Radiation pneumonitis prediction with dual-radiomics for esophageal cancer underwent radiotherapy.

BACKGROUND: To integrate radiomics and dosiomics features from multiple regions in the radiation pneumonia (RP grade ≥ 2) prediction for esophageal cancer (EC) patients underwent radiotherapy (RT). METHODS: Total of 143 EC patients in the authors' hospital (training and internal validation: 70%:30%) and 32 EC patients from another hospital (external validation) underwent RT from 2015 to 2022 were retrospectively reviewed and analyzed. Patients were dichotomized as positive (RP+) or negative (RP-) according to CTCAE V5.0. Models with radiomics and dosiomics features extracted from single region of interest (ROI), multiple ROIs and combined models were constructed and evaluated. A nomogram integrating radiomics score (Rad_score), dosiomics score (Dos_score), clinical factors, dose-volume histogram (DVH) factors, and mean lung dose (MLD) was also constructed and validated. RESULTS: Models with Rad_score_Lung&Overlap and Dos_score_Lung&Overlap achieved a better area under curve (AUC) of 0.818 and 0.844 in the external validation in comparison with radiomics and dosiomics models with features extracted from single ROI. Combining four radiomics and dosiomics models using support vector machine (SVM) improved the AUC to 0.854 in the external validation. Nomogram integrating Rad_score, and Dos_score with clinical factors, DVH factors, and MLD further improved the RP prediction AUC to 0.937 and 0.912 in the internal and external validation, respectively. CONCLUSION: CT-based RP prediction model integrating radiomics and dosiomics features from multiple ROIs outperformed those with features from a single ROI with increased reliability for EC patients who underwent RT.

Enhanced Prediction of Malignant Cerebral Edema in Large Vessel Occlusion with Successful Recanalization Through Automated Weighted Net Water Uptake.

BACKGROUND: Malignant cerebral edema (MCE) is associated with both net water uptake (NWU) and infarct volume. We hypothesized that NWU weighted by the affected Alberta Stroke Program Early Computed Tomography Score (ASPECTS) regions could serve as a quantitative imaging biomarker of aggravated edema development in acute ischemic stroke with large vessel occlusion (LVO). The aim of this study was to evaluate the performance of weighted NWU (wNWU) to predict MCE in patients with mechanical thrombectomy (MT). METHODS: We retrospectively analyzed consecutive patients who underwent MT due to LVO. NWU was computed from nonenhanced computed tomography scans upon admission using automated ASPECTS software. wNWU was derived by multiplying NWU with the number of affected ASPECTS regions in the ischemic hemisphere. Predictors of MCE were assessed through multivariate logistic regression analysis and receiver operating characteristic curves. RESULTS: NWU and wNWU were significantly higher in MCE patients than in non-MCE patients. Vessel recanalization status influenced the performance of wNWU in predicting MCE. In patients with successful recanalization, wNWU was an independent predictor of MCE (adjusted odds ratio 1.61; 95% confidence interval [CI] 1.24-2.09; P < 0.001). The model integrating wNWU, National Institutes of Health Stroke Scale, and collateral score exhibited an excellent performance in predicting MCE (area under the curve 0.80; 95% CI 0.75-0.84). Among patients with unsuccessful recanalization, wNWU did not influence the development of MCE (adjusted odds ratio 0.99; 95% CI 0.60-1.62; P = 0.953). CONCLUSIONS: This study revealed that wNWU at admission can serve as a quantitative predictor of MCE in LVO with successful recanalization after MT and may contribute to the decision for early intervention.

Low blood flow ratio is associated with hemorrhagic transformation secondary to mechanical thrombectomy in patients with acute ischemic stroke.

