Search

Objective To explore the genetic changes in the progression of castration-resistant prostate cancer (CRPC) and neuroendocrine prostate cancer (NEPC) and the reason why these cancers resist existing therapies. Methods We employed our CRPC cell line microarray and other CRPC or NEPC datasets to screen the target gene NEIL3. Lentiviral transfection and RNA interference were used to construct overexpression and knockdown cell lines. Cell and animal models of radiotherapy were established by using a medical electron linear accelerator. Flow cytometry was used to detect apoptosis or cell cycle progression. Western blot and qPCR were used to detect changes in the protein and RNA levels. Results TCGA and clinical patient datasets indicated that NEIL3 was downregulated in CRPC and NEPC cell lines, and NEIL3 was correlated with a high Gleason score but a good prognosis. Further functional studies demonstrated that NEIL3 had no effect on the proliferation and migration of PCa cells. However, cell and animal radiotherapy models revealed that NEIL3 could facilitate the radiotherapy sensitivity of PCa cells, while loss of NEIL3 activated radiotherapy resistance. Mechanistically, we found that NEIL3 negatively regulated the expression of ATR, and higher NEIL3 expression repressed the ATR/CHK1 pathway, thus regulating the cell cycle. Conclusions We demonstrated that NEIL3 may serve as a diagnostic or therapeutic target for therapy-resistant patients.

Neurofibromatosis type 1 (NF1) is an autosomal dominant tumor predisposition syndrome that involves the central and peripheral nervous systems. Accurate detection and segmentation of neurofibromas are essential for assessing tumor burden and longitudinal tumor size changes. Automatic convolutional neural networks (CNNs) are sensitive and vulnerable as tumors' variable anatomical location and heterogeneous appearance on MRI. In this study, we propose deep interactive networks (DINs) to address the above limitations. User interactions guide the model to recognize complicated tumors and quickly adapt to heterogeneous tumors. We introduce a simple but effective Exponential Distance Transform (ExpDT) that converts user interactions into guide maps regarded as the spatial and appearance prior. Comparing with popular Euclidean and geodesic distances, ExpDT is more robust to various image sizes, which reserves the distribution of interactive inputs. Furthermore, to enhance the tumor-related features, we design a deep interactive module to propagate the guides into deeper layers. We train and evaluate DINs on three MRI data sets from NF1 patients. The experiment results yield significant improvements of 44% and 14% in DSC comparing with automated and other interactive methods, respectively. We also experimentally demonstrate the efficiency of DINs in reducing user burden when comparing with conventional interactive methods. The source code of our method is available at \url{https://github.com/Jarvis73/DINs}., Accepted by IEEE Journal of Biomedical and Health Informatics (JBHI)

Purpose: To evaluate the feasibility of dual-energy computed tomography (DECT) virtual noncalcium (VNCa) images for detecting vertebral bone marrow edema (BME), both in qualitative and quantitative analyses. Materials and Methods: Nineteen cases with acute spinal trauma were enrolled. DECT and MR Imaging were performed over an interval of 0–3 days within 3 weeks of injury. Both gray-scale and color-coded VNCa images were evaluated in an independent and blind manner using a three-scale grading system (2 = distinct BME, 1 = suspicious BME, and 0 = no edema) by two radiologists. CT value of bone marrow were measured for each region and subjected to receiver operating characteristic (ROC) curve analysis for diagnosis of BME on VNCa images from DECT. Subsequently, as a standard of reference, MR imaging was evaluated by a third radiologist for the presence of traumatic BME. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for the detection of BME using VNCa images were evaluated. Kappa analysis and one-way ANOVA were performed to evaluate the consistency and variance of VNCa images in the BME. Results: For two observers, the sensitivity of VNCa images for detecting BME (grade 2) were 97.8% and 96.7%, with the specificity 76% and 72%, the PPV 93.8% and 92.7%, the NPV 90.5% and 85.7%, respectively. And the sensitivity for detecting BME (grade 1–2) were 94.3% and 94.3%, with the specificity 96.7% and 93.3%, the PPV 98.8% and 97.6%, the NPV 85.3% and 84.8%, respectively. The consistency test between the two observers showed that Kappa value was 0.650 (P < 0.001). The VNCa images had higher CT value in the positive regions than in the negative regions (P < 0.05). Statistically significant differences in the CT value were identified in different regions with the three-scale grading system (thoracic levels: F = 136.690, P < 0.001; lumbar levels: F = 92.689, P < 0.001). Conclusions: Acute traumatic BME in the vertebrae can be detected on VNCa images in both qualitative and quantitative analyses. DECT VNCa images are expected to serve as an additional method for the evaluation of BME.

Locating the neurofibromatosis boundaries is one of the most challenging tasks due to the properties of low contrast and irregular blurred boundaries. To handle this problem, we propose Deep Parametric Active Contours (DPAC), a novel end-to-end framework that integrates the precise boundary localization abilities of the Active Contour Models (ACMs) with the robust nonlinear priori feature extraction abilities of the Convolutional Neural Networks (CNN). DPAC employs CNN to predict the parameter maps of the ACM per instance and incorporates them into the energy functional of the ACM to locate the neurofibromatosis boundaries, which makes DPAC end-to-end trainable. We evaluate the proposed method on three neurofibromatosis datasets that we created, and experimental results show that our DPAC method can improve the boundary similarity and precision of the classic ACMs. Even in the case with a small amount of data, our method can achieve good performance outperforming similar methods.

