A Prediction Model for Detecting Dysthyroid Optic Neuropathy Based on Clinical Factors and Imaging Markers of the Optic Nerve and Cerebrospinal Fluid in the Optic Nerve Sheath.

OBJECTIVE: This study aimed to develop and test a model for predicting dysthyroid optic neuropathy (DON) based on clinical factors and imaging markers of the optic nerve and cerebrospinal fluid (CSF) in the optic nerve sheath. METHODS: This retrospective study included patients with thyroid-associated ophthalmopathy (TAO) without DON and patients with TAO accompanied by DON at our hospital. The imaging markers of the optic nerve and CSF in the optic nerve sheath were measured on the water-fat images of each patient and, together with clinical factors, were screened by Least absolute shrinkage and selection operator. Subsequently, we constructed a prediction model using multivariate logistic regression. The accuracy of the model was verified using receiver operating characteristic curve analysis. RESULTS: In total, 80 orbits from 44 DON patients and 90 orbits from 45 TAO patients were included in our study. Two variables (optic nerve subarachnoid space and the volume of the CSF in the optic nerve sheath) were found to be independent predictive factors and were included in the prediction model. In the development cohort, the mean area under the curve (AUC) was 0.994, with a sensitivity of 0.944, specificity of 0.967, and accuracy of 0.901. Moreover, in the validation cohort, the AUC was 0.960, the sensitivity was 0.889, the specificity was 0.893, and the accuracy was 0.890. CONCLUSIONS: A combined model was developed using imaging data of the optic nerve and CSF in the optic nerve sheath, serving as a noninvasive potential tool to predict DON.

A Radiomics-Based Nomogram Using Ultrasound Carotid Plaque Evaluation For Predicting Cerebro-Cardiovascular Events In Asymptomatic Patients.

RATIONALE AND OBJECTIVES: This study aims to assess whether a radiomics-based nomogram correlates with a higher risk of future cerebro-cardiovascular events in patients with asymptomatic carotid plaques. Additionally, it investigates the nomogram's contribution to the revised Framingham Stroke Risk Profile (rFSRP) for predicting cerebro-cardiovascular risk. MATERIALS AND METHODS: Predictive models aimed at identifying an increased risk of future cerebro-cardiovascular events were developed and internally validated at one center, then externally validated at two other centers. Survival curves, constructed using the Kaplan-Meier method, were compared through the log-rank test. RESULTS: This study included a total of 2009 patients (3946 images). The final nomogram was generated using multivariate Cox regression variables, including dyslipidemia, lumen diameter, plaque echogenicity, and ultrasonography (US)-based radiomics risk. The Harrell's concordance index (C-index) for predicting events-free survival (EFS) was 0.708 in the training cohort, 0.574 in the external validation cohort 1, 0.632 in the internal validation cohort, and 0.639 in the external validation cohort 2. The final nomogram showed a significant increase in C-index compared to the clinical, conventional US, and US-based radiomics models (all P < 0.05). Furthermore, the final nomogram-assisted method significantly improved the sensitivity and accuracy of radiologists' visual qualitative score of plaque (both P < 0.001). Among 1058 patients with corresponding 1588 plaque US images classified as low-risk by the rFSRP, 75 (7.1%) patients with corresponding 93 (5.9%) carotid plaque images were appropriately reclassified to the high-risk category by the final nomogram. CONCLUSION: The radiomics-based nomogram demonstrated accurate prediction of cerebro-cardiovascular events in patients with asymptomatic carotid plaques. It also improved the sensitivity and accuracy of radiologists' visual qualitative score of carotid plaque and enhanced the risk stratification ability of rFSRP. SUMMARY: The radiomics-based nomogram allowed accurate prediction of cerebro-cardiovascular events, especially ipsilateral ischemic stroke in patients with asymptomatic carotid atherosclerotic plaques. KEY RESULTS: The radiomics-based nomogram allowed accurate prediction of cerebro-cardiovascular events, especially ipsilateral ischemic stroke in patients with asymptomatic carotid atherosclerotic plaques. The radiomics-based nomogram improved the sensitivity and accuracy of radiologists' visual qualitative score of carotid plaque. The radiomics-based nomogram improved the discrimination of high-risk populations from low-risk populations in asymptomatic patients with carotid atherosclerotic plaques and the risk stratification capability of the rFSRP.

Identification and validation of a novel anoikis-related prognostic model for prostate cancer.