BACKGROUND AND PURPOSE: A significant decrease of cerebral blood flow (CBF) is a risk factor for hemorrhagic transformation (HT) in acute ischemic stroke (AIS). This study aimed to ascertain whether the ratio of different CBF thresholds derived from computed tomography perfusion (CTP) is an independent risk factor for HT after mechanical thrombectomy (MT). METHODS: A retrospective single center cohort study was conducted on patients with AIS undergoing MT at the First Affiliated Hospital of Wenzhou Medical University from August 2018 to December 2023. The perfusion parameters before thrombectomy were obtained according to CTP automatic processing software. The low blood flow ratio (LFR) was defined as the ratio of brain volume with relative CBF <20 % over volume with relative CBF <30 %. HT was evaluated on the follow-up CT images. Binary logistic regression was used to analyze the correlation between parameters that differ between the two groups with regards to HT occurrence. The predictive efficacy was assessed utilizing the receiver operating characteristic curve. RESULTS: In total, 243 patients met the inclusion criteria. During the follow-up, 46.5 % of the patients (113/243) developed HT. Compared with the Non-HT group, the HT group had a higher LFR (0.47 (0.34-0.65) vs. 0.32 (0.07-0.56); P < 0.001). According to the binary logistic regression analysis, the LFR (aOR: 6.737; 95 % CI: 1.994-22.758; P = 0.002), Hypertension history (aOR: 2.231; 95 % CI: 1.201-4.142; P = 0.011), plasma FIB levels before MT (aOR: 0.641; 95 % CI: 0.456-0.902; P = 0.011), and the mismatch ratio (aOR: 0.990; 95 % CI: 0.980-0.999; P = 0.030) were independently associated with HT secondary to MT. The area under the curve of the regression model for predicting HT was 0.741. CONCLUSION: LFR, a ratio quantified via CTP, demonstrates potential as an independent risk factor of HT secondary to MT.

Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach.

BACKGROUND: Precise prediction of esophageal squamous cell carcinoma (ESCC) invasion depth is crucial not only for optimizing treatment plans but also for reducing the need for invasive procedures, consequently lowering complications and costs. Despite this, current techniques, which can be invasive and costly, struggle with achieving the necessary precision, highlighting a pressing need for more effective, non-invasive alternatives. METHOD: We developed ResoLSTM-Depth, a deep learning model to distinguish ESCC stages T1-T2 from T3-T4. It integrates ResNet-18 and Long Short-Term Memory (LSTM) networks, leveraging their strengths in spatial and sequential data processing. This method uses arterial phase CT scans from ESCC patients. The dataset was meticulously segmented by an experienced radiologist for effective training and validation. RESULTS: Upon performing five-fold cross-validation, the ResoLSTM-Depth model exhibited commendable performance with an accuracy of 0.857, an AUC of 0.901, a sensitivity of 0.884, and a specificity of 0.828. These results were superior to the ResNet-18 model alone, where the average accuracy is 0.824 and the AUC is 0.879. Attention maps further highlighted influential features for depth prediction, enhancing model interpretability. CONCLUSION: ResoLSTM-Depth is a promising tool for ESCC invasion depth prediction. It offers potential for improvement in the staging and therapeutic planning of ESCC.

Histone 3 lysine 9 acetylation-specific reprogramming regulates esophageal squamous cell carcinoma progression and metastasis.

Dysregulation of histone acetylation is widely implicated in tumorigenesis, yet its specific roles in the progression and metastasis of esophageal squamous cell carcinoma (ESCC) remain unclear. Here, we profiled the genome-wide landscapes of H3K9ac for paired adjacent normal (Nor), primary ESCC (EC) and metastatic lymph node (LNC) esophageal tissues from three ESCC patients. Compared to H3K27ac, we identified a distinct epigenetic reprogramming specific to H3K9ac in EC and LNC samples relative to Nor samples. This H3K9ac-related reprogramming contributed to the transcriptomic aberration of targeting genes, which were functionally associated with tumorigenesis and metastasis. Notably, genes with gained H3K9ac signals in both primary and metastatic lymph node samples (common-gained gene) were significantly enriched in oncogenes. Single-cell RNA-seq analysis further revealed that the corresponding top 15 common-gained genes preferred to be enriched in mesenchymal cells with high metastatic potential. Additionally, in vitro experiment demonstrated that the removal of H3K9ac from the common-gained gene MSI1 significantly downregulated its transcription, resulting in deficiencies in ESCC cell proliferation and migration. Together, our findings revealed the distinct characteristics of H3K9ac in esophageal squamous cell carcinogenesis and metastasis, and highlighted the potential therapeutic avenue for intervening ESCC through epigenetic modulation via H3K9ac.

Deep Learning-Based Multiparametric MRI Model for Preoperative T-Stage in Rectal Cancer.

