Amylose content controls the V-type structural formation and in vitro digestibility of maize starch-resveratrol complexes and their effect on human gut microbiota.

The chain structure of starch affects its interaction with polyphenol molecules which in turn determines the nutritional function of starch. In this study, starch with different amylose content including waxy maize starch (WMS), normal maize starch (NMS) and G50 high-amylose maize starch (G50) were selected to complex with resveratrol (RA) in high-pressure homogenization (HPH) environment, and structural changes of the complexes, together with their effects on in vitro digestibility and gut microbiota were discussed. The results showed that with increasing amylose content, RA could form more inclusion complex with starch through non-covalent bonds accompanied by the increased single helix structure, V-type crystalline structure, compact nano-aggregates and total ordered structure content, which thus endowed the complex lower digestibility and intestinal probiotic function. Notably, when RA addition reached 3 %, the resistant starch (RS) content of HP-G50-3 % rose to 29.2 %, correspondingly increased the relative abundance of beneficial gut microbiota such as Megamonas and Bifidobacterium, as well as the total short-chain fatty acids (SCFAs) content. Correlation analysis showed that V-type crystalline structure positively correlated with the growth of Pediococcu and Blautia (p < 0.05) for producing SCFAs. These findings provided feasible ideas for the development of personalized nutritional starch-based foods.

Non-invasive precise staging of liver fibrosis using deep residual network model based on plain CT images.

PURPOSE: The aim of this study was to explore the application of five-class deep residual network models based on plain CT images and clinical features for the precise staging of liver fibrosis. METHODS: This retrospective clinical study included 347 patients who underwent liver CT, with pathological staging of liver fibrosis as the gold standard. We established three ResNet models to stage liver fibrosis. The output diagnosis labels of models were 0, 1, 2, 3 and 4, which correspond to F0, F1, F2, F3, and F4 stages. Confusion matrices were used to evaluate the performances of models to precisely stage liver fibrosis. The performance for diagnosing cirrhosis (F4), advanced fibrosis (≥ F3) and significant fibrosis (≥ F2) of models was evaluated with receiver operating characteristic (ROC) analyses. RESULTS: The kappa coefficients of the five-class ResNet model (based on plain CT images), the five-class ResNet clinical model (based on clinical features), and the five-class mixed ResNet model (based on plain CT images and clinical features) for precise staging liver fibrosis were 0.566, 0.306, and 0.63, respectively. The recall rates and precision rates for F0, F1, F2, and F3 of three models were lower than 60%. The ROC AUC values of the five-class ResNet model, the five-class ResNet clinical model, and the five-class mixed ResNet model for diagnosing cirrhosis, advanced fibrosis, and significant fibrosis were 0.95, 0.88, and 0.82, 0.80, 0.72, and 0.70, 0.95, 0.90, and 0.83, respectively. CONCLUSIONS: The five-class ResNet models are of high value in the diagnosis of liver cirrhosis, advanced liver fibrosis, and significant liver fibrosis. However, for the precise staging of liver fibrosis, the models cannot accurately distinguish other liver fibrosis stages except F4. Plain CT images combined with clinical features have the potential to improve the performance of the ResNet models in diagnosing liver fibrosis.

Deep residual nets model for staging liver fibrosis on plain CT images.

PURPOSE: The early diagnosis of liver fibrosis is crucial for the prevention of liver cirrhosis and liver cancer. As gold standard for staging liver fibrosis, liver biopsy is an invasive procedure that carries the risk of serious complications. The aim of this study was to evaluate the performance of the residual neural network (ResNet), a non-invasive methods, for staging liver fibrosis using plain CT images. METHODS: This retrospective study involved 347 patients subjected to liver CT scanning and liver biopsy. For each patient, we selected three axial images adjacent to the puncture location in the eighth or ninth inter-space on the right side. After processing and enhancement (rotation, translation, and amplification), these images were used as input data for the ResNet model. The model used a fivefold cross-validation method. In each fold, the images of approximately 80% of the total sample size (278 patients) were used for training the ResNet model, the other 20% (69 patients) were used for testing the trained network, with the liver biopsy pathology results as gold standard. The proportion of patients in each fibrosis stage was equal for training and test groups. The final result was the mean of the fivefold cross-validation in the test group. The performance of the ResNet model was evaluated for the test group by receiver operating characteristic (ROC) analysis. RESULTS: For the ResNet model, the area under the ROC curve (AUC) for assessing cirrhosis (F4), advanced fibrosis (F3 or higher), significant fibrosis (F2 or higher), and mild fibrosis (F1 or higher) was 0.97, 0.94, 0.90, and 0.91, respectively. CONCLUSIONS: The ResNet model analysis of plain CT images exhibited high diagnostic efficiency for liver fibrosis staging. As a convenient, fast, and economical non-invasive diagnostic method, the ResNet model can be used to assist radiologists and clinicians in liver fibrosis evaluations.