BACKGROUND: Anoikis resistance is a hallmark characteristic of oncogenic transformation, which is crucial for tumor progression and metastasis. The aim of this study was to identify and validate a novel anoikis-related prognostic model for prostate cancer (PCa). METHODS: We collected a gene expression profile, single nucleotide polymorphism mutation and copy number variation (CNV) data of 495 PCa patients from the TCGA database and 140 PCa samples from the MSKCC dataset. We extracted 434 anoikis-related genes and unsupervised consensus cluster analysis was used to identify molecular subtypes. The immune infiltration, molecular function, and genome alteration of subtypes were evaluated. A risk signature was developed using Cox regression analysis and validated with the MSKCC dataset. We also identify potential drugs for high-risk group patients. RESULTS: Two subtypes were identified. C1 exhibited a higher level of CNV amplification, immune score, stromal score, aneuploidy score, homologous recombination deficiency, intratumor heterogeneity, single-nucleotide variant neoantigens, and tumor mutational burden compared to C2. C2 showed a better survival outcome and had a high level of gamma delta T cell and activated B cell infiltration. The risk signature consisting of four genes (HELLS, ZWINT, ABCC5, and TPSB2) was developed (area under the curve = 0.780) and was found to be an independent prognostic factor for overall survival in PCa patients. Four CTRP-derived and four PRISM-derived compounds were identified for high-risk patients. CONCLUSIONS: The anoikis-related prognostic model developed in this study could be a useful tool for clinical decision-making. This study may provide a new perspective for the treatment of anoikis-related PCa.

A Comparative Study of Two Radiomics-Based Blood Flow Modes with Thyroid Imaging Reporting and Data System in Predicting Malignancy of Thyroid Nodules and Reducing Unnecessary Fine-Needle Aspiration Rate.

RATIONALE AND OBJECTIVES: We aimed to compare superb microvascular imaging (SMI)-based radiomics methods, and contrast-enhanced ultrasound (CEUS)-based radiomics methods to the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) for classifying thyroid nodules (TNs) and reducing unnecessary fine-needle aspiration biopsy (FNAB) rate. MATERIALS AND METHODS: This retrospective study enrolled a dataset of 472 pathologically confirmed TNs. Radiomics characteristics were extracted from B-mode ultrasound (BMUS), SMI, and CEUS images, respectively. After eliminating redundant features, four radiomics scores (Rad-scores) were constructed. Using multivariable logistic regression analysis, four radiomics prediction models incorporating Rad-score and corresponding US features were constructed and validated in terms of discrimination, calibration, decision curve analysis, and unnecessary FNAB rate. RESULTS: The diagnostic performance of the BMUS + SMI radiomics method was better than ACR TI-RADS (area under the curve [AUC]: 0.875 vs. 0.689 for the training cohort, 0.879 vs. 0.728 for the validation cohort) (P < 0.05), and comparable with BMUS + CEUS radiomics method (AUC: 0.875 vs. 0.878 for the training cohort, 0.879 vs. 0.865 for the validation cohort) (P > 0.05). Decision curve analysis showed that the BMUS+SMI radiomics method could achieve higher net benefits than the BMUS radiomics method and ACR TI-RADS when the threshold probability was between 0.13 and 0.88 in the entire cohort. When applying the BMUS+SMI radiomics method, the unnecessary FNAB rate reduced from 43.4% to 13.9% in the training cohort and from 45.6% to 18.0% in the validation cohorts in comparison to ACR TI-RADS. CONCLUSION: The dual-modal SMI-based radiomics method is convenient and economical and can be an alternative to the dual-modal CEUS-based radiomics method in helping radiologists select the optimal clinical strategy for TN management.

Predicting the malignancy of extremity soft-tissue tumors by an ultrasound-based radiomics signature.

BACKGROUND: Accurate differentiation of extremity soft-tissue tumors (ESTTs) is important for treatment planning. PURPOSE: To develop and validate an ultrasound (US) image-based radiomics signature to predict ESTTs malignancy. MATERIAL AND METHODS: A dataset of US images from 108 ESTTs were retrospectively enrolled and divided into the training cohort (78 ESTTs) and validation cohort (30 ESTTs). A total of 1037 radiomics features were extracted from each US image. The most useful predictive radiomics features were selected by the maximum relevance and minimum redundancy method, least absolute shrinkage, and selection operator algorithm in the training cohort. A US-based radiomics signature was built based on these selected radiomics features. In addition, a conventional radiologic model based on the US features from the interpretation of two experienced radiologists was developed by a multivariate logistic regression algorithm. The diagnostic performances of the selected radiomics features, the US-based radiomics signature, and the conventional radiologic model for differentiating ESTTs were evaluated and compared in the validation cohort. RESULTS: In the validation cohort, the area under the curve (AUC), sensitivity, and specificity of the US-based radiomics signature for predicting ESTTs malignancy were 0.866, 84.2%, and 81.8%, respectively. The US-based radiomics signature had better diagnostic predictability for predicting ESTT malignancy than the best single radiomics feature and the conventional radiologic model (AUC = 0.866 vs. 0.719 vs. 0.681 for the validation cohort, all P <0.05). CONCLUSION: The US-based radiomics signature could provide a potential imaging biomarker to accurately predict ESTT malignancy.

