model was developed using these categorical measures; the predictive value of the model was tested using the Area Under the Receiver operating characteristic (AU-ROC) curve for logistic models. P-values <0.05 were considered statistically significant. Results: There were consistent differences in T1lesion/T1liver, and T2lesion/T2liver, and ADClesion/ADCliver between benign hepatocellular lesions versus the sHCC group (p<0.001, p<0.001, p = 0.045, respectively). Lesion-to-background liver enhancement in the portal venous and delayed phases was different between the benign lesions versus sHCC (p=0.001). ROC analysis for the logistic model that included the T1 ratio, T2 ratio, and portal venous enhancement ratio demonstrated excellent discriminatory power with the area under the curve of 0.94. Conclusion: Multiparametric MR imaging is a useful method to help differentiate benign liver lesions from sHCC in patients with Budd-Chiari syndrome.

Verapamil modulates NFAT2 to inhibit tumor growth and potentiates PD1ab immune checkpoint inhibitor therapy in cervical cancer treatment.

PURPOSE: Current evidence suggests a high co-prevalence of hypertension and cervical cancer. Accordingly, blood pressure control is indicated during anti-tumor drug therapy in this patient population. Over the past few years, immunotherapy has made great strides in treating different cancers. However, the role and clinical significance of verapamil as a first-line anti-hypertensive drug during immunotherapy remain poorly understood, emphasizing the need for further studies. METHODS: Murine cervical cancer models were employed to assess the effect of verapamil monotherapy and combination with PD1ab. Immunohistochemistry was conducted to quantify the abundance of CD8+ T cell and Ki67+ cells. Several in-vitro and in-vivo assays were used to study the effects of verapamil and explore the preliminary mechanism. RESULTS: Monotherapy with verapamil or PD1ab immune checkpoint inhibitor significantly suppressed the growth of subcutaneously grafted U14 cells in WT BABL/c mice, respectively, with increased survival time of mice. Consistent results were observed in the melanoma model. Furthermore, we substantiated that verapamil significantly impaired tumor proliferation and migration of SiHa human cervical cancer cells in vitro and in vivo. In silico analysis using TCGA data revealed that NFAT2 expression negatively correlated with patient survival. The CCK8 assay revealed that verapamil abrogated the stimulatory effect of NFAT2 after knockdown of NFAT2. CONCLUSIONS: Our results suggest that verapamil inhibits tumor growth by modulating NFAT2 expression and enhancing tumor immune responses to PD1ab, which can be harnessed for cervical cancer therapy, especially for patients with comorbid hypertension. Indeed, further clinical trials are warranted to increase the robustness of our findings.

Effect of Montelukast Combined with Budesonide on Inflammatory Response and Pulmonary Function in Children with Cough Variant Asthma: A Meta-analysis.

This meta-analysis aimed to compare the efficacy of montelukast (MKST) combined with budesonide (BUD) and BUD alone in the treatment of pulmonary inflammation and pulmonary function in children with cough variant asthma (CVA). Five electronic databases were searched for studies about MKST+BUD therapy and BUD alone therapy on inflammation and pulmonary function in CVA children from inception to November 23, 2021. Twenty-two articles were included. The results showed that, compared with BUD alone, the combination treatment could achieve better improvement of pulmonary function and lower levels of inflammation (MKST+BUD group: FEV1: SMD = 2.77, 95% CI: 2.07, 3.46; FVC: SMD = 2.54, 95% CI: 1.82, 3.27; PEF: SMD = 2.27, 95% CI: 1.79, 2.75; IgE: SMD = -7.95, 95% CI: -9.66, -6.25; TNF-α: SMD = -4.67, 95% CI: -6.04, -3.31; IL-8: SMD = -8.18, 95% CI: -11.46, -4.90; BUD alone group: FEV1: SMD = 1.83, 95% CI: 1.34, 2.31; FVC: SMD = 1.39, 95% CI: 0.93, 1.84; PEF: SMD = 1.51, 95% CI: 1.13, 1.89; IgE: SMD = -4.93, 95% CI: -6.14, -3.72; TNF-α: SMD = -2.78, 95% CI: -3.76, -1.80; IL-8: SMD = -4.94, 95% CI: -7.10, -2.79). To conclude, compared with BUD alone, MKST+BUD therapy was found to be more effective in improving pulmonary function and reducing inflammation in CVA children. Key Words: Montelukast, Budesonide, Cough variant asthma, Children, Pulmonary function, Inflammatory markers, Meta-analysis.

