D-A-D type based NIR fluorescence probe for monitoring the cysteine levels in pancreatic cancer cell during ferroptosis.

Cysteine (Cys) as a crucial precursor for intracellular glutathione (GSH) synthesis, plays an important role in the redox regulation in ferroptosis, Therefore, evaluating intracellular Cys levels is worthy to better understand ferroptosis-related physiological process. In this work, we constructed a novel NIR coumarin-derived fluorescent probe (NCDFP-Cys) based on a dual-ICT system, the NCDFP-Cys can show fluorescence turn-on response at 717 nm toward Cys over other amino acids, and possess large Stokes shift (Δλ = 167 nm), low detection limit, hypotoxicity. More significantly, NCDFP-Cys has been utilized to monitor the intracellular Cys fluctuation in pancreatic cancer cells during ferroptosis induced by Erastin and RSL3 respectively, and revealing the difference of Cys levels changes in different activator-triggered ferroptosis pathways.

Smoking Behavior and its Effect on Psychiatric Symptoms of Patients with Stable Schizophrenia.

Context: Schizophrenia is a common and clinically disabling mental disorder. Many patients with schizophrenia smoke. Research on the effects of smoking on schizophrenia's symptoms are inconsistent. Objective: The study intended to investigate the smoking status of patients with stable schizophrenia to determine the effects of smoking on schizophrenia-related symptoms. Design: The research team performed an case-control study. Setting: The study took place at Beijing Huilongguan Hospital in Beijing, Changping District, China. Participants: Participants were 160 patients at the hospital who had been diagnosed with stable schizophrenia between April 2018 and March 2020. Groups: The research team divided participants into two groups based on their current smoking status: (1) a smoking group with 72 participants and (2) a nonsmoking group with 88 participants. Outcome Measures: The research team: (1) examined the types of antipsychotic drugs that participants received; (2) used a schizophrenia-related scale, the Positive and Negative Syndrome Scale (PANSS), to examine participants' status; (3) examined the smoking habits of the smoking group; and (4) analyzed the correlation between the PANSS score and the smoking group's smoking index. Results: No significant difference existed between the groups in the type of medicine used (P > .05). The smoking group's PANSS total (P = .014), positive symptom (P = .039), and negative symptom (P = .003) scores were significantly lower than those of the nonsmoking group (P < .05). No significant difference existed between the groups in the general psychopathological symptom score (P > .05). The smoking group started smoking between 13 and 24 years of age, with an mean age of 19.11 ± 4.10 years. The group smoked 10-30 cigarettes/d, with a mean smoking amount of 18.4 ± 3.1 cigarettes/d, and the smoking index was 344.7 ± 48.0. The smoking group's smoking index was significantly negatively correlated with the positive symptom, negative symptom, and total PANSS scores (all P = .000). No correlation existed between the smoking index and the general psychopathological symptom score (P > .05). Conclusions: Smoking patients with stable schizophrenia generally exhibit fewer symptoms than nonsmoking patients, which relate to the alleviation of mental tension that smoking can provide.

DenseNet model incorporating hybrid attention mechanisms and clinical features for pancreatic cystic tumor classification.

PURPOSE: The aim of this study is to develop a deep learning model capable of discriminating between pancreatic plasma cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN) by leveraging patient-specific clinical features and imaging outcomes. The intent is to offer valuable diagnostic support to clinicians in their clinical decision-making processes. METHODS: The construction of the deep learning model involved utilizing a dataset comprising abdominal magnetic resonance T2-weighted images obtained from patients diagnosed with pancreatic cystic tumors at Changhai Hospital. The dataset comprised 207 patients with SCN and 93 patients with MCN, encompassing a total of 1761 images. The foundational architecture employed was DenseNet-161, augmented with a hybrid attention mechanism module. This integration aimed to enhance the network's attentiveness toward channel and spatial features, thereby amplifying its performance. Additionally, clinical features were incorporated prior to the fully connected layer of the network to actively contribute to subsequent decision-making processes, thereby significantly augmenting the model's classification accuracy. The final patient classification outcomes were derived using a joint voting methodology, and the model underwent comprehensive evaluation. RESULTS: Using the five-fold cross validation, the accuracy of the classification model in this paper was 92.44%, with an AUC value of 0.971, a precision rate of 0.956, a recall rate of 0.919, a specificity of 0.933, and an F1-score of 0.936. CONCLUSION: This study demonstrates that the DenseNet model, which incorporates hybrid attention mechanisms and clinical features, is effective for distinguishing between SCN and MCN, and has potential application for the diagnosis of pancreatic cystic tumors in clinical practice.

Artificial Intelligence to Predict Lymph Node Metastasis at CT in Pancreatic Ductal Adenocarcinoma.