An NIR-II/MR dual modal nanoprobe for liver cancer imaging.

Hepatocellular carcinoma (HCC) is a malignancy of the liver worldwide and surgical resection remains the most effective treatment. However, it is still a great challenge to locate small lesions and define the border of diffused HCC even with the help of preoperative imaging examination. Here, we reported a rare-earth-doped nanoparticle NaGdF4:Nd 5%@NaGdF4@Lips (named Gd-REs@Lips), which simultaneously performed powerful functions in both magnetic resonance imaging (MRI) and second near-infrared fluorescence window imaging (NIR-II, 1000-1700 nm). Imaging studies on orthotopic models with xenografts established from HCC patients indicated that Gd-REs@Lips efficiently worked as a T2-weighted imaging contrast agent to increase the signal intensity difference between liver cancer tissues and surrounding normal liver tissues on MRI, and it can also serve as a negative NIR-II imaging contrast enabling the precise detection of liver cancer. More importantly, benefiting from the high sensitivity of NIR-II imaging, Gd-REs@Lips allowed the visualization of tiny metastasis lesions (2 mm) on the liver surface. It is expected that the dual NIR-II/MRI modal nanoprobe developed holds high potential to fill the gap between the preoperative imaging detection of cancer lesions and intra-operative guidance, and it further brings new opportunities to address HCC-related medical challenges.

Existing contradictions and suggestions: flipped classroom in radiology courses of musculoskeletal disease under Chinese medical educational mode from medical imaging student perspective.

BACKGROUND: Flipped classrooms have already begun to be used in many universities aboard, and they now make up for some of the short comings of the traditional classroom. We introduced the concept of flipped classrooms into a radiology class in China and evaluated the students' performance to find out whether it was a better learning method. Furthermore, we have attempted to identify the problems of application of flipped classrooms (as practiced under the Chinese education system) and make suggestions. METHODS: Facilities made videos and prepared clinical cases and short lectures for the flipped classroom. A total of 55 undergraduate radiology students were asked to finish pre-class learning and pre-learning assessment, participate in a flipped classroom about bone malignant tumours, and complete questionnaires. Teachers were also need to finish the survey. RESULTS: 1) The students showed good performances in the pre-learning assessment. The mean scores for three pre-learning assessment were 89.77, 96.54, and 93.71, respectively; the median scores were 90, 97.5, and 94, respectively. 2) After they attended the flipped classroom, their mastery of knowledge (case-solving skills, basic feature command, comparison ability, and overall knowledge command) showed improvements; after flipped classroom, the scores for these knowledge factors improved to 81.25, 85.42, 85.42, and 85.42%, respectively, compared to the scores they obtained before taking the flipped classroom (1.25, 68.75, 64.58, and 72.92% respectively). 3) The students' discussion time and student-teacher-communication time increased, and the students' questions were solved satisfactorily. 4) CTDI-CV showed no improvement in critical thinking skills after taking the course. 5) The time spent in previewing (pre-class video watching, material reading, and pre-learning assessment) increased significantly. CONCLUSIONS: Flipped classrooms, when tested in a radiology classroom setting, show many advantages, making up for some inadequacies of didactic classrooms. They provide students with better learning experiences. We can continue to practice flipped classroom methods under the curriculum, but we still need to make improvements to make it more suitable for the Chinese medical education mode.

The utility of APT and IVIM in the diagnosis and differentiation of squamous cell carcinoma of the cervix: A pilot study.

