Development and validation of a Radiopathomics model based on CT scans and whole slide images for discriminating between Stage I-II and Stage III gastric cancer.

OBJECTIVE: This study aimed to develop and validate an artificial intelligence radiopathological model using preoperative CT scans and postoperative hematoxylin and eosin (HE) stained slides to predict the pathological staging of gastric cancer (stage I-II and stage III). METHODS: This study included a total of 202 gastric cancer patients with confirmed pathological staging (training cohort: n = 141; validation cohort: n = 61). Pathological histological features were extracted from HE slides, and pathological models were constructed using logistic regression (LR), support vector machine (SVM), and NaiveBayes. The optimal pathological model was selected through receiver operating characteristic (ROC) curve analysis. Machine learnin algorithms were employed to construct radiomic models and radiopathological models using the optimal pathological model. Model performance was evaluated using ROC curve analysis, and clinical utility was estimated using decision curve analysis (DCA). RESULTS: A total of 311 pathological histological features were extracted from the HE images, including 101 Term Frequency-Inverse Document Frequency (TF-IDF) features and 210 deep learning features. A pathological model was constructed using 19 selected pathological features through dimension reduction, with the SVM model demonstrating superior predictive performance (AUC, training cohort: 0.949; validation cohort: 0.777). Radiomic features were constructed using 6 selected features from 1834 radiomic features extracted from CT scans via SVM machine algorithm. Simultaneously, a radiopathomics model was built using 17 non-zero coefficient features obtained through dimension reduction from a total of 2145 features (combining both radiomics and pathomics features). The best discriminative ability was observed in the SVM_radiopathomics model (AUC, training cohort: 0.953; validation cohort: 0.851), and clinical decision curve analysis (DCA) demonstrated excellent clinical utility. CONCLUSION: The radiopathomics model, combining pathological and radiomic features, exhibited superior performance in distinguishing between stage I-II and stage III gastric cancer. This study is based on the prediction of pathological staging using pathological tissue slides from surgical specimens after gastric cancer curative surgery and preoperative CT images, highlighting the feasibility of conducting research on pathological staging using pathological slides and CT images.

CT findings and clinical characteristics in distinguishing renal urothelial carcinoma mimicking renal cell carcinoma from clear cell renal cell carcinoma.

BACKGROUND: We aimed to characterize the clinical and multiphase computed tomography (CT) features, which can distinguish renal urothelial carcinoma (RUC) mimicking renal cell carcinoma (RCC) from clear cell renal cell carcinoma (ccRCC) with collecting system invasion (CSI). METHODS: Data from 56 patients with RUC (46 men and 10 women) and 366 patients with ccRCC (262 men and 104 women) were collected and assessed retrospectively. The median age was 65.50 (IQR: 56.25-69.75) and 53.50 (IQR: 42.25-62.5) years, respectively. Univariate and multivariate logistic regression analyses were performed on clinical and CT characteristics to determine independent factors for distinguishing RUC and ccRCC, and an integrated predictive model was constructed. Differential diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS: The independent predictors for differentiating RUC from ccRCC were infiltrative growth pattern, hydronephrosis, heterogeneous enhancement, preserving reniform contour, and hematuria. The differential diagnostic performance of the integrated predictive model-1 (AUC: 0.947, sensitivity: 89.07%, specificity: 89.29%) and model-2 (AUC: 0.960, sensitivity: 92.1%, specificity: 89.3%) were both better than that of the infiltrative growth pattern (AUC: 0.830, sensitivity: 71.9%, specificity: 92.9%), heterogeneous enhancement (AUC: 0.771, sensitivity: 86.3%, specificity: 67.9%), preserving reniform contour (AUC = 0.758, sensitivity: 85.5%, specificity: 66.1%), hydronephrosis (AUC: 0.733, sensitivity: 87.7%, specificity: 58.9%), or hematuria (AUC: 0.706, sensitivity: 79.5%, specificity: 51.8%). CONCLUSION: The CT and clinical characteristics showed extraordinary discriminative abilities in the differential diagnosis of RUC and ccRCC, which might provide helpful information for clinical decision-making.

Values of multiparametric and biparametric MRI in diagnosing clinically significant prostate cancer: a multivariate analysis.