To construct and validate a nomogram model that integrated the CT radiomic features and the TNM staging for risk stratification of thymic epithelial tumors (TETs). A total of 136 patients with pathology-confirmed TETs who underwent CT examination were collected from two institutions. According to the WHO pathological classification criteria, patients were classified into low-risk and high-risk groups. The TNM staging was determined in terms of the 8th edition AJCC/UICC staging criteria. LASSO regression was performed to extract the optimal features correlated to risk stratification among the 704 radiomic features calculated. A nomogram model was constructed by combining the Radscore and the TNM staging. The clinical performance was evaluated by ROC analysis, calibration curve, and decision curve analysis (DCA). The Kaplan-Meier (KM) analysis was employed for survival analysis. Five optimal features identified by LASSO regression were employed to calculate the Radscore correlated to risk stratification. The nomogram model showed a better performance in both training cohort (AUC = 0.84, 95%CI 0.75–0.91) and external validation cohort (AUC = 0.79, 95%CI 0.69–0.88). The calibration curve and DCA analysis indicated a better accuracy of the nomogram model for risk stratification than either Radscore or the TNM staging alone. The KM analysis showed a significant difference between the two groups stratified by the nomogram model (p = 0.02). A nomogram model that integrated the radiomic signatures and the TNM staging could serve as a reliable model of risk stratification in predicting the prognosis of patients with TETs. • The radiomic features could be associated with the TET pathophysiology. • TNM staging and Radscore could independently stratify the risk of TETs. • The nomogram model is more objective and more comprehensive than previous methods.

PLC-L2, whose related members of its family facilitate in the differentiation of T cells, is a potential epigenetic marker for PCa. The density of tumor-infiltrating CD8+ T cells in PCa is associated with a better prognosis. It remains unclear how PLC-L2 affects the TIL and PCa. A human tissue microarray (TMA) and PCa patients from the Cancer Genome Atlas were used to address the correlations between PLC-L2 and the infiltrated immune cells and to evaluate the relationship between PLC-L2 and prognosis of PCa patients. More advanced PCa expressed less PLC-L2 than less advanced PCa, in terms of pathological grade, clinical stage, and the presence of lymph node and distant metastasis. Besides, the expression of PLC-L2 had positive correlations with CD8+ T cells and antigen-presenting cells such as macrophage and dendritic cells. PLC-L2 expression was also positively correlated with local immune cytolytic ability, TNF-alpha, IFN-gamma, IL-12A, IL-12B, and IL-18. Furthermore, PLC-L2 expression was an independent factor of a favorable prognosis in PCa patients. This study reveals that PLC-L2 might recruit CD8+ T cells and promote their cytolytic ability in PCa. PLC-L2 accompanied by CD8+ cytotoxic T cells could suppress the development of PCa and subsequently increase prognosis in PCa patients.

BACKGROUND Several MRI features are proposed to distinguish between plexiform neurofibromas (PNF) and malignant peripheral nerve sheath tumors (MPNST) in neurofibromatosis type 1 (NF1), including tumor size, margins, and degree of heterogeneity. However, most of these features are descriptive in nature, subject to intra-/interrater variability, and based on small single-institution studies. The goal of this study was to identify radiomic features that can differentiate between NF1-associated PNF and MPNST. METHODS 31 MPNSTs and 24 PNFs from five centers were segmented on short TI inversion recovery sequences using a semi-automated segmentation software (3DQI). Standard pre-processing was performed, including N4 bias field correction, intensity normalization (using a mean of 120 SI and standard deviation of 80 SI), and resampling to 1 mm3 voxel resolution. 1688 radiomic features were extracted from the tumor region of interest using PyRadiomics, an open-source Python radiomics package. To classify tumors as PNF or MPNST, we implemented the Boruta algorithm and correlation removal for selection of important features. A Random Forest model was built using the top ten selected features. Five-fold cross-validation was performed and repeated 100 times. Model performance was evaluated using the area under the ROC curve (AUC), sensitivity, specificity, accuracy, and confidence intervals. RESULTS The top ten features included in the model were five intensity features, two shape features, and three texture features. The model demonstrated an AUC of 0.891 (95% CI 0.882-0.899), sensitivity of 0.744, specificity of 0.847, and accuracy of 0.802 (95% CI 0.792-0.813). CONCLUSIONS Our machine learning model demonstrated high performance in classifying tumors as either PNF or MPNST in NF1 individuals. Inclusion of additional tumors for model training and testing on an independent dataset are underway. Ultimately, our model may enable improved differentiation between PNF and MPNST compared to descriptive MRI features, permit early patient risk stratification, and improve patient outcomes.