PURPOSE: This study aimed to investigate whether amide proton transfer (APT) imaging, compared with intravoxel incoherent motion (IVIM) imaging-derived parameters, can differentiate squamous cell carcinoma of the cervix (SCCC) from the normal cervical stroma and distinguish poorly differentiated SCCC from well-moderately differentiated SCCC. METHODS: This prospective study enrolled 32 patients, comprising 20 patients with well-moderately differentiated SCCC and 12 patients with poorly differentiated SCCC. 20 healthy volunteers were enrolled as a control group. A bi-exponential model (BEM) analysis was performed to derive ADC, pure molecular diffusion coefficient (D), pseudo-diffusion coefficient (D*) and perfusion fraction (f). The APT signal intensity (APT SI), ADC, D, D* and f were measured. The parameters between the groups were compared with independent t-tests. Diagnostic performance was evaluated with a ROC analysis. RESULTS: The APT SI of SCCC (2.92 ±â€¯0.24%) was higher than that of normal cervical stroma (2.72 ±â€¯0.36%) with P = 0.020. The comparison of the AUCs for the diagnosis of SCCC was ADC > f > APT SI > D* > D. A significant difference was found in the APT SI between the well-moderately differentiated SCCC group (2.82 ±â€¯0.15%) and the poorly differentiated SCCC group (3.09 ±â€¯0.27%) with P = 0.006. Except for D (P = 0.012), the ADC, D* and f values were not significantly different between the groups (P > 0.05). The comparison of the AUCs for distinguishing poorly differentiated SCCC was APT SI > D > ADC > D* = f. CONCLUSION: APT imaging may be a useful technique in the diagnosis and predicting the differentiation of SCCC.

Combinative evaluation of primary tumor and lymph nodes in predicting pelvic lymphatic metastasis in early-stage cervical cancer: A multiparametric PET-CT study.

PURPOSE: The purpose of this study is to investigate the value of combining tumor and pelvic lymph node (PLN) characteristics on PET-CT in predicting PLN metastasis of patients with early-stage cervical cancer, specifically to further reduce the false-negative cases of diagnosis. METHODS: The [18F] FDG PET-CT imaging data of 394 patients who were newly diagnosed with cervical cancer (FIGO stage, Ia-IIa) were retrospectively studied. We measured size, total lesion glycolysis (TLG) of tumor, metabolic tumor volume (MTV), maximum and mean standardized uptake value (SUVmax, SUVmean) of tumor and lymph node (LN). Diagnostic efficiency was evaluated using receiver operator characteristic (ROC) curve. We also investigated additional CT diagnosis information in PET-negative cases. RESULTS: Our results indicated both lymph node and tumor parameters were independent risk factors for lymphatic metastasis in early-stage cervical cancer. The diagnosis based on above meaningful parameters, we name it 'combination diagnosis', offered significantly higher predictive value than that based on SUV measurement alone, which the values of AUC were 0.842 and 0.784 respectively (P < 0.05). In PET-negative cases, we also found that tumor TLG, suspicious LN in lymphatic drainage pathway, long/short axis of LN ≤ 2, heterogeneity of LN significantly associated with PLN metastasis. ROC analysis showed combination diagnosis of all these parameters above produced an AUC value of 0.859 (P < 0.05, 95% CI, 0.811-0.899), which was significantly higher than either using tumor TLG alone (AUC = 0.622, Z = 3.919, P < 0.05) or indices derived from CT alone (AUC = 0.727, 0.668, 0.695. Z = 3.620, 5.356, 3.696, P < 0.05). CONCLUSIONS: We proposed a combination diagnosis method that can better predict PLN metastasis for patients with early-stage cervical cancer. In PET-negative cases, combination diagnosis of TLG of tumor and CT indicators also produced improved prediction by reducing false-negative cases of diagnosis. This combination diagnosis approach has significant implications in cervical cancer patient management and treatment planning.

Effects of radiation dose levels and spectral iterative reconstruction levels on the accuracy of iodine quantification and virtual monochromatic CT numbers in dual-layer spectral detector CT: an iodine phantom study.