BACKGROUND: To investigate the value of semi-quantitative and quantitative parameters (PI-RADS score, T2WI score, ADC, Ktrans, and Kep) based on multiparametric MRI (mpMRI) or biparametric MRI (bpMRI) combined with prostate specific antigen density (PSAD) in detecting clinically significant prostate cancer (csPCa). METHODS: A total of 561 patients (276 with csPCa; 285 with non-csPCa) with biopsy-confirmed prostate diseases who underwent preoperative mpMRI were included. Prostate volume was measured for calculation of PSAD. Prostate index lesions were scored on a five-point scale on T2WI images (T2WI score) and mpMRI images (PI-RADS score) according to the PI-RADS v2.1 scoring standard. DWI and DCE-MRI images were processed to measure the quantitative parameters of the index lesion, including ADC, Kep, and Ktrans values. The predictors of csPCa were screened by logistics regression analysis. Predictive models of bpMRI and mpMRI were established. ROC curves were used to evaluate the efficacy of parameters and the model in diagnosing csPCa. RESULTS: The independent diagnostic accuracy of PSA density, PI-RADS score, T2WI score, ADCrec, Ktrans, and Kep for csPCa were 80.2%, 89.5%, 88.3%, 84.6%, 58.5% and 61.6%, respectively. The diagnostic accuracy of bpMRI T2WI score and ADC value combined with PSAD was higher than that of PI-RADS score. The combination of mpMRI PI­RADS score, ADC value with PSAD had the highest diagnostic accuracy. CONCLUSIONS: PI-RADS score according to the PI-RADS v2.1 scoring standard was the most accurate independent diagnostic index. The predictive value of bpMRI model for csPCa was slightly lower than that of mpMRI model, but higher than that of PI-RADS score.

Prognostic analysis and risk stratification of lung adenocarcinoma undergoing EGFR-TKI therapy with time-serial CT-based radiomics signature.

OBJECTIVES: To evaluate the value of time-serial CT radiomics features in predicting progression-free survival (PFS) for lung adenocarcinoma (LUAD) patients after epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) therapy. MATERIALS AND METHODS: LUAD patients treated with EGFR-TKIs were retrospectively included from three independent institutes and divided into training and validation cohorts. Intratumoral and peritumoral features were extracted from time-serial non-contrast chest CT (including pre-therapy and first follow-up images); moreover, the percentage variation per unit time (day) was introduced to adjust for the different follow-up periods of each patient. Test-retest was performed to exclude irreproducible features, while the Boruta algorithm was used to select critical radiomics features. Radiomics signatures were constructed with random forest survival models in the training cohort and compared against baseline clinical characteristics through Cox regression and nonparametric testing of concordance indices (C-indices). RESULTS: The training cohort included 131 patients (74 women, 56.5%) from one institute and the validation cohort encompassed 41 patients (24 women, 58.5%) from two other institutes. The optimal signature contained 10 features and 7 were unit time feature variations. The comprehensive radiomics model outperformed the pre-therapy clinical characteristics in predicting PFS (training: 0.78, 95% CI: [0.72, 0.84] versus 0.55, 95% CI: [0.49, 0.62], p < 0.001; validation: 0.72, 95% CI: [0.60, 0.84] versus 0.54, 95% CI: [0.42, 0.66], p < 0.001). CONCLUSION: Radiomics signature derived from time-serial CT images demonstrated optimal prognostic performance of disease progression. This dynamic imaging biomarker holds the promise of monitoring treatment response and achieving personalized management. KEY POINTS: • The intrinsic tumor heterogeneity can be highly dynamic under the therapeutic effect of EGFR-TKI treatment, and the inevitable development of drug resistance may disrupt the duration of clinical benefit. Decision-making remained challenging in practice to detect the emergence of acquired resistance during the early response phase. • Time-serial CT-based radiomics signature integrating intra- and peritumoral features offered the potential to predict progression-free survival for LUAD patients treated with EGFR-TKIs. • The dynamic imaging signature allowed for prognostic risk stratification.

Multi-echo Dixon and breath-hold T2-corrected multi-echo single-voxel MRS for quantifying hepatic iron overload in rabbits.