PurposeThere is a major shortage of reliable early detection methods for pancreatic cancer in high-risk groups. The focus of this preliminary study was to use Time Intensity-Density Curve (TIDC) and Marley Equation analyses, in conjunction with 3D volumetric and perfusion imaging to demonstrate their potential as imaging biomarkers to assist in the early detection of Pancreatic Ductal Adenocarcinoma (PDAC).Experimental DesignsA quantitative retrospective and prospective study was done by analyzing multi-phase Computed Tomography (CT) images of 28 patients undergoing treatment at different stages of pancreatic adenocarcinoma using advanced 3D imaging software to identify the perfusion and radio density of tumors.ResultsTIDC and the Marley Equation proved useful in quantifying tumor aggressiveness. Perfusion delays in the venous phase can be linked to Vascular Endothelial Growth Factor (VEGF)-related activity which represents the active part of the tumor. 3D volume analysis of the multiphase CT scan of the patient showed clear changes in arterial and venous perfusion indicating the aggressive state of the tumor.ConclusionTIDC and 3D volumetric analysis can play a significant role in defining the response of the tumor to treatment and identifying early-stage aggressiveness.

BackgroundAlthough immune checkpoint inhibitors (ICIs), especially programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) axis blockers, exhibit prominent antitumor effects against numerous malignancies, their benefit for patients with prostate cancer (PCa) has been somewhat marginal. This study aimed to assess the feasibility of B7-H3 or HHLA2 as alternative immunotherapeutic targets in PCa.MethodsImmunohistochemistry was performed to evaluate the expression pattern of PD-L1, B7-H3 and HHLA2 and the infiltration of CD8+ and Foxp3+ lymphocytes in 239 PCa tissues from two independent cohorts. The correlations between B7-H3 and HHLA2 and clinicopathological features, including the presence of CD8+ and Foxp3+ tumor-infiltrating lymphocytes (TILs), were explored.ResultsHHLA2 expression was much higher than PD-L1 expression but lower than B7-H3 expression in PCa tissues. High expression of both B7-H3 and HHLA2 was significantly associated with higher Gleason score and tumor stage, lymph node metastasis and dismal overall survival (OS) and cancer-specific survival (CSS). Moreover, a high B7 score, defined as high B7-H3 expression and/or high HHLA2 expression, was an independent prognostic predictor for PCa. Of note, a high B7 score was negatively correlated with CD8+ TILs. Importantly, a new immune classification, based on the B7 score and CD8+ TILs, successfully stratified OS and CSS in PCa.ConclusionsBoth B7-H3 and HHLA2 have a critical impact on the immunosuppressive microenvironment, and the B7 score could be used as an independent prognostic factor for PCa. The B7 score combined with CD8+ TILs could be used as a new immune classification to stratify the risk of death, especially cancer-related death, for patients with PCa. These findings may provide insights that could improve response to immune-related comprehensive therapy for PCa in the future.

We developed a method to create customizable phantoms suitable for endocavitary imaging and interventional research, based on the fabrication of an acrylic phantom mold, and development of a phantom matrix composed of gelatin, agar, graphite particles, and propanol. Our phantom was mechanically stable, easily fabricated, and highly adjustable, and its ultrasound (US) and magnetic resonance imaging (MRI) scans showed the qualification for the procedure guidance compared with the human prostate image using the same US system. To test the feasibility of the phantom for the research, the seeds placement guided by MRI/US fusion was performed, and the overall test error (distance from the seed center to the virtual lesion center in olives) was 2.59 ± 0.59 mm. We have created a simple, low-cost, configurable, gelatin-based phantom and tested its feasibility for simulating endorectal interventional US procedures. The design of the phantom mold and matrix is likely to be useful to the broader medical training community, and the preliminary data from the experiment of MRI/US-guided seeds placement showed its potential to test the clinical hypothesis in US research.

OBJECTIVE This study was to investigate the CT quantification of COVID-19 pneumonia and its impacts on the assessment of disease severity and the prediction of clinical outcomes in the management of COVID-19 patients. MATERIALS AND METHODS Ninety-nine COVID-19 patients who were confirmed by positive nucleic acid test (NAT) of RT-PCR and hospitalized from January 19, 2020 to February 19, 2020 were collected for this retrospective study. All patients underwent arterial blood gas test, routine blood test, chest CT examination, and physical examination on admission. In addition, fellow-up clinical data including the disease severity, clinical treatment, and clinical outcomes were collected for each patient. Lung volume, lesion volume, non-lesion lung volume (NLLV) (lung volume – lesion volume), and fraction of non-lesion lung volume (%NLLV) (non-lesion lung volume / lung volume) were quantified in CT images by using two U-Net models trained for segmentation of lung and COVID-19 lesions in CT images. Furthermore, we calculated 20 histogram textures for lesions volume and non-lesion lung volumes, respectively. To investigate the validity of CT quantification in the management of COVID-19, we built Random Forest (RF) models for the purpose of classification and regression to assess the disease severity (Moderate, Severe, and Critical) and to predict the need and length of ICU stay, the duration of oxygen inhalation, hospitalization, sputum NAT-positive, and patient prognosis. The performance of RF classifiers was evaluated using the area under the receiver operating characteristic (ROC) curves (AUC) and that of RF regressors using the root-mean-square error (RMSE). RESULTS Patients were classified into three groups of disease severity: moderate (n=25), severe (n=47) and critical (n=27), according to the clinical staging. Of which, a total of 32 patients, 1 (1/25) moderate, 6 (6/47) severe and 25 critical (25/27), respectively, were admitted to ICU. The median values of ICU stay were 0, 0, 12 days, the duration of oxygen inhalation 10, 15, and 28 days, the hospitalization 12, 16, and 28 days, and the sputum NAT-positive 8, 9, 13 days, in three severity groups, respectively. The clinical outcomes were complete recovery (n=3), partial recovery with residual pulmonary damage (n=80), prolonged recovery (n=15), and death (n=1). The %NLLV in three severity groups were 92.18±9.89%, 82.94±16.49%, and 66.19±24.15% with p-value

