Progress in using cross-linking technologies to increase the thermal stability of colloidal delivery systems.

In recent years, crosslinking technology has been found and widely used in food, textile, pharmaceutical, bioengineering and other fields. Crosslinking is a reaction in which two or more molecules bond to each other to form a stable three-dimensional network structure to improve the strength, heat resistance and other properties of substances. The researchers found that the cross-linking technology has a significant effect on improving the thermal stability of the colloidal delivery system. In this paper, crosslinking techniques that can be used to improve the thermal stability of colloidal delivery systems are reviewed, including enzyme-, ion-, chemical-, and combined cross-linking. Initially, the underlying mechanisms of these crosslinking technologies is reviewed. Then, the impacts of crosslinking on the heat-stability of colloidal delivery systems are discussed. Finally, the application of crosslinked delivery systems in improving the thermal stability of probiotics, polyphenols, pigments, and nutrients in foods and food packaging materials is introduced. The ability of proteins and polysaccharides to form heat-stable colloidal delivery systems can be improved by crosslinking. Nevertheless, more research is required to establish the impact of different crosslinking on the thermal stability of a broader range of different delivery systems, as well as to ensure their safety and efficacy.

Quantitative MR imaging biomarkers for distinguishing inflammatory pancreatic mass and pancreatic cancer-a systematic review and meta-analysis.

OBJECTIVES: To evaluate the diagnostic performance of quantitative magnetic resonance (MR) imaging biomarkers in distinguishing between inflammatory pancreatic masses (IPM) and pancreatic cancer (PC). METHODS: A literature search was conducted using PubMed, Embase, the Cochrane Library, and Web of Science through August 2023. Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to evaluate the risk of bias and applicability of the studies. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated using the DerSimonian-Laird method. Univariate meta-regression analysis was used to identify the potential factors of heterogeneity. RESULTS: Twenty-four studies were included in this meta-analysis. The two main types of IPM, mass-forming pancreatitis (MFP) and autoimmune pancreatitis (AIP), differ in their apparent diffusion coefficient (ADC) values. Compared with PC, the ADC value was higher in MFP but lower in AIP. The pooled sensitivity/specificity of ADC were 0.80/0.85 for distinguishing MFP from PC and 0.82/0.84 for distinguishing AIP from PC. The pooled sensitivity/specificity for the maximal diameter of the upstream main pancreatic duct (dMPD) was 0.86/0.74, with a cutoff of dMPD ≤ 4 mm, and 0.97/0.52, with a cutoff of dMPD ≤ 5 mm. The pooled sensitivity/specificity for perfusion fraction (f) was 0.82/0.68, and 0.82/0.77 for mass stiffness values. CONCLUSIONS: Quantitative MR imaging biomarkers are useful in distinguishing between IPM and PC. ADC values differ between MFP and AIP, and they should be separated for consideration in future studies. CLINICAL RELEVANCE STATEMENT: Quantitative MR parameters could serve as non-invasive imaging biomarkers for differentiating malignant pancreatic neoplasms from inflammatory masses of the pancreas, and hence help to avoid unnecessary surgery. KEY POINTS: • Several quantitative MR imaging biomarkers performed well in differential diagnosis between inflammatory pancreatic mass and pancreatic cancer. • The ADC value could discern pancreatic cancer from mass-forming pancreatitis or autoimmune pancreatitis, if the two inflammatory mass types are not combined. • The diameter of main pancreatic duct had the highest specificity for differentiating autoimmune pancreatitis from pancreatic cancer.

Predicting Invasiveness of Lung Adenocarcinoma at Chest CT with Deep Learning Ternary Classification Models.