Radiomics based on T2-weighted and diffusion-weighted MR imaging for preoperative prediction of tumor deposits in rectal cancer.

AIM: Preoperative diagnosis of tumor deposits (TDs) in patients with rectal cancer remains a challenge. This study aims to develop and validate a radiomics nomogram based on the combination of T2-weighted (T2WI) and diffusion-weighted MR imaging (DWI) for the preoperative identification of TDs in rectal cancer. MATERIALS AND METHODS: A total of 199 patients with rectal cancer who underwent T2WI and DWI were retrospectively enrolled and divided into a training set (n â€‹= â€‹159) and a validation set (n â€‹= â€‹40). The total incidence of TDs was 37.2 â€‹% (74/199). Radiomics features were extracted from T2WI and apparent diffusion coefficient (ADC) images. A radiomics nomogram combining Rad-score (T2WI â€‹+ â€‹ADC) and clinical factors was subsequently constructed. The area under the receiver operating characteristic curve (AUC) was then calculated to evaluate the models. The nomogram is also compared to three machine learning model constructed based on no-Rad scores. RESULTS: The Rad-score (T2WI â€‹+ â€‹ADC) achieved an AUC of 0.831 in the training and 0.859 in the validation set. The radiomics nomogram (the combined model), incorporating the Rad-score (T2WI â€‹+ â€‹ADC), MRI-reported lymph node status (mLN-status), and CA19-9, showed good discrimination of TDs with an AUC of 0.854 for the training and 0.923 for the validation set, which was superior to Random Forests, Support Vector Machines, and Deep Learning models. The combined model for predicting TDs outperformed the other three machine learning models showed an accuracy of 82.5 â€‹% in the validation set, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 66.7 â€‹%, 92.0 â€‹%, 83.3 â€‹%, and 82.1 â€‹%, respectively. CONCLUSION: The radiomics nomogram based on Rad-score (T2WI â€‹+ â€‹ADC) and clinical factors provides a promising and effective method for the preoperative prediction of TDs in patients with rectal cancer.

Dual-modal radiomics nomogram based on contrast-enhanced ultrasound to improve differential diagnostic accuracy and reduce unnecessary biopsy rate in ACR TI-RADS 4-5 thyroid nodules.

BACKGROUND: American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS, TR) 4 and 5 thyroid nodules (TNs) demonstrate much more complicated and overlapping risk characteristics than TR1-3 and have a rather wide range of malignancy possibilities (> 5%), which may cause overdiagnosis or misdiagnosis. This study was designed to establish and validate a dual-modal ultrasound (US) radiomics nomogram integrating B-mode ultrasound (BMUS) and contrast-enhanced ultrasound (CEUS) imaging to improve differential diagnostic accuracy and reduce unnecessary fine needle aspiration biopsy (FNAB) rates in TR 4-5 TNs. METHODS: A retrospective dataset of 312 pathologically confirmed TR4-5 TNs from 269 patients was collected for our study. Data were randomly divided into a training dataset of 219 TNs and a validation dataset of 93 TNs. Radiomics characteristics were derived from the BMUS and CEUS images. After feature reduction, the BMUS and CEUS radiomics scores (Rad-score) were built. A multivariate logistic regression analysis was conducted incorporating both Rad-scores and clinical/US data, and a radiomics nomogram was subsequently developed. The performance of the radiomics nomogram was evaluated using calibration, discrimination, and clinical usefulness, and the unnecessary FNAB rate was also calculated. RESULTS: BMUS Rad-score, CEUS Rad-score, age, shape, margin, and enhancement direction were significant independent predictors associated with malignant TR4-5 TNs. The radiomics nomogram involving the six variables exhibited excellent calibration and discrimination in the training and validation cohorts, with an AUC of 0.873 (95% CI, 0.821-0.925) and 0.851 (95% CI, 0.764-0.938), respectively. The marked improvements in the net reclassification index and integrated discriminatory improvement suggested that the BMUS and CEUS Rad-scores could be valuable indicators for distinguishing benign from malignant TR4-5 TNs. Decision curve analysis demonstrated that our developed radiomics nomogram was an instrumental tool for clinical decision-making. Using the radiomics nomogram, the unnecessary FNAB rate decreased from 35.3 to 14.5% in the training cohort and from 41.5 to 17.7% in the validation cohorts compared with ACR TI-RADS. CONCLUSION: The dual-modal US radiomics nomogram revealed superior discrimination accuracy and considerably decreased unnecessary FNAB rates in benign and malignant TR4-5 TNs. It could guide further examination or treatment options.