BACKGROUND: Three-dimensional (3D) multi-echo-Dixon (ME-Dixon) and breath-hold T2-corrected multi-echo single-voxel MR spectroscopy (HISTO) can simultaneously quantify liver fat and liver iron. However, their diagnostic efficacy and application scope for quantitative iron in co-existing fatty liver have not been adequately evaluated. PURPOSE: To evaluate the accuracy of ME-Dixon and HISTO for quantitative analysis of hepatic iron in rabbits with iron deposition and fatty liver using liver-iron concentration (LIC) as a reference standard. MATERIAL AND METHODS: ME-Dixon, HISTO, and conventional two-dimensional multi-echo gradient echo (GRE) sequences were performed on 42 rabbits. The following parameters were calculated: R2* from ME-Dixon and GRE; proton density fat fraction (PDFF) from the ME-Dixon, HISTO (normal TE range), and HISTO-H (extended TE range); and R2_water from HISTO and HISTO-H. The LIC and liver-fat concentration (LFC) were measured through chemical analysis, and their relationship with the MRI parameters were assessed. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to evaluate the diagnostic efficiency. RESULTS: LIC was significantly correlated with R2_HISTO-H, R2*_Dixon, and R2*_GRE (r = 0.858, 0.910, 0.931, respectively; P < 0.001) and weakly with R2_HISTO (r = 0.424; P = 0.008). A strong correlation was also observed between the LFC and PDFF obtained from HISTO, HISTO-H, and ME-Dixon (r = 0.776, 0.811, 0.888, respectively; P < 0.001). ME-Dixon showed the best performance with moderate iron overload (AUC = 0.983). CONCLUSION: 3D ME-Dixon is useful for quantifying the LIC, especially with co-existing fatty liver. Its diagnostic performance is also superior to that of the HISTO sequence.

Development and validation of an MRI-based radiomic model for predicting overall survival in nasopharyngeal carcinoma patients with local residual tumors after intensity-modulated radiotherapy.

BACKGROUND: To investigate the potential value of the pretreatment MRI-based radiomic model in predicting the overall survival (OS) of nasopharyngeal carcinoma (NPC) patients with local residual tumors after intensity-modulated radiotherapy (IMRT). METHODS: A total of 218 consecutive nonmetastatic NPC patients with local residual tumors after IMRT [training cohort (n = 173) and validation cohort (n = 45)] were retrospectively included in this study. Clinical and MRI data were obtained. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features from pretreatment MRI. The clinical, radiomic, and combined models for predicting OS were constructed. The models' performances were evaluated using Harrell's concordance index (C-index), calibration curve, and decision curve analysis. RESULTS: The C-index of the radiomic model was higher than that of the clinical model, with the C-index of 0.788 (95% CI 0.724-0.852) versus 0.672 (95% CI 0.599-0.745) in the training cohort and 0.753 (95% CI 0.604-0.902) versus 0.634 (95% CI 0.593-0.675) in the validation cohort. Calibration curves showed good agreement between the radiomic model-predicted probability of 2- and 3-year OS and the actual observed probability in the training and validation groups. Decision curve analysis showed that the radiomic model had higher clinical usefulness than the clinical model. The discrimination of the combined model improved significantly in the training cohort (P < 0.01) but not in the validation cohort, with the C-index of 0.834 and 0.734, respectively. The radiomic model divided patients into high- and low-risk groups with a significant difference in OS in both the training and validation cohorts. CONCLUSIONS: Pretreatment MRI-based radiomic model may improve OS prediction in NPC patients with local residual tumors after IMRT and may assist in clinical decision-making.

Radiomics in Addition to Computed Tomography-Based Body Composition Nomogram May Improve the Prediction of Postoperative Complications in Gastric Cancer Patients.

OBJECTIVES: The study aimed to determine the impact of computed tomography (CT)-based body composition and radiomics nomogram on the prediction of postoperative complications in gastric cancer. METHODS: The clinical data of 457 individuals with surgically confirmed gastric cancer, 320 patients in the training cohort (TC) and 137 patients in the validation cohort (VC), were retrospectively analyzed. Body composition data were measured using CT. Postoperative complications were graded using the Clavien-Dindo system. Dedicated radiomics prototype software was used to segment lesions and extract characteristics from preoperative portal venous-phase CT images. Clinical, radiomics, and combined models were developed using logistic regression analysis. Model performance was evaluated using the area under the curve (AUC) of receiver operating characteristic curve, and the prediction ability of the optimal model was evaluated using calibration curves and decision curve analysis (DCA). RESULTS: Nutritional Risk Screening 2002 (NRS2002) score, sarcopenia, and blood loss were independent predictors of postoperative complications in gastric cancer. A radiomics signature was created using 19 conserved radiomics features. The nomogram based on both the clinical model and radiomics signature showed the greatest predictive performance, with AUCs of 0.763 (95% confidence interval [CI], 0.708-0.817) and 0.748 (95% CI: 0.667-0.818) in the TC and VC, respectively. The calibration curve and DCA revealed that the nomogram was beneficial in clinical practice for the preoperative prediction of postoperative complications. CONCLUSIONS: The combined model consisting of NRS2002 score, sarcopenia, blood loss, and a radiomics signature holds potential application value for the individualized prediction of postoperative complications in gastric cancer patients.

Comparison of Conventional Gadoxetate Disodium-Enhanced MRI Features and Radiomics Signatures With Machine Learning for Diagnosing Microvascular Invasion.