Background Preoperative discrimination of preinvasive, minimally invasive, and invasive adenocarcinoma at CT informs clinical management decisions but may be challenging for classifying pure ground-glass nodules (pGGNs). Deep learning (DL) may improve ternary classification. Purpose To determine whether a strategy that includes an adjudication approach can enhance the performance of DL ternary classification models in predicting the invasiveness of adenocarcinoma at chest CT and maintain performance in classifying pGGNs. Materials and Methods In this retrospective study, six ternary models for classifying preinvasive, minimally invasive, and invasive adenocarcinoma were developed using a multicenter data set of lung nodules. The DL-based models were progressively modified through framework optimization, joint learning, and an adjudication strategy (simulating a multireader approach to resolving discordant nodule classifications), integrating two binary classification models with a ternary classification model to resolve discordant classifications sequentially. The six ternary models were then tested on an external data set of pGGNs imaged between December 2019 and January 2021. Diagnostic performance including accuracy, specificity, and sensitivity was assessed. The χ2 test was used to compare model performance in different subgroups stratified by clinical confounders. Results A total of 4929 nodules from 4483 patients (mean age, 50.1 years ± 9.5 [SD]; 2806 female) were divided into training (n = 3384), validation (n = 579), and internal (n = 966) test sets. A total of 361 pGGNs from 281 patients (mean age, 55.2 years ± 11.1 [SD]; 186 female) formed the external test set. The proposed strategy improved DL model performance in external testing (P < .001). For classifying minimally invasive adenocarcinoma, the accuracy was 85% and 79%, sensitivity was 75% and 63%, and specificity was 89% and 85% for the model with adjudication (model 6) and the model without (model 3), respectively. Model 6 showed a relatively narrow range (maximum minus minimum) across diagnostic indexes (accuracy, 1.7%; sensitivity, 7.3%; specificity, 0.9%) compared with the other models (accuracy, 0.6%-10.8%; sensitivity, 14%-39.1%; specificity, 5.5%-17.9%). Conclusion Combining framework optimization, joint learning, and an adjudication approach improved DL classification of adenocarcinoma invasiveness at chest CT. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Sohn and Fields in this issue.

Effects of frying on the surface oil absorption of wheat, potato, and pea starches.

The effects of structural changes on surface oil absorption characteristics of wheat starch, pea starch and potato starch during frying under different water content (20%, 30%, 40%, 50%) were studied. Fried potato starch with a 40% water content exhibited the highest surface oil content. When the initial moisture content reached 30%, the scattering intensity of the crystal layer structure decreased for wheat and pea starches, while the scattering peak for potato starch completely disappeared. At 40% moisture content, the amorphous phase ratio values for fried potato, wheat and pea starches were 13.50%, 11.78% and 11.24%, respectively, and the nitrogen adsorption capacity of fried starch decreased in turn. These findings that the structure of potato starch was more susceptible to degradation compared to pea starch and wheat starch, resulting in higher surface oil absorbed by potato starch during frying process.

B1 Power Modification for Amide Proton Transfer Imaging in Parotid Glands: A Strategy for Image Quality Accommodation and Evaluation of Tumor Detection Feasibility.

BACKGROUND: In the application of APTw protocols for evaluating tumors and parotid glands, inhomogeneity and hyperintensity artifacts have remained an obstacle. This study aimed to improve APTw imaging quality and evaluate the feasibility of difference B1 values to detect parotid tumors. METHODS: A total of 31 patients received three APTw sequences to acquire 32 lesions and 30 parotid glands (one patient had lesions on both sides). Patients received T2WI and 3D turbo-spin-echo (TSE) APTw imaging on a 3.0 T scanner for three sequences (B1 = 2 µT, 1 µT, and 0.7 µT in APTw 1, 2, and 3, respectively). APTw image quality was evaluated using four-point Likert scales in terms of integrity and hyperintensity artifacts. Image quality was compared between the three sequences. An evaluable group and a trustable group were obtained for APTmean value comparison. RESULTS: Tumors in both APT2 and APT3 had fewer hyperintensity artifacts than in APT1. With B1 values decreasing, tumors had less integrity in APTw imaging. APTmean values of tumors were higher than parotid glands in traditional APT1 sequence though not significant, while the APTmean subtraction value was significantly different. CONCLUSIONS: Applying a lower B1 value could remove hyperintensity but could also compromise its integrity. Combing different APTw sequences might increase the feasibility of tumor detection.

Application of magnetic resonance imaging radiomics in endometrial cancer: a systematic review and meta-analysis.