MRI Radiomics Features of Adenohypophysis Determine the Activation of Hypothalamic-Pituitary-Gonadal Axis in Peri-Puberty Children.

BACKGROUND: The status of the hypothalamic-pituitary-gonadal (HPG) axis is important for assessing the onset of physiological or pathological puberty. The reference standard gonadotropin-releasing hormone (GnRH) stimulation test requires hospital admission and repeated blood samples. A simple noninvasive method would be beneficial. OBJECTIVES: To explore a noninvasive method for evaluating HPG axis activation in children using an MRI radiomics model. STUDY TYPE: Retrospective. POPULATION: Two hundred thirty-nine children (83 male; 3.6-14.6 years) with hypophysial MRI and GnRH stimulation tests, randomly divided a training set (168 children) and a test set (71 children). FIELD STRENGTH/SEQUENCE: 3.0 T, 3D isotropic fast spin echo (CUBE) T1-weighted imaging (T1WI) sequences. ASSESSMENT: Radiomics features were extracted from sagittal 3D CUBE T1WI, and imaging signatures were generated using the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation. Diagnostic performance for differential diagnosis of HPG status was compared between a radiomics model and MRI features (adenohypophyseal height [aPH] and volume [aPV]). STATISTICAL TESTS: Receiver operating characteristic (ROC) and decision curve analysis (DCA). A P value <0.05 was considered statistically significant. RESULTS: Eight hundred fifty-one radiomics features were extracted and reduced to 10 by the LASSO method in the training cohort. The radiomics model based on CUBE T1WI showed good performance in assessment of HPG axis activation with an area under the ROC curve (AUC) of 0.81 (95% CI: 0.71, 0.91) in the test set. The AUC of the radiomics model was significantly higher than that of aPH (0.81 vs. 0.65) but there was no significant difference compared to aPV (0.81 vs. 0.78, P = 0.58). In DCA analysis, the radiomics signature showed higher net benefit over the aPV and aPH models. DATA CONCLUSIONS: The MRI radiomics model has potential to assess HPG axis activation status noninvasively, potentially providing valuable information in the diagnosis of patients with pathological puberty onset. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Deep learning predicts cervical lymph node metastasis in clinically node-negative papillary thyroid carcinoma.

OBJECTIVES: Precise determination of cervical lymph node metastasis (CLNM) involvement in patients with early-stage thyroid cancer is fairly significant for identifying appropriate cervical treatment options. However, it is almost impossible to directly judge lymph node metastasis based on the imaging information of early-stage thyroid cancer patients with clinically negative lymph nodes. METHODS: Preoperative US images (BMUS and CDFI) of 1031 clinically node negative PTC patients definitively diagnosed on pathology from two independent hospitals were divided into training set, validation set, internal test set, and external test set. An ensemble deep learning model based on ResNet-50 was built integrating clinical variables, BMUS, and CDFI images using a bagging classifier to predict metastasis of CLN. The final ensemble model performance was compared with expert interpretation. RESULTS: The ensemble deep convolutional neural network (DCNN) achieved high performance in predicting CLNM in the test sets examined, with area under the curve values of 0.86 (95% CI 0.78-0.94) for the internal test set and 0.77 (95% CI 0.68-0.87) for the external test set. Compared to all radiologists averaged, the ensemble DCNN model also exhibited improved performance in making predictions. For the external validation set, accuracy was 0.72 versus 0.59 (p = 0.074), sensitivity was 0.75 versus 0.58 (p = 0.039), and specificity was 0.69 versus 0.60 (p = 0.078). CONCLUSIONS: Deep learning can non-invasive predict CLNM for clinically node-negative PTC using conventional US imaging of thyroid cancer nodules and clinical variables in a multi-institutional dataset with superior accuracy, sensitivity, and specificity comparable to experts. CRITICAL RELEVANCE STATEMENT: Deep learning efficiently predicts CLNM for clinically node-negative PTC based on US images and clinical variables in an advantageous manner. KEY POINTS: • A deep learning-based ensemble algorithm for predicting CLNM in PTC was developed. • Ultrasound AI analysis combined with clinical data has advantages in predicting CLNM. • Compared to all experts averaged, the DCNN model achieved higher test performance.

Ultrasound radiomics based on axillary lymph nodes images for predicting lymph node metastasis in breast cancer.