OBJECTIVE. This study aimed to determine the best model for predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC) using conventional gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (gadoxetate disodium)-enhanced MRI features and radiomics signatures with machine learning. MATERIALS AND METHODS. This retrospective study included 269 patients with a postoperative pathologic diagnosis of HCC. Gadoxetate disodium-enhanced MRI features were assessed, including T1 relaxation time, tumor margin, tumor size, peritumoral enhancement, peritumoral hypointensity, and ADC. Radiomics models were constructed and validated by machine learning. The least absolute shrinkage and selection operator (LASSO) was used for feature selection, and radiomics-based LASSO models were constructed with six classifiers. Predictive capability was assessed using the ROC AUC. RESULTS. Histologic examination confirmed MVI in 111 (41.3%) of the 269 patients. ADC value, nonsmooth tumor margin, and 20-minute T1 relaxation time showed diagnostic accuracy with AUC values of 0.850, 0.847, and 0.846, respectively (p < .05 for all). A total of 1395 quantitative imaging features were extracted. In the hepatobiliary phase (HBP) model, the support vector machine (SVM), extreme gradient boosting (XGBoost), and logistic regression (LR) classifiers showed greater diagnostic efficiency for predicting MVI, with AUCs of 0.942, 0.938, and 0.936, respectively (p < .05 for all). CONCLUSION. ADC value, nonsmooth tumor margin, and 20-minute T1 relaxation time show high diagnostic accuracy for predicting MVI. Radiomics signatures with machine learning can further improve the ability to predict MVI and are best modeled during HBP. The SVM, XGBoost, and LR classifiers may serve as potential biomarkers to evaluate MVI.

Diagnostic Value of Gd-EOB-DTPA-Enhanced MRI for the Expression of Ki67 and Microvascular Density in Hepatocellular Carcinoma.

CONTRACT GRANT SPONSOR: National Natural Science Foundation of China; Contract grant number: 81260214. BACKGROUND: Recent studies have highlighted the diagnostic value of Gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in small hepatocellular carcinoma (HCC). Ki67 and CD34 are histologic markers that reflect the proliferation of tumor cells and the microvascular density (MVD). PURPOSE: To explore the diagnostic value of Gd-EOB-DTPA-enhanced MRI for Ki67 expression and MVD in HCC. STUDY TYPE: Retrospective. SUBJECTS: In all, 180 patients with HCC. FIELD STRENGTH/SEQUENCE: 3.0T, Gd-EOB-DTPA-enhanced T1 WI volumetric interpolated breath-hold examination (VIBE) axial fat suppression plain, and enhanced scanning. ASSESSMENT: The T1 relaxation time (T1 rt) and signal intensity (SI) of the lesion were measured. The Ki67 expressions and MVD were evaluated by immunohistochemistry. STATISTICAL TEST: Receiver operating characteristic (ROC) curves were used to analyze the diagnostic efficacy of T1 rt for high Ki67 expression (≥50%) and high MVD (≥100). RESULTS: The T1 rt-20min, rrT1 rt-20min, and SI-hepatobiliary phase (SI-HBP) were strongly correlated with Ki67, the r values were 0.846, -0.765, and -0.760 (P < 0.05), respectively. There were moderate correlations with CD34, with r values -0.444, 0.336, and -0.463 (P < 0.05), respectively. The T1 rt-Pre, T1 rt-20min, SI-Pre, and SI-HBP were significantly different both between the high and low ki67 expression groups (P < 0.05) and between the high MVD and low MVD groups (P < 0.05). In the two groups the T1 rt-20min and SI-HBP was 800.06 ± 128.91 vs. 530.06 ± 139.29 (P < 0.05) and 122.29 ± 39.39 vs. 173.49 ± 46.15 (P < 0.05); T1 rt-20min was found to have high diagnostic efficiency for high ki67 expression (area under the curve [AUC], 0.937; P < 0.05) T1 rt-20min had moderate diagnostic value for high MVD (AUC, 0.716; P < 0.05). DATA CONCLUSION: The T1 rt and SI of Gd-EOB-DTPA-enhanced MRI were correlated with Ki67 expression and MVD. T1 rt-20min has a high diagnostic value for high ki67 expression and high MVD in HCC tissues. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1755-1763.

Magnetic resonance imaging: Proton density fat fraction for assessment of pancreatic fatty infiltration during progression of T2DM bama minipigs.

