Search

Objective: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. Materials and Methods: Between June 2019 and November 2019, 103 patients (mean age, 40.8 +/- 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively. Results: With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean +/- standard deviation [SD], 19.5 +/- 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 +/- 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 +/- 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. Conclusion: The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.

Background O(6)-methylguanine-DNA methyltransferase (MGMT) promoter methylation is an important prognostic factor for gliomas and is associated with tumor angiogenesis. Arteriolar cerebral blood volume (CBVa) obtained from inflow-based vascular-space-occupancy (iVASO) magnetic resonance imaging (MRI) is assumed to be an indicator of tumor microvasculature. Its preoperative predictive ability for MGMT promoter methylation remains unclear. Purpose To investigate the role of iVASO-CBVa histogram features in determining MGMT promoter methylation status of grade II-IV gliomas. Study type Retrospective SUBJECTS: Forty-six patients consisting of 20 MGMT methylated and 26 unmethylated gliomas. Field strength/sequence 3.0 T magnetic resonance images containing iVASO MRI, T1 -weighted image (T1 WI), T2 -weighted image, T2 -weighted fluid attenuated inversion recovery image images, and enhanced T1 WI. Assessment Sixteen structural imaging features were visually evaluated on structural MRI and 14 CBVa histogram features were extracted from iVASO-CBVa maps. Statistical tests Imaging features were screened and ranked using Fisher's exact test, Mann-Whitney U-test, and randomforest algorithm. Features with higher importance were selected to develop logistic regression models to determine MGMT methylation status. Receiver operating characteristics (ROC) curve with the area under the curve (AUC) and leave-one-out cross-validation (LOOCV) were used to assess effectiveness and stability. Results The top two CBVa histogram features were root mean squared (RMS) and variance. The top two structural imaging features were contrast-enhancing component of the tumor (CET) location and tumor location. Both the CBVa model of RMS and variance (ROC, AUC = 0.867; LOOCV, AUC = 0.819) and the model of structural features (ROC, AUC = 0.882; LOOCV, AUC = 0.802) accurately identified MGMT methylation. The fusion model of CBVa RMS and CET location improved diagnostic performance (ROC, AUC = 0.931; LOOCV, AUC =0.906). DATA CONCLUSION: iVASO-CBVa has potential in evaluating MGMT methylation status in grade II-IV gliomas. Level of evidence 4 TECHNICAL EFFICACY: Stage 2.

Background To systematically investigate structural and functional abnormalities in subregions of the basal forebrain (BF) using structural and resting-state fMRI, and to examine their clinical relevance in short-term and chronic insomnia disorder (ID). Methods Thirty-four patients with short-term ID, 41 patients with chronic ID, and 46 healthy controls (HCs) were recruited. Grey matter volume and seed-based resting-state functional connectivity (RSFC) in each BF subregion (Ch1,2,3 and 4) were computed and compared among the three groups. Spearman correlation was used to estimate the relationships between MRI-based alterations and clinical variables. Results The short-term group exhibited lower RSFC with the bilateral striatum and bilateral Ch_4 than HCs and the chronic group. In the left Ch_4, subjects in the chronic group exhibited lower RSFC with the left middle cingulate cortex than HCs and the short-term group. The short-term group exhibited lower RSFC with the left parahippocampal gyrus (PHG) than HCs and the chronic group. The chronic group exhibited the highest RSFC with the left middle frontal gyrus (MFG), followed by HCs and the short-term group. In the right Ch_4, the chronic group exhibited the lowest RSFC with the right superior temporal gyrus, followed by HCs and the short-term group. Moreover, in the short-term group, negative correlations were found between the left Ch_4 and left MFG RSFC and Epworth Sleepiness Scale scores. Conclusions These findings suggest that the Ch_4 may be a key node for establishing diagnostic and categorical biomarkers of ID, which could be useful in developing more effective treatment strategies for insomnia.

Background The tumor immune microenvironment can provide prognostic and predictive information. A previously validated ImmunoScore of Gastric Cancer (ISGC) evaluates both lymphoid and myeloid cells in the tumor core and invasive margin with immunohistochemical staining of surgical specimens. We aimed to develop a noninvasive radiomics-based predictor of ISGC. Patients and methods In this retrospective study including four independent cohorts of 1778 patients, we extracted 584 quantitative features from the intratumoral and peritumoral regions on contrast-enhanced computed tomography images. A radiomic signature [radiomics ImmunoScore (RIS)] was constructed to predict ISGC using regularized logistic regression. We further evaluated its association with prognosis and chemotherapy response. Results A 13-feature radiomic signature for ISGC was developed and validated in three independent cohorts (area under the curve = 0.786, 0.745, and 0.766). The RIS signature was significantly associated with both disease-free and overall survival in the training and all validation cohorts [hazard ratio (HR) range: 0.296–0.487, all P Conclusion The RIS is a reliable tool for evaluation of immunoscore and retains the prognostic significance in gastric cancer. Future prospective studies are required to confirm its potential to predict treatment response and select patients who will benefit from chemotherapy.

Growing evidence has supported that the nucleus accumbens (NAc), especially its shell portion, has been involved in epileptogenesis. However, relevant studies on vivo human brain are quite limited. In this study, we investigated left mesial temporal lobe epilepsy (MTLE) related function connectivity (FC) changes of NAc subregions using resting-state functional magnetic resonance imaging. We calculated functional connectivity from two NAc subregions to both whole brain and 16 related targets. Two-sample t-test (Alphasim multiple comparisons corrected) was performed to identify the effect of the disease on each seed's whole brain network. Repeated-measures ANOVA and Post hoc pairwise t test (Bonferroni corrections) were performed to visualize the seed to target FC group differences in each subdivision. In whole brain FC networks, neither the left or right core show different FC changes. The left shell showed decreased FC with a cluster located around the right inferior frontal gyrus. The right shell portion showed increased FC with a cluster located around the left inferior temporal gyrus. The seed to targets results showed that the left shell of LTLE group exhibited lower FC with left posterior-parahippocampal gyrus and right caudate, putamen, thalamus, paracingulate gyrus but higher FC with right subcallosal cortex. The right core of LTLE group exhibited higher FC with right frontal pole and the right shell exhibited lower FC with left thalamus and left anterior-parahippocampal gyrus. This is the first study to investigate the functional connectivity changes of NAc subdivisions of epilepsy in vivo human brain. Our results showed that the left MTLE related FC changes on NAc are mainly on shell portion rather than core. The decrease FC between the left shell and right frontal area and the decrease FC between the right shell and left temporal area suggested they serve vital roles for MTLE.

BACKGROUND Non-Gaussian diffusion models and T1 rho quantification may reflect the changes in tissue heterogeneity in hepatic sinusoidal obstruction syndrome (SOS). PURPOSE To investigate the feasibility of diffusion kurtosis imaging (DKI), stretched exponential model (SEM), and T1 rho quantification in detecting and staging SOS in a monocrotaline (MCT)-induced rat model. STUDY TYPE Animal study. POPULATION Thirty male Sprague-Dawley rats gavaged with MCT to induce hepatic SOS and six male rats without any intervention. FIELD STRENGTH/SEQUENCE 3.0T, DWI with five b-values (0-2000 s/mm2 ) and T1 rho with five spin lock times (1-60 msec). ASSESSMENT MRI was performed 1 day before and 1, 3, 5, 7, and 10 days after MCT administration. The corrected apparent diffusion coefficient (Dapp ), kurtosis coefficient (Kapp ), distributed diffusion coefficient (DDC), and intravoxel water molecular diffusion heterogeneity (α) were calculated from the corresponding non-Gaussian diffusion model. The T1 rho value was calculated using a monoexponential model. Specimens obtained from the six timepoints were categorized into normal liver (n = 6), early-stage (n = 16), and late-stage (n = 14) SOS in accordance with the pathological score. STATISTICAL TESTS Parametric statistical methods and receiver operating characteristic (ROC) curves were employed to determine diagnostic accuracy. RESULTS The Dapp , Kapp , DDC, α, and T1 rho values were correlated with pathological score with r values of -0.821, 0.726, -0.828, -0.739, and 0.714 (all P

Laparoscopic salpingo-oophorectomy and pelvic lymphadenectomy place the ureter at risk. Although traditional anatomic studies indicate that ureters generally cross over the common iliac artery in the pelvic inlet, the view is often different during surgery. Hence, digital three-dimensional models have been utilized to solve this conundrum. Our study aimed to evaluate the anatomic relation between the iliac artery and the ureter in the pelvic inlet.A medical image processing system processed data collected from 129 female patients, and digital 3D models of the iliac artery and ureter were used to study their anatomic relation. Left- and right-side data were compared using Pearson's χTwo different anatomic relations were found immediately posterior to the ureter: Common iliac arteries were present in 33.2% and external iliac arteries in 76.8%. The mean distances from the point at which the ureter crossed over the iliac artery until the bifurcation of the artery was 8.8 ± 12.2 mm in the left pelvis and 12.4 ± 10.8 mm in the right pelvis - different from that of the traditional anatomic study.In the future, these models and digital anatomical data could help avoid surgical complications.