PURPOSE: We aimed to systematically assess the methodological quality and clinical potential application of published magnetic resonance imaging (MRI)-based radiomics studies about endometrial cancer (EC). METHODS: Studies of EC radiomics analyses published between 1 January 2000 and 19 March 2023 were extracted, and their methodological quality was evaluated using the radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pairwise correlation analyses and separate meta-analyses of studies exploring differential diagnoses and risk prediction were also performed. RESULTS: Forty-five studies involving 3 aims were included. The mean RQS was 13.77 (range: 9-22.5); publication bias was observed in the areas of 'index test' and 'flow and timing'. A high RQS was significantly associated with therapy selection-aimed studies, low QUADAS-2 risk, recent publication year, and high-performance metrics. Raw data from 6 differential diagnosis and 34 risk prediction models were subjected to meta-analysis, revealing diagnostic odds ratios of 23.81 (95% confidence interval [CI] 8.48-66.83) and 18.23 (95% CI 13.68-24.29), respectively. CONCLUSION: The methodological quality of radiomics studies involving patients with EC is unsatisfactory. However, MRI-based radiomics analyses showed promising utility in terms of differential diagnosis and risk prediction.

Magnetic-optical dual-modality imaging monitoring chemotherapy efficacy of pancreatic ductal adenocarcinoma with a low-dose fibronectin-targeting Gd-based contrast agent.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is a lethal hypovascular tumor surrounded by dense fibrosis. Albumin-bound paclitaxel and gemcitabine (AG) chemotherapy is the mainstay of PDAC treatment through depleting peritumoral fibrosis and killing tumor cells; however, it remains challenging due to the lack of a noninvasive imaging method evaluating fibrotic changes during AG chemotherapy. In this study, we developed a dual-modality imaging platform that enables noninvasive, dynamic, and quantitative assessment of chemotherapy-induced fibrotic changes through near-infrared fluorescence molecular imaging (FMI) and magnetic resonance imaging (MRI) using an extradomain B fibronectin (EDB-FN)-targeted imaging probe (ZD2-Gd-DOTA-Cy7). METHODS: The ZD2-Gd-DOTA-Cy7 probe was constructed by conjugating a peptide (Cys-TVRTSAD) to Gd-DOTA and the near-infrared dye Cy7. PDAC murine xenograft models were intravenously injected with ZD2-Gd-DOTA-Cy7 at a Gd concentration of 0.05 mmol/kg or free Cy7 and Gd-DOTA as control. The normalized tumor background ratio (TBR) on FMI and the T1 reduction ratio on MRI were quantitatively analyzed. For models receiving AG chemotherapy or saline, MRI/FMI was performed before and after treatment. Histological analyses were performed for validation. RESULTS: The ZD2-Gd-DOTA-Cy7 concentration showed a linear correlation with the fluorescence intensity and T1 relaxation time in vitro. The optimal imaging time was 30 min after injection of the ZD2-Gd-DOTA-Cy7 (0.05 mmol/kg), only half of the clinic dosage of gadolinium. Additionally, ZD2-Gd-DOTA-Cy7 generated a 1.44-fold and 1.90-fold robust contrast enhancement compared with Cy7 (P < 0.05) and Gd-DOTA (P < 0.05), respectively. For AG chemotherapy monitoring, the T1 reduction ratio and normalized TBR in the fibrotic tumor areas were significantly increased by 1.99-fold (P < 0.05) and 1.78-fold (P < 0.05), respectively, in the control group compared with those in the AG group. CONCLUSION: MRI/FMI with a low dose of ZD2-Gd-DOTA-Cy7 enables sensitive imaging of PDAC and the quantitative assessment of fibrotic changes during AG chemotherapy, which shows potential clinical applications for precise diagnosis, post-treatment monitoring, and disease management.

Tomoelastography and Pancreatic Extracellular Volume Fraction Derived From MRI for Predicting Clinically Relevant Postoperative Pancreatic Fistula.