BACKGROUND: Recent studies have highlighted the correlation between diabetes and pancreatic fat infiltration. However, pancreatic fat content (PFC) is rarely confirmed by pathological results, and a change of PFC during progression of type 2 diabetes (T2DM) is currently controversial. PURPOSE: To evaluate the relationship of MRI-pancreatic proton density fat fraction to serologic changes and histology in an experimental model of diabetes. STUDY TYPE: Prospective animal study. ANIMAL MODEL: Thirteen Bama pigs were randomly assigned to diabetes (n = 7) or control (n = 6) groups. Pigs in the diabetic group received high-fat/high-sugar feed, combined with three doses of streptozotocin injections. FIELD STRENGTH/SEQUENCE: 3.0T, IDEAL-IQ sequence. ASSESSMENT: Starting in the fifth month, biochemical changes were evaluated; all pigs underwent axial MRI with the IDEAL-IQ sequence to measured pancreatic fat fraction (PFF). PFC was measured by the Soxhlet extraction method. Pancreatic fat distribution and pancreas islet morphology were observed by histopathology. STATISTICAL TESTS: A Mann-Whitney U-test, independent-samples t-test, Pearson correlation, Spearman correlation, single-measure intraclass correlation coefficient (ICC) were performed. RESULTS: During the development of T2DM, the PFF, weight, fasting blood glucose (FBG), triglyceride (TG), total cholesterol (TCHO), low-density lipoprotein (LDL), and HOMA-IR (insulin resistance) of the experimental group showed an upward trend; fasting insulin (INS), high-density lipoprotein (HDL), and HOMA-ß showed decreasing trends. At the end of the fifteenth month, FBG (mmol/L) was 18.06 ± 6.03 and 5.06 ± 1.41 (P < 0.001), PFF (%) was 36.52 ± 4.07 and 27.75 ± 3.73 (P = 0.002), INS (mU/L) was 21.59 ± 2.93 and 29.32 ± 3.27 (P = 0.001), HOMA-IR was 16.83 ± 4.22 and 6.70 ± 2.45 (P < 0.001), HOMA-ß was 1.50 ± 0.24 and 2.77 ± 0.45 (P < 0.001), between the experimental and control groups. There were strong and moderate positive correlations between PFF and PFC (r = 0.968, P < 0.001), and FBG (r = 0.657, P = 0.015), and HOMA-IR (r = 0.608, P = 0.028). DATA CONCLUSION: MRI-proton density fat fraction can measure the fat content of the pancreas with great accuracy and repeatability; PFF is a potential biomarker that can reflect the different stages of diabetes development. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1905-1913.

Comparison of Imaging and Clinically Relevant Features of Combined Hepatocellular Carcinoma and Cholangiocarcinoma with Hepatocellular Carcinoma.

BACKGROUND The aim of this study was to compare the clinical, imaging, pathological, and prognostic characteristics of combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CC) and hepatocellular carcinoma (HCC). MATERIAL AND METHODS Medical records of 21 patients with cHCC-CC and 21 patients with HCC were retrospectively reviewed. Patients underwent a computed tomography (CT) examination within 1 month before surgery and did not receive preoperative interventional therapy. Clinical, imaging, pathological, and prognostic characteristics of cHCC-CC and HCC were compared. RESULTS On multi-phase contrast-enhanced CT, cHCC-CC could be differentiated from HCC based on the presence of a pseudocapsule (p<0.0001; χ²=14.538) and extensive necrosis (p=0.009; χ²=8.400). The changes in the arterial phase and venous phase (V>A) and arterial phase and delayed phase (D>A) of CT enhanced scanning in HCC and cHCC-CC were statistically significant (both p<0.0001, χ²=28.560 and 25.846). Immunohistochemistry showed more HCC were positive for VEGF (P=0.012, χ²=7.785). A Kaplan-Meier survival analysis showed no statistically significant difference in progression-free survival (PFS) after treatment between patients with cHCC-CC and those with HCC (p=0.526). CONCLUSIONS Multi-phase contrast-enhanced CT may be useful for preoperative diagnosis of cHCC-CC in tumors with a diffuse boundary, no pseudocapsule, extensive necrosis (>50%), and a dilated bile duct, and when the CT value in the delayed phase is higher than in the arterial phase. VEGF expression is more likely to be positive in HCC than cHCC-CC. There was no significant difference between cHCC-CC and HCC in prognosis, but cHCC-CC was more likely to recur after treatment than HCC.

Quantification of pancreatic proton density fat fraction in diabetic pigs using MR imaging and IDEAL-IQ sequence.