PurposeThis study aimed to identify preoperative gadoxetic acid-enhanced MRI features and establish a nomogram for predicting early recurrence (≤ 2 years) of hepatocellular carcinoma (HCC) after ablation therapy.MethodsA total of 160 patients who underwent gadoxetic acid-enhanced MRI and ablation HCC therapy from January 2015 to June 2018, were included retrospectively and divided into a training cohort (n = 112) and a validation cohort (n = 48). Independent clinical risk factors and gadoxetic acid-enhanced MRI features associated with early recurrence were identified by univariate and multivariate logistic regression analysis and used for construction of a nomogram. The performance of the nomogram was evaluated by discrimination, calibration, and clinical utility.ResultsAlpha-fetoprotein (AFP) level, tumor number, arterial peritumoral enhancement, satellite nodule and peritumoral hypointensity at hepatobiliary phases in the training cohort were identified as independent risk factors for early recurrence after ablation. A new nomogram that was constructed with these five features showed an area under the curve (AUC) of 0.843 (95%CI 0.771-0.916) and 0.835 (95%CI 0.713-0.956) in the training and validation cohort, respectively. The calibration curve and decision curve analysis (DCA) suggested that the nomogram had good consistency and clinical utility.ConclusionsA new nomogram that was constructed using four preoperative gadoxetic acid-enhanced MRI features and serum AFP level can predict the risk of early HCC recurrence after ablation therapy with AUC up to 0.843. The strong performance of this nomogram may help hepatologists to categorize patients’ recurrent risk to guide selecting treatment options and improve postoperative management.

Background We attempted to train and validate a model of deep learning for the preoperative prediction of the response of patients with intermediate-stage hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE). Method All computed tomography (CT) images were acquired for 562 patients from the Nan Fang Hospital (NFH), 89 patients from Zhu Hai Hospital Affiliated with Jinan University (ZHHAJU), and 138 patients from the Sun Yat-sen University Cancer Center (SYUCC). We built a predictive model from the outputs using the transfer learning techniques of a residual convolutional neural network (ResNet50). The prediction accuracy for each patch was revaluated in two independent validation cohorts. Results In the training set (NFH), the deep learning model had an accuracy of 84.3% and areas under curves (AUCs) of 0.97, 0.96, 0.95, and 0.96 for complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD), respectively. In the other two validation sets (ZHHAJU and SYUCC), the deep learning model had accuracies of 85.1% and 82.8% for CR, PR, SD, and PD. The ResNet50 model also had high AUCs for predicting the objective response of TACE therapy in patches and patients of three cohorts. Decision curve analysis (DCA) showed that the ResNet50 model had a high net benefit in the two validation cohorts. Conclusion The deep learning model presented a good performance for predicting the response of TACE therapy and could help clinicians in better screening patients with HCC who can benefit from the interventional treatment. Key Points • Therapy response of TACE can be predicted by a deep learning model based on CT images. • The probability value from a trained or validation deep learning model showed significant correlation with different therapy responses. • Further improvement is necessary before clinical utilization. Electronic supplementary material The online version of this article (10.1007/s00330-019-06318-1) contains supplementary material, which is available to authorized users.

Purpose To assess the role of histogram analysis of apparent diffusion coefficient (ADC) maps based on whole-tumor in differentiating intracranial solitary fibrous tumor/hemangiopericytoma (SFT/HPC) from angiomatous meningioma (AM). Materials and methods Pathologically confirmed intracranial SFT/HPC (n = 15) and AM (n = 20) were retrospectively collected and their clinical and conventional MRI features were analyzed. Diffusion-weighted (DW) images (b = 0 and 1000 s/mm2) were processed with the mono-exponential model. Regions of interest covering the whole tumor were drawn on all slices of the ADC maps to obtain histogram parameters, including mean ADC (ADCmean), median ADC (ADCmedian), maximum ADC (ADCmax), minimum ADC (ADCmin), skewness and kurtosis, as well as the 5th, 10th, 25th, 75th, 90th and 95th percentile ADC (ADC5, ADC10, ADC25, ADC75, ADC90 and ADC95). Differences of histogram parameters between SFT/HPC and AM were compared using Mann-Whitney U test. Receiver operating characteristic (ROC) curve was used to determine the diagnostic performance. Results The ADCmin (P = 0.001) and ADC5 (P = 0.045) were significantly lower in SFT/HPCs than in AMs, while no significant difference was found in sex, age, conventional MRI features or any other histogram parameters between the two entities (P = 0.051−1.000). ADCmin showed the best diagnostic performance (area under curve [AUC], 0.86; sensitivity, 81.3%; specificity, 83.3%) in differentiating SFT/HPC from AM with optimal cutoff value being 569.00 × 10−6 mm2/s, followed by ADC5 (AUC, 0.72; sensitivity, 68.8%; specificity, 75%) with optimal cutoff value being 781.97 × 10−6 mm2/s. Conclusion SFT/HPC and AM share similar conventional MR appearances. Whole-tumor histogram analysis of ADC maps may be a useful tool for differential diagnosis, with ADCmin and ADC5 being potential parameters.

Objectives To investigate the diagnostic value of clivopalate angle (CPA) for basilar invagination (BI) at magnetic resonance imaging (MRI). Methods In this retrospective case-control study, CPA, clivodens angle (CDA), and clivoaxial angle (CXA) were measured on midsagittal MR images from 112 patients with BI (22 men; mean age, 43.9 years ± 13.1 years; range, 21–79 years) and 200 control subjects (57 men; mean age, 47.1 years ± 13.3 years; range, 20–80 years). Intraclass correlation coefficient (ICC), linear regression, Mann-Whitney U test, binary logistic regression, and receiver operating characteristic (ROC) curve were used for statistical analysis. Results Clivopalate angle showed better inter-observer agreement (ICC = 0.951) than CXA (0.867) or CDA (0.853). CPA significantly correlated with CXA (R = 0.811, p

This study aimed to develop and validate a prognostic nomogram for recurrence-free survival (RFS) after surgery in the absence of adjuvant therapy to guide the selection for adjuvant imatinib therapy based on Residual Neural Network (ResNet).The ResNet model was developed based on contrast-enhanced computed tomography (CE-CT) in a training cohort consisted of 80 patients pathologically diagnosed gastrointestinal sromal tumors (GISTs) and validated in internal and external validation cohort respectively. Independent clinicopathologic factors were integrated with the ResNet model to construct the individualized nomogram. The performance of the nomogram was evaluated in regard to discrimination, calibration, and clinical usefulness.The ResNet model was significantly associated with RFS. Integrable predictors in the individualized ResNet nomogram included the tumor site, size, and mitotic count. Compared with modified NIH, AFIP, and clinicopathologic nomogram, both ResNet nomogram and ResNet model showed a better discrimination capability with AUCs of 0·947(95%CI, 0·910–0·984) for 3-year-RFS, 0·918(0·852–0·984) for 5-year-RFS, and AUCs of 0·912 (0·851–0·973) for 3-year-RFS, 0·887(0·816–0·960) for 5-year-RFS, respectively. Calibration curve shows the good calibration of the nomogram in terms of the agreement between the estimated and the observed 3- and 5- year outcomes. Decision curve analysis showed that the ResNet nomogram had a higher overall net benefit.In conclusion, we presented a deep learning-based prognostic nomogram to predict RFS after resection of localized primary GISTs with excellent performance and could be a potential tool to select patients for adjuvant imatinib therapy. Keywords: Gastrointestinal Stromal Tumors, Deep Learning, Residual Neural Network, Recurrence-free Survival, Imatinib