BACKGROUND: Conventional MRI staging can be challenging in the preoperative assessment of rectal cancer. Deep learning methods based on MRI have shown promise in cancer diagnosis and prognostication. However, the value of deep learning in rectal cancer T-staging is unclear. PURPOSE: To develop a deep learning model based on preoperative multiparametric MRI for evaluation of rectal cancer and to investigate its potential to improve T-staging accuracy. STUDY TYPE: Retrospective. POPULATION: After cross-validation, 260 patients (123 with T-stage T1-2 and 134 with T-stage T3-4) with histopathologically confirmed rectal cancer were randomly divided to the training (N = 208) and test sets (N = 52). FIELD STRENGTH/SEQUENCE: 3.0 T/Dynamic contrast enhanced (DCE), T2-weighted imaging (T2W), and diffusion-weighted imaging (DWI). ASSESSMENT: The deep learning (DL) model of multiparametric (DCE, T2W, and DWI) convolutional neural network were constructed for evaluating preoperative diagnosis. The pathological findings served as the reference standard for T-stage. For comparison, the single parameter DL-model, a logistic regression model composed of clinical features and subjective assessment of radiologists were used. STATISTICAL TESTS: The receiver operating characteristic curve (ROC) was used to evaluate the models, the Fleiss' kappa for the intercorrelation coefficients, and DeLong test for compare the diagnostic performance of ROCs. P-values less than 0.05 were considered statistically significant. RESULTS: The Area Under Curve (AUC) of the multiparametric DL-model was 0.854, which was significantly higher than the radiologist's assessment (AUC = 0.678), clinical model (AUC = 0.747), and the single parameter DL-models including T2W-model (AUC = 0.735), DWI-model (AUC = 0.759), and DCE-model (AUC = 0.789). DATA CONCLUSION: In the evaluation of rectal cancer patients, the proposed multiparametric DL-model outperformed the radiologist's assessment, the clinical model as well as the single parameter models. The multiparametric DL-model has the potential to assist clinicians by providing more reliable and precise preoperative T staging diagnosis. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Multi-modality radiomics nomogram based on DCE-MRI and ultrasound images for benign and malignant breast lesion classification.

Objective: To develop a multi-modality radiomics nomogram based on DCE-MRI, B-mode ultrasound (BMUS) and strain elastography (SE) images for classifying benign and malignant breast lesions. Material and Methods: In this retrospective study, 345 breast lesions from 305 patients who underwent DCE-MRI, BMUS and SE examinations were randomly divided into training (n = 241) and testing (n = 104) datasets. Radiomics features were extracted from manually contoured images. The inter-class correlation coefficient (ICC), Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection and radiomics signature building. Multivariable logistic regression was used to develop a radiomics nomogram incorporating radiomics signature and clinical factors. The performance of the radiomics nomogram was evaluated by its discrimination, calibration, and clinical usefulness and was compared with BI-RADS classification evaluated by a senior breast radiologist. Results: The All-Combination radiomics signature derived from the combination of DCE-MRI, BMUS and SE images showed better diagnostic performance than signatures derived from single modality alone, with area under the curves (AUCs) of 0.953 and 0.941 in training and testing datasets, respectively. The multi-modality radiomics nomogram incorporating the All-Combination radiomics signature and age showed excellent discrimination with the highest AUCs of 0.964 and 0.951 in two datasets, respectively, which outperformed all single modality radiomics signatures and BI-RADS classification. Furthermore, the specificity of radiomics nomogram was significantly higher than BI-RADS classification (both p < 0.04) with the same sensitivity in both datasets. Conclusion: The proposed multi-modality radiomics nomogram based on DCE-MRI and ultrasound images has the potential to serve as a non-invasive tool for classifying benign and malignant breast lesions and reduce unnecessary biopsy.

Diagnosis of architectural distortion on digital breast tomosynthesis using radiomics and deep learning.