BACKGROUND: Recent studies have highlighted the correlation between diabetes and pancreatic fat infiltration. Notably, pancreatic fat content (PFC) is a potential biomarker in diabetic patients, and magnetic resonance imaging (MRI) provides an effective method for noninvasive assessment of pancreatic fat infiltration. However, most reports of quantitative measurement of pancreatic fat have lacked comparisons of pathology results. The primary objective of this study was to determine the feasibility and accuracy of pancreatic MRI by using pancreatic fat fraction (PFF) measurements with the IDEAL-IQ sequence; the secondary objective was to explore changes in PFC between pigs with and without diabetes. METHODS: In this prospective study, 13 Bama Mini-pigs (7 females, 6 males; median age, 2 weeks) were randomly assigned to diabetes (n = 7) or control (n = 6) groups. Pigs in the diabetes group received high fat/high sugar feed, combined with streptozotocin injections. At the end of 15 months, biochemical changes were evaluated. All pigs underwent axial MRI with the IDEAL-IQ sequence to measure PFF; PFC of fresh pancreatic parenchyma was measured by the Soxhlet extraction method; and pancreatic fat distribution was observed by histopathology. Results of all analyses were compared between the diabetes and control groups by using the Mann-Whitney U-test. Correlations of PFF and PFC, fasting blood glucose (GLU), and serum insulin (INS) were calculated by using the Spearman correlation coefficient. Single-measure intraclass correlation coefficient (ICC) was used to assess interreader agreement. RESULTS: There were significant differences between diabetes and control groups: GLU (mmol/L) was 18.06 ± 6.03 and 5.06 ± 1.41 (P < 0.001); INS (mU/L) was 21.59 ± 2.93 and 29.32 ± 3.27 (P = 0.003); PFC (%) was 34.60 ± 3.52 and 28.63 ± 3.25 (P = 0.027); and PFF (%) was 36.51 ± 4.07 and 27.75 ± 3.73 (P = 0.003). There was a strongly positive correlation between PFF and PFC (r = 0.934, P < 0.001); there were moderate correlations between PFF and GLU (r = 0.736, P = 0.004; positive correlation), and between PFF and INS (r = - 0.747, P = 0.003; negative correlation). Excellent interreader agreement was observed for PFF measurements (ICC, 0.954). CONCLUSIONS: Pancreatic fat infiltration shows a clear association with diabetes. MRI with the IDEAL-IQ sequence can be used to accurately and reproducibly quantify PFC.

Sarcopenia is Associated with Perioperative Outcomes in Gastric Cancer Patients Undergoing Gastrectomy.

BACKGROUND: Sarcopenia is a syndrome in which skeletal muscle reduction is the main manifestation of age-related and/or disease-related malnutrition associated with postoperative complications and mortality. OBJECTIVES: The aim of the current study was to investigate the association between sarcopenia and postoperative complications as well as the nutrition risk of patients with gastric cancer (GC) who received gastrectomy. In addition, a comparative analysis was performed to evaluate the diagnostic accuracy of total psoas muscle area (TPA) and skeletal muscle area (SMA) in sarcopenia. METHODS: Preoperative computed tomography scans were obtained from 279 GC patients who received a gastrectomy from June 2011 to May 2016. TPA and SMA at the level of the third lumbar vertebra (L3) were used as the sarcopenia diagnostic index. Patients were diagnosed with sarcopenia via the total psoas muscle index (TPI) and skeletal muscle index (SMI) methods. TPI and SMI were normalized with the square of the patient's height (m2) by TPA and SMA. The Clavien-Dindo complications score system was used to classify the complication extent after gastrectomy. Univariate and multivariate logistic regression analyses were carried out to evaluate the risk factors for postoperative complications. RESULTS: A total of 68 and 125 patients were diagnosed with sarcopenia by TPI and SMI, respectively. Eighty-eight (31.5%) patients experienced postoperative complications. Patients with sarcopenia also had a significantly extended postoperative stay (TPI-sarcopenia, 15.0 days vs. non-sarcopenia, 11.0 days, p < 0.001; and SMI-sarcopenia, 14.0 days vs. non-sarcopenia, 11.0 days, p < 0.001) and hospital stay (TPI-sarcopenia, 22.5 days vs. non-sarcopenia, 17.0 days, p < 0.001; and SMI-sarcopenia, 21.0 days vs. non-sarcopenia, 16.5 days, p < 0.001). Multivariate logistic analysis showed that both TPI-sarcopenia (OR 7.561, p < 0.001) and SMI-sarcopenia (OR 10.085, p < 0.001) were associated with the risk of postoperative complications. Furthermore, univariate analysis showed a high correlation between nutrition risk screening 2002 (NRS2002) and sarcopenia (p < 0.001). A total of 54 (79.4%) of the 68 patients who were classified as having sarcopenia by TPI and 94 (75.3%) of the 125 patients who were classified as having sarcopenia by SMI were diagnosed with nutritional risk. CONCLUSIONS: Sarcopenia is associated with the total length of hospital stay, postoperative hospital stay, and severe complications in GC patients undergoing gastrectomy. Moreover, SMI may be a more meaningful index than TPI in reducing the rate of misdiagnosis and in predicting adverse perioperative risk. In addition, sarcopenia may cause severe malnutrition and increases perioperative adverse risk. Thus, both sarcopenia and the NRS2002 nutritional score should be assessed during preoperative nutritional screening and evaluation for GC patients.