Objective: The aim of this study is to evaluate whether radiomics imaging signatures based on computed tomography (CT) could predict peritoneal metastasis (PM) in gastric cancer (GC) and to develop a nomogram for preoperative prediction of PM status. Methods: We collected CT images of pathological T4 gastric cancer in 955 consecutive patients of two cancer centers to analyze the radiomics features retrospectively and then developed and validated the prediction model built from 292 quantitative image features in the training cohort and two validation cohorts. Lasso regression model was applied for selecting feature and constructing radiomics signature. Predicting model was developed by multivariable logistic regression analysis. Radiomics nomogram was developed by the incorporation of radiomics signature and clinical T and N stage. Calibration, discrimination, and clinical usefulness were used to evaluate the performance of the nomogram. Results: In training and validation cohorts, PM status was associated with the radiomics signature significantly. It was found that the radiomics signature was an independent predictor for peritoneal metastasis in multivariable logistic analysis. For training and internal and external validation cohorts, the area under the receiver operating characteristic curves (AUCs) of radiomics signature for predicting PM were 0.751 (95%CI, 0.703–0.799), 0.802 (95%CI, 0.691–0.912), and 0.745 (95%CI, 0.683–0.806), respectively. Furthermore, for training and internal and external validation cohorts, the AUCs of radiomics nomogram for predicting PM were 0.792 (95%CI, 0.748–0.836), 0.870 (95%CI, 0.795–0.946), and 0.815 (95%CI, 0.763–0.867), respectively. Conclusions: CT-based radiomics signature could predict peritoneal metastasis, and the radiomics nomogram can make a meaningful contribution for predicting PM status in GC patient preoperatively.

Rationale and Objectives The purpose of this study was to explore conventional MRI features that can accurately differentiate central nervous system embryonal tumor, not otherwise specified (CNS ETNOS) from glioblastoma (GBM) in adults. Materials and Methods Preoperative conventional MRI images of 30 CNS ETNOS and 98 GBMs were analyzed by neuroradiologists retrospectively to identify valuable MRI features. Five blinded neuroradiologists independently reviewed all these MRI images, and scored MRI features on a five-point scale. Kendall's coefficient of concordance was used to measure inter-rater agreement. Diagnostic value was assessed by the area under the curve (AUC) of receiver operating curve, and sensitivity and specificity were also calculated. Results Seven MRI features, including isointensity on T1WI, T2WI, and FLAIR, ill-defined margin, severe peritumoral edema, ring enhancement, and broad-based attachment sign, were helpful for the differential diagnosis of these two entities. Among these features, ring enhancement showed the highest inter-rater concordance (0.80). Ring enhancement showed the highest AUC value (0.79), followed by severe peritumoral edema (0.67). The combination of seven features showed the highest AUC value (0.86), followed by that of three features (ill-defined margin, severe peritumoral edema, and ring enhancement) (0.83). Conclusion Enhancement pattern, peritumoral edema, and margin are valuable for the discrimination between CNS ETNOS and GBM in adults.

Insomnia disorder (ID) is reclassified into short-term and chronic subtypes based on recent etiological advances, however, neural mechanisms underlying the subtypes are rarely examined. In this study, we investigated gray matter volume and resting-state functional connectivity (RSFC) alterations of hippocampal subregions in short-term and chronic ID using multimodal MRI. We found convergent and divergent alterations between both ID groups in specific hippocampal subregions [right cornu ammonis 1 (CA1), subicular complex (Subc), and caudal hippocampus, (cHipp)] with prefrontal cortex [bilateral medial prefrontal cortex (MPFC), and right middle frontal gyrus] and limbic/paralimbic regions (bilateral middle cingulate cortex and left parahippocampal gyrus). Intriguingly, the RSFC of the right CA1/cHipp, particularly the intersection between these two subregions, with bilateral MPFC exhibited gradual increases from healthy controls to short-term ID and from short-term ID to chronic ID. Moreover, a negative correlation between the right CA1-left parahippocampal gyrus RSFC and Epworth Sleepiness Scale scores, and a positive correlation between the right CA1-bilateral MPFC RSFC and Insomnia Severity Index scores were found in the chronic ID group (P

We aimed to develop a deep learning-based signature to predict prognosis and benefit from adjuvant chemotherapy using preoperative computed tomography (CT) images.Current staging methods do not accurately predict the risk of disease relapse for patients with gastric cancer.We proposed a novel deep neural network (S-net) to construct a CT signature for predicting disease-free survival (DFS) and overall survival in a training cohort of 457 patients, and independently tested it in an external validation cohort of 1158 patients. An integrated nomogram was constructed to demonstrate the added value of the imaging signature to established clinicopathologic factors for individualized survival prediction. Prediction performance was assessed with respect to discrimination, calibration, and clinical usefulness.The DeLIS was associated with DFS and overall survival in the overall validation cohort and among subgroups defined by clinicopathologic variables, and remained an independent prognostic factor in multivariable analysis (P0.001). Integrating the imaging signature and clinicopathologic factors improved prediction performance, with C-indices: 0.792-0.802 versus 0.719-0.724, and net reclassification improvement 10.1%-28.3%. Adjuvant chemotherapy was associated with improved DFS in stage II patients with high-DeLIS [hazard ratio = 0.362 (95% confidence interval 0.149-0.882)] and stage III patients with high- and intermediate-DeLIS [hazard ratio = 0.611 (0.442-0.843); 0.633 (0.433-0.925)]. On the other hand, adjuvant chemotherapy did not affect survival for patients with low-DeLIS, suggesting a predictive effect (Pinteraction = 0.048, 0.016 for DFS in stage II and III disease).The proposed imaging signature improved prognostic prediction and could help identify patients most likely to benefit from adjuvant chemotherapy in gastric cancer.

To develop and validate a radiomics signature for the prediction of gastric cancer (GC) survival and chemotherapeutic benefits. In this multicenter retrospective analysis, we analyzed the radiomics features of portal venous-phase computed tomography in 1591 consecutive patients. A radiomics signature was generated by using the Lasso-Cox regression model in 228 patients and validated in internal and external validation cohorts. Radiomics nomograms integrating the radiomics signature were constructed, demonstrating the incremental value of the radiomics signature to the traditional staging system for individualized survival estimation. The performance of the nomograms was assessed with respect to calibration, discrimination, and clinical usefulness. The radiomics signature consisted of 19 selected features and was significantly associated with DFS (disease-free survival) and OS (overall survival). Multivariate analysis demonstrated that the radiomics signature was an independent prognostic factor. Incorporating the radiomics signature into the radiomics-based nomograms resulted in better performance for the estimation of DFS and OS than the clinicopathological nomograms and TNM staging system, with improved accuracy of the classification of survival outcomes. Further analysis showed that stage II and III patients with higher radiomics scores exhibited a favorable response to chemotherapy. In conclusion, the newly developed radiomics signature is a powerful predictor of DFS and OS, and it may predict which patients with stage II and III GC benefit from chemotherapy.

To develop and evaluate a radiomics nomogram for differentiating the malignant risk of gastrointestinal stromal tumours (GISTs). A total of 222 patients (primary cohort: n = 130, our centre; external validation cohort: n = 92, two other centres) with pathologically diagnosed GISTs were enrolled. A Relief algorithm was used to select the feature subset with the best distinguishing characteristics and to establish a radiomics model with a support vector machine (SVM) classifier for malignant risk differentiation. Determinant clinical characteristics and subjective CT features were assessed to separately construct a corresponding model. The models showing statistical significance in a multivariable logistic regression analysis were used to develop a nomogram. The diagnostic performance of these models was evaluated using ROC curves. Further calibration of the nomogram was evaluated by calibration curves. The generated radiomics model had an AUC value of 0.867 (95% CI 0.803–0.932) in the primary cohort and 0.847 (95% CI 0.765–0.930) in the external cohort. In the entire cohort, the AUCs for the radiomics model, subjective CT findings model, clinical index model and radiomics nomogram were 0.858 (95% CI 0.807–0.908), 0.774 (95% CI 0.713–0.835), 0.759 (95% CI 0.697–0.821) and 0.867 (95% CI 0.818–0.915), respectively. The nomogram showed good calibration. This radiomics nomogram predicted the malignant potential of GISTs with excellent accuracy and may be used as an effective tool to guide preoperative clinical decision-making. • CT-based radiomics model can differentiate low- and high-malignant-potential GISTs with satisfactory accuracy compared with subjective CT findings and clinical indexes. • Radiomics nomogram integrated with the radiomics signature, subjective CT findings and clinical indexes can achieve individualised risk prediction with improved diagnostic performance. • This study might provide significant and valuable background information for further studies such as response evaluation of neoadjuvant imatinib and recurrence risk prediction.