Metastasis is the major cause of prostate cancer (PCa)‐related mortality. Epithelial‐mesenchymal transition (EMT) is a vital characteristic feature that empowers cancer cells to adapt and survive at the beginning of metastasis. Therefore, it is essential to identify the regulatory mechanism of EMT in metastatic prostate cancer (mPCa) and to develop a novel therapy to block PCa metastasis. Here, we discovered a novel PCa metastasis oncogene, DEP domain containing 1B (DEPDC1B), which was positively correlated with the metastasis status, high Gleason score, advanced tumor stage, and poor prognosis. Functional assays revealed that DEPDC1B enhanced the migration, invasion, and proliferation of PCa cells in vitro and promoted tumor metastasis and growth in vivo. Mechanistic investigations clarified that DEPDC1B induced EMT and enhanced proliferation by binding to Rac1 and enhancing the Rac1‐PAK1 pathway. This DEPDC1B‐mediated oncogenic effect was reversed by a Rac1‐GTP inhibitor or Rac1 knockdown. In conclusion, we discover that the DEPDC1B‐Rac1‐PAK1 signaling pathway may serve as a multipotent target for clinical intervention in mPCa., Highlights 1. DEPDC1B positively correlates with prostate cancer metastasis, tumor stage, Gleason score, and poor prognosis. 2. DEPDC1B enhances prostate cancer cell metastasis and tumor growth in vitro and in vivo. 3. DEPDC1B enhances the Rac1‐PAK1 signaling pathway to induce EMT. 4. DEPDC1B contributing to metastasis and proliferation through Rac1‐PAK1 signaling.

Background Cervical lymph node (LN) status is a critical factor related to the treatment and prognosis of papillary thyroid carcinoma (PTC). The aim of this study was to investigate the preoperative predictions of cervical LN metastasis in PTC using computed tomography (CT) radiomics.Methods A total of 134 PTC patients who underwent CT examinations were enrolled in the study at two institutes between January 2018 and January 2020. Of these patients, 289 cervical LNs (institute 1: 206 LNs from 88 patients; institute 2: 83 LNs from 46 patents) were selected. All the cases had been confirmed by surgery and pathology. Each LN was segmented and 1408 radiomic features were calculated radiomic features in noncontrast and contrast-enhanced CT images. Features were selected using the Boruta algorithm followed by an iterative culling-out algorithm. We compared four machine learning classifiers, including random forest (RF), support vector machine (SVM), neural network (NN), and naïve bayes (NB) for the classification of LN metastasis. The models were first trained and validated by 10-fold cross-validation using data from institute 1 and then tested using independent data from institute 2. The performance of the models was compared using the area under the receiver operating characteristic curves (AUC).Results Seven radiomic features were selected for building the models − 3 histogram statistical textures, 1 gray level co-occurrence matrix texture, and 3 gray level zone size matrix textures. The AUCs of the radiomic models with 10-fold cross-validation were 0.941 (95% confidence interval [CI]: 0.93–0.95), 0.943 (95% CI: 0.93–0.95), 0.914 (95% CI: 0.90–0.95), and 0.905 (95% CI: 0.88–0.91) for RF, SVM, NN, and NB, respectively. The AUCs for the testing data were 0.926 (95% CI: 0.86–0.98), 0.932 (95% CI: 0.88–0.98), 0.925 (95% CI: 0.86–0.97), and 0.912 (95% CI: 0.83–0.98) for RF, SVM, NN, and NB, respectively.Conclusions CT radiomic model demonstrated robustness in preoperative classification of LN metastases for patients with PTC, which may provide significant support for clinical decision making and prognosis evaluation.