Objectives: To determine whether ultrasound radiomics can be used to distinguish axillary lymph nodes (ALN) metastases in breast cancer based on ALN imaging. Methods: A total of 147 breast cancer patients with 41 non-metastatic lymph nodes and 109 metastatic lymph nodes were divided into a training set (105 ALN) and a validation set (45 ALN). Radiomics features were extracted from ultrasound images and a radiomics signature (RS) was built. The Intraclass correlation coefficients (ICCs), Spearman correlation analysis, and least absolute shrinkage and selection operator (LASSO) methods were used to select the ALN status-related features. All images were assessed by two radiologists with at least 10 years of experience in ALN ultrasound examination. The performance levels of the model and radiologists in the training and validation subgroups were then evaluated and compared. Result: Radiomics signature accurately predicted the ALN status, achieved an area under the receiver operator characteristic curve of 0.929 (95%CI, 0.881-0.978) and area under curve(AUC) of 0.919 (95%CI, 95%CI, 0.841-0.997) in training and validation cohorts respectively. The radiomics model performed better than two experts' prediction of ALN status in both cohorts (P<0.05). Besides, prediction in subgroups based on baseline clinicopathological information also achieved good discrimination performance, with an AUC of 0.937, 0.918, 0.885, 0.930, and 0.913 in HR+/HER2-, HER2+, triple-negative, tumor sized ≤ 3cm and tumor sized>3 cm, respectively. Conclusion: The radiomics model demonstrated a good ability to predict ALN status in patients with breast cancer, which might provide essential information for decision-making.

Nomogram for predicting lymph node metastasis in patients with ovarian cancer using ultrasonography: a multicenter retrospective study.

BACKGROUND: Ovarian cancer is a common cancer among women globally, and the assessment of lymph node metastasis plays a crucial role in the treatment of this malignancy. The primary objective of our study was to identify the risk factors associated with lymph node metastasis in patients with ovarian cancer and develop a predictive model to aid in the selection of the appropriate surgical procedure and treatment strategy. METHODS: We conducted a retrospective analysis of data from patients with ovarian cancer across three different medical centers between April 2014 and August 2022. Logistic regression analysis was employed to establish a prediction model for lymph node metastasis in patients with ovarian cancer. We evaluated the performance of the model using receiver operating characteristic (ROC) curves, calibration plots, and decision analysis curves. RESULTS: Our analysis revealed that among the 368 patients in the training set, 101 patients (27.4%) had undergone lymph node metastasis. Maximum tumor diameter, multifocal tumor, and Ki67 level were identified as independent risk factors for lymph node metastasis. The area under the curve (AUC) of the ROC curve in the training set was 0.837 (95% confidence interval [CI]: 0.792-0.881); in the validation set this value was 0.814 (95% CI: 0.744-0.884). Calibration plots and decision analysis curves revealed good calibration and clinical application value. CONCLUSIONS: We successfully developed a model for predicting lymph node metastasis in patients with ovarian cancer, based on ultrasound examination results and clinical data. Our model accurately identified patients at high risk of lymph node metastasis and may guide the selection of appropriate treatment strategies. This model has the potential to significantly enhance the precision and efficacy of clinical management in patients with ovarian cancer.

Effect of inoculated Daqu on the spontaneous fermentation of Chinese liquor.

Inoculated fermentation is widely used to improve the efficiency and quality of food production. However, it is still unclear how the inoculated multi-species starters influence food fermentation. Here we prepared two different types of Daqu, with/without the inoculation of Bacillus licheniformis in spontaneous Daqu fermentation, and studied their effects on liquor fermentation. These two types of Daqu were different in microbial community, and the inoculated Daqu had significantly higher relative abundance of Bacillus (69.2%) and lower relative abundance of Lactobacillus (3.2%) than those of Daqu without inoculation (Bacillus, 13.5%; Lactobacillus, 14.0%). After using with these two types of Daqu, metatranscriptomic analysis revealed that Kazachstania, Naumovozyma, Saccharomyces, Nakaseomyces and Lactobacillus were the transcriptional active genera during liquor fermentation. The transcription of Lactobacillus decreased on days 10 and 20 in liquor fermentation with the inoculated Daqu. The transcription of Kazachstania, Naumovozyma and Saccharomyces decreased on day 10 but increased on day 20 with the inoculated Daqu. Although lactate dehydrogenase decreased in Lactobacillus, alcohol dehydrogenase, aldehyde dehydrogenase and lactate dehydrogenase increased in Saccharomyces on day 20 in fermentation with inoculated Daqu, it indicated an extended succession of Saccharomyces in liquor fermentation. This would facilitate the increase of ethanol, acetic acid and lactic acid contents in liquor fermentation with inoculated Daqu. This work would be beneficial for improving Chinese liquor fermentation.