Purpose: To implement two Artificial Intelligence (AI) methods, radiomics and deep learning, to build diagnostic models for patients presenting with architectural distortion on Digital Breast Tomosynthesis (DBT) images. Materials and Methods: A total of 298 patients were identified from a retrospective review, and all of them had confirmed pathological diagnoses, 175 malignant and 123 benign. The BI-RADS scores of DBT were obtained from the radiology reports, classified into 2, 3, 4A, 4B, 4C, and 5. The architectural distortion areas on craniocaudal (CC) and mediolateral oblique (MLO) views were manually outlined as the region of interest (ROI) for the radiomics analysis. Features were extracted using PyRadiomics, and then the support vector machine (SVM) was applied to select important features and build the classification model. Deep learning was performed using the ResNet50 algorithm, with the binary output of malignancy and benignity. The Gradient-weighted Class Activation Mapping (Grad-CAM) method was utilized to localize the suspicious areas. The predicted malignancy probability was used to construct the ROC curves, compared by the DeLong test. The binary diagnosis was made using the threshold of ≥ 0.5 as malignant. Results: The majority of malignant lesions had BI-RADS scores of 4B, 4C, and 5 (148/175 = 84.6%). In the benign group, a substantial number of patients also had high BI-RADS ≥ 4B (56/123 = 45.5%), and the majority had BI-RADS ≥ 4A (102/123 = 82.9%). The radiomics model built using the combined CC+MLO features yielded an area under curve (AUC) of 0.82, the sensitivity of 0.78, specificity of 0.68, and accuracy of 0.74. If only features from CC were used, the AUC was 0.77, and if only features from MLO were used, the AUC was 0.72. The deep-learning model yielded an AUC of 0.61, significantly lower than all radiomics models (p<0.01), which was presumably due to the use of the entire image as input. The Grad-CAM could localize the architectural distortion areas. Conclusion: The radiomics model can achieve a satisfactory diagnostic accuracy, and the high specificity in the benign group can be used to avoid unnecessary biopsies. Deep learning can be used to localize the architectural distortion areas, which may provide an automatic method for ROI delineation to facilitate the development of a fully-automatic computer-aided diagnosis system using combined AI strategies.

Differentiating Primary Tumors for Brain Metastasis with Integrated Radiomics from Multiple Imaging Modalities.

Objectives: To differentiate the primary site of brain metastases (BMs) is of high clinical value for the successful management of patients with BM. The purpose of this study is to investigate a combined radiomics model with computer tomography (CT) and magnetic resonance imaging (MRI) images in differentiating BMs originated from lung and breast cancer. Methods: Pretreatment cerebral contrast enhanced CT and T1-weighted MRI images of 78 patients with 179 BMs from primary lung and breast cancer were retrospectively analyzed. Radiomic features were extracted from contoured BM lesions and selected using the Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) logistic regression. Binary logistic regression (BLR) and support vector machine (SVM) models were built and evaluated based on selected radiomic features from CT alone, MRI alone, and combined images to differentiate BMs originated from lung and breast cancer. Results: A total of 10 and 6 optimal radiomic features were screened out of 1288 CT and 1197 MRI features, respectively. The mean area under the curves (AUCs) of the BLR and SVM models using fivefolds cross-validation were 0.703 vs. 0.751, 0.718 vs. 0.754, and 0.781 vs. 0.803 in the training dataset and 0.708 vs. 0.763, 0.715 vs. 0.717, and 0.771 vs. 0.805 in the testing dataset for models with CT alone, MRI alone, and combined CT and MRI radiomic features, respectively. Conclusions: Radiomics model based on combined CT and MRI features is feasible and accurate in the differentiation of the primary site of BMs from lung and breast cancer.

Dynamic change of COVID-19 lung infection evaluated using co-registration of serial chest CT images.

Purpose: To evaluate the volumetric change of COVID-19 lesions in the lung of patients receiving serial CT imaging for monitoring the evolution of the disease and the response to treatment. Materials and methods: A total of 48 patients, 28 males and 20 females, who were confirmed to have COVID-19 infection and received chest CT examination, were identified. The age range was 21-93 years old, with a mean of 54 ± 18 years. Of them, 33 patients received the first follow-up (F/U) scan, 29 patients received the second F/U scan, and 11 patients received the third F/U scan. The lesion region of interest (ROI) was manually outlined. A two-step registration method, first using the Affine alignment, followed by the non-rigid Demons algorithm, was developed to match the lung areas on the baseline and F/U images. The baseline lesion ROI was mapped to the F/U images using the obtained geometric transformation matrix, and the radiologist outlined the lesion ROI on F/U CT again. Results: The median (interquartile range) lesion volume (cm3) was 30.9 (83.1) at baseline CT exam, 18.3 (43.9) at first F/U, 7.6 (18.9) at second F/U, and 0.6 (19.1) at third F/U, which showed a significant trend of decrease with time. The two-step registration could significantly decrease the mean squared error (MSE) between baseline and F/U images with p < 0.001. The method could match the lung areas and the large vessels inside the lung. When using the mapped baseline ROIs as references, the second-look ROI drawing showed a significantly increased volume, p < 0.05, presumably due to the consideration of all the infected areas at baseline. Conclusion: The results suggest that the registration method can be applied to assist in the evaluation of longitudinal changes of COVID-19 lesions on chest CT.