Purpose To evaluate the value of intravoxel incoherent motion (IVIM) histogram analysis based on whole tumor volume in predicting microvascular invasion (MVI) of single hepatocellular carcinoma (HCC). Materials and Methods The study enrolled 41 patients with pathologically proven HCCs who underwent IVIM diffusion-weighted imaging with nine b values and contrast-enhanced magnetic resonance imaging (MRI). Histogram parameters including mean; skewness; kurtosis; and percentiles (5th, 10th, 25th, 50th, 75th, 90th, 95th) were derived from apparent diffusion coefficient (ADC), perfusion fraction (f), true diffusion coefficient (D), and pseudo diffusion coefficient (D*). Quantitative histogram parameters and clinical data were compared between HCCs with and without MVI. For significant parameters, receiver operating characteristic (ROC) curves were further plotted to compare the diagnosis performance for identifying MVI. Results The mean, 5th, 10th, 25th, 50th, and 75th percentiles of D, and the 5th, 10th, and 25th percentiles of ADC between HCCs with and without MVI were statistically significant (all P＜0.05). The histogram parameters of D* and f showed no statistically significant differences between HCCs with and without MVI (all P＞0.05). The areas under the ROC curves (AUCs) were 0.707–0.874 for D and 0.668–0.720 for ADC. The largest AUC of D (5th percentile) showed significantly higher accuracy than that of ADC or tumor size (P = 0.009–0.046). With a cut-off of 0.403 × 10−3 mm²/s, the 5th percentile of D value provided a sensitivity of 81% and a specificity of 85% in the prediction of MVI. Conclusions Histogram analysis of IVIM based on whole tumor volume can be useful for predicting MVI. The 5th percentile of D was most useful value to predict MVI of HCC.

To assess the value of T1ρ,T1ρ on hepatobiliary phase (HBP) and diffusion metrics in staging of Non-alcoholic fatty liver disease (NAFLD) activity scores, inflammation, fibrosis in NASH rabbits model. Non-alcoholic steatohepatitis (NASH) rabbits model was induced by feeding a varied duration of high-fat, high-cholesterol diet. T1ρ,T1ρ (HBP) 20min after administration of Gd-EOB-DTPA, and Intravoxel incoherent motion imaging (IVIM) diffusion-weighted imaging were performed on a 3.0T magnetic resonance (MR) imaging unit. The diagnostic value of each parameter for NAS, inflammation and fibrosis severity were determined. T1ρ (r=0.658) and T1ρ (HBP) (r=0.750) have strong association with NASH overall activity, T1ρ (HBP) is strongly relevant to inflammation stage (r=0.812). There was negative association between f and inflammation (r=-0.480), whilst no significant relation between other three parameters (apparent diffusion coefficient (ADC), pseudo-diffusion coefficient (D*) and true diffusion coefficient (D)) and inflammation or overall activity. The areas under the receiver operating characteristic curves (AUCs) of f, ADC, T1ρ and T1ρ-HBP were 0.871, 0.728, 0.849 and 0.949 for differentiating NASH; 0.731, 0.552, 0.925 and 0.922 for G2-3 inflammation; and 0.767, 0.625, 0.816, and 0.882 for S1-2 fibrosis. Comparison of ROC curve showed T1ρ (HBP) had an optimal diagnostic performance for NASH [T1ρ (HBP) vs ADC, AUC:0.949 vs 0.728, P=0.043], inflammation [T1ρ (HBP) vs ADC, AUC:0.922 vs 0.552, P=0.003], fibrosis [T1ρ (HBP) vs ADC, AUC:0.882 vs 0.625, P=0.046]. The combination of T1ρ (HBP)+perfusion fraction (f) showed highest diagnostic value for NASH (AUC:0.971), inflammation (AUC:0.935). Among T1ρ imaging and IVIM diffusion metrics, combination of T1rho (HBP)+f was found to be superior noninvasive imaging biomarker for NASH activity assessment.

To increase our understanding of the imaging features of central neurocytoma (CN) and improve the preoperative MRI diagnosis accuracy. Preoperative MR images of 30 CNs and another 68 intraventricular non-CN tumours were analysed by one experienced neuroradiologist retrospectively to identify previously reported features and new features of CN. Six blinded radiologists independently reviewed all these MRI images, and scored all characteristic features on a five-point scale. Diagnostic value was assessed by the area under the receiver operating characteristic curve (AUC); sensitivity, specificity and accuracy were also calculated. In addition to the ‘scalloping’ sign, ‘broad-based attachment’ sign and ‘soap-bubble’ sign, three new MRI features of CN were identified, including the ‘peripheral cysts’ sign, ‘fluid-fluid level’ sign and the ‘gemstone’ sign. The scalloping sign showed the highest AUC value (0.82), followed by the peripheral cysts sign (0.75) and broad-based attachment sign (0.75). The scalloping sign exhibited the highest specificity (82%), followed by the fluid-fluid level sign (79%) and gemstone (78%) sign. The broad-based attachment sign (85%) was the most sensitive feature, followed by the soap-bubble sign (84%) and peripheral cysts sign (77%). There are six characteristic MRI features that help to improve the preoperative diagnostic accuracy of CN. • This study is the largest magnetic resonance imaging (MRI) cohort on central neurocytoma (CN). • Three new features helpful for the diagnosis of CN were reported. • Diagnostic value of six MRI features of CN was preliminarily determined.

BACKGROUND The major factor for the appropriate treatment strategies for ischemia patients is its onset timing. PURPOSE/HYPOTHESIS To study to evaluate the diagnostic accuracy of T1 relaxation time in a rotating frame (T1 ρ) and apparent diffusion coefficient (ADC) from MRI to estimate ischemia stages. STUDY TYPE Prospective. POPULATION/SUBJECTS/PHANTOM/SPECIMEN/ANIMAL MODEL In all, 73 patients (49 males, aged 29-78 years and 24 females, aged 22-94 years) with ischemia were prospectively imaged with T1 ρ and diffusion MRI during the postischemic period. FIELD STRENGTH/SEQUENCE 3T/T1 ρ and diffusion-weighted imaging (DWI). ASSESSMENT Ischemic parenchyma included tissue with elevated signal areas on DWI and correlative hypointense areas on ADC maps. STATISTICAL TESTS The sensitivity of variables to ischemia time was quantified by analyzing the respective correlations of these values with onset time. RESULTS ΔT1 ρ (ipsilateral-contralateral differences in T1 ρ) (R2 = 0.956) and T1 ρipsi (ipsilateral ischemia T1 ρ values) (R2 = 0.941) were elevated in all ischemic lesions; these values increased linearly as a function of time, unlike ΔADC (ipsilateral-contralateral differences in ADC) (R2 = -0.410) and ADCipsi (ipsilateral ischemia ADC values) (R2 = 0.550). ΔT1 ρ and T1 ρipsi were significantly different between all stages (P

Purpose Systemic lupus erythematosus (SLE) is a chronic inflammatory female-predominant autoimmune disease that can affect the central nervous system and exhibit neuropsychiatric symptoms. In SLE patients without neuropsychiatric symptoms (non-NPSLE), recent diffusion tensor imaging studies showed white matter abnormalities in their brains. The present study investigated the entire brain white matter structural connectivity in non-NPSLE patients by using probabilistic tractography and connectivity-based analyses. Methods Whole-brain structural networks of 29 non-NPSLE patients and 29 healthy controls (HCs) were examined. The structural networks were constructed with interregional probabilistic connectivity. Graph theory analysis was performed to investigate the topological properties, and network-based statistic was employed to assess the alterations of the interregional connections among non-NPSLE patients and controls. Results Compared with HCs, non-NPSLE patients demonstrated significantly decreased global and local network efficiencies and showed increased characteristic path length. This finding suggests that the global integration and local specialization were impaired. Moreover, the regional properties (nodal efficiency and degree) in the frontal, occipital, and cingulum regions of the non-NPSLE patients were significantly changed and negatively correlated with the disease activity index. The distribution pattern of the hubs measured by nodal degree was altered in the patient group. Finally, the non-NPSLE group exhibited decreased structural connectivity in the left median cingulate-centered component and increased connectivity in the left precuneus-centered component and right middle temporal lobe-centered component. Conclusion This study reveals an altered topological organization of white matter networks in non-NPSLE patients. Furthermore, this research provides new insights into the structural disruptions underlying the functional and neurocognitive deficits in non-NPSLE patients., Highlights • The global integration and local specialization of the white matter networks were disrupted in non-NPSLE patients. • The brain regions with significantly altered nodal properties were mainly involved in language, visual, and DMN systems. • The properties of some identified regions presented negative correlations with the SLE disease activity index scores. • The distribution pattern of the hubs measured by nodal degree was altered in the non-NPSLE patients. • The non-NPSLE group exhibited a subnetwork of decreased connectivity and a subnetwork of increased connectivity.