Role of combined clinical-radiomics model based on contrast-enhanced MRI in predicting the malignancy of breast non-mass enhancements without an additional diffusion-weighted imaging sequence.

Background: In our previous study, we developed a combined diagnostic model based on time-intensity curve (TIC) types and radiomics signature on contrast-enhanced magnetic resonance imaging (CE-MRI) for non-mass enhancement (NME). The model had a high diagnostic ability for differentiation without the additional diffusion-weighted imaging (DWI) sequence. In this study, we aimed to compare the diagnostic performance of the combined clinical-radiomics model based on CE-MRI and DWI in discriminating Breast Imaging-Reporting and Data System (BI-RADS) 4 NME breast lesions, ductal carcinoma in situ (DCIS), and invasive carcinoma. Methods: This retrospective study enrolled 364 NME lesions (343 patients). Of these, 183 malignant and 84 benign breast lesions classified as BI-RADS 4 NMEs by the initial diagnosis were reclassified based on the combined clinical-radiomics model and DWI, respectively. The nomogram score (NS) values for malignancy risk derived from the combined clinical-radiomics model and the minimal apparent diffusion coefficient (ADC) values from DWI were calculated and compared. The percentage of false positives were estimated in comparison with the original classification. Receiver operating characteristic (ROC) curve analysis was performed to determine the diagnostic value of the NS and minimal ADC values in distinguishing benign and malignant lesions, DCIS, and invasive breast carcinoma. An ablation experiment was used to test the value of the additional DWI sequence. Results: The diagnostic value of the NS values [area under curve (AUC) =0.843; 95% CI: 0.789-0.896] for discriminating the 267 NME breast lesions categorized as BI-RADS 4 was significantly higher than the minimal ADC values (AUC =0.662; 95% CI: 0.590-0.735). The NS values showed higher sensitivity, specificity, and accuracy compared with the minimal ADC values (sensitivity: 80.3% vs. 65.6%; specificity: 79.8% vs. 65.5%; accuracy: 80.1% vs. 65.5%). The NS values and minimal ADC values did not achieve high diagnostic accuracy in discriminating between DCIS and invasive cancer. However, the diagnostic performance of the combined NS-ADC model (AUC =0.731; 95% CI: 0.655-0.806) was higher than that of the NS values alone (P=0.008) and comparable to that of the minimal ADC values (P=0.440). Conclusions: The combined clinical-radiomics model based on CE-MRI could improve the diagnostic performance in discriminating the BI-RADS 4 NME lesions without an additional DWI sequence. However, DWI may improve the diagnostic performance in discriminating DCIS from invasive cancer.

Ultrastable Emulsion Stabilized by the Konjac Glucomannan-Xanthan Gum Complex.

Surfactant-free emulsions are currently gaining increased interest due to their technofunctional, health-promoting, economic, biocompatible, and sustainable characteristics. Herein, we report an ultrastable, surfactant-free emulsion stabilized by the konjac glucomannan (KGM)-xanthan gum (XG) complex. The results suggested that KGM-XG tended to adsorb onto the oil/water interface, causing a reduction in interfacial tension. The emulsion droplets were less than 1 µm in diameter and had a narrow size distribution. Using laser confocal microscopy and cryo-SEM, it was observed that KGM-XG generated a compact film on the surface of emulsion droplets while simultaneously constructing a three-dimensional network in the continuous phase. Both of these factors contributed to the stability of the emulsion. The present study presents a straightforward approach for producing highly stable emulsions stabilized by polysaccharides. These emulsions can be effectively utilized to enhance the water resistance of cellulose paper, which is extensively employed in the packaging industry.

Physicochemical Properties, Thermal Stability, and Pyrolysis Behavior of Antioxidative Lignin from Water Chestnut Shell Obtained with Ternary Deep Eutectic Solvents.