Image Quality Control in Lumbar Spine Radiography Using Enhanced U-Net Neural Networks.

Purpose: To standardize the radiography imaging procedure, an image quality control framework using the deep learning technique was developed to segment and evaluate lumbar spine x-ray images according to a defined quality control standard. Materials and Methods: A dataset comprising anteroposterior, lateral, and oblique position lumbar spine x-ray images from 1,389 patients was analyzed in this study. The training set consisted of digital radiography images of 1,070 patients (800, 798, and 623 images of the anteroposterior, lateral, and oblique position, respectively) and the validation set included 319 patients (200, 205, and 156 images of the anteroposterior, lateral, and oblique position, respectively). The quality control standard for lumbar spine x-ray radiography in this study was defined using textbook guidelines of as a reference. An enhanced encoder-decoder fully convolutional network with U-net as the backbone was implemented to segment the anatomical structures in the x-ray images. The segmentations were used to build an automatic assessment method to detect unqualified images. The dice similarity coefficient was used to evaluate segmentation performance. Results: The dice similarity coefficient of the anteroposterior position images ranged from 0.82 to 0.96 (mean 0.91 ± 0.06); the dice similarity coefficient of the lateral position images ranged from 0.71 to 0.95 (mean 0.87 ± 0.10); the dice similarity coefficient of the oblique position images ranged from 0.66 to 0.93 (mean 0.80 ± 0.14). The accuracy, sensitivity, and specificity of the assessment method on the validation set were 0.971-0.990 (mean 0.98 ± 0.10), 0.714-0.933 (mean 0.86 ± 0.13), and 0.995-1.000 (mean 0.99 ± 0.12) for the three positions, respectively. Conclusion: This deep learning-based algorithm achieves accurate segmentation of lumbar spine x-ray images. It provides a reliable and efficient method to identify the shape of the lumbar spine while automatically determining the radiographic image quality.

Brain network connectivity feature extraction using deep learning for Alzheimer's disease classification.

Early diagnosis and therapeutic intervention for Alzheimer's disease (AD) is currently the only viable option for improving clinical outcomes. Combining structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) to diagnose AD has yielded promising results. Most studies assume fixed time lags when constructing functional networks. Since the propagation delays between brain signals are constantly changing, these methods cannot reflect more detailed relationships between brain regions. In this work, we use a deep learning-based Granger causality estimator for brain connectivity construction. It exploits the strength of long short-term memory in ever-changing time series processing. This research involves data analysis from sMRI and rs-fMRI. We use sMRI to analyze the cerebral cortex properties and use rs-fMRI to analyze the graph metrics of functional networks. We extract a small subset of optimal features from both types of data. A support vector machine (SVM) is trained and tested to classify AD (n = 27) from healthy controls (n = 20) using rs-fMRI and sMRI features. Using a subset of optimal features in SVM, we achieve a classification accuracy of 87.23% for sMRI, 78.72% for rs-fMRI, and 91.49% for combined sMRI with rs-fMRI. The results show the potential to identify AD from healthy controls by integrating rs-fMRI and sMRI. The integration of sMRI and rs-fMRI modalities can provide supplemental information to improve the diagnosis of AD relative to either the sMRI or fMRI modalities alone.

Integrative Analysis of 5-Hydroxymethylcytosine and Transcriptional Profiling Identified 5hmC-Modified lncRNA Panel as Non-Invasive Biomarkers for Diagnosis and Prognosis of Pancreatic Cancer.