Background Although deep learning has brought revolutionary changes in health care, reliance on manually selected cross-sectional images and segmentation remain methodological barriers. Purpose To develop and validate an automated preoperative artificial intelligence (AI) algorithm for tumor and lymph node (LN) segmentation with CT imaging for prediction of LN metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Materials and Methods In this retrospective study, patients with surgically resected, pathologically confirmed PDAC underwent multidetector CT from January 2015 to April 2020. Three models were developed, including an AI model, a clinical model, and a radiomics model. CT-determined LN metastasis was diagnosed by radiologists. Multivariable logistic regression analysis was conducted to develop the clinical and radiomics models. The performance of the models was determined on the basis of their discrimination and clinical utility. Kaplan-Meier curves, the log-rank test, or Cox regression were used for survival analysis. Results Overall, 734 patients (mean age, 62 years ± 9 [SD]; 453 men) were evaluated. All patients were split into training (n = 545) and validation (n = 189) sets. Patients who had LN metastasis (LN-positive group) accounted for 340 of 734 (46%) patients. In the training set, the AI model showed the highest performance (area under the receiver operating characteristic curve [AUC], 0.91) in the prediction of LN metastasis, whereas the radiologists and the clinical and radiomics models had AUCs of 0.58, 0.76, and 0.71, respectively. In the validation set, the AI model showed the highest performance (AUC, 0.92) in the prediction of LN metastasis, whereas the radiologists and the clinical and radiomics models had AUCs of 0.65, 0.77, and 0.68, respectively (P < .001). AI model-predicted positive LN metastasis was associated with worse survival (hazard ratio, 1.46; 95% CI: 1.13, 1.89; P = .004). Conclusion An artificial intelligence model outperformed radiologists and clinical and radiomics models for prediction of lymph node metastasis at CT in patients with pancreatic ductal adenocarcinoma. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chu and Fishman in this issue.

Diffusion-Weighted Imaging of the Abdomen: Correction for Gradient Nonlinearity Bias in Apparent Diffusion Coefficient.

BACKGROUND: Gradient nonlinearity (GNL) introduces spatial nonuniformity bias in apparent diffusion coefficient (ADC) measurements, especially at large offsets from the magnet isocenter. PURPOSE: To investigate the effects of GNL in abdominal ADC measurements and to develop an ADC bias correction procedure. STUDY TYPE: Retrospective. PHANTOM/POPULATION: Two homemade ultrapure water phantoms/25 patients with histologically confirmed pancreatic ductal adenocarcinoma (PDAC). FIELD STRENGTH/SEQUENCE: A 3.0 T/diffusion-weighted imaging (DWI) with single-shot echo-planar imaging sequence. ASSESSMENT: ADC bias was computed in the three orthogonal directions at different offset locations. The spatial-dependent correctors of ADC bias were generated from the ADCs of phantom 1. The ADCs were estimated before and after corrections for the phantom 1 with both the proposed approach and the theoretical GNL correction method. For the patients, ADCs were measured in abdominal tissues including left and right liver lobes, PDAC, spleen, bilateral kidneys, and bilateral paraspinal muscles. STATISTICAL TEST: Friedman tests and Wilcoxon tests. RESULTS: The ADC bias measured by phantom 1 was 9.7% and 12.6% higher in the right-left and anterior-posterior directions and 9.2% lower in the superior-inferior direction at the 150 mm offsets from the magnetic isocenter. The corrected vs. the uncorrected ADCs measurements (median: 2.20 × 10-3  mm2 /sec for both the proposed method and the theoretical GNL method vs. 2.31 × 10-3  mm2 /sec, respectively) and their relative ADC errors (0.014, 0.016, and 0.054, respectively) were lower in the phantom 1. The relative ADC errors substantially decreased after correction in the phantom 2 (median: 0.048 and -0.008, respectively). The ADCs of all the abdominal tissues were lower after correction except for the left liver lobes (P = 0.13). DATA CONCLUSION: GNL bias in abdominal ADC can be measured by a DWI phantom. The proposed correction procedure was successfully applied for the bias correction in abdominal ADC. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 1.

Replacing secretin-enhanced MRCP with MRI radiomics model based on a fully automated pancreas segmentation for assessing pancreatic exocrine function in chronic pancreatitis.