BACKGROUND: Pancreatic stiffness and extracellular volume fraction (ECV) are potential imaging biomarkers for pancreatic fibrosis. Clinically relevant postoperative fistula (CR-POPF) is one of the most severe complications after pancreaticoduodenectomy. Which imaging biomarker performs better for predicting the risk of CR-POPF remains unknown. PURPOSE: To evaluate the diagnostic performance of ECV and tomoelastography-derived pancreatic stiffness for predicting the risk of CR-POPF in patients undergoing pancreaticoduodenectomy. STUDY TYPE: Prospective. POPULATION: Eighty patients who underwent multiparametric pancreatic MRI before pancreaticoduodenectomy, among whom 16 developed CR-POPF and 64 did not. FIELD STRENGTH/SEQUENCE: 3 T/tomoelastography and precontrast and postcontrast T1 mapping of the pancreas. ASSESSMENT: Pancreatic stiffness was measured on the tomographic c-map, and pancreatic ECV was calculated from precontrast and postcontrast T1 maps. Pancreatic stiffness and ECV were compared with histological fibrosis grading (F0-F3). The optimal cutoff values for predicting CR-POPF were determined, and the correlation between CR-POPF and imaging parameters was evaluated. STATISTICAL TESTS: The Spearman's rank correlation and multivariate linear regression analysis was conducted. The receiver operating characteristic curve analysis and logistic regression analysis was performed. A double-sided P < 0.05 indicated a statistically significant difference. RESULTS: Pancreatic stiffness and ECV both showed a significantly positive correlation with histological pancreatic fibrosis (r = 0.73 and 0.56, respectively). Patients with advanced pancreatic fibrosis had significantly higher pancreatic stiffness and ECV compared to those with no/mild fibrosis. Pancreatic stiffness and ECV were also correlated with each other (r = 0.58). Lower pancreatic stiffness (<1.38 m/sec), lower ECV (<0.28), nondilated main pancreatic duct (<3 mm) and pathological diagnosis other than pancreatic ductal adenocarcinoma were associated with higher risk of CR-POPF at univariate analysis, and pancreatic stiffness was independently associated with CR-POPF at multivariate analysis (odds ratio: 18.59, 95% confidence interval: 4.45, 77.69). DATA CONCLUSION: Pancreatic stiffness and ECV were associated with histological fibrosis grading, and pancreatic stiffness was an independent predictor for CR-POPF. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 5.

Exploring a simplified way to diagnose pelvic lipomatosis: prediction of pelvic fat volume using a single cross-sectional image.

Background: Pelvic lipomatosis (PL) is a rare disease characterized by the overgrowth of pelvic adipose tissue (AT). We investigated the relationships between areas of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) and pelvic fat volume (PFV), and analyzed the feasibility of diagnosing PL from a single cross-sectional image. Methods: The study included 50 patients and 50 controls. We used nnU-Net to segment SAT and VAT automatically. L3 vertebra was set as the zero point (L0), and a total of 201 slices were obtained with a 1 mm interval (L-100 - L+100). We selected 5 pelvic slices, including slices of the anterior superior margin of the S1-S4 vertebrae and the slice above the bilateral femoral head (FH). SAT areas, VAT areas, and PFVs were calculated by computational software. Areas and volumes of 2 groups were compared by t-test or rank-sum test. The correlations among areas and PFV were calculated. Logistic regression models were developed to identify the best slice for predicting PL. Receiver operating characteristic (ROC) curves were performed, and the area under the curve (AUC) and thresholds [with sensitivity (SEN) and specificity (SPE)] were calculated. Results: VAT areas of L-94 - L-100, L+79 - L+100, S1-S4, and FH indicated statistical differences between patients and controls (P<0.05). The linear regression model with VAT area as the independent variable was established to estimate PFV (FH level: r=0.745, P<0.001, R2=0.555). Among the univariate logistic regression models, VAT area at FH as the independent variable had the highest performance in predicting PL (AUC: 0.893, SEN: 74%, SPE: 94%), followed by S4 level (AUC: 0.800, SEN: 88%, SPE: 66%). The overall accuracy of the logistic regression model including VAT areas at S4 and FH in predicting PL was 88% (AUC: 0.927, SEN: 90%, SPE: 88%). Conclusions: VAT areas at the level of FH can help estimate the value of PFV. VAT areas of S4 and FH provide greater power than a single image for the diagnosis of PL.

T1 and T2 mapping for identifying malignant lymph nodes in head and neck squamous cell carcinoma.