Pancreatic adenocarcinoma (PAAD) is the fourth leading cause of cancer-related deaths worldwide. 5-Hydroxymethylcytosine (5hmC)-mediated epigenetic regulation has been reported to be involved in cancer pathobiology and has emerged to be promising biomarkers for cancer diagnosis and prognosis. However, 5hmC alterations at long non-coding RNA (lncRNA) genes and their clinical significance remained unknown. In this study, we performed the genome-wide investigation of lncRNA-associated plasma cfDNA 5hmC changes in PAAD by plotting 5hmC reads against lncRNA genes, and identified six PAAD-specific lncRNAs with abnormal 5hmC modifications compared with healthy individuals. Then we applied machine-learning and Cox regression approaches to develop predictive diagnostic (5hLRS) and prognostic (5hLPS) models using the 5hmC-modified lncRNAs. The 5hLRS demonstrated excellent performance in discriminating PAAD from healthy controls with an area under the curve (AUC) of 0.833 in the training cohort and 0.719 in the independent testing cohort. The 5hLPS also effectively divides PAAD patients into high-risk and low-risk groups with significantly different clinical outcomes in the training cohort (log-rank test p = 0.04) and independent testing cohort (log-rank test p = 0.0035). Functional analysis based on competitive endogenous RNA (ceRNA) and enrichment analysis suggested that these differentially regulated 5hmC modified lncRNAs were associated with angiogenesis, circulatory system process, leukocyte differentiation and metal ion homeostasis that are known important events in the development and progression of PAAD. In conclusion, our study indicated the potential clinical utility of 5hmC profiles at lncRNA loci as valuable biomarkers for non-invasive diagnosis and prognostication of cancers.

Molecular Mechanism Exploration of Pancreatitis Based on miRNA Expression Profile.

BACKGROUND: Pancreatitis is a popular disease around the world, and can also lead to pancreatic cancer. Pancreatitis can be distinguished into two types, acute pancreatitis (AP) and chronic pancreatitis (CP). Every year, AP leads to approximately 275,000 new cases and is the most frequent gastrointestinal disease in American. METHODS: The miRNA expression profile of pancreatic cancer and pancreatitis was downloaded from GEO with accession id GSE24279. First, the differentially expressed miRNAs with |fold change| ≥ 2 and p-value ≤ 0.05 and then the target genes of significantly differentially expressed miRNAs in pancreatitis were identified and the interaction network was constructed. Also the biological functions of the target genes were explored based on GO and KEGG enrichment. Finally, the expression values of hsa-miR-373-5p and hsa-miR-374a-5p were validated using RT-PCR. RESULTS: A total of 40 and 13 differentially expressed miRNAs were screened out for pancreatic and pancreatitis, respectively. Two miRNAs, hsa-miR-373-5p and hsa-miR-374a-5p, had significantly down-regulated expression in pancreatitis. Target gene analysis showed that hsa-miR-373-5p probably participates in the development of pancreatitis by regulating MBL2, MAT2B, and BCL10. In addition, has-miR-374a-5p can regulate the expression of NCK1, MMP14. Those genes are involved in nuclear factor kappa B and p38 signaling in the early stage of pancreatitis. Also, NCK1 can regulate pancreatic ß-cell proinsulin content and participate in the progression of pancreatic cancer development. CONCLUSIONS: In summary, the findings in this study deciphered the potential miRNA regulation mechanism in pancreatitis, and identified valuable biomarkers for the diagnosis of pancreatitis.

Reduced artifacts and improved diagnostic value of 640-slice computed tomography in patients with cardiac pacemakers.

OBJECTIVE: The aim of this study was to compare the feasibility of 640-slice with 64-slice computed tomography (CT) coronary angiography for diagnosing coronary lesions in patients with pacemakers. METHODS: Forty-five and 50 patients with pacemakers and with suspected or known coronary artery disease underwent 64-slice (64 group) and 640-slice (640 group) CT scans, respectively. All segments of the vessels were evaluated according to the 15-segment model recommended by the American Heart Association. RESULTS: The incidence of moderate or severe artifacts was significantly lower (7.27% vs. 32.17%) and the diagnosable rate for coronary lesions was higher (98.91% vs. 94.19%) in the 640 compared with the 64 group. In the 64 group, the incidence of artifacts in patients with a heart rate >65 bpm (20.98%) was higher than in those with a heart rate <65 bpm (15.67%), although the difference was not significant, while the incidence of artifacts was significantly higher in patients with heart arrhythmia (21.40%) compared with in those with normal heart rhythm (15.09%). CONCLUSIONS: Among patients with pacemakers and a higher heart rate or heart arrhythmia, 640-slice CT may be more effective than 64-slice CT for diagnosing coronary lesions, by reducing moderate and severe artifacts.