OBJECTIVES: To develop and validate a radiomics nomogram based on a fully automated pancreas segmentation to assess pancreatic exocrine function. Furthermore, we aimed to compare the performance of the radiomics nomogram with the pancreatic flow output rate (PFR) and conclude on the replacement of secretin-enhanced magnetic resonance cholangiopancreatography (S-MRCP) by the radiomics nomogram for pancreatic exocrine function assessment. METHODS: All participants underwent S-MRCP between April 2011 and December 2014 in this retrospective study. PFR was quantified using S-MRCP. Participants were divided into normal and pancreatic exocrine insufficiency (PEI) groups using the cut-off of 200 µg/L of fecal elastase-1. Two prediction models were developed including the clinical and non-enhanced T1-weighted imaging radiomics model. A multivariate logistic regression analysis was conducted to develop the prediction models. The models' performances were determined based on their discrimination, calibration, and clinical utility. RESULTS: A total of 159 participants (mean age [Formula: see text] standard deviation, 45 years [Formula: see text] 14;119 men) included 85 normal and 74 PEI. All the participants were divided into a training set comprising 119 consecutive patients and an independent validation set comprising 40 consecutive patients. The radiomics score was an independent risk factor for PEI (odds ratio = 11.69; p < 0.001). In the validation set, the radiomics nomogram exhibited the highest performance (AUC, 0.92) in PEI prediction, whereas the clinical nomogram and PFR had AUCs of 0.79 and 0.78, respectively. CONCLUSION: The radiomics nomogram accurately predicted pancreatic exocrine function and outperformed pancreatic flow output rate on S-MRCP in patients with chronic pancreatitis. KEY POINTS: • The clinical nomogram exhibited moderate performance in diagnosing pancreatic exocrine insufficiency. • The radiomics score was an independent risk factor for pancreatic exocrine insufficiency, and every point rise in the rad-score was associated with an 11.69-fold increase in pancreatic exocrine insufficiency risk. • The radiomics nomogram accurately predicted pancreatic exocrine function and outperformed the clinical model and pancreatic flow output rate quantified by secretin-enhanced magnetic resonance cholangiopancreatography on MRI in patients with chronic pancreatitis.

Application of a pyramid pooling Unet model with integrated attention mechanism and Inception module in pancreatic tumor segmentation.

BACKGROUND: The segmentation and recognition of pancreatic tumors are crucial tasks in the diagnosis and treatment of pancreatic diseases. However, due to the relatively small proportion of the pancreas in the abdomen and significant shape and size variations, pancreatic tumor segmentation poses considerable challenges. PURPOSE: To construct a network model that combines a pyramid pooling module with Inception architecture and SE attention mechanism (PIS-Unet), and observe its effectiveness in pancreatic tumor images segmentation, thereby providing supportive recommendations for clinical practitioners. MATERIALS AND METHODS: A total of 303 patients with histologically confirmed pancreatic cystic neoplasm (PCN), including serous cystic neoplasm (SCN) and mucinous cystic neoplasm (MCN), from Shanghai Changhai Hospital between March 2011 and November 2021 were included. A total of 1792 T2-weighted imaging (T2WI) slices were used to build a CNN model. The model employed a pyramid pooling Inception module with a fused attention mechanism. The attention mechanism enhanced the network's focus on local features, while the Inception module and pyramid pooling allowed the network to extract features at different scales and improve the utilization efficiency of global information, thereby effectively enhancing network performance. RESULTS: Using three-fold cross-validation, the model constructed by us achieved a dice score of 85.49 ± 2.02 for SCN images segmentation, and a dice score of 87.90 ± 4.19 for MCN images segmentation. CONCLUSION: This study demonstrates that using deep learning networks for the segmentation of PCNs yields favorable results. Applying this network as an aid to physicians in PCN diagnosis shows potential for clinical applications.

Preoperative Radiomics Approach to Evaluating Tumor-Infiltrating CD8+ T Cells in Patients With Pancreatic Ductal Adenocarcinoma Using Noncontrast Magnetic Resonance Imaging.

BACKGROUND: CD8+ T cell in pancreatic ductal adenocarcinoma (PDAC) is closely related to the prognosis and treatment response of patients. Accurate preoperative CD8+ T-cell expression can better identify the population benefitting from immunotherapy. PURPOSE: To develop and validate a machine learning classifier based on noncontrast magnetic resonance imaging (MRI) for the preoperative prediction of CD8+ T-cell expression in patients with PDAC. STUDY TYPE: Retrospective cohort study. POPULATION: Overall, 114 patients with PDAC undergoing MR scan and surgical resection; 97 and 47 patients in the training and validation cohorts. FIELD STRENGTH/SEQUENCE/3 T: Breath-hold single-shot fast-spin echo T2-weighted sequence and noncontrast T1-weighted fat-suppressed sequences. ASSESSMENT: CD8+ T-cell expression was quantified using immunohistochemistry. For each patient, 2232 radiomics features were extracted from noncontrast T1- and T2-weighted images and reduced using the Wilcoxon rank-sum test and least absolute shrinkage and selection operator method. Linear discriminative analysis was used to construct radiomics and mixed models. Model performance was determined by its discriminative ability, calibration, and clinical utility. STATISTICAL TESTS: Kaplan-Meier estimates, Student's t-test, the Kruskal-Wallis H test, and the chi-square test, receiver operating characteristic curve, and decision curve analysis. RESULTS: A log-rank test showed that the survival duration in the CD8-high group (25.51 months) was significantly longer than that in the CD8-low group (22.92 months). The mixed model included all MRI characteristics and 13 selected radiomics features, and the area under the curve (AUC) was 0.89 (95% confidence interval [CI], 0.77-0.92) and 0.69 (95% CI, 0.53-0.82) in the training and validation cohorts. The radiomics model included 13 radiomics features, which showed good discrimination in the training cohort (AUC, 0.85; 95% CI, 0.77-0.92) and the validation cohort (AUC, 0.76; 95% CI, 0.61-0.87). DATA CONCLUSIONS: This study developed a noncontrast MRI-based radiomics model that can preoperatively determine CD8+ T-cell expression in patients with PDAC and potentially immunotherapy planning. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Two nomograms for differentiating mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis.