BACKGROUND: This study seeks to assess the utility of T1 and T2 mapping in distinguishing metastatic lymph nodes from reactive lymphadenopathy in patients with head and neck squamous cell carcinoma (HNSCC), using diffusion-weighted imaging (DWI) as a comparison. METHODS: Between July 2017 and November 2019, 46 HNSCC patients underwent neck MRI inclusive of T1 and T2 mapping and DWI. Quantitative measurements derived from preoperative T1 and T2 mapping and DWI of metastatic and non-metastatic lymph nodes were compared using independent samples t-test or Mann-Whitney U test. Receiver operating characteristic curves and the DeLong test were employed to determine the most effective diagnostic methodology. RESULTS: We examined a total of 122 lymph nodes, 45 (36.9%) of which were metastatic proven by pathology. Mean T2 values for metastatic lymph nodes were significantly lower than those for benign lymph nodes (p < 0.001). Conversely, metastatic lymph nodes exhibited significantly higher apparent diffusion coefficient (ADC) and standard deviation of T1 values (T1SD) (p < 0.001). T2 generated a significantly higher area under the curve (AUC) of 0.890 (0.826-0.954) compared to T1SD (0.711 [0.613-0.809]) and ADC (0.660 [0.562-0.758]) (p = 0.007 and p < 0.001). Combining T2, T1SD, ADC, and lymph node size achieved an AUC of 0.929 (0.875-0.983), which did not significantly enhance diagnostic performance over using T2 alone (p = 0.089). CONCLUSIONS: The application of T1 and T2 mapping is feasible in differentiating metastatic from non-metastatic lymph nodes in HNSCC and can improve diagnostic efficacy compared to DWI.

Effect of preoperative PI-RADS assessment on pathological outcomes in patients who underwent radical prostatectomy.

OBJECTIVE: To assess the effect of preoperative MRI with standardized Prostate Imaging-Reporting and Data System (PI-RADS) assessment on pathological outcomes in prostate cancer (PCa) patients who underwent radical prostatectomy (RP). PATIENTS AND METHODS: This retrospective cohort study included patients who had undergone prostate MRI and subsequent RP for PCa between January 2017 and December 2022. The patients were divided into the PI-RADS group and the non-PI-RADS group according to evaluation scheme of presurgery MRI. The preoperative characteristics and postoperative outcomes were retrieved and analyzed. The pathological outcomes included pathological T stage (pT2 vs. pT3-4) and positive surgical margins (PSMs). Patients were further stratified according to statistically significant preoperative variables to assess the difference in pathological outcomes. A propensity score matching based on the above preoperative characteristics was additionally performed. RESULTS: A total of 380 patients were included in this study, with 201 patients in the PI-RADS group and 179 in the non-PI-RADS group. The two groups had similar preoperative characteristics, except for clinical T stage (cT). As for pathological outcomes, the PI-RADS group showed a significantly lower percentage of pT3-4 (21.4% vs. 48.0%, p < 0.001), a lower percentage of PSMs (31.3% vs. 40.9%, p = 0.055), and a higher concordance between the cT and pT (79.1% vs. 64.8%, p = 0.003). The PI-RADS group also showed a lower proportion of pT3-4 (p < 0.001) in the cT1-2 subgroup and the cohort after propensity score matching. The PSM rate of cT3 patients was reduced by 39.2% in the PI-RADS group but without statistical significance (p = 0.089). CONCLUSIONS: Preoperative MRI with standardized PI-RADS assessment could benefit the decision-making of patients by reducing the rate of pathologically confirmed non-organ-confined PCa after RP and slightly reducing the PSM rate compared with non-PI-RADS assessment.

Should All Pancreatic Cystic Lesions with Worrisome or High-Risk Features Be Resected? A Clinical and Radiological Machine Learning Model May Help to Answer.

RATIONALE AND OBJECTIVES: According to current guidelines, pancreatic cystic lesions (PCLs) with worrisome or high-risk features may have overtreatment. The purpose of this study was to build a clinical and radiological based machine-learning (ML) model to identify malignant PCLs for surgery among preoperative PCLs with worrisome or high-risk features. MATERIALS AND METHODS: Clinical and radiological details of 317 pathologically confirmed PCLs with worrisome or high-risk features were retrospectively analyzed and applied to ML models including Support Vector Machine, Logistic Regression (LR), Decision Tree, Bernoulli NB, Gaussian NB, K Nearest Neighbors and Linear Discriminant Analysis. The diagnostic ability for malignancy of the optimal model with the highest diagnostic AUC in the cross-validation procedure was further evaluated in internal (n = 77) and external (n = 50) testing cohorts, and was compared to two published guidelines in internal mucinous cyst cohort. RESULTS: Ten clinical and radiological feature-based LR model was the optimal model with the highest AUC (0.951) in the cross-validation procedure. In the internal testing cohort, LR model reached an AUC, accuracy, sensitivity, and specificity of 0.927, 0.909, 0.914, and 0.905; in the external testing cohort, LR model reached 0.948, 0.900, 0.963, and 0.826. When compared to the European guidelines and the ACG guidelines, LR model demonstrated significantly better accuracy and specificity in identifying malignancy, while maintaining the same high sensitivity. CONCLUSION: Clinical- and radiological-based LR model can accurately identify malignant PCLs in patients with worrisome or high-risk features, possessing diagnostic performance better than the European guidelines as well as ACG guidelines.