Spectral CT Analysis of Solitary Pulmonary Nodules for Differentiating Malignancy from Benignancy: The Value of Iodine Concentration Spatial Distribution Difference.

OBJECTIVE: The objective is to assess the value of spatial distribution difference in iodine concentration between malignant and benign solitary pulmonary nodules (SPNs) by analyzing multiple parameters of spectral CT. METHODS: Sixty patients with 39 malignant nodules and 21 benign nodules underwent chest contrast CT scans using spectral imaging mode during pulmonary arterial phase (PP), arterial phase (AP), and venous phase (VP). Iodine concentrations of proximal and distal regions in pulmonary nodules on iodine-based material decomposition images were recorded. Normalized iodine concentration (NIC) and the differences in NIC between the proximal and the distal regions (dNIC) were calculated. The two-sample t-test and Mann-Whitney U-test were performed to compare the multiple parameters generated from spectral CT between malignant and benign nodules. Receiver operating characteristic (ROC) curves were generated to calculate sensitivity and specificity. RESULTS: NIC in the proximal region (NICpro) and NIC in the distal region (NICdis) between malignant and benign nodules at AP (NICpro, P=0.012; NICdis, P=0.024), and VP (NICpro, P=0.005; NICdis, P =0.004) were significantly different. NICpro at PP (P = 0.037) was also found significantly different between malignant and benign nodules; however, no significant differences were found in NICdis at PP (P = 0.093). In addition, the dNIC of malignant nodules was significantly higher than that of benign ones at PP (median and interquartiles (0.31, 0.11, 0.57 versus -0.26, -0.5, -0.1); p≤0.001), AP (mean dNIC, 0.093 ±0.094 versus -0.075±0.060; p≤0.001), and VP (mean dNIC, 0.171±0.137 versus -0.183±0.127; p≤0.001). The sensitivity and specificity (93%, 95%, respectively) of dNIC during VP were higher than other parameters, with a threshold value of -0.07. CONCLUSIONS: Spectral CT imaging with multiple parameters such as NICpro, NICdis, and dNIC may be a new method for differentiating malignant SPNs from benign ones.

Whole-brain CT perfusion imaging using increased sampling intervals: A pilot study.

The aim of the present study was to investigate the feasibility of whole-brain perfusion imaging using the increased sampling interval protocol for 320-detector row dynamic-volume computed tomography (CT). A total of 12 volunteers were recruited. The novel protocols with 11 volumes (defined as protocol P11) and 15 volumes (defined as protocol P15) were performed on the volunteers to evaluate whether P11 and P15 are able to acquire comparable results to the standard protocol with 19 volumes (defined as protocol P19) according to the as-low-as-reasonably-achievable principle. All data were acquired using a dynamic-volume CT scanner with a 16 cm-wide detector with 320 rows. The scanned transverse images from volunteers were analyzed using the Volume-Engineered System workstation. The MedCalc software package was used for Bland-Altman analysis of all variables. The data inconsistency of mean transit time (MTT), cerebral blood volume (CBV), cerebral blood flow (CBF), and time to peak (TTP) between P11/P15 and P19 were all <5%, and the data were trustworthy. The mean differences of MTT, CBV, CBF and TTP between P15 and P19 were less than those between P11 and P19. The consistencies of perfusion parameters acquired with protocols P15 and P19 were higher compared with those acquired with P11. In whole-brain perfusion, the new protocol P15 has higher consistency with P19 than P11, and the radiation dose may be reduced by ~16% without degradation of perfusion parameters. Therefore, P15 should be recommended as a routine procedure in whole-brain perfusion imaging.

Quantitative assessment on blood-brain barrier permeability of acute spontaneous intracerebral hemorrhage in basal ganglia: a CT perfusion study.