Background The aim of this study was to apply electrocardiography (ECG)-gated prospective-triggered multidetector row computed tomography angiography with iterative model reconstruction (IMR) to optimize imaging of the bronchial arteries in patients with the chief complaint of hemoptysis. Methods This was a prospective observational study. Between August 2015 and June 2016, we enrolled 31 consecutive patients with the chief complaint of hemoptysis who were scheduled to undergo computed tomography of the bronchial artery. Patients were randomly divided into 3 groups: group A, with filtered back-projection reconstruction; group B, with iDose reconstruction; and group C, with ECG-gated prospective-triggered multidetector row computed tomography angiography with IMR. Image quality, visibility, and traceability were compared. Results Image quality, including signal-to-noise and contrast-to-noise ratios, visibility, and traceability, was best in group C. Conclusions With the help of IMR and ECG-synchronized prospective-triggered technology, the bronchial artery anatomy can be accurately depicted in patients with massive hemoptysis.

It was a different study from traditional anatomy to construct three-dimensional (3D) model of the corona mortis (CMOR) through contrasted enhanced computed tomography (CT) scanning. This will provide an aid for the understanding of the fine anatomy of CMOR, as well as surgical produces. A medical image processing system was used to process computed tomography scanning data collected from 330 cases of woman patients. Accurate digital 3D models of the CMOR were used to study the incidence, location, and the morphological patterns. These models have a rotation of 360° and allow displaying from different views. The incidence was 51.1% for the venous CMOR, 14.1% for the arterial CMOR in the 660 sides pelvic wall and vessel models. The mean distance from the symphysis pubis for the venous CMOR was 66.87 mm, and 59.60 mm for the arterial CMOR. The morphological patterns we identified were classified in three types (I-III). The model of CMOR serves as a useful tool for the learning and investigation of the CMOR. In the future, the digital anatomical data can be beneficial to illustrating the operation approach and decreasing the surgical complications.

Background To develop liposomes loaded with iodinated agents as nanosized CT/MRI bimodal contrast agents for monitoring liposome-mediated drug delivery. Methods Rhodamine-labeled iodixanol (VisipaqueTM)-loaded liposomes (IX-lipo) were prepared and tested for their properties as a diamagnetic CEST contrast agent in vitro. Mice bearing subcutaneous CT26 colon tumors were injected i.v. with 1 g/kg (535 mg iodine/kg) IX-lipo, and in vivo CT and CEST MR images were acquired on day 3. CT and CEST MR images were also acquired for tumor-bearing mice co-injected with IX-lipo and tumor necrosis factor (TNF-α). Results In addition to CT contrast, IX-lipo exhibited a strong CEST contrast similar to its non-liposomal form, with a detectability of ~2 nM per liposome. Both CT imaging and CEST MRI showed that i.v. injection of IX-lipo resulted in a rim enhancement of CT26 tumors with a heterogeneous central distribution. In contrast, co-injection of TNF-α caused a significantly augmented CT/MRI contrast in the tumor center. The intratumoral biodistribution of IX-lipo correlated well to the rhodamine patterns observed with fluorescence microscopy. Conclusions We have developed a CT/MRI bimodal imaging approach for monitoring the delivery and biodistribution of liposomes by loading them with the clinically approved X-ray/CT contrast agent iodixanol. Our approach may be easily adapted for other-FDA approved iodinated agents and thus has great translational potential.

Purpose This study sought to monitor the dynamic process of lymph node (LN) metastasis with intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), and to investigate the impact of disease course on the detection of metastatic LNs by IVIM-DWI. Methods Twenty female New Zealand rabbits with 2.5–3.0 kg body weight were studied. VX2 cells and egg yolk emulsion were randomly inoculated into one thigh to induce metastatic and inflammatory popliteal LNs, respectively. Eight rabbits underwent IVIM-DWI (14 b values, 0–2000 s/mm2) 2 h prior to, and 14, 21, and 28 days after inoculation (D0, D14, D21, D28). The apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were measured and compared between the metastatic and the inflammatory groups at each time point. Three rabbits randomly chosen from the remaining twelve rabbits were sacrificed at each time point to perform hematoxylin and eosin staining and histologic evaluation. Results The patterns of dynamic change of D*, ADC, and D were different between the metastatic and the inflammatory LNs. The metastatic group had a lower D* value at D14 (p = .003), and greater ADC and D values at both D21 (p = .001, p = .001) and D28 (p = .021, p = .001), compared to the inflammatory group. The f value of the metastatic group was greater than that of the inflammatory only at D28 (p = .001). Conclusions IVIM-DWI can reflect the dynamic process of LN metastasis, and disease course has a significant influence on the ability of IVIM-DWI to detect metastatic nodes.

LIM domain containing 2 (LIMD2) is a small LIM-only protein that has been demonstrated to promote tumor progression; however, the expression and function of LIMD2 in non-small cell lung cancer (NSCLC) has not previously been reported. In the present study, reverse transcription-quantitative PCR and western blot analysis were conducted to examine the mRNA and protein expression levels of LIMD2. Cell Counting Kit-8, Transwell and wound-healing assays were performed in order to examine cell proliferation, invasion and migration, respectively. The data revealed that the LIMD2 expression levels were significantly increased in NSCLC tissues and cell lines, compared with adjacent non-tumor tissues and normal lung epithelial cells, respectively. In addition, the high expression of LIMD2 was significantly associated with lymph node metastasis, distant metastasis and advanced clinical stage in NSCLC. The patients with NSCLC with a high expression of LIMD2 exhibited shorter survival times than those with low LIMD2 expression. The knockdown of LIMD2 caused remarkable decreases in NSCLC cell proliferation, migration and invasion. Bioinformatics analysis and luciferase reporter gene assay data further confirmed that LIMD2 was a direct target gene of microRNA-124 (miR-124), a well-known tumor suppressor in NSCLC. The expression of LIMD2 was negatively regulated by miR-124 in NSCLC cells. In addition, miR-124 was downregulated in NSCLC tissues compared with adjacent non-tumor tissues, and an inverse correlation was observed between the expression of LIMD2 and miR-124 in NSCLC tissues. In conclusion, the present study demonstrates that LIMD2 serves an oncogenic role in NSCLC, suggesting that it may be used as a potential therapeutic target for the treatment of NSCLC.

The data presented in this article is related to the research article entitled “A One-Pot Method for Building Colloidal Nanoparticles into Bulk Dry Powders with Nanoscale Magnetic, Plasmonic and Catalytic Functionalities” (Ye et al., 2019) The data shows the hydrophobicity of the nanoparticle (NP) building blocks used for constructing NMPs obtained through contact angle measurements, along with the effect of NP hydrophobicity on the stability of the parent Pickering emulsions. SEM data of the morphology of NMPs is presented. Finally, a mathematical model is presented to predict the average diameter of NMPs produced via different experimental parameters. Keywords: Promoter, Emulsion, Self-assembly, Nano-powder

To investigate the discriminative capabilities of different machine learning–based classification models on the differentiation of small (

Here, we present surface-enhanced Raman data for the calculation of signal uniformity and enhancement factor in SENSERS (surface-exposed nanoparticle sheet enhanced Raman spectroscopy). SEM was used to characterize the microstructure of the solid sample. The interaction between the solid sample and surface-exposed nanoparticle sheet was characterized using SERS and SEM. Based on these data a “skin” versus “sheet” type calculation method was used to calculate the magnitude of Raman signal enhancement within SENSERS. The data presented in this article is related to the research article entitled “Pressing Solids Directly Into Sheets of Plasmonic Nanojunctions Enables Solvent-Free Surface-Enhanced Raman Spectroscopy” (Xu et al., 2018).