Imaging Examination and Quantitative Detection and Analysis of Gastrointestinal Diseases Based on Data Mining Technology.

The medical image storage and transmission system completes the collection, storage, management, diagnosis and information processing of digital medical image information generated from digital medical devices, accumulates a large amount of data resources, and uses these valuable data resources to extract corresponding diseases. Diagnostic rules that help improve the accuracy of clinical disease diagnosis have always been the subject of medical research and management. Gastrointestinal diseases are common high-risk digestive diseases. This paper studies the imaging detection and quantitative detection and analysis of gastrointestinal diseases based on data mining, aiming to improve the accuracy of doctors' clinical diagnosis, reduce the misdiagnosis and misdiagnosis of patients' diseases, and reduce the burden on patients. With the high computing speed and computational accuracy of the computer, combined with the flexible analysis and judgment ability of the human body, the doctor can help the semi-structured and unstructured diagnosis problems. Experiments demonstrate the effectiveness and robustness of the proposed method.

Imaging characteristics of cerebral extraventricular neurocytoma with pathological correlation.

PURPOSE: Extraventricular neurocytoma (EVN) is an exceedingly rare tumor. In this study, we sought to characterize the imaging and pathological features of this uncommon tumor. METHODS: Retrospective review of 18 patients (9 male; 9 female) with pathologically confirmed EVN treated at a single center between 2005 and 2017. RESULTS: All patients had a solitary lesion. Sixteen lesions were found in hemispheres. The greatest tumor diameter ranged from 2.6 to 8 cm. The lesions were generally solid with cystic components; the solid portion appeared isodense or hyperdense on CT, isointense to hypointense on T1WI, and slightly hyperintense on T2WI. Heterogeneous hyperintensity interspersed with isointense or hypointense areas suggestive of hemorrhage, calcification or vascular flow voids were seen on T2WI. Heterogeneous enhancement was noted in 17 lesions; no enhancement was observed in one lesion. Cystic components were observed in 13 lesions; 9 of these showed characteristic perilesional cysts (9/13). Mild to moderate peritumoral edema (15/18), calcification (4/16), intratumoral hemorrhage (11/18) and vascular flow voids (10/16) were observed in some lesions. Pathologically, tumor cells showed round nucleus and fine neuropil matrix. Foci of calcification in the solid portion of the tumor were seen in five cases. Microcystic changes were observed in almost all cases. Some lesions exhibited positive staining for synaptophysin (Syn) (15/16) and neuronal nuclei (NeuN) (7/8). MIB-1 was determined for 10 patients; seven of these had an MIB-1 ≥ 3. These six patients experienced recurrence; four of them relapsed twice. CONCLUSIONS: EVNs occur as single intracranial solid mass with cystic components (especially peripherally located cysts); solid portion exhibits slight hyperintensity or heterogeneous signal intensity. Mild to moderate peritumoral edema, calcification, intratumoral hemorrhage and vascular flow voids were characteristic features of extraventricular neurocytoma. Positive staining for synaptophysin and neuronal nuclei confirmed the diagnosis. A combination of atypical pathologic features and atypical radiologic features should be considered for prognostic assessment.

Therapeutic Evaluation of Radiotherapy with Contrast-Enhanced Ultrasound in Non-Resectable Hepatocellular Carcinoma Patients with Portal Vein Tumor Thrombosis.