Recent progress in regulating starch digestibility using natural additives and sustainable processing operations.

The development of a healthier and more sustainable food supply is a main concern of consumers, industry, governments, and international institutions. Foods containing high levels of rapidly digestible starches have been linked to a rise in the number of people suffering from diet-related chronic diseases. Consequently, there is interest in reducing the digestibility of starch to improve their healthiness. The ability of natural additives including proteins, dietary fibers, and polyphenols, and sustainable processing technologies such as high-intensity ultrasonic, pulsed electric field, non-thermal plasma, γ-ray irradiation that regulate reduce starch digestibility in foods are reviewed. The potential mechanisms of action, advantages, and disadvantages of each approach at inhibiting starch digestibility is highlighted. The potential for commercializing these technologies is discussed, and areas where further research are required are emphasized. Natural additives and sustainable processing operations can effectively reduce the digestibility of starch and inhibit postprandial sugar "spikes" in the bloodstream by adjusting the structural changes, which can be used to create healthier and more sustainable foods and have broad application prospects.

Free-Breathing StarVIBE Sequence for the Detection of Extranodal Extension in Head and Neck Cancer: An Image Quality and Diagnostic Performance Study.

(1) Background: This study aims to evaluate the image quality of abnormal cervical lymph nodes in head and neck cancer and the diagnostic performance of detecting extranodal extension (ENE) using free-breathing StarVIBE. (2) Methods: In this retrospective analysis, 80 consecutive head and neck cancer patients underwent StarVIBE before neck dissection at an academic center. Image quality was compared with conventional VIBE available for 28 of these patients. A total of 73 suspicious metastatic lymph nodes from 40 patients were found based on morphology and enhancement pattern on StarVIBE. Sensitivity (SN), specificity (SP), and odds ratios were calculated for each MR feature from StarVIBE to predict pathologic ENE. (3) Results: StarVIBE showed significantly superior image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) for enlarged lymph nodes compared to VIBE. The MR findings of "invading adjacent planes" (SN, 0.54; SP, 1.00) and "matted nodes" (SN, 0.72; SP, 0.89) emerged as notable observations. The highest diagnostic performance was attained by combining these two features (SN, 0.93; SP, 0.89). (4) Conclusions: This study confirms that StarVIBE offers superior image quality for abnormal lymph nodes compared to VIBE, and it can accurately diagnose ENE by utilizing a composite MR criterion in head and neck cancer.

An MRI-based grading system for preoperative risk estimation of positive surgical margin after radical prostatectomy.

OBJECTIVE: To construct a simplified grading system based on MRI features to predict positive surgical margin (PSM) after radical prostatectomy (RP). METHODS: Patients who had undergone prostate MRI followed by RP between January 2017 and January 2021 were retrospectively enrolled as the derivation group, and those between February 2021 and November 2022 were enrolled as the validation group. One radiologist evaluated tumor-related MRI features, including the capsule contact length (CCL) of lesions, frank extraprostatic extension (EPE), apex abutting, etc. Binary logistic regression and decision tree analysis were used to select risk features for PSM. The area under the curve (AUC), sensitivity, and specificity of different systems were calculated. The interreader agreement of the scoring systems was evaluated using the kappa statistic. RESULTS: There were 29.8% (42/141) and 36.4% (32/88) of patients who had PSM in the derivation and validation cohorts, respectively. The first grading system was proposed (mrPSM1) using two imaging features, namely, CCL ≥ 20 mm and apex abutting, and then updated by adding frank EPE (mrPSM2). In the derivation group, the AUC was 0.705 for mrPSM1 and 0.713 for mrPSM2. In the validation group, our grading systems showed comparable AUC with Park et al.'s model (0.672-0.686 vs. 0.646, p > 0.05) and significantly higher specificity (0.732-0.750 vs. 0.411, p < 0.001). The kappa value was 0.764 for mrPSM1 and 0.776 for mrPSM2. Decision curve analysis showed a higher net benefit for mrPSM2. CONCLUSION: The proposed grading systems based on MRI could benefit the risk stratification of PSM and are easily interpretable. CRITICAL RELEVANCE STATEMENT: The proposed mrPSM grading systems for preoperative prediction of surgical margin status after radical prostatectomy are simplified compared to a previous model and show high specificity for identifying the risk of positive surgical margin, which might benefit the management of prostate cancer. KEY POINTS: • CCL ≥ 20 mm, apex abutting, and EPE were important MRI features for PSM. • Our proposed MRI-based grading systems showed the possibility to predict PSM with high specificity. • The MRI-based grading systems might facilitate a structured risk evaluation of PSM.