PURPOSE: Blood-brain barrier (BBB) damage aggravates perihematomal edema, and edema volume predicts prognosis independently. But the BBB permeability at the late stage of acute intracerebral hemorrhage (ICH) patients is uncertain. We aimed to assess the BBB permeability of spontaneous basal ganglia ICH using computed tomographic perfusion (CTP) and investigates its relationship with hematoma and perihematomal edema volume. METHODS: We performed CTP on 54 consecutive ICH patients within 24 to 72 h after symptom onset. Permeability-surface area product (PS) derived from CTP imaging was measured in hematoma, "high-PS spot," perihematoma, normal-appearing, hemispheric, and contralateral regions. Hematoma and edema volumes were calculated from non-contrast CT. RESULTS: "High-PS spot" and perihematoma regions had higher PS than the contralateral regions (p < 0.001). Hematoma PS was lower than that in the contralateral regions (p < 0.001). Perihematoma PS of the large-hematoma group was higher than that of the small-hematoma group (p = 0.011). Perihematomal edema volume correlated positively with hematoma volume (ß = 0.864, p < 0.001) and perihematoma PS (ß = 0.478, p < 0.001). Perihematoma PS correlated positively with hematoma volume (ß = 0.373, p = 0.005). CONCLUSIONS: Locally elevated perihematoma PS was found in most spontaneous basal ganglia ICH patients within 24 to 72 h after symptom onset. Perihematoma PS was higher in larger hematomas and was associated with larger edema volume. At this period, BBB leakage is likely to be an important factor in edema formation.

Surgical resection of pulmonary metastases from colorectal cancer: 11 years of experiences.

OBJECTIVE: To analyze the benefits and prognostic factors after surgical resection of pulmonary metastases from colorectal cancer (CRC). METHODS: From Jan. 2004 to Jan. 2015, continuous 88 cases diagnosed with pulmonary metastases from CRC, including 15 cases of synchronous metastases and 73 metachronous metastases, were analyzed in the retrospective study. RESULTS: All of these 88 cases underwent curative pulmonary resection including 8 cases of simultaneous surgery. The one-year, three-year and five-year survival of the 88 cases were 93.4%, 60.2% and 35.7%, respectively. 63 patients just have one metastasis, and 25 patients have more than one metastasis. Additionally, the one-year, three-year and five-year survival was 98.1%, 70.2% and 40.3% respectively in one metastasis group, while 80.1%, 37.9% and 22.5% respectively in more than one metastasis group (p = 0.003). DFS of 37 metachronous metastases were equal or greater than 18 months, and DFS of 36 metachronous metastases were less than 18 months. The one-year, three-year and five-year survival was 97.8%, 77.9% and 41.4% respectively in the DFS≥18 month group, while 88.2%, 44.6% and 28.1% respectively in the DFS<18 month group (p = 0.01). CONCLUSION: Surgical resection of pulmonary metastases from colorectal cancer can improve survival rate in selected patients. It seems that the number of metastases is an independence prognostic factor in surgical treatment. Furthermore, longer DFI implies longer survival for resectable CRC pulmonary metastases.

Integrated analysis of mRNA and miRNA expression profiles in pancreatic ductal adenocarcinoma.

In the present study, to investigate the potential molecular mechanism of pancreatic ductal adenocarcinoma (PDAC), mRNA and miRNA expression profiles were integrated for systematic analysis. Results showed that a total of 76 common differentially expressed genes (DEGs) were identified from 2 mRNA expression profiles that contained 39 tumor and 15 normal samples. Notably, the tumor and normal samples were able to be clearly classified into 4 groups based on the DEGs. mRNA­miRNA regulation network analysis indicated that 22 out of the 76 DEGs including MUC4, RRM2 and CCL2 are regulated by 5 reported miRNAs. Survival analysis using SurvExpress database demonstrated that the common DEGs were able to significantly differentiate low- and high-risk PDAC groups in 4 datasets. In summary, various biological processes are probably involved in the development and progression of PDAC. Firstly, activation of MUC4 induces nuclear translocation of ß-catenin and promotes the process of angiogenesis that provides necessary nutrition or oxygen for cancer cells. Then, RRM2 induces the invasiveness of PDAC via NF-κB. Finally, the formation of an immunosuppressive tumor microenvironment by recruiting regulatory T cells with high expression of CCL2 further promotes cancer cell proliferation and vascularization. Identification of valuable biological processes and genes can be helpful for the understanding of the molecular mechanism of PDAC.