The molecular weight of lignin extracted from lignocellulosic biomass is an important factor in determining its valorization in industrial processes. Herein, this work aims to explore the extraction of high molecular weight and bioactive lignin from water chestnut shells under mild conditions. Five kinds of deep eutectic solvents were prepared and applied to isolate lignin from water chestnut shells. The extracted lignin was further characterized with element analysis, gel permeation chromatography, and Ultraviolet-visible and Fourier-transform infrared spectroscopy. The distribution of pyrolysis products was identified and quantified with thermogravimetric analysis-Fourier-transform infrared spectroscopy and pyrolysis-gas chromatograph-mass spectrometry. The results showed that choline chloride/ethylene glycol/p-toluenesulfonic acid (1:1.8:0.2 molar ratio) exhibited the highest fractionation efficiency for lignin (84.17% yield) at 100 °C for 2 h. Simultaneously, the lignin showed high purity (90.4%), high relative molecular weight (37,077 g/mol), and excellent uniformity. Furthermore, the aromatic ring structure of lignin remained intact, consisting mainly of p-hydroxyphenyl, syringl, and guaiacyl subunits. The lignin generated a large number of volatile organic compounds during the depolymerization process, mainly composed of ketones, phenols, syringols, guaiacols, esters, and aromatic compounds. Finally, the antioxidant activity of the lignin sample was evaluated with the 1,1-diphenyl-2-picrylhydrazyl radical scavenging assay; the lignin from water chestnut shells showed excellent antioxidant activity. These findings confirm that lignin from water chestnut shells has a broad application prospect in valuable chemicals, biofuels and bio-functional materials.

A facile strategy to fabricate high-barrier, water- and oil-repellent paper with carboxymethyl cellulose/collagen fiber/modified polyvinyl alcohol.

Due to the increasingly serious environmental and human health hazards brought by traditional food packaging materials, paper-based packaging materials have become increasingly popular among consumers in recent years. Currently, the fabrication of fluorine-free degradable water- and oil-repellent paper using low-cost bio-based polymers by a simple method is a hot subject in the field of food packaging. In this work, we used carboxymethyl cellulose (CMC), collagen fiber (CF), and modified polyvinyl alcohol (MPVA) to create coatings that were impervious to water and oil. The homogeneous mixture of CMC and CF generated electrostatic adsorption to impart excellent oil repellency to the paper. PVA was chemically modified by sodium tetraborate decahydrate, and the MPVA coating imparted excellent water-repellent properties to the paper. Finally, the water- and oil-proof paper showed excellent water repellency (Cobb value: 1.12 g/m2), oil repellency (kit rating: 12/12), low air permeability (0.3 µm/Pa·s), and stronger mechanical properties (4.19 kN/m). This non-fluorinated degradable water- and oil-repellent paper with high barrier properties prepared by a convenient method is expected to be in widespread use in the food packaging field.

Heteroleptic/Heterometallic Sierpinski Triangle with Room Temperature Fluorescence Emission and Photocatalytic Amine Oxidation Activity.

As a result of their optical and redox properties, bipyridyl (bpy) and terpyridyl (tpy) ruthenium complexes play vital roles in numerous domains. Herein, the design and synthesis of two bipyridyl and terpyridyl ruthenium(II) building units L1 and L2 are explained. A [Ru(bpy)3]2+ functionalized triangle S1 and a Sierpinski triangle S2 were synthesized in almost quantitative yields by the self-assembly of L1 with Zn2+ ions and by the heteroleptic self-assembly of L1 and L2 with Zn2+ ions, respectively. The Sierpinski triangle S2 contains the coordination metals [Ru(bpy)3]2+, [Ru(tpy)2]2+, and [Zn(tpy)2]2+. According to research on the catalytic activity of amine oxidation on supramolecules S1 and S2, the benzylamine substrates were nearly entirely transformed to N-benzylidenebenzylamine derivatives after 1 h under a Xe lamp. Furthermore, the observed ruthenium-containing terpyridyl supramolecule S2 maintains high luminous performance at ambient temperature. This discovery opens up new possibilities for the rational molecular design of terpyridyl ruthenium fluorescent materials and catalytic functional materials.

A multiparametric clinic-ultrasomics nomogram for predicting extremity soft-tissue tumor malignancy: a combined retrospective and prospective bicentric study.

OBJECTIVE: We aimed at building and testing a multiparametric clinic-ultrasomics nomogram for prediction of malignant extremity soft-tissue tumors (ESTTs). MATERIALS AND METHODS: This combined retrospective and prospective bicentric study assessed the performance of the multiparametric clinic-ultrasomics nomogram to predict the malignancy of ESTTs, when compared with a conventional clinic-radiologic nomogram. A dataset of grayscale ultrasound (US), color Doppler flow imaging (CDFI), and elastography images for 209 ESTTs were retrospectively enrolled from one hospital, and divided into the training and validation cohorts. A multiparametric ultrasomics signature was built based on multimodal ultrasomic features extracted from the grayscale US, CDFI, and elastography images of ESTTs in the training cohort. Another conventional radiologic score was built based on multimodal US features as interpreted by two experienced radiologists. Two nomograms that integrated clinical risk factors and the multiparameter ultrasomics signature or conventional radiologic score were respectively developed. Performance of the two nomograms was validated in the retrospective validation cohort, and tested in a prospective dataset of 51 ESTTs from the second hospital. RESULTS: The multiparametric ultrasomics signature was built based on seven grayscale ultrasomic features, three CDFI ultrasomic features, and one elastography ultrasomic feature. The conventional radiologic score was built based on five multimodal US characteristics. Predictive performance of the multiparametric clinic-ultrasomics nomogram was superior to that of the conventional clinic-radiologic nomogram in the training (area under the receiver operating characteristic curve [AUC] 0.970 vs. 0.890, p = 0.006), validation (AUC: 0.946 vs. 0.828, p = 0.047) and test (AUC: 0.934 vs. 0.842, p = 0.040) cohorts, respectively. Decision curve analysis of combined training, validation and test cohorts revealed that the multiparametric clinic-ultrasomics nomogram had a higher overall net benefit than the conventional clinic-radiologic model. CONCLUSION: The multiparametric clinic-ultrasomics nomogram can accurately predict the malignancy of ESTTs.