BACKGROUND: The purpose of this study is to investigate the accuracy of iodine quantification and virtual monochromatic CT numbers obtained with the dual-layer spectral CT (DLCT) using a phantom at different radiation dose levels and spectral iterative reconstruction (IR) levels. METHODS: An abdomen phantom with seven iodine inserts (2.0, 2.5, 5.0, 7.5, 10.0, 15.0, 20.0 mg/mL) was imaged using a DLCT scanner. Five repeated scans were performed at computed tomography dose index volume (CTDIvol) levels of 5, 10, 15, 20, 25 mGy at tube voltages of 120 and 140 kVp, respectively. Spectral-based images were reconstructed using four spectral IR levels (spectral level of 0, 2, 4, 6). Iodine density images and virtual monochromatic images (VMI) at energy levels of 50, 70 and 120 keV were created. The absolute percentage bias (APB) of the measured iodine concentration and the true iodine concentration, and the measured VMI CT numbers and the theoretical VMI CT numbers were compared to determine the difference of radiation dose levels and different spectral IR levels. RESULTS: At CTDIvol levels of 25, 20, 15, 10 mGy, radiation dose levels had no effect on the accuracy of iodine quantitation; at CTDIvol level of 5 mGy, the accuracy of iodine quantification was the poorest, with the mean APBiodine of 4.33% (P<0.05). There was no significant difference in the accuracy of iodine quantitation between 120 and 140 kVp (P=0.648). At energy levels of 50, 70 and 120 keV, there was no significant difference in the accuracy of the VMI CT numbers among the CTDIvol levels of 25, 20 and 15 mGy. However, the accuracy of VMI CT numbers was significantly degraded at the CTDIvol levels of 10 and 5 mGy (P<0.05). At energy level of 50 keV, the accuracy of VMI CT numbers was not affected by tube voltages (kVps) used (P=0.125). At the energy levels of 70 and 120 keV, 140 kVp produced a smaller bias than 120 kVp, with the mean APBHU at 120 and 140 kVp being of 3.62% vs. 2.99% for 70 keV (P<0.01), and 11.65% vs. 9.28% for 120 keV (P<0.01), respectively. Spectral IR levels did not affect the accuracy of iodine quantification and VMI CT numbers (P=0.998, P=0.963). CONCLUSIONS: The accuracy of iodine quantification and VMI CT numbers was only affected by very low radiation dose levels. At the clinically applied radiation dose levels of >10 mGy, the accuracy of both iodine quantification and VMI CT numbers is relatively stable and high.

MRI-based texture analysis of the primary tumor for pre-treatment prediction of bone metastases in prostate cancer.

PURPOSE: To identify texture features of multiparametric MRI (mp-MRI) for pre-treatment prediction of bone metastases (BM) in patients with prostate cancer (PCa). PATIENTS AND METHODS: One-hundred and seventy-six patients with clinicopathologically confirmed PCa were enrolled，and the data was gathered from January 2008 to January 2018. A total of 976 texture features were extracted from T2-weighted (T2-w) and dynamic contrast-enhanced T1-weighted (DCE T1-w) MRI. Step regression, ridge regression and LASSO regression method model was applied to select features and develop the predicting model for BM. The performance of the radiomics features, PSA level and Gleason Score were explored with the respect to the receiver operating characteristics (ROC) curve. Multivariable logistic regression analysis starting with the following clinical risk factors (PSA level, Gleason Score and age) and imaging biomarkers were applied to develop diagnostic model for BM in PCa. RESULTS: The texture features, which consisted of 15 selected features, were significantly associated with BM (P < 0.01). The combined MRI features derived from T2-w and DCE T1-w showed better prognostic performance (AUC = 0.898) than features derived from single sequence (T2WI AUC = 0.875, DCE T1-w AUC = 0.870) and Gleason Score (AUC = 0.731) for pre-treatment prediction of BM in PCa. MRI -based imaging biomarker combined with clinical risk factors (free PSA, age and Gleason score) yielded the highest AUC(AUC = 0.916). Multivariate regression analysis showed that the imaging biomarker was an independent risk factor for the detection of bone metastases along with f-PSA level (free PSA) and Gleason score. CONCLUSION: Multiparametric MRI-based texture feature was significant predictor for BM in PCa. Clinical risk factors combined with MRI-based texture feature could further improve the prediction performance, which provide an illustrative example of precision medicine and may affect treatment strategies.

Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach.