Comparison between Gradient-Echo and Spin-Echo EPI MR Elastography at 3 T in quantifying liver stiffness of patients with and without iron overload; a prospective study.

OBJECTIVES: To compare the maximum axial area of the confidence mask and the calculated liver stiffness (LS) on gradient-echo (GRE) and spin-echo echo planar imaging (SE-EPI) MR elastography (MRE) in patients with and without iron deposition. METHODS: 104 patients underwent MRE by GRE and SE-EPI sequences at 3 T. R2* values >88 Hz in the liver were categorized in the iron overload group. The maximum axial area and the corresponding LS values were measured by manually contouring the whole area on one slice with the largest confidence mask at both GRE and SE-EPI sequences. RESULTS: In patients with iron overload, SE-EPI provided larger maximum axial confidence area in unfailed images (57.6 ± 41.7 cm2) compared to GRE (45.7 ± 29.1 cm2) (p-value = 0.007). In five patients with iron overload, imaging failed at GRE sequence, whereas at the SE-EPI sequence the maximum area of the confidence mask had a mean value of 33.5 ± 54.9 cm2. In livers without iron overload (R2*: 50.7 ± 13.1 Hz), the maximum area on the confidence mask was larger at SE-EPI (118.3 ± 41.2 cm2) than on GRE (105.1 ± 31.7 cm2) (P-value = 0.003). There was no significant difference in mean LS between SE-EPI (2.0 ± 0.3 kPa) and GRE (2.1 ± 0.5 kPa) in livers with iron overload (P value = 0.24). Similarly, in the group without iron overload, mean LS was 2.3 ± 0.7 kPa at SE-EPI and 2.4 ± 0.8 kPa at GRE sequences (P-value = 0.11). CONCLUSIONS: SE-EPI MRE can successfully provide similar LS measurements as GRE MRE. Furthermore, it provides a larger measurable area on the confidence mask in both groups with and without iron overload.

Multifunctional nanomedicines-enabled chemodynamic-synergized multimodal tumor therapy via Fenton and Fenton-like reactions.

Chemodynamic therapy (CDT) is well-known for using the tumor microenvironment to activate the Fenton reaction or Fenton-like reaction to generate strong oxidative hydroxyl radicals for tumor-specific treatment. It is highly selective and safe, without depth limitation of tissue penetration, and shows its potential as a new green therapeutic method with great clinical application. However, the catalytic efficiency of reagents involved in the Fenton reaction is severely affected by the inherent microenvironmental limitations of tumors and the strict Fenton reaction-dependent conditions. With the increasing application of nanotechnology in the medical field, combined therapies based on different types of functional nanomaterials have opened up new avenues for the development of next-generation CDT-enhanced system. This review will comprehensively exemplify representative results of combined therapies of CDT with other antitumor therapies such as chemotherapy, phototherapy, sonodynamic therapy, radiation therapy, magnetic hyperthermia therapy, immunotherapy, starvation therapy, gas therapy, gene therapy, oncosis therapy, or a combination thereof for improving antitumor efficiency from hundreds of the latest literature, introduce strategies such as the ingenious design of nanomedicines and tumor microenvironment regulations to enhance the combination therapy, and further summarize the challenges and future perspective of CDT-based multimodal anticancer therapy.