Immune checkpoint inhibitors (ICIs) treatment is becoming a new hope for cancer treatment. However, most prostate cancer (PCa) patients do not benefit from it. In order to achieve the accuracy of ICIs treatment in PCa and reduce unnecessary costs for patients, we have analyzed the data from TCGA database to find a indicator that can assist the choice of treatment. By analyzing the data of PCa patients with TMB analysis and immune infiltration analysis, we found the expression of immune cells in different immune infiltration groups. Commonly used markers of ICIs, expressed on CD8+ T cell, were highly expressed in the high immune group. Then we used the forimmune cytolytic activity (CYT) to determine its relationship with the target of ICIs treatment. Through the analysis of CYT score and the ligands of immune checkpoints, we found that there was a significant correlation between them. With the increase of CYT score, the expression of CD80/86, PD-L1/L2, TNFSF14, and LGALS9 also increased gradually. Similarly, CD8+ T cells were significantly increased in the CYT high group compared with the CYT low group in PRAD. The present research provides novel insights into the immune microenvironment of PRAD and potential immunotherapies. The proposed CYT score is a clinically promising indicator that can serve as a marker to assist anti-PD-L1 or other ICIs treatment. At the same time, it also provides a basis for the selection of other immune checkpoint drugs.

Background The purpose of this study was to investigate the value of wavelet-transformed radiomic MRI in predicting the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) for patients with locally advanced breast cancer (LABC). Methods Fifty-five female patients with LABC who underwent contrast-enhanced MRI (CE-MRI) examination prior to NAC were collected for the retrospective study. According to the pathological assessment after NAC, patient responses to NAC were categorized into pCR and non-pCR. Three groups of radiomic textures were calculated in the segmented lesions, including (1) volumetric textures, (2) peripheral textures, and (3) wavelet-transformed textures. Six models for the prediction of pCR were Model I: group (1), Model II: group (1) + (2), Model III: group (3), Model IV: group (1) + (3), Model V: group (2) + (3), and Model VI: group (1) + (2) + (3). The performance of predicting models was compared using the area under the receiver operating characteristic (ROC) curves (AUC). Results The AUCs of the six models for the prediction of pCR were 0.816 ± 0.033 (Model I), 0.823 ± 0.020 (Model II), 0.888 ± 0.025 (Model III), 0.876 ± 0.015 (Model IV), 0.885 ± 0.030 (Model V), and 0.874 ± 0.019 (Model VI). The performance of four models with wavelet-transformed textures (Models III, IV, V, and VI) was significantly better than those without wavelet-transformed textures (Model I and II). In addition, the inclusion of volumetric textures or peripheral textures or both did not result in any improvements in performance. Conclusions Wavelet-transformed textures outperformed volumetric and/or peripheral textures in the radiomic MRI prediction of pCR to NAC for patients with LABC, which can potentially serve as a surrogate biomarker for the prediction of the response of LABC to NAC.

Objectives Plexiform neurofibromas (PNs) are complex, histologically benign peripheral nerve sheath tumors that are challenging to measure by simple line measurements. Computer-aided volumetric segmentation of PN has become the recommended method to assess response in clinical trials directed at PN. Different methods for volumetric analysis of PN have been developed. The goal of this study is to test the level of agreement in volume measurements and in interval changes using two separate methods of volumetric magnetic resonance imaging analysis. Methods Three independent volume measurements were performed on 15 PN imaged at three time-points using 3DQI software at Massachusetts General Hospital (MGH) and National Cancer Institute (NCI) and MEDx software at NCI. Results Median volume differences at each time-point comparing MGH-3DQI and NCI-3DQI were −0.5, −4.2, and −19.9 mL; comparing NCI-3DQI and NCI-MEDx were −21.0, −47.0, and −21.0 mL; comparing MGH-3DQI and NCI-MEDx were −10.0, −70.3, and −29.9 mL. Median differences in percentage change over time comparing MGH-3DQI and NCI-3DQI were −1.7, 1.1, and −1.0%; comparing NCI-3DQI and NCI-MEDx were −2.3, 3.3, and −1.1%; comparing MGH-3DQI and NCI-MEDx were −0.4, 2.0, and −1.5%. Volume differences were Conclusions The volumes, interval changes, and progression status classifications were in good agreement. The comparison of two volumetric analysis methods suggests no systematic differences in tumor assessment. A prospective comparison of the two methods is planned.

DNA damage response (DDR) plays an important role in the progression of cancers, including prostate cancer (PCa). Topoisomerase II-binding protein 1 (TopBP1) is an essential promotor of ATR-mediated DDR. Herein, we investigated the association between TopBP1 and PCa and determined its effect on the progression of PCa. The expression and clinical features of TopBP1 were analyzed using large-scale cohort of tissue microarray analyses and The Cancer Genome Atlas database, which indicated that TopBP1 was positively correlated with high Gleason Score, advanced clinical and pathological stages, the metastasis status. Multivariate analysis revealed that the upregulation of TopBP1 was an independent predictor for a worse biochemical recurrence-free survival (BCR-free survival). Furthermore, we discovered that downregulation of TopBP1 significantly suppressed the growth and migration ability of PCa lines by loss-of-function assays in vitro. Further mechanistic investigations clarified that TopBP1 promoted proliferation and migration by activating ATR-Chk1 signaling pathway.