BACKGROUND: Diabetes mellitus is a chronic disease with a steadfast increase in prevalence. Due to the chronic course of the disease combining with devastating complications, this disorder could easily carry a financial burden. The early diagnosis of diabetes remains as one of the major challenges medical providers are facing, and the satisfactory screening tools or methods are still required, especially a population- or community-based tool. METHODS: This is a retrospective cross-sectional study involving 15,323 subjects who underwent the annual check-up in the Department of Family Medicine of Shengjing Hospital of China Medical University from January 2017 to June 2017. With a strict data filtration, 10,436 records from the eligible participants were utilized to develop a prediction model using the J48 decision tree algorithm. Nine variables, including age, gender, body mass index (BMI), hypertension, history of cardiovascular disease or stroke, family history of diabetes, physical activity, work-related stress, and salty food preference, were considered. RESULTS: The accuracy, precision, recall, and area under the receiver operating characteristic curve (AUC) value for identifying potential diabetes were 94.2%, 94.0%, 94.2%, and 94.8%, respectively. The structure of the decision tree shows that age is the most significant feature. The decision tree demonstrated that among those participants with age ≤ 49, 5497 participants (97%) of the individuals were identified as nondiabetic, while age > 49, 771 participants (50%) of the individuals were identified as nondiabetic. In the subgroup where people were 34 < age ≤ 49 and BMI ≥ 25, when with positive family history of diabetes, 89 (92%) out of 97 individuals were identified as diabetic and, when without family history of diabetes, 576 (58%) of the individuals were identified as nondiabetic. Work-related stress was identified as being associated with diabetes. In individuals with 34 < age ≤ 49 and BMI ≥ 25 and without family history of diabetes, 22 (51%) of the individuals with high work-related stress were identified as nondiabetic while 349 (88%) of the individuals with low or moderate work-related stress were identified as not having diabetes. CONCLUSIONS: We proposed a classifier based on a decision tree which used nine features of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of diabetes. The classifier indicates that a decision tree analysis can be successfully applied to screen diabetes, which will support clinical practitioners for rapid diabetes identification. The model provides a means to target the prevention of diabetes which could reduce the burden on the health system through effective case management.

Actinidia arguta (Hardy Kiwi) Root Extract Exerts Anti-cancer Effects via Mcl-1-Mediated Apoptosis in Cholangiocarcinoma.

Cholangiocarcinoma (CCA) is a highly aggressive and chemoresistant liver malignancy. Thus, identification of strategies to overcome insensitivity to apoptosis and growth inhibition is a growing focus of research in this malignancy. This study evaluated the potential anti-cancer effects of an ethanol extract from the Actinidia arguta (Hardy Kiwi) root (RAE) on CCA. Our data demonstrated that RAE decreased cell viability and induced apoptosis by activation of Caspase 3, Caspase 8, and Poly (ADP-ribose) polymerase (PARP) in two CCA cell lines. RAE induced a decrease in Mcl-1 in cultured CCA cells and in xenograft CCA tumors. Administration of RAE every other day led to significant growth inhibition in tumor burden xenograft CCA mice. Western blotting analysis of paired human CCA and normal adjacent tissues from the same patient revealed that CCA tissues exhibited significantly higher Mcl-1 expression than normal tissues. Taken together, our findings demonstrated the anti-cancer effects of RAE on CCA both in vitro and in vivo. These data suggest that RAE may be a promising anti-CCA agent and could be beneficial in the treatment of CCA through the targeting of Mcl-1.

Amide proton transfer imaging to evaluate the grading of squamous cell carcinoma of the cervix: A comparative study using 18 F FDG PET.