OBJECTIVES: To develop and validate a CT nomogram and a radiomics nomogram to differentiate mass-forming chronic pancreatitis (MFCP) from pancreatic ductal adenocarcinoma (PDAC) in patients with chronic pancreatitis (CP). METHODS: In this retrospective study, the data of 138 patients with histopathologically diagnosed MFCP or PDAC treated at our institution were retrospectively analyzed. Two radiologists analyzed the original cross-sectional CT images based on predefined criteria. Image segmentation, feature extraction, and feature reduction and selection were used to create the radiomics model. The CT and radiomics models were developed using data from a training cohort of 103 consecutive patients. The models were validated in 35 consecutive patients. Multivariable logistic regression analysis was conducted to develop a model for the differential diagnosis of MFCP and PDAC and visualized as a nomogram. The nomograms' performances were determined based on their differentiating ability and clinical utility. RESULTS: The mean age of patients was 53.7 years, 75.4% were male. The CT nomogram showed good differentiation between the two entities in the training (area under the curve [AUC], 0.87) and validation (AUC, 0.94) cohorts. The radiomics nomogram showed good differentiation in the training (AUC, 0.91) and validation (AUC, 0.93) cohorts. Decision curve analysis showed that patients could benefit from the CT and radiomics nomograms, if the threshold probability was 0.05-0.85 and > 0.05, respectively. CONCLUSIONS: The two nomograms reasonably accurately differentiated MFCP from PDAC in patients with CP and hold potential for refining the management of pancreatic masses in CP patients. KEY POINTS: • A CT nomogram and a computed tomography-based radiomics nomogram reasonably accurately differentiated mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis (CP). • The two nomograms can monitor the cancer risk in patients with CP and hold promise to optimize the management of pancreatic masses in patients with CP.

Negative pressure irrigation on water use efficiency, yield and quality of Brassica chinensis L.

BACKGROUND: Water is critical to the production of crops, especially when faced with seasonal drought or freshwater scarcity. We compared the effect of negative pressure irrigation (NPI) on water use efficiency (WUE), nutrient uptake, yield and quality of Brassica chinensis L. using a greenhouse plot experiment. Three different water supply pressures (-5, -10 and -15 kPa), and a conventional irrigation (CK) treatment, were arranged in a randomized design with three replications. RESULTS: Our results suggest that plant height, leaf area, number of leaves and ratio of root to shoot were significantly correlated with water supply pressure. Specifically, our results show that B. chinensis L. yield was increased 50% with NPI versus CK. Water supply pressure had a significant effect on N and P nutrient uptake and no significant effect on K. The average concentration of vitamin C was greatest with -5 kPa treatment and consecutively declined. According to our results, NPI can save up to 36.8% of water used and improve WUE by 61.3% during growth of B. chinensis L. Our results suggest that the optimum irrigation management strategy is -5 kPa treatment. CONCLUSION: NPI versus CK can provide more stable irrigation water and retain soil moisture during plant growth, resulting in an increased WUE and yield with suitable water supply pressure. While our results suggest that NPI can enhance B. chinensis L. yield and perhaps also quality, future research should explore the mechanism of NPI in relation to yield and water use efficiency. © 2021 Society of Chemical Industry.

Noncontrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibroblast Activation Protein Expression in Patients With Pancreatic Ductal Adenocarcinoma.