Objective: The purpose of this study was to systematically review the diagnostic performance of gadoxetic acid-enhanced magnetic resonance imaging (Gd-EOB-DTPA-MRI) for differentiation of hepatocellular adenoma (HCA) and focal nodular hyperplasia (FNH), as well as HCA classification by using the low signal intensity (SI) in the hepatobiliary phase (HBP). Methods: A systematic process was used to review all published data in MEDLINE database about Gd-EOB-DTPA-MRI applied to differentiation of HCA and FNH, and classification of HCA by using low SI in the HBP. The pooled sensitivity and specificity were calculated to assess the diagnostic value of low SI in the HBP. Results: A review of 45 articles identified 10 eligible studies with a total of 288 HCA lesions. The pooled proportion of low SI in the HBP of HCA were 91% (95% CI: 0.81-0.97). In specific, the subtypes of HCA were 75% (95% CI: 0.64-0.85) for I-HCA, 100% (95% CI: 0.95-1.00) for H-HCA, 92% (95% CI: 0.70-1.00) for U-HCA, and 59% (95% CI: 0.00-1.00) for b-HCA, respectively. The pooled specificity and sensitivity of low SI in the HBP for distinguishing FNH from HCA were 95% (95% CI: 0.92-0.98) and 92% (95% CI: 0.87-0.96), respectively. Conclusion: Low SI in the HBP of Gd-EOB-DTPA-MRI is associated with higher accuracy for distinguishing HCA from FNH. However, the diagnostic accuracy may be overvalued, especially for the diagnosis of subtypes of b-HCA and I-HCA. Therefore, the risk factors and conventional imaging findings should be take into account simultaneously.

BACKGROUND Neurofibromatosis type 2 (NF2) and schwannomatosis (SWN) are related genetic tumor predisposition syndromes caused by distinct germline pathogenic variants on chromosome 22. Both conditions are characterized by the presence of cranial, peripheral, and/or spinal nerve schwannomas. The long-term growth behavior of schwannomas is unknown but knowledge thereof would help guide patient surveillance and selection for treatment and improvement of clinical trial design. Whole-body MRI (WBMRI) can detect whole-body schwannoma burden in a single image acquisition session. METHODS 12 NF2 and 10 SWN patients who underwent a WBMRI between 2007-2010 underwent a repeat WBMRI between 2018-2019. Schwannomas were segmented on short tau inversion recovery (STIR) sequences. Tumor volume was calculated using a three-dimensional tumor quantification software (3DQI). Tumor growth and shrinkage were defined as a volume change ≥ 20% over the entire study period. RESULTS Median time between scans was 10 years. A total of 103 schwannomas (46 NF2-associated, 57 SWN-associated) were analyzed. In both NF2 and SWN, 50% of tumors grew. Median growth was 88.3% in NF2 and 100.4% in SWN. All growing NF2-associated schwannomas grew in the setting of exposure to systemic therapy whereas only one growing SWN-associated tumor had been treated systemically. Excluding resected tumors, 19.4% of schwannomas shrank. Median shrinkage was 48.5% in NF2 and 37.4% in SWN. All shrinking NF2-associated tumors had been treated with systemic therapy whereas none of the shrinking SWN-associated tumors had been. 19 new tumors (7 NF2-associated, 12 SWN-associated) developed in 8 patients. CONCLUSIONS Half of NF2- and SWN-associated schwannomas grow significantly over a decade. In NF2 patients, growth occurs despite systemic treatment whereas, in SWN patients, schwannomas may shrink spontaneously without treatment. These findings may suggest a more aggressive tumor phenotype in NF2. Continued patient enrollment and correlation of MRI findings with functional outcomes and hormone exposure history are ongoing.

Schwannomatosis has been linked to germline mutations in the SMARCB1 and LZTR1 genes, and is frequently associated with pain. In a cohort study, we assessed the mutation status of 37 patients with clinically diagnosed schwannomatosis and compared to clinical data, whole body MRI (WBMRI), visual analog pain scale, and Short Form 36 (SF-36) bodily pain subscale. We identified a germline mutation in LZTR1 in 5 patients (13.5%) and SMARCB1 in 15 patients (40.5%), but found no germline mutation in 17 patients (45.9%). Peripheral schwannomas were detected in 3 LZTR1-mutant (60%) and 10 SMARCB1-mutant subjects (66.7%). Among those with peripheral tumors, the median tumor number was 4 in the LZTR1 group (median total body tumor volume 30 cc) and 10 in the SMARCB1 group (median volume 85cc), (P=.2915 for tumor number and P = .2289 for volume). mutation was associated with an increased prevalence of spinal schwannomas (100% vs 41%, P = .0197). The median pain score was 3.9/10 in the LZTR1 group and 0.5/10 in the SMARCB1 group (P = .0414), and SF-36 pain-associated quality of life was significantly worse in the LZTR1 group (P = .0106). Pain scores correlated with total body tumor volume (rho = 0.32471, P = .0499), but not with number of tumors (rho = 0.23065, P = .1696). We found no significant difference in quantitative tumor burden between mutational groups, but spinal schwannomas were more common in LZTR1-mutant patients. Pain was significantly higher in LZTR1-mutant than in SMARCB1-mutant patients, though spinal tumor location did not significantly correlate with pain. This suggests a possible genetic association with schwannomatosis-associated pain.