BACKGROUND: Amide proton transfer (APT) imaging has shown great potential value in the diagnosis of cancer, but has yet not been applied in cervical carcinoma patients. PURPOSE: To investigate the utility of APT imaging in estimating histologic grades of squamous cell carcinoma of the cervix (SCCC), compared with the standardized uptake value (SUV). STUDY TYPE: Prospective. POPULATION: Thirty-one patients with SCCC (median age 51 years) were included. FIELD STRENGTH/SEQUENCE: Ingenia 3.0 T CX, Axial T1 -weighted imaging (T1 WI), Axial T2 WI, 3D turbo spin echo sequence for APT imaging. ASSESSMENT: Patient pathology was confirmed by surgery and the patients were divided into three groups based on histologic grades: Grade 1 (n = 9), Grade 2 (n = 12), and Grade 3 (n = 10). The APT signal intensity (APT SI), maximum SUV (SUVmax ) and mean SUV (SUVmean ) for each grade were assessed by experienced radiologists in a blinded manner. STATISTICAL TESTS: The obtained parameters were compared by one-way analysis of variance with Tukey honest significant difference post-hoc test. The correlations between the parameters and histologic grades were analyzed by Spearman's correlation coefficient. The Pearson correlation coefficients of the APT SI with the SUVmax and SUVmean were also calculated. RESULTS: The APT SIs for the three grades were significantly different (P = 0.0002). The APT SIs of Grade 2 and Grade 3 had significant differences (P = 0.009). The Spearman correlation coefficients for the correlations between the parameters and histological grade were as follows: APT SI: 0.684 (P = 0.00002), SUVmax : 0.318 (P = 0.082), and SUVmean : 0.261 (P = 0.157). The Pearson correlation coefficients of the APT SI with the SUVmax and SUVmean were 0.108 (P = 0.564) and 0.178 (P = 0.337), respectively. DATA CONCLUSION: The APT SI was positively correlated with the SCCC grades. APT imaging maybe a promising method for predicting SCCC histologic grades. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:261-268.

FSIP1 regulates autophagy in breast cancer.

Fibrous sheath interacting protein 1 (FSIP1) is a cancer antigen expressed in the majority of breast cancer tissues and is associated with poor prognosis. However, the role of FSIP1 in the progression and drug sensitivity of triple-negative breast cancer (TNBC) has not been explored. Here, we show that FSIP1 deficiency by shRNA-mediated knockdown or CRISPR-Cas9-mediated knockout significantly inhibits the proliferation and invasion of TNBC cells and impairs chemotherapy-induced growth inhibition in vivo. Computational modeling predicted that FSIP1 binds to ULK1, and this was established by coimmunoprecipitation. FSIP1 deficiency promoted autophagy, enhanced AMP-activated protein kinase (AMPK) signaling, and decreased mechanistic target of rapamycin (mTOR) and Wnt/ß-catenin activity. In contrast, knockdown of AMPK or inhibition of autophagy restored the sensitivity to chemotherapy drugs in TNBC cells. Our findings uncover a role of FSIP1 as well as mechanisms underlying FSIP1 action in drug sensitivity and may, therefore, aid in design of TNBC therapies.

Multiple well-differentiated cardiac liposarcoma with a concomitant myocardial lipoma: A case report.

A cardiac lipoma and a cardiac liposarcoma are very uncommon conditions; however, the simultaneous development of the two is extremely rare. In addition, the symptoms of these conditions are not evident in the vast majority of cases, making them easily missed during diagnosis. The present case report describes a case of well-differentiated cardiac liposarcoma with a concomitant myocardial lipoma that was misdiagnosed as multiple cardiac lipomas. The ultrasound, computed tomography, and magnetic resonance imaging presentations of the tumour were described. In addition, relevant literature regarding these two types of tumours was reviewed and compared in order to help improve the identification of these types of tumours. This, in turn, may be beneficial in the selection of more appropriate examination and treatment strategies.

Evaluation of 18F-FDG PET/CT parameters for reflection of aggressiveness and prediction of prognosis in early-stage cervical cancer.