BACKGROUND: Fibroblast activation protein (FAP) in pancreatic ductal adenocarcinoma (PDAC) is closely related to the prognosis and treatment of patients. Accurate preoperative FAP expression can better identify the population benefitting from FAP-targeting drugs. PURPOSE: To develop and validate a machine learning classifier based on noncontrast MRI for the preoperative prediction of FAP expression in patients with PDAC. STUDY TYPE: Retrospective cohort study. POPULATION: Altogether, 129 patients with pathology-confirmed PDAC undergoing MR scan and surgical resection; 90 patients in a training cohort, and 39 patients in a validation cohort. FIELD STRENGTH/SEQUENCE/3T: Breath-hold single-shot fast-spin echo T2-weighted sequence and unenhanced and noncontrast T1-weighted fat-suppressed sequences. ASSESSMENT: FAP expression was quantified using immunohistochemistry. For each patient, 1409 radiomics features were extracted from T1- and T2-weighted images and reduced using the least absolute shrinkage and selection operator logistic regression algorithm. A multilayer perceptron (MLP) network classifier was developed using the training and validation set. The MLP network classifier performance was determined by its discriminative ability, calibration, and clinical utility. STATISTICAL TESTS: Kaplan-Meier estimates, student's t-test, the Kruskal-Wallis H test, and the chi-square test, univariable regression analysis, receiver operating characteristic curve, and decision curve analysis were used. RESULTS: A log-rank test showed that the survival of patients with low FAP expression (24.43 months) was significantly longer (P < 0.05) than that in the FAP-high group (13.50 months). The prediction model showed good discrimination in the training set (area under the curve [AUC], 0.84) and the validation set (AUC, 0.77). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 75.00%, 79.41%, 0.77, 0.86, and 0.66, respectively, whereas those for the validation set were 85.00%, 63.16%, 0.74, 0.71, and 0.80, respectively. DATA CONCLUSIONS: The MLP network classifier based on noncontrast MRI can accurately predict FAP expression in patients with PDAC. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Generalized additive mixed model to evaluate the association between total pulmonary infection volume and volume ratio, and clinical types, in patients with COVID-19 pneumonia: a propensity score analysis.

OBJECTIVES: To investigate the association between longitudinal total pulmonary infection volume and volume ratio over time and clinical types in COVID-19 pneumonia patients. METHODS: This retrospective review included 367 patients with COVID-19 pneumonia. All patients underwent CT examination at baseline and/or one or more follow-up CT. Patients were categorized into two clinical types (moderate and severe groups). The severe patients were matched to the moderate patients via propensity scores (PS). The association between total pulmonary infection volume and volume ratio and clinical types was analyzed using a generalized additive mixed model (GAMM). RESULTS: Two hundred and seven moderate patients and 160 severe patients were enrolled. The baseline clinical and imaging variables were balanced using PS analysis to avoid patient selection bias. After PS analysis, 172 pairs of moderate patients were allocated to the groups; there was no difference in the clinical and CT characteristics between the two groups (p > 0.05). A total of 332 patients, including 396 CT scans, were assessed. The impact of total pulmonary infection volume and volume ratio with time was significantly affected by clinical types (p for interaction = 0.01 and 0.01, respectively) using GAMM. Total pulmonary infection volume and volume ratio of the severe group increased by 14.66 cm3 (95% confidence interval [CI]: 3.92 to 25.40) and 0.45% (95% CI: 0.13 to 0.77) every day, respectively, compared to that of the moderate group. CONCLUSIONS: Longitudinal total pulmonary infection volume and volume ratio are independently associated with the clinical types of COVID-19 pneumonia. KEY POINTS: • The impact of total pulmonary infection volume and volume ratio over time was significantly affected by the clinical types (p for interaction = 0.01 and 0.01, respectively) using the GAMM. • Total pulmonary infection volume and volume ratio of the severe group increased by 14.66 cm3 (95% CI: 3.92 to 25.40) and 0.45% (95% CI: 0.13 to 0.77) every day, respectively, compared to that of the moderate group.

Tumor Size on Microscopy, CT, and MRI Assessments Versus Pathologic Gross Specimen Analysis of Pancreatic Neuroendocrine Tumors.

OBJECTIVE. The purpose of the present study was to assess the consistency of measurements of pancreatic neuroendocrine tumor (PNET) tumor size obtained using pre-operative imaging, pathologic gross specimen analysis, and microscopic examination of large pathologic sections; evaluate the impact of differences in pathologic and radiologic measurements of size on T categorization; and investigate the exact relationships among tumor size measurements obtained from microscopic analysis, CT, MRI, and pathologic gross specimen analysis. MATERIALS AND METHODS. We enrolled 64 patients with pathologically confirmed PNETs who underwent radiologic examination between December 2016 and September 2019. Tumor sizes were measured by CT, MRI, pathologic gross specimen analysis, and microscopic examination. The relationship between the tumor sizes calculated by MRI and microscopy was analyzed using univariate and multivariate logistic regression models. RESULTS. The measurements of tumor sizes calculated by pathologic and radiologic assessments and CT and MRI assessments showed good concordance, but measurements calculated by microscopic analysis and other methods showed poor concordance. When T categories from pathologic gross specimen analysis were considered the reference, alterations in T category were found in the microscopic assessments of 12 of 64 patients (18.75%), CT assessments of 15 of 64 patients (23.44%), and MRI assessments of 13 of 64 patients (20.31%). In the fully adjusted model, microscopic size (ß, 1.05; 95% CI, 0.98-1.12; p < .001), CT size (ß, 0.90; 95% CI, 0.78-1.02; p < .001), and MRI size (ß, 0.92; 95% CI, 0.81-1.04; p < .001) were significantly correlated with gross tumor size. CONCLUSION. Tumor sizes measured by microscopy, CT, and MRI were significantly associated with the gross size of PNETs. This finding provides physicians with new tools for rapid identification of gross tumor size.