Purpose To compare the measurement of glenoid bone surface area (GBSA) and glenoid bone loss (GBL) between 3-dimensional computed tomography (3D CT) and an autosegmentation approach for 3D magnetic resonance imaging (MRI) of patients with recurrent shoulder instability. Methods Eight subjects (2 women and 6 men; age range, 15-72 years [mean, 44 ± 19 years]) were consecutively enrolled who had both CT and MRI of the shoulder for clinical shoulder instability. Inclusion criteria were patients with shoulder instability or other shoulder injury who had both a CT scan and MRI performed of the same shoulder. All patients underwent a 3D CT scan and a 3-Tesla 3D MRI with additional volumetric and autosegmented sequences. En face views of the glenoid for both CT and MRI were auto- and manually measured for overall GBSA and GBL using best-fit circle technique; the amount of GBL was compared with loss of GBSA and was expressed as a percentage of bone loss. Results There were no differences in GBL measured by 3D CT (41 mm2, 6.6%) vs 3D MRI (40 mm2, 6.5%, P = .852). The mean GBSA was not different among the manual- and autocalculated 3D CT (644 mm2 vs 640 mm2, P = .482). In addition, the manual MRI scan glenoid area was similar to the autocalculated 3D MRI (622 mm2 vs 618 mm2, respectively; P = .482). Overall regression analysis demonstrated excellent correlation between CT and MRI for both GBSA and GBL calculations (R2 = 0.84-0.90). Conclusions 3D MRI of the glenoid is nearly identical to 3D CT scans for measurement of GBSA and GBL, making 3D MRI a reliable alternative to a CT scan for a preoperative shoulder evaluation of the glenoid pathology. This study shows that a 3D MRI could be a radiation-free and reliable alternative to a preoperative CT shoulder scan. Level of Evidence Level III, case-control study.

ObjectiveTo investigate the genotype–phenotype correlation between neurofibromatosis 1 (NF1) germline mutations and imaging features of neurofibromas on whole-body MRI (WBMRI) by using radiomics image analysis techniques.Materials and methodsTwenty-nine patients with NF1 who had known germline mutations determined by targeted next-generation sequencing were selected from a previous WBMRI study using coronal short tau inversion recovery sequence. Each tumor was segmented in WBMRI and a set of 59 imaging features was calculated using our in-house volumetric image analysis platform, 3DQI. A radiomics heatmap of 59 imaging features was analyzed to investigate the per-tumor and per-patient associations between the imaging features and mutation domains and mutation types. Linear mixed-effect models and one-way analysis of variance tests were performed to assess the similarity of tumor imaging features within mutation groups, between mutation groups, and between randomly selected groups.ResultsA total of 218 neurofibromas (97 discrete neurofibromas and 121 plexiform neurofibromas) were identified in 19 of the 29 patients. The unsupervised hierarchical clustering in heatmap analysis revealed 6 major image feature patterns that were significantly correlated with gene mutation domains and types with strong to very strong associations of genotype–phenotype correlations in both per-tumor and per-patient studies (p < 0.05, Cramer V > 0.5), whereas tumor size and locations showed no correlations with imaging features (p = 0.79 and p = 0.42, respectively). The statistical analyses revealed that the number of significantly different features (SDFs) within mutation groups were significantly lower than those between mutation groups (mutation domains: 10.9 ± 9.5% vs 31.9 ± 23.8% and mutation types: 31.8 ± 30.7% vs 52.6 ± 29.3%). The first and second quartile p values of within-patient groups were more than 2 times higher than those between-patient groups. However, the numbers of SDFs between randomly selected groups were much lower (approximately 5.2%).ConclusionThis preliminary study identified the NF1 radiogenomics linkage between NF1 causative mutations and MRI radiomic features, i.e., the correlation between NF1 genotype and imaging phenotype on WBMRI.

Purpose: To establish small-sized superparamagnetic polymeric micelles for magnetic resonance and fluorescent dual-modal imaging, we investigated the feasibility of MR imaging (MRI) and macrophage-targeted in vitro. Methods: A new class of superparamagnetic iron oxide nanoparticles (SPIONs) and Nile red-co-loaded mPEG-Lys3-CA4-NR/SPION polymeric micelles was synthesized to label Raw264.7 cells. The physical characteristics of the polymeric micelles were assessed, the T2 relaxation rate was calculated, and the effect of labeling on the cell viability and cytotoxicity was also determined in vitro. In addition, further evaluation of the application potential of the micelles was conducted via in vitro MRI. Results: The diameter of the mPEG-Lys3-CA4-NR/SPION polymeric micelles was 33.8 ± 5.8 nm on average. Compared with the hydrophilic SPIO, mPEG-Lys3-CA4-NR/SPION micelles increased transversely (r2), leading to a notably high r2 from 1.908 µg/mL-1S-1 up to 5.032 µg/mL-1S-1, making the mPEG-Lys3-CA4-NR/SPION micelles a highly sensitive MRI T2 contrast agent, as further demonstrated by in vitro MRI. The results of Confocal Laser Scanning Microscopy (CLSM) and Prussian blue staining of Raw264.7 after incubation with micelle-containing medium indicated that the cellular uptake efficiency is high. Conclusion: We successfully synthesized dual-modal MR and fluorescence imaging mPEG-Lys3-CA4-NR/SPION polymeric micelles with an ultra-small size and high MRI sensitivity, which were effectively and quickly uptaken into Raw 264.7 cells. mPEG-Lys3-CA4-NR/SPION polymeric micelles might become a new MR lymphography contrast agent, with high effectiveness and high MRI sensitivity.