PURPOSE: The aim of this study was to investigate the value of five typical fluorine-18-fluorodeoxyglucose PET/computed tomography (CT) parameters (including SUVmax, SUVmean, SUVpeak, MTV and TLG) in the reflecting aggressiveness and predicting prognosis in patients with early-stage cervical cancer. PATIENTS AND METHODS: Our study enrolled 85 primary cervical cancer patients who underwent preoperative fluorine-18-fluorodeoxyglucose PET/CT scans. We investigated the association of five parameters derived from PET/CT with clinicopathological characteristics. Immunohistochemistry was utilized to evaluate the expression of glucose transporter protein-1 (GLUT-1), TP53-induced glycolysis and apoptosis regulator (TIGAR), and vascular endothelial growth factor (VEGF), and the correlation between PET/CT parameters and the expression of GLUT-1, TIGAR, and VEGF was analyzed. In addition, we also investigated the correlation between disease-free survival of cervical cancer patients and PET/CT parameters. RESULTS: The mean peak standardized uptake value (SUV)peak showed significant differences between all three International Federation of Gynecology and Obstetrics stages (P<0.001-0.032). The patients with lymph node metastasis had significantly higher SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) compared with those with the absence of lymph node metastasis (P=0.038, 0.024, and 0.010, respectively). All the five parameters showed an association with tumor size and cervical stromal invasion depth. Immunohistochemistry results indicated that the SUVpeak showed the highest association with the expression of GLUT-1, TIGAR, and VEGF (rs=0.705, P<0.001, rs=-0.466, P<0.001, rs=0.580, P<0.001, respectively) in these five parameters. The survival analysis showed that SUVpeak, MTV, and TLG were correlated with disease-free survival (P=0.042, P<0.001, and P=0.004, respectively). CONCLUSION: Early-stage cervical cancer with high SUVpeak may reflect more aggressive behavior, and SUVpeak, MTV, and TLG can be used to predict the prognosis of early-stage cervical cancer patients. It may help with the choice of individualized treatment options.

Value of [18F]FDG PET radiomic features and VEGF expression in predicting pelvic lymphatic metastasis and their potential relationship in early-stage cervical squamous cell carcinoma.

PURPOSE: The purpose of this study was to explore the value of 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography/computed tomography ([18F]FDG PET/CT) radiomic features combined with vascular endothelial growth factor (VEGF) expression in predicting pelvic lymphatic metastasis in patients with early-stage cervical squamous cell carcinoma and the added value of radiomic features in predicting VEGF expression. MATERIALS AND METHODS: Ninety-four newly diagnosed cervical squamous cell carcinoma patients (training dataset: n = 64, validation cohort: n = 30) in stage Ia to IIa, according to the International Federation of Gynecology and Obstetrics (FIGO) staging system, who underwent [18F]FDG PET/CT were retrospectively analyzed. Radiomic features of the [18F]FDG PET scans were extracted, and the value of the lymph node sizes, metabolic parameters (both tumor and lymph nodes), radiomic features and VEGF expression level in predicting lymphatic metastasis were evaluated by receiver operating characteristics curves (ROC) and were compared using DeLong test. Moreover, we studied the associations between the [18F]FDG PET radiomic features and VEGF expression. RESULTS: Total lesion glycolysis (TLG) and the expression of VEGF were significantly higher in subjects with lymphatic metastasis than in those without. The homogeneity feature derived from the histogram, the skewness, had a certain value in predicting lymphatic metastasis (AUC = 0.803 in training dataset, P < 0.05, 95% CI 0.684, 0.892; AUC = 0.757 in validation dataset, P < 0.05, 95% CI 0.545, 0.904). Additionally, the combination of this radiomic feature and VEGF expression had a significantly superior predictive value (AUC = 0.878, P < 0.05, 95% CI 0.772- 0.947), compared to that of the conventional parameters. Moreover, 26 radiomic features derived from the histogram and GLCM features correlated with VEGF expression. CONCLUSIONS: In patients with early-stage cervical squamous cell carcinoma, PLN metastasis can be predicted by TLG and the textural feature of homogeneity. Radiomic features in combination with the VEGF expression level improved the prediction accuracy. In addition, some features derived from the histogram and gray-level co-occurrence matrices (GLCM) may have a certain value in predicting the VEGF expression level.

Pancreatic Stiffness Quantified with MR Elastography: Relationship to Postoperative Pancreatic Fistula after Pancreaticoenteric Anastomosis.