Background: To evaluate whether radiomic feature-based computed tomography (CT) imaging signatures allow prediction of lymph node (LN) metastasis in gastric cancer (GC) and to develop a preoperative nomogram for predicting LN status. Methods: We retrospectively analyzed radiomics features of CT images in 1,689 consecutive patients from three cancer centers. The prediction model was developed in the training cohort and validated in internal and external validation cohorts. Lasso regression model was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop the predicting model. We incorporated the radiomics signature, clinical T and N stage, and independent clinicopathologic risk factors, and this was presented with a radiomics nomogram. The performance of the nomogram was assessed with calibration, discrimination, and clinical usefulness. Results: The radiomics signature was significantly associated with pathological LN stage in training and validation cohorts. Multivariable logistic analysis found the radiomics signature was an independent predictor for LN metastasis. The nomogram showed good discrimination and calibration. Conclusions: The newly developed radiomic signature was a powerful predictor of LN metastasis and the radiomics nomogram could facilitate the preoperative individualized prediction of LN status.

Aims N6-Methyladenosine (m6A) is the most frequent posttranscriptional modification and plays important roles in tumorigenesis and metastasis. The roles of fat mass and obesity-associated (FTO) in metabolic diseases have been widely explored. However, the molecular mechanisms and physiological functions of FTO in prostate cancer remain largely unknown. This study aimed to explore the exact functions of FTO in the progression of prostate cancer metastasis. Main methods Dot blot and m6A RNA methylation quantification assays were performed to determine m6A levels. The protein and mRNA expression levels were detected using immunoblot (IB) and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analyses. Cell invasion and migration abilities were measured using transwell and wound healing assays. Bioinformatics was used to measure the expression level of FTO and possible correlation between FTO levels and advanced tumor stage. Immunofluorescence (IF) was performed to measure the cellular localization of FTO. Key findings FTO was downregulated in prostate cancer tissues and cell lines, and the m6A content was increased. Importantly, patients with lower FTO expression had advanced tumor stage and higher Gleason scores. Gain- and loss-of-function assays revealed that FTO inhibits prostate cancer cell invasion and migration in vitro. Moreover, we confirmed that FTO can decrease the total m6A level. Significance The present study revealed that the FTO m6A demethylase inhibits prostate cancer cell invasion and migration by regulating total m6A levels.

The purpose of this study was to determine whether intravoxel incoherent motion (IVIM) -derived parameters and apparent diffusion coefficient (ADC) could act as imaging biomarkers for predicting antifungal treatment response.Forty-six consecutive patients (mean age, 33.9 ± 13.0 y) with newly diagnosed invasive fungal infection (IFI) in the lung according to EORTC/MSG criteria were prospectively enrolled. All patients underwent diffusion-weighted magnetic resonance (MR) imaging at 3.0 T using 11 b values (0-1000 sec/mmf values were significantly lower in the unfavourable response group (12.6%±4.4%) than in the favourable response group (30.2%±8.6%) (Z=4.989, P0.001). However, the ADC, D, and D* were not significantly different between the two groups (P0.05). Receiver operating characteristic curve analyses showed f to be a significant predictor for differentiation, with a sensitivity of 93.8% and a specificity of 92.9%.IVIM-MRI is potentially useful in the prediction of antifungal treatment response to patients with IFI in the lung. Our results indicate that a low perfusion fraction f may be a noninvasive imaging biomarker for unfavourable response.• Recognition of IFI indicating clinical outcome is important for treatment decision-making. • IVIM can reflect diffusion and perfusion information of IFI lesions separately. • Perfusion characteristics of IFI lesions could help differentiate treatment response. • An initial low f may predict unfavourable response in IFI.

OBJECT Craniopharyngiomas (CPs) are rare epithelial tumors that are often associated with an enigmatic and unpredictable growth pattern. Understanding the growth patterns of these tumors has a direct impact on surgical planning and may enhance the safety of radical tumor removal. The aim of this study was to analyze the growth patterns and surgical treatment of CPs with a focus on the involvement of the hypothalamopituitary axis and the relationship of the tumor to the arachnoid membrane and surrounding structures. METHODS Clinical data from 226 consecutive patients with primary CP were retrospectively reviewed. Tumor location and the relationship of the tumor to the third ventricle floor and the pituitary stalk were evaluated using preoperative MRI and intraoperative findings. A topographic classification scheme was proposed based on the site of tumor origin and tumor development. The clinical relevance of this classification on patient presentation and outcomes was also analyzed. RESULTS The growth of CPs can be broadly divided into 3 groups based on the site of tumor origin and on tumor-meningeal relationships: Group I, infrasellar/infradiaphragmatic CPs (Id-CPs), which mainly occurred in children; Group II, suprasellar subarachnoid extraventricular CPs (Sa-CPs), which were mainly observed in adults and rarely occurred in children; and Group III, suprasellar subpial ventricular CPs (Sp-CPs), which commonly occurred in both adults and children. Tumors in each group may develop complex growth patterns during vertical expansion along the pituitary stalk. Tumor growth patterns were closely related to both clinical presentation and outcomes. Patients with Sp-CPs had more prevalent weight gain than patients with Id-CPs or Sa-CPs; the rates of significant weight gain were 41.7% for children and 16.7% for adults with Sp-CPs, 2.2% and 7.1% for those with Id-CPs, and 12.5% and 2.6% for those with Sa-CPs (p < 0.001). Moreover, patients with Sp-CPs had increased hypothalamic dysfunction after radical removal; 39% of patients with Sp-CPs, 14.5% with Id-CPs, and 17.4% with Sa-CPs had high-grade hypothalamic dysfunction in the first 2 postoperative years (p < 0.001). CONCLUSIONS The classification of CPs based on growth pattern may elucidate the best course of treatment for this formidable tumor. More tailored, individualized surgical strategies based on tumor growth patterns are mandatory to provide long-term tumor control and to minimize damage to hypothalamic structures. Differences in the distribution of growth patterns between children and adults imply that hierarchical comparison is necessary when investigating outcomes and survival across treatment paradigms in patients with CP.

Background It is very difficult to predict the early response to NAC only on the basis of change in tumor size. ADC value derived from DWI promises to be a valuable parameter for evaluating the early response to treatment. This study aims to establish the optimal time window of predicting the early response to neoadjuvant chemotherapy (NAC) for different subtypes of locally advanced breast carcinoma using diffusion-weighted imaging (DWI). Methods We conducted an institutional review board-approved prospective clinical study of 142 patients with locally advanced breast carcinoma. All patients underwent conventional MR and DW examinations prior to treatment and after first, second, third, fourth, sixth and eighth cycle of NAC. The response to NAC was classified into a pathologic complete response (pCR) and a non-pCR group. DWI parameters were compared between two groups, and the optimal time window for predicting tumor response was established for each chemotherapy regimen. Results For all the genomic subtypes, there were significant differences in baseline ADC value between pCR and non-pCR group (p Conclusions The time point prior to treatment can be considered as the optimal time point regardless of genomic subtype. For each chemotherapy regimen, the optimal time point during chemotherapy varies across different genomic subtypes.