[CT-Based Weighted Radiomic Score Predicts Tumor Response to Immunotherapy in Non-Small Cell Lung Cancer].

Objective To develop a CT-based weighted radiomic model that predicts tumor response to programmed death-1(PD-1)/PD-ligand 1(PD-L1)immunotherapy in patients with non-small cell lung cancer.Methods The patients with non-small cell lung cancer treated by PD-1/PD-L1 immune checkpoint inhibitors in the Peking Union Medical College Hospital from June 2015 to February 2022 were retrospectively studied and classified as responders(partial or complete response)and non-responders(stable or progressive disease).Original radiomic features were extracted from multiple intrapulmonary lesions in the contrast-enhanced CT scans of the arterial phase,and then weighted and summed by an attention-based multiple instances learning algorithm.Logistic regression was employed to build a weighted radiomic scoring model and the radiomic score was then calculated.The area under the receiver operating characteristic curve(AUC)was used to compare the weighted radiomic scoring model,PD-L1 model,clinical model,weighted radiomic scoring + PD-L1 model,and comprehensive prediction model.Results A total of 237 patients were included in the study and randomized into a training set(n=165)and a test set(n=72),with the mean ages of(64±9)and(62±8)years,respectively.The AUC of the weighted radiomic scoring model reached 0.85 and 0.80 in the training set and test set,respectively,which was higher than that of the PD-L1-1 model(Z=37.30,P<0.001 and Z=5.69,P=0.017),PD-L1-50 model(Z=38.36,P<0.001 and Z=17.99,P<0.001),and clinical model(Z=11.40,P<0.001 and Z=5.76,P=0.016).The AUC of the weighted scoring model was not different from that of the weighted radiomic scoring + PD-L1 model and the comprehensive prediction model(both P>0.05).Conclusion The weighted radiomic scores based on pre-treatment enhanced CT images can predict tumor responses to immunotherapy in patients with non-small cell lung cancer.

Recent advances in natural gums as additives to help the construction and application of edible biopolymer gels: the example of hydrogels and oleogels.

Novel, innovative approaches like edible gels (hydrogels and oleogels) are important food materials with great scientific interest due to their positive impacts on structural and functional foods and other unique properties. Biopolymers (protein, starch and other polysaccharides) can be excellent and cost-effective materials for the formed edible gels. Recently, natural gums, although also as biopolymers, are preferred as additives to further improve the textural and functional properties of edible gels, which have received extensive attention. However, these studies have not been outlined in previous reviews. In this review, we highlighted the advantages of gums as additives to construct edible gels. Moreover, the various roles (including electrostatic or covalent interactions) for natural gums in regulation of food gel properties (solvent-holding and rheological properties) are highlighted. Finally, the use of natural gums as additives to improve the stability and targeted delivery of phytochemicals in food gels and their application in food systems are summarized. The information covered in this article may be useful for the design of functional foods that can better meet personalized needs of people.

Health benefits, mechanisms of interaction with food components, and delivery of tea polyphenols: a review.

Tea polyphenols (TPs) are the most important active component of tea and have become a research focus among natural products, thanks to their antioxidant, lipid-lowering, liver-protecting, anti-tumor, and other biological activities. Polyphenols can interact with other food components, such as protein, polysaccharides, lipids, and metal ions to further improve the texture, flavor, and sensory quality of food, and are widely used in food fields, such as food preservatives, antibacterial agents and food packaging. However, the instability of TPs under conditions such as light or heat and their low bioavailability in the gastrointestinal environment also hinder their application in food. In this review, we summarized the health benefits of TPs. In order to better use TPs in food, we analyzed the form and mechanism of interaction between TPs and main food components, such as polysaccharides and proteins. Moreover, we reviewed research into optimizing the applications of TPs in food by bio-based delivery systems, such as liposomes, nanoemulsions, and nanoparticles, so as to improve the stability and bioactivity of TPs in food application. As an effective active ingredient, TPs have great potential to be applied in functional food to produce benefits for human health.