Purpose To describe the relationship between conventional magnetic resonance (MR) imaging parameters and MR elastography of the pancreas in association with pancreatic histologic features and occurrence of postoperative pancreatic fistula (POPF). Materials and Methods Patients who underwent preoperative MR imaging (MR elastography and diffusion-, T1-, and T2-weighted imaging) followed by pancreatectomy with pancreaticoenteric anastomosis were included. The relationships between preoperative MR imaging parameters, demographic data, and intraoperative factors with POPF risk were analyzed with logistic regression analyses. The correlation of MR imaging parameters with histologic characteristics was evaluated with multivariate regression analysis. Results A total of 112 patients (64 men, 48 women; median age, 58 years) were evaluated. Forty-two patients (37.5%) developed POPF and 20 (17.9%) developed high-grade POPF (grades B and C). Lower pancreatic stiffness (≤1.43 kPa; odds ratio [OR], 9.196; 95% confidence interval [CI]: 1.92, 43.98), nondilated main pancreatic duct (MPD) diameter (<3 mm; OR, 7.298; 95% CI: 1.51, 35.34), and larger stump area (≥211 mm2; OR, 9.210; 95% CI: 1.53, 55.26) were risk factors for POPF. Lower pancreatic stiffness (≤1.27 kPa; OR, 8.389; 95% CI: 1.88, 37.41) was the only independent predictor of high-grade POPF. Log-transformed pancreatic stiffness was independently associated with fibrosis (ß = 0.060; 95% CI: 0.052, 0.068), acinar atrophy (ß = 0.015; 95% CI: 0.003, 0.028), and lipomatosis (ß = -0.016; 95% CI: -0.026, -0.006). Conclusion Preoperative MR assessment of pancreatic stiffness, MPD diameter, and stump area are important predictors of POPF.

Sensorimotor cortex atrophy in patients with cervical spondylotic myelopathy.

Previous studies have shown compensatory adaptive changes in cerebral functions before surgery in patients with cervical spondylotic myelopathy (CSM), especially in the sensorimotor cortices. However, the structural changes in the sensorimotor cortices in patients with CSM remain poorly understood. The aim of this study was to assess the volumetric changes in the sensorimotor cortices using morphological MRI and to correlate these changes with clinical scales. We hypothesize that CSM causes atrophy in the sensorimotor cortices, which results in functional changes during CSM progression. The study participants included 30 CSM patients and 25 matched healthy controls. The patients underwent brain morphological MRI before surgery. Compared with the healthy controls, the patients with CSM showed significant atrophy in the primary somatosensory cortex (S1), the primary motor cortex (M1), the somatosensory association cortex, and the supplementary motor area. The gray matter volumes in the S1 and M1 were correlated positively with the motor scores of the Japanese Orthopedic Association in patients with CSM. The change in supplementary motor area correlated with the sphincter scores of the Japanese Orthopedic Association in CSM patients. Our findings provide new insights into the compensatory reaction in CSM patients.

Differentiation of benign and malignant solid pancreatic masses using magnetic resonance elastography with spin-echo echo planar imaging and three-dimensional inversion reconstruction: a prospective study.

OBJECTIVES: To determine the diagnostic performance of MR elastography (MRE) and compare it with serum CA19-9 in differentiating malignant from benign pancreatic masses, with emphasis on differentiating between pancreatic ductal adenocarcinoma (PDAC) and mass-forming pancreatitis (MFP). METHODS: We performed a prospective, consecutive, 24-month study in 85 patients with solid pancreatic masses confirmed by histopathologic examinations. The mass stiffness and stiffness ratio (calculated as the ratio of mass stiffness to the parenchymal stiffness) were assessed. The diagnostic accuracy was analysed by calculating the area under the ROC curve (AUROC). RESULTS: The final diagnosis included 54 malignant tumours (43 patients with PDAC) and 31 benign masses (24 patients with MFP). The stiffness ratio showed better diagnostic performance than the mass stiffness and serum CA19-9 for the differentiation between malignant and benign masses (AUC: 0.912 vs. 0.845 vs. 0.702; P = 0.026, P < 0.001) and, specifically, between PDAC and MFP (AUC: 0.955 vs. 0.882 vs. 0.745; P = 0.026, P = 0.003). The sensitivity, specificity, and accuracy of stiffness ratio for the differentiation of PDAC and MFP were all higher than 0.9. CONCLUSIONS: MRE presents an effective and quantitative strategy for non-invasive differentiation between PDAC and MFP based on their mechanical properties. KEY POINTS: • 3D MRE is useful for calculating stiffness of solid pancreatic tumours. • Stiffness ratio outperformed stiffness and CA19-9 for differentiating PDAC from MFP. • Incorporation of 3D MRE into a standard MRI protocol is recommended.