BACKGROUND Therapeutic evaluation of 3-dimensional conformal radiotherapy (3DCRT) is rarely reported for non-resectable hepatocellular carcinoma (HCC) with portal vein tumor thrombus (PVTT). The aim of this study was to determine the value of contrast-enhanced ultrasound (CEUS) in evaluating the therapeutic response of HCC with PVTT treated with 3DCRT. MATERIAL AND METHODS PVTT reduction rate in the study was determined after 3DCRT using time intensity curve (TIC) analysis software before and after radiotherapy. Seventy-nine HCC patients with PVTT treated with 3DCRT were studied. HCC and PVTT were performed by CEUS, before and after 3DCRT, over time. The parameters of blood flow, including arrival time (AT), time to peak (TTP), peak intensity (PI), washout time (WT), and area under the curve (AUC), were quantified and evaluated on still images by CEUS. RESULTS After 3DCRT, typing and staging of PVTT in 38 patients was decreased, the reduction rate was 48.1%. HCC was effective in 45 patients, the effective rate was 57%; No differences were found between the PVTT reduction rate and the HCC effective rate (χ2=2.96, P>0.05). In the effective group, the PI and AUC of HCCs and PVTTs after 3DCRT were significantly lower than before 3DCRT, while the other parameters of TIC were not significantly different before and after 3DCRT. CONCLUSIONS CEUS might be a useful monitoring option for the evaluation of HCC with PVTT treated with 3DCRT. CEUS might be useful as an important choice for monitoring and evaluation HCC with PVTT after 3DCRT. TIC parameters might provide quantitative data for efficacy evaluation, which helps to modify treatment strategies timely and accurately.

Evaluating segmental liver function using T1 mapping on Gd-EOB-DTPA-enhanced MRI with a 3.0 Tesla.

BACKGROUND: Assessing the liver function provides valuable information to evaluate surgical risk and plan accordingly. Current studies focus on whole liver function evaluation. However, assessment of segmental liver function is equally important in the clinical practice. The purpose of this study was to investigate whether Gd-EOB-DTPA-enhanced MRI can evaluate the liver function of each segment by using T1 mapping at 3 Tesla MRI. METHODS: One hundred three patients were classified into one of 4 groups: a normal liver function (NLF) group (n = 38), a liver cirrhosis with Child-Pugh A (LCA) group (n = 33), a liver cirrhosis with Child-Pugh B (LCB) group (n = 21), and a liver cirrhosis with Child-Pugh C (LCC) group (n = 11). All patients underwent Gd-EOB-DTPA-enhanced MRI scans. T1 relaxation times were measured on the liver superimposing T1 mapping images. Reduction rate (△%) of T1 relaxation time of the liver parenchyma were calculated. RESULTS: After 20 min of Gd-EOB-DTPA enhancement, the T1 relaxation time of all liver segments in the LCC group were different from those in all the other groups, and more liver segments from the LCB and LCA groups different from the NLF group (p < 0.05). For the LCB group, the areas under the receiver operating characteristic curves (AUCs) of different liver segments for hepatobiliary phase (HBP) were 0.654-0.904 on T1 relaxation time, and 0.709-0.905 on △%. For the LCC group, the AUCs of different liver segments for HBP were 0.842-0.997 on T1 relaxation time, and 0.887-0.990 on △%. CONCLUSIONS: For LCB patients, segmental liver function evaluation is possible using Gd-EOB-DTPA-enhanced MRI T1 mapping. For LCC patients, all liver segments can be used to evaluate liver function and both T1 relaxation time and the △% of T1 relaxation time have good diagnostic performance.

Gd-EOB-DTPA-enhanced MRI T1 mapping for assessment of liver function in rabbit fibrosis model: comparison of hepatobiliary phase images obtained at 10 and 20 min.

OBJECTIVES: To compare hepatobiliary phase (HBP) images obtained at 10 and 20 min after Gd-EOB-DTPA-enhanced MRI for assessment of liver function in rabbit fibrosis model on 3.0 T MR imaging. METHODS: 34 animals were separated into three groups: 5 for a control group, 14 for a mild fibrosis group, and 15 for a severe fibrosis group based on pathological proof. T1 relaxation times (T1rt) were measured on T1 mapping and reduction rates of T1rt (rrT1rt) were calculated. HBP images were obtained at 10 and 20 min after Gd-EOB-DTPA enhancement. Indocyanine green retention rates at 15 min (ICG R15) were performed for all animals. RESULTS: T1rt on pre-enhancement imaging showed no significant difference (p > 0.05) among all groups. T1rt on 10 min HBP and 20 min HBP showed significant difference (p < 0.05) among all groups. T1rt and rrT1rt in three groups showed no-significant difference (p > 0.05) between 10 min HBP and 20 min HBP. T1rt on both 10 and 20 min HBP showed significant correlation with ICG R15 (p < 0.05); rrT1rt on both 10 min HBP and 20 min HBP showed significant inverse correlation with ICG R15 (p < 0.05). CONCLUSIONS: Comparing 10 min HBP and 20 min HBP T1 mapping after Gd-EOB-DTPA enhancement, our results suggest that 10 min HBP T1 mapping is feasible for quantitatively assessing liver function.