MRI-based radiomics approach for differentiation of hypovascular non-functional pancreatic neuroendocrine tumors and solid pseudopapillary neoplasms of the pancreas.

BACKGROUND: This study aims to investigate the value of radiomics parameters derived from contrast enhanced (CE) MRI in differentiation of hypovascular non-functional pancreatic neuroendocrine tumors (hypo-NF-pNETs) and solid pseudopapillary neoplasms of the pancreas (SPNs). METHODS: Fifty-seven SPN patients and twenty-two hypo-NF-pNET patients were enrolled. Radiomics features were extracted from T1WI, arterial, portal and delayed phase of MR images. The enrolled patients were divided into training cohort and validation cohort with the 7:3 ratio. We built four radiomics signatures for the four phases respectively and ROC analysis were used to select the best phase to discriminate SPNs from hypo-NF-pNETs. The chosen radiomics signature and clinical independent risk factors were integrated to construct a clinic-radiomics nomogram. RESULTS: SPNs occurred in younger age groups than hypo-NF-pNETs (P < 0.0001) and showed a clear preponderance in females (P = 0.0185). Age was a significant independent factor for the differentiation of SPNs and hypo-NF-pNETs revealed by logistic regression analysis. With AUC values above 0.900 in both training and validation cohort (0.978 [95% CI, 0.942-1.000] in the training set, 0.907 [95% CI, 0.765-1.000] in the validation set), the radiomics signature of the arterial phase was picked to build a clinic-radiomics nomogram. The nomogram, composed by age and radiomics signature of the arterial phase, showed sufficient performance for discriminating SPNs and hypo-NF-pNETs with AUC values of 0.965 (95% CI, 0.923-1.000) and 0.920 (95% CI, 0.796-1.000) in the training and validation cohorts, respectively. Delong Test did not demonstrate statistical significance between the AUC of the clinic-radiomics nomogram and radiomics signature of arterial phase. CONCLUSION: CE-MRI-based radiomics approach demonstrated great potential in the differentiation of hypo-NF-pNETs and SPNs.

Circ_BMP2K enhances the regulatory effects of miR-455-3p on its target gene SUMO1 and thereby inhibits the activation of cardiac fibroblasts.

Research has shown that some circular RNAs (circRNA) are abnormally expressed in the process of myocardial fibrosis, but the mechanism behind this was unknown. In the process of inducing cardiac fibroblast (CF) activation with TGF-ß1 or Ang II, the expression of circRNA circ_BMP2K and miR-455-3p were significantly inhibited, whereas the expression of SUMO1 was promoted. The results from our dual luciferase reporter gene assays, RIP assays, and pull-down assays show that miR-455-3p directly binds circ_BMP2K, thereby mutually promoting their expression levels. SUMO1 is a target gene of miR-455-3p, and circ_BMP2K enhances the inhibitory effects of miR-455-3p on the expression of SUMO1. Further study showed that both circ_BMP2K and miR-455-3p inhibited the expression of alpha-SMA as well as type I and type III collagen, whereas SUMO1 promoted their expression, and miR-455-3p inhibitors or overexpression of SUMO1 reversed the effects of circ_BMP2K and miR-455-3p. Circ_BMP2K and miR-455-3p inhibits cell proliferation and migration and promotes the apoptosis of CFs, but SUMO1 has the opposite effects; miR-455-3p inhibitors or overexpression of SUMO1 reverses the effects of circ_BMP2K and miR-455-3p. Thus, circ_BMP2K promotes the expression of miR-455-3p, down-regulates the expression of SUMO1, and finally, inhibits the activation, growth, and migration of CFs. These results could provide important therapeutic targets and a theoretical basis for regulating the process of myocardial fibrosis.

Esophageal Histological Precursor Lesions and Subsequent 8.5-Year Cancer Risk in a Population-Based Prospective Study in China.