A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility.

OBJECTIVES: We aimed to present the state of the art of CT- and MRI-based radiomics in the context of ovarian cancer (OC), with a focus on the methodological quality of these studies and the clinical utility of these proposed radiomics models. METHODS: Original articles investigating radiomics in OC published in PubMed, Embase, Web of Science, and the Cochrane Library between January 1, 2002, and January 6, 2023, were extracted. The methodological quality was evaluated using the radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pairwise correlation analyses were performed to compare the methodological quality, baseline information, and performance metrics. Additional meta-analyses of studies exploring differential diagnoses and prognostic prediction in patients with OC were performed separately. RESULTS: Fifty-seven studies encompassing 11,693 patients were included. The mean RQS was 30.7% (range - 4 to 22); less than 25% of studies had a high risk of bias and applicability concerns in each domain of QUADAS-2. A high RQS was significantly associated with a low QUADAS-2 risk and recent publication year. Significantly higher performance metrics were observed in studies examining differential diagnosis; 16 such studies as well as 13 exploring prognostic prediction were included in a separate meta-analysis, which revealed diagnostic odds ratios of 25.76 (95% confidence interval (CI) 13.50-49.13) and 12.55 (95% CI 8.38-18.77), respectively. CONCLUSION: Current evidence suggests that the methodological quality of OC-related radiomics studies is unsatisfactory. Radiomics analysis based on CT and MRI showed promising results in terms of differential diagnosis and prognostic prediction. CRITICAL RELEVANCE STATEMENT: Radiomics analysis has potential clinical utility; however, shortcomings persist in existing studies in terms of reproducibility. We suggest that future radiomics studies should be more standardized to better bridge the gap between concepts and clinical applications.

CT-based radiomics nomogram for prediction of survival after transarterial chemoembolization with drug-eluting beads in patients with hepatocellular carcinoma and portal vein tumor thrombus.

OBJECTIVES: To develop and validate a CT-based radiomics model for the prediction of the overall survival (OS) of patients with hepatocellular carcinoma (HCC) and portal vein tumor thrombus (PVTT) treated with drug-eluting beads transarterial chemoembolization (DEB-TACE). METHODS: Patients were retrospectively enrolled from two institutions for the constitution of training (n = 69) and validation (n = 31) cohorts with a median follow-up of 15 months. A total of 396 radiomics features were extracted from each baseline CT image. Features selected by variable importance and minimal depth were used for random survival forest model construction. The performance of the model was assessed using the concordance index (C-index), calibration curves, integrated discrimination index (IDI), net reclassification index (NRI), and decision curve analysis. RESULTS: Type of PVTT and tumor number were proved to be significant clinical indicators for OS. Arterial phase images were used to extract radiomics features. Three radiomics features were selected for model construction. The C-index for the radiomics model was 0.759 in the training cohort and 0.730 in the validation cohort. To improve the predictive performance, clinical indicators were integrated into the radiomics model to form a combined model with a C-index of 0.814 in the training cohort and 0.792 in the validation cohort. The IDI was significant in both cohorts for the combined model versus the radiomics model in predicting 12-month OS. CONCLUSIONS: Type of PVTT and tumor number affected the OS of HCC patients with PVTT treated with DEB-TACE. Moreover, the combined clinical-radiomics model had a satisfactory performance. CLINICAL RELEVANCE STATEMENT: A CT-based radiomics nomogram, which consisted of 3 radiomics features and 2 clinical indicators, was recommended to predict 12-month overall survival of patients with hepatocellular carcinoma and portal vein tumor thrombus initially treated with drug-eluting beads transarterial chemoembolization. KEY POINTS: • Type of portal vein tumor thrombus and tumor number were significant predictors of the OS. • Integrated discrimination index and net reclassification index provided a quantitative evaluation of the incremental impact added by new indicators for the radiomics model. • A nomogram based on a radiomics signature and clinical indicators showed satisfactory performance in predicting OS after DEB-TACE.