Purpose To investigate whether the apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM), and stretched exponential model (SEM) based on histogram analyses derived from the whole-tumor volume combined with prognostic factors can be used to assess the response to chemotherapy and radiation therapy (CRT) in locally advanced rectal cancer (LARC). Materials and methods This study included 60 patients with LARC who underwent diffusion-weighted imaging with 9b values (0–1000s/mm2) before CRT. Histograms derived from the whole-tumor volume were used to obtain the ADC, IVIM (Dslow, Dfast, and f), and SEM parameters (distributed diffusion coefficient (DDC) and α). The histogram metrics and prognostic factors before CRT were compared between pathological complete response (pCR) and non-pCR patients. The receiver operating characteristic (ROC) and the area under the ROC curve (AUC) were generated to analyze the histogram metrics and prognostic factors. Results A significant difference was only found in the tumor volume between the pCR and non-pCR groups (p = 0.033, AUC = 0.740). The ADC mean, DDC median, and most of the histogram metrics were significantly lower in the pCR group than the non-pCR group (p = 0.000–0.025), and AUC was highest for the ADC mean (0.890). Only the Dslow median differed significantly between the two groups (p = 0.023, AUC = 0.721). However, the Dfast, f, and α histogram metrics did not differ significantly between the pCR and non-pCR groups. The AUC for the ADC mean combined with the tumor volume was 0.908, with a sensitivity of 100% and specificity of 81%. The inter-observer agreements were good or excellent for the ADC and SEM histogram parameters but generally fair for IVIM. Conclusion The whole-tumor ADC mean combined with the tumor volume was highly accurate for predicting pCR. The IVIM models were inferior to ADC and SEM at predicting pCR.

To evaluate the image quality of ultralow-dose computed tomography (ULDCT) reconstructed with knowledge-based iterative model reconstruction (IMR) in patients with pulmonary tuberculosis (TB). This IRB-approved prospective study enrolled 59 consecutive patients (mean age, 43.9 ± 16.6 years; F:M 18:41) with known or suspected pulmonary TB. Patients underwent a low-dose CT (LDCT) using automatic tube current modulation followed by an ULDCT using fixed tube current. Raw image data were reconstructed with filtered-back projection (FBP), hybrid iterative reconstruction (iDose), and IMR. Objective measurements including CT attenuation, image noise, and contrast-to-noise ratio (CNR) were assessed and compared using repeated-measures analysis of variance. Overall image quality and visualization of normal and pathological findings were subjectively scored on a five-point scale. Radiation output and subjective scores were compared by the paired Student t test and Wilcoxon signed-rank test, respectively. Compared with FBP and iDose, IMR yielded significantly lower noise and higher CNR values at both dose levels (p

Purpose To determine whether imaging parameters derived from intravoxel incoherent motion (IVIM) diffusion weighted imaging (DWI) vary according to tumor-stroma ratio(TSR) or dominant stroma type of breast cancer. Methods We prospectively enrolled 77 patients with breast cancer who underwent IVIM DWI on a 3.0 T MR scanner. The values of IVIM parameters (D, D* and f) were measured. After surgery, TSR or dominant stroma type was evaluated. The relationship between imaging parameters and tumor stroma characteristics was analyzed. Results The mean D and f values were lower in stroma-poor tumor than in stroma-rich tumor (P = 0.012, 0.015). The mean D value was lower in the collagen-dominant type than in fibroblast-dominant or lymphocyte-dominant type (P = 0.032, 0.043). According to multivariate linear regression analyses, tumor size (P = 0.007), TSR (P = 0.008), dominant stroma type (collagen dominant, P = 0.012), and histological grade (P = 0.031) were independently correlated with D value; and tumor size (P = 0.011), TSR (P = 0.021) and histological grade (P = 0.037) were independently correlated with f value. Conclusion In breast cancer, D and f values show significant differences according to TSR, and D value is lower in collagen dominant type than in fibroblast dominant or lymphocyte dominant types.

Predicting malignant potential is one of the most critical components of a computer-aided diagnosis system for gastrointestinal stromal tumors (GISTs). These tumors have been studied only on the basis of subjective computed tomography findings. Among various methodologies, radiomics, and deep learning algorithms, specifically convolutional neural networks (CNNs), have recently been confirmed to achieve significant success by outperforming the state-of-the-art performance in medical image pattern classification and have rapidly become leading methodologies in this field. However, the existing methods generally use radiomics or deep convolutional features independently for pattern classification, which tend to take into account only global or local features, respectively. In this paper, we introduce and evaluate a hybrid structure that includes different features selected with radiomics model and CNNs and integrates these features to deal with GISTs classification. The Radiomics model and CNNs are constructed for global radiomics and local convolutional feature selection, respectively. Subsequently, we utilize distinct radiomics and deep convolutional features to perform pattern classification for GISTs. Specifically, we propose a new pooling strategy to assemble the deep convolutional features of 54 three-dimensional patches from the same case and integrate these features with the radiomics features for independent case, followed by random forest classifier. Our method can be extensively evaluated using multiple clinical datasets. The classification performance (area under the curve (AUC): 0.882; 95% confidence interval (CI): 0.816–0.947) consistently outperforms those of independent radiomics (AUC: 0.807; 95% CI: 0.724–0.892) and CNNs (AUC: 0.826; 95% CI: 0.795–0.856) approaches.

Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging technique that quantifies the magnetic susceptibility distribution within biological tissues. QSM calculates the underlying magnetic susceptibility by deconvolving the tissue magnetic field map with a unit dipole kernel. However, this deconvolution problem is ill-posed. The morphology enabled dipole inversion (MEDI) introduces total variation (TV) to regularize the susceptibility reconstruction. However, MEDI results still contain artifacts near tissue boundaries because MEDI only imposes TV constraint on voxels inside smooth regions. We introduce a Morphology-Adaptive TV (MATV) for improving TV-regularized QSM. The MATV method first classifies imaging target into smooth and nonsmooth regions by thresholding magnitude gradients. In the dipole inversion for QSM, the TV regularization weights are a monotonically decreasing function of magnitude gradients. Thus, voxels inside smooth regions are assigned with larger weights than those in nonsmooth regions. Using phantom and in vivo datasets, we compared the performance of MATV with that of MEDI. MATV results had better visual quality than MEDI results, especially near tissue boundaries. Preliminary brain imaging results illustrated that MATV has potential to improve the reconstruction of regions near tissue boundaries.

Purpose To compare the diagnostic quality of reduced radiation dose computed tomography (CT) with iterative model reconstruction (IMR) versus that of conventional low-dose CT in patients with pulmonary invasive fungal infection. Materials and Methods This prospective observational study included 48 patients (mean age ± standard deviation, 39.9 years ± 11.3) known to have or suspected of having pulmonary invasive fungal infection between October 2016 and July 2017. Patients underwent CT with IMR (at 80 kV with 20 mA) immediately after low-dose CT (at 80 kV with automatic exposure control). Images were reconstructed by using a hybrid iterative reconstruction (HIR) algorithm and IMR. Two radiologists independently assessed subjective image quality, noise, and visibility of normal and abnormal findings by using a five-point scale. Objective measurements, including image noise, contrast-to-noise ratio (CNR), and corresponding figure of merit (FOM), were compared by using repeated-measures analysis of variance with Bonferroni post hoc tests for multiple comparisons. Results The mean effective dose was 0.3 mSv ± 0.3 for CT with IMR and 0.7 mSv ± 0.2 for low-dose CT (P.01). When the image noise and CNR were normalized to the effective dose, CT images obtained with IMR had significantly higher FOM than did other image series (P.0001). Subjectively, visibility of CT features of invasive fungal infection on CT scans reconstructed with IMR was rated as noninferior to that on low-dose CT scans reconstructed with HIR, except for the halo sign. Conclusion CT with IMR had approximately 60% dose reduction compared with conventional low-dose CT, with reduced noise and improved depiction of abnormal findings, in patients with pulmonary invasive fungal infection.

PURPOSE We aimed to develop and validate a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). METHODS A total of 304 eligible patients with HCC were randomly divided into training (n=184) and independent validation (n=120) cohorts. Portal venous and arterial phase computed tomography data of the HCCs were collected to extract radiomic features. Using the least absolute shrinkage and selection operator algorithm, the training set was processed to reduce data dimensions, feature selection, and construction of a radiomics signature. Then, a prediction model including the radiomics signature, radiologic features, and alpha-fetoprotein (AFP) level, as presented in a radiomics nomogram, was developed using multivariable logistic regression analysis. The radiomics nomogram was analyzed based on its discrimination ability, calibration, and clinical usefulness. Internal cohort data were validated using the radiomics nomogram. RESULTS The radiomics signature was significantly associated with MVI status (P < 0.001, both cohorts). Predictors, including the radiomics signature, nonsmooth tumor margin, hypoattenuating halos, internal arteries, and alpha-fetoprotein level were reserved in the individualized prediction nomogram. The model exhibited good calibration and discrimination in the training and validation cohorts (C-index [95% confidence interval]: 0.846 [0.787-0.905] and 0.844 [0.774-0.915], respectively). Its clinical usefulness was confirmed using a decision curve analysis. CONCLUSION The radiomics nomogram, as a noninvasive preoperative prediction method, shows a favorable predictive accuracy for MVI status in patients with HBV-related HCC.