INTRODUCTION: Data on the associations between esophageal histological lesions and risk of esophageal squamous cell carcinoma (ESCC) in general populations are limited. We aimed to investigate these associations in a large Chinese general population to inform future Chinese ESCC screening guidelines. METHODS: We performed endoscopic screening of 21,111 participants aged 40-69 years from 3 high-risk areas of China in 2005-2009, and followed the cohort through 2016. Cumulative incidence and mortality rates of ESCC were calculated by baseline histological diagnosis, and hazard ratios of ESCC, overall and by age and sex, were assessed using the Cox proportional hazards models. RESULTS: We identified 143 new ESCC cases (0.68%) and 62 ESCC deaths (0.29%) during a median follow-up of 8.5 years. Increasing grades of squamous dysplasia were associated with the increasing risk of ESCC incidence and mortality. The cumulative ESCC incidence rates for severe dysplasia/carcinoma in situ, moderate dysplasia (MD), and mild dysplasia were 15.5%, 4.5%, and 1.4%, respectively. Older individuals (50-69 years) had 3.1 times higher ESCC incidence than younger individuals (40-49 years), and men had 2.4 times higher ESCC incidence than women. DISCUSSION: This study confirmed that increasing grades of squamous dysplasia are associated with increasing risk of ESCC and that severe dysplasia and carcinoma in situ require clinical treatment. This study suggests that in high-risk areas of China, patients with endoscopically worrisome MD should also receive therapy, the first screening can be postponed to 50 years, and endoscopic surveillance intervals for unremarkable MD and mild dysplasia can be lengthened to 3 and 5 years, respectively.

Noncontrast Radiomics Approach for Predicting Grades of Nonfunctional Pancreatic Neuroendocrine Tumors.

BACKGROUND: Endoscopic ultrasound-guided fine-needle aspiration is associated with the accurate determination of tumor grade. However, because it is an invasive procedure there is a need to explore alternative noninvasive procedures. PURPOSE: To develop and validate a noncontrast radiomics model for the preoperative prediction of nonfunctional pancreatic neuroendocrine tumor (NF-pNET) grade (G). STUDY TYPE: Retrospective, single-center study. SUBJECTS: Patients with pathologically confirmed PNETs (139) were included. FIELD STRENGTH/SEQUENCE: 3T/breath-hold single-shot fast-spin echo T2 -weighted sequence and unenhanced and dynamic contrast-enhanced T1 -weighted fat-suppressed sequences. ASSESSMENT: Tumor features on contrast MR images were evaluated by three board-certified abdominal radiologists. STATISTICAL TESTS: Multivariable logistic regression analysis was used to develop the clinical model. The least absolute shrinkage and selection operator method and linear discriminative analysis (LDA) were used to select the features and to construct a radiomics model. The performance of the models was assessed using the training cohort (97 patients) and the validation cohort (42 patients), and decision curve analysis (DCA) was applied for clinical use. RESULTS: The clinical model included 14 imaging features, and the corresponding area under the curve (AUC) was 0.769 (95% confidence interval [CI], 0.675-0.863) in the training cohort and 0.729 (95% CI, 0.568-0.890) in the validation cohort. The LDA included 14 selected radiomics features that showed good discrimination-in the training cohort (AUC, 0.851; 95% CI, 0.758-0.916) and the validation cohort (AUC, 0.736; 95% CI, 0.518-0.874). In the decision curves, if the threshold probability was 0.17-0.84, using the radiomics score to distinguish NF-pNET G1 and G2/3, offered more benefit than did the use of a treat-all-patients or treat-none scheme. DATA CONCLUSION: The developed radiomics model using noncontrast MRI could help differentiate G1 and G2/3 tumors, to make the clinical decision, and screen pNETs grade. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1124-1136.

Relationship between clinical types and radiological subgroups defined by latent class analysis in 2019 novel coronavirus pneumonia caused by SARS-CoV-2.

OBJECTIVES: To investigate whether meaningful subgroups sharing the CT features of patients with COVID-19 pneumonia could be identified using latent class analysis (LCA) and explore the relationship between the LCA-derived subgroups and clinical types. METHODS: This retrospective review included 499 patients with confirmed COVID-19 pneumonia between February 11 and March 8, 2020. Subgroups sharing the CT features were identified using LCA. Univariate and multivariate logistic regression models were utilized to analyze the association between clinical types and the LCA-derived subgroups. RESULTS: Two radiological subgroups were identified using LCA. There were 228 subjects (45.69%) in class 1 and 271 subjects (54.31%) in class 2. The CT findings of class 1 were smaller pulmonary infection volume, more peripheral distribution, more GGO, more maximum lesion range ≤ 5 cm, a smaller number of lesions, less involvement of lobes, less air bronchogram, less dilatation of vessels, less hilar and mediastinal lymph node enlargement, and less pleural effusion than the CT findings of class 2. Univariate analysis demonstrated that older age, therapy, presence of fever, presence of hypertension, decreased lymphocyte count, and increased CRP levels were significant parameters associated with an increased risk for class 2. Multivariate analyses revealed that the patients with clinically severe type disease had a 1.97-fold risk of class 2 than the patients with clinically moderate-type disease. CONCLUSIONS: The demographic and clinical differences between the two radiological subgroups based on the LCA were significantly different. Two radiological subgroups were significantly associated with clinical moderate and severe types. KEY POINTS: • Two radiological subgroups were identified using LCA. • Older age, therapy, presence of fever, presence of hypertension, decreased lymphocyte count, and increased CRP levels were significant parameters with an increased risk for class 2 defined by LCA. • Patients with clinically severe type had a 1.97-fold higher risk of class 2 defined by LCA in comparison with patients showing clinically moderate-type disease.