BACKGROUND Neurofibromas affect 40–50% of neurofibromatosis type 1 (NF1) patients and can cause significant morbidity and mortality. They grow more rapidly during childhood and adolescence but studies in adults are limited by their retrospective nature and follow-up time < 3 years. The long-term natural history of neurofibromas remains unknown. No guidelines exist on the need and frequency of surveillance imaging for patients. Whole-body MRI (WBMRI) can detect whole-body tumor burden, including internal neurofibromas. METHODS 17 adult NF1 patients who underwent WBMRI between 2007–2010 (Scan 1) underwent repeat WBMRI between 2018–2019 (Scan 2). Internal neurofibromas were segmented on short tau inversion recovery (STIR) sequences and tumor volume was calculated using a computerized volumetry and three-dimensional segmentation software. Circumscribed tumors were defined as discrete; invasive tumors or those involving multiple nerves were defined as plexiform. Tumor growth and shrinkage were defined as volume change ≥ 20% over the entire study period. RESULTS Median patient age was 43 years during Scan 1 and 53 years during Scan 2. Median time between Scan 1 and 2 was 9 years. A total of 140 neurofibromas were assessed. 24% of tumors grew by a median 63% (6.8% per year). 54% of tumors spontaneously decreased in volume by a median 60% (7% per year) without treatment. On a per-patient basis, 18% of patients had overall tumor growth and 41% overall tumor shrinkage. 8 new tumors developed in 7 patients. 16 tumors resolved entirely without medical or surgical intervention. Growth behavior did not correlate with discrete or plexiform morphology. CONCLUSION A subset of internal neurofibromas in adult NF1 patients grow significantly over a long-term period, suggesting that continued monitoring of these patients may be warranted. Surprisingly, more than half of neurofibromas shrink spontaneously without intervention. Continued patient enrollment and correlation of imaging findings with functional outcomes are underway.

Purpose of review Pneumothorax, a potentially life-threatening condition, is present in about one-third of chest trauma patients. Traditionally, pneumothorax has been diagnosed and managed by use of chest radiography, which has been found inaccurate and inconsistent. With the ubiquitous application of multidetector computed tomography (MDCT) in emergency care, MDCT quantification of pneumothoraces becomes an emerging technique for accurate determination of the size of pneumothoraces. The use of MDCT quantification provides a promising means to improve pneumothorax management. Recent findings Recent studies have demonstrated that MDCT is the gold standard for detecting pneumothorax and MDCT provides an effective imaging modality for the accurate measurement of the volume of pneumothoraces. The use of MDCT volumetric quantification of pneumothoraces has been evidenced in the improvement of performance in pneumothorax management for clinically stable chest trauma patients. Summary The MDCT volumetric quantification of pneumothoraces is a new concept in the care of chest trauma patients and has the potential to improve pneumothorax management. Further clinical studies are needed to establish a MDCT-based clinical guideline for pneumothorax management.

PURPOSE The purpose of our study was to measure the dual-energy index (DEI) value of colonic luminal air in both phantom and clinical fecal-tagging dual-energy computed tomography (CT) colonography (DE-CTC) images and to demonstrate its impact on dual-energy electronic cleansing. METHODS For the phantom study, a custom-ordered colon phantom was scanned by a dual-energy CT scanner (SOMATON Definition Flash; Siemens Healthcare, Forchheim, Germany) at two photon energies: 80 and 140 kVp. Before imaging, the phantom was filled with a 300-mL mixture of simulated fecal materials tagged by a nonionic iodinated contrast agent at three contrast concentrations: 20, 40, and 60 mg/mL. Ten regions-of-interest (ROIs) were randomly placed in each of the colonic luminal air, abdominal fat, bony structure, and tagged material in each scan. For the clinical study, 22 DE-CTC (80 and 140 kVp) patient cases were collected, who underwent a low-fiber, low-residue diet bowel preparation and orally administered iodine-based fecal tagging. Twenty ROIs were randomly placed in each of the colonic luminal air, abdominal fat, abdominal soft tissue, and tagged fecal material in each scan. For each ROI, the mean CT values in both 80- and 140-kVp images were measured, and then its DEI was calculated. RESULTS In the phantom study, the mean DEI values of luminal air were 0.270, 0.298, 0.386, and 0.402 for the four groups of tagging conditions: no tagged material and tagged with three groups of contrast concentrations at 20, 40, and 60 mg/mL. In the clinical study, the mean DEI values were 0.341, -0.012, -0.002, and 0.188 for colonic luminal air, abdominal fat, abdominal soft tissue, and tagged fecal material, respectively. CONCLUSIONS In