To analyze the relationship between fetal head size and maternal pelvis size using magnetic resonance imaging (MRI) with a 3-D reconstruction technique. A total of 301 nulliparous full-term Chinese pregnant women with cephalic presentation were enrolled and received MRI examinations before labor onset. Data were collected and imported into Mimics software to reconstruct the maternal pelvis and fetus. Of 301 pregnant women, 212 underwent vaginal delivery and 32 received cesarean section. The body mass index (BMI) was significantly different between the vaginal delivery group and the suspected cephalopelvic disproportion (CPD) group; the larger the BMI, the higher was the risk of CPD. The transverse diameter of the pelvic inlet and the posterior sagittal diameter of the midpelvis were significantly larger in the vaginal delivery group, compared with the suspected CPD group. Fetal weight > 3.5 kg could be used as a diagnostic indicator for CPD. BMI is a risk factor for CPD, and fetal weight

Objective. To investigate the potential of L5 peptide-guided pretargeting approach to identify GPC3-expressing hepatocellular carcinoma (HCC) using ultrasmall superparamagnetic iron oxide (USPIO) as the MR probe. Methods. Immunofluorescence with carboxyfluorescein- (FAM-) labeled L5 peptide was performed in HepG2 cells. Polyethylene glycol-modified USPIO (PEG-USPIO) and its conjugation with streptavidin (SA-PEG-USPIO) were synthesized, and their hydrodynamic diameters, zeta potential, T2 relaxivity, and cytotoxicity were measured. In vitro and in vivo two-step pretargeting MR imaging was performed on HepG2 cells and tumor-bearing mice after the administration of biotinylated L5 peptide (first step), followed by SA-PEG-USPIO (second step). Prussian blue staining was performed to assess iron deposition in tumors. Results. The high specificity of L5 peptide for GPC3 was demonstrated. Generation of SA-PEG-USPIO nanoparticles with good biocompatibility (an average hydrodynamic diameter of 35.97 nm and a zeta potential of −7.91 mV), superparamagnetism (R2 = 0.1039 × 103 mM−1s−1), and low toxicity was achieved. The pretargeting group showed more enhancement than the nonpretargeting group both in vitro (60% vs 20%, P<0.05) and in vivo (32% vs 6%, P<0.001). Substantial iron deposition was only observed in HepG2 cells and tumors in the pretargeting group. Conclusion. L5 peptide-guided, two-step pretargeting approach with USPIO as the MR imaging probe is a lucrative strategy to specifically identify GPC3-expressing HCC.

Previous studies have shown that employing the low dose computed tomography (CT) technique based on low tube potential reduces the radiation dose required for the coronary artery examination protocol. However, low tube potential may adversely influence the CT number of plaque composition. Therefore, we aimed to determine whether quantitative atherosclerotic plaque analysis by a multi-slice, low radiation dose CT protocol using 80 kilovolts (kV) yields results comparable to those of the standard 120 kV protocol. Artificial plaque samples (n = 17) composed of three kinds of plaque were scanned at 120 and 80 kV. Relative low-density and medium-density plaque component volumes obtained by three protocols (80 kV, 60 Hounsfield units [HU] threshold; 120 kV, 60 HU threshold; and 80 kV, 82 HU threshold) were compared. Using the 60 HU threshold, relative volume of the low-density plaque component obtained at 80 kV was lower than that obtained at 120 kV (27 ± 3% vs. 51 ± 5%, P

The nucleus accumbens (NAc), an important target of deep brain stimulation for some neuropsychiatric disorders, is thought to be involved in epileptogenesis, especially the shell portion. However, little is known about the exact parcellation within the NAc, and its structural abnormalities or connections alterations of each NAc subdivision in temporal lobe epilepsy (TLE) patients. Here, we used diffusion probabilistic tractography to subdivide the NAc into core and shell portions in individual TLE patients to guide stereotactic localization of NAc shell. The structural and connection abnormalities in each NAc subdivision in the groups were then estimated. We successfully segmented the NAc in 24 of 25 controls, 14 of 16 left TLE patients, and 14 of 18 right TLE patients. Both left and right TLE patients exhibited significantly decreased fractional anisotropy (FA) and increased radial diffusivity (RD) in the shell, while there was no significant alteration in the core. Moreover, relatively distinct structural connectivity of each NAc subdivision was demonstrated. More extensive connection abnormalities were detected in the NAc shell in TLE patients. Our results indicate that neuronal degeneration and damage caused by seizure mainly exists in NAc shell and provide anatomical evidence to support the role of NAc shell in epileptogenesis. Remarkably, those NAc shell tracts with increased connectivities in TLE patients were found decreased in FA, which indicates disruption of fiber integrity. This finding suggests the regeneration of aberrant connections, a compensatory and repair process ascribed to recurrent seizures that constitutes part of the characteristic changes in the epileptic network.

Myocardial bridges (MB) have traditionally been considered a benign condition, but recent studies have demonstrated that the clinical complications can be dangerous. The transluminal attenuation gradient (TAG) obtained from coronary computed tomography angiography (CCTA) data (Retrospective ECG-triggered method) has been used in detecting significant stenosis in coronary artery caused by atherosclerosis. Contrast opacification difference (COD) was the parameters calculated as the change between attenuation of mural artery and the median attenuation of presumptive vessel segment; it was evaluated along with TAGstandardized (TAGs) and MB length for predicting MB with systolic compression (MB-SC) in patients diagnosed as MB in left anterior descending coronary artery (LAD) by CCTA or invasive coronary angiograph (ICA). A total of 107 MB patients were divided into three groups based on systolic compression (SC), including: Group 1 (MB without SC); Group 2 (MB with mild SC); and Group 3 (MB with significant SC). ANOVA and Kruskal–Wallis analysis indicated TAGs showed the most significant differences for MB identification. This study revealed that TAGs decreasing and COD increasing were dominated in MB with significant SC. COD had a higher sensitivity and a higher negative predictive value for detecting MB with significant SC than TAGs.

Danting Cui,1 Xiaodan Lu,1 Chenggong Yan,1 Xiang Liu,1 Meirong Hou,1 Qi Xia,2 Yikai Xu,1 Ruiyuan Liu2,3 1Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 2School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, People’s Republic of China; 3School of Biomedical Engineering, Southern Medical University, Guangzhou, People’s Republic of China Abstract: Bombesin (BBN), an analog of gastrin-releasing peptide (GRP), specifically binds to GRP receptors, which are overexpressed in human prostate cancer (PC). Here, we synthesized a BBN-modified gadolinium oxide (Gd2O3) nanoprobe containing fluorescein (Gd2O3-5(6)-carboxyfluorescein [FI]-polyethylene glycol [PEG]-BBN) for targeted magnetic resonance (MR)/optical dual-modality imaging of PC. The Gd2O3-FI-PEG-BBN nanoparticles exhibited a relatively uniform particle size with an average diameter of 52.3nm and spherical morphology as depicted by transmission electron microscopy. The longitudinal relaxivity (r1) of Gd2O3-FI-PEG-BBN (r1 =4.23mM–1s–1) is comparable to that of clinically used Magnevist (Gd-DTPA). Fluorescence microscopy and in vitro cellular MRI demonstrated GRP receptor-specific and enhanced cellular uptake of the Gd2O3-FI-PEG-BBN in PC-3 tumor cells. Moreover, Gd2O3-FI-PEG-BBN showed more remarkable contrast enhancement than the corresponding nontargeted Gd2O3-FI-PEG according to in vivo MRI and fluorescent imaging. Tumor immunohistochemical analysis further demonstrated improved accumulation of the targeted nanoprobe in tumors. BBN-conjugated Gd2O3 may be a promising nanoplatform for simultaneous GRP receptor-targeted molecular cancer diagnosis and antitumor drug delivery in future clinical applications. Keywords: magnetic resonance imaging, gadolinium oxide, bombesin, gastrin-releasing peptide receptor, molecular imaging