Comparison of chest CT findings between COVID-19 pneumonia and other types of viral pneumonia: a two-center retrospective study.

OBJECTIVES: To compare the pulmonary chest CT findings of patients with COVID-19 pneumonia with those with other types of viral pneumonia. METHODS: This retrospective review includes 154 patients with RT-PCR-confirmed COVID-19 pneumonia diagnosed between February 11 and 20, 2020, and 100 patients with other types of viral pneumonia diagnosed between April 2011 and December 2020 from two hospitals. High-resolution CT (HRCT) of the chest was performed. Data on location, distribution, attenuation, maximum lesion range, lobe involvement, number of lesions, air bronchogram signs, Hilar and mediastinal lymph node enlargement, and pleural effusion were collected. Associations between imaging characteristics and COVID-19 pneumonia were analyzed with univariate and multivariate logistic regression models. RESULTS: A peripheral distribution was associated with a 13.04-fold risk of COVID-19 pneumonia, compared with a diffuse distribution. A maximum lesion range > 10 cm was associated with a 9.75-fold risk of COVID-19 pneumonia, compared with a maximum lesion range ≤ 5 cm, and the involvement of 5 lobes was associated with an 8.45-fold risk of COVID-19 pneumonia, compared with a maximum lesion range ≤ 2. No pleural effusion was associated with a 3.58-fold risk of COVID-19 pneumonia compared with the presence of pleural effusion. Hilar and mediastinal lymph node enlargement was associated with a 2.79-fold risk of COVID-19 pneumonia. CONCLUSION: A peripheral distribution, a lesion range > 10 cm, involvement of 5 lobes, presence of hilar and mediastinal lymph node enlargement, and no pleural effusion were significantly associated with 2019-novel coronavirus pneumonia. KEY POINTS: • A peripheral distribution, a lesion range > 10 cm, involvement of 5 lobes, presence of hilar and mediastinal lymph node enlargement, and no pleural effusion were significantly associated with COVID-19 compared with other types of viral pneumonia.

Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2.

OBJECTIVES: To develop and validate a radiomics model for predicting 2019 novel coronavirus (COVID-19) pneumonia. METHODS: For this retrospective study, a radiomics model was developed on the basis of a training set consisting of 136 patients with COVID-19 pneumonia and 103 patients with other types of viral pneumonia. Radiomics features were extracted from the lung parenchyma window. A radiomics signature was built on the basis of reproducible features, using the least absolute shrinkage and selection operator method (LASSO). Multivariable logistic regression model was adopted to establish a radiomics nomogram. Nomogram performance was determined by its discrimination, calibration, and clinical usefulness. The model was validated in 90 consecutive patients, of which 56 patients had COVID-19 pneumonia and 34 patients had other types of viral pneumonia. RESULTS: The radiomics signature, consisting of 3 selected features, was significantly associated with COVID-19 pneumonia (p < 0.05) in both training and validation sets. The multivariable logistic regression model included the radiomics signature and distribution; maximum lesion, hilar, and mediastinal lymph node enlargement; and pleural effusion. The individualized prediction nomogram showed good discrimination in the training sample (area under the receiver operating characteristic curve [AUC], 0.959; 95% confidence interval [CI], 0.933-0.985) and in the validation sample (AUC, 0.955; 95% CI, 0.899-0.995) and good calibration. The mixed model achieved better predictive efficacy than the clinical model. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. CONCLUSIONS: The radiomics model derived has good performance for predicting COVID-19 pneumonia and may help in clinical decision-making. KEY POINTS: • A radiomics model showed good performance for prediction 2019 novel coronavirus pneumonia and favorable discrimination for other types of pneumonia on CT images. • A central or peripheral distribution, a maximum lesion range > 10 cm, the involvement of all five lobes, hilar and mediastinal lymph node enlargement, and no pleural effusion is associated with an increased risk of 2019 novel coronavirus pneumonia. • A radiomics model was superior to a clinical model in predicting 2019 novel coronavirus pneumonia.