Predicting Malignancy of Thyroid Micronodules: Radiomics Analysis Based on Two Types of Ultrasound Elastography Images.

RATIONALE AND OBJECTIVES: To develop a multimodal ultrasound radiomics nomogram for accurate classification of thyroid micronodules. MATERIALS AND METHODS: A retrospective study including 181 thyroid micronodules within 179 patients was conducted. Radiomics features were extracted from strain elastography (SE), shear wave elastography (SWE) and B-mode ultrasound (BMUS) images. Minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms were used to select malignancy-related features. BMUS, SE, and SWE radiomics scores (Rad-scores) were then constructed. Multivariable logistic regression was conducted using radiomics signatures along with clinical data, and a nomogram was ultimately established. The calibration, discriminative, and clinical usefulness were considered to evaluate its performance. A clinical prediction model was also built using independent clinical risk factors for comparison. RESULTS: An aspect ratio ≥ 1, mean elasticity index, BMUS Rad-score, SE Rad-score, and SWE Rad-score were identified as the independent predictors for predicting malignancy of thyroid micronodules by multivariable logistic regression. The radiomics nomogram based on these characteristics showed favorable calibration and discriminative capabilities (AUCs: 0.903 and 0.881 for training and validation cohorts, respectively), all outperforming clinical prediction model (AUCs: 0.791 and 0.626, respectively). The decision curve analysis also confirmed clinical usefulness of the nomogram. The significant improvement of net reclassification index and integrated discriminatory improvement indicated that multimodal ultrasound radiomics signatures might work as new imaging markers for classifying thyroid micronodules. CONCLUSION: The nomogram combining multimodal ultrasound radiomics features and clinical factors has the potential to be used for accurate diagnosis of thyroid micronodules in the clinic.

Functional and Morphological Brain Alterations in Dysthyroid Optic Neuropathy: A Combined Resting-State fMRI and Voxel-Based Morphometry Study.

BACKGROUND: Increasing evidence has indicated that the entire visual pathway from retina to visual cortex may be involved in dysthyroid optic neuropathy (DON) pathological mechanisms. PURPOSE: To explore the functional and morphological brain characteristics in DON and their relationship with ophthalmologic performance. STUDY TYPE: Retrospective. POPULATION: A total of 30 DON patients, 40 thyroid-associated ophthalmopathy (TAO) without DON patients and 21 healthy-controls (HCs). FIELD STRENGTH/SEQUENCE: A 3.0 T, 3D T1-weighted spoiled gradient-recalled echo and gradient-recalled echo-planar imaging. ASSESSMENT: Functional and structural alterations in brain regions were evaluated with fractional amplitude of low-frequency fluctuations, degree centrality (DC), and gray matter volume (GMV). Clinical activity score (CAS) is assessed across patients. STATISTICAL TEST: One-way analysis of variance with post hoc two sample t-tests (GRF-corrected, voxel level: P < 0.005, cluster level: P < 0.05) and correlation analysis (significance level: P < 0.05). RESULTS: Compared to HCs, DON patients had significantly decreased DC values in the bilateral BA17 and BA18 regions. Compared to the TAO group, DON patients had decreased GMV in the left anterior cingulate cortex, left middle frontal gyrus, left lingual gyrus, left parietal gyrus, right Rolandic operculum, left supplementary motor area, and right middle temporal gyrus. In addition, GMV in the right Rolandic operculum was significantly positively correlated with CAS (correlation coefficient: r = 0.448). DATA CONCLUSION: This study showed significant morphological and functional alterations in visual cortex and morphological alterations in partial default mode network regions of DON patients, which may provide insights into the mechanism of vision loss and may facilitate the diagnosis and treatment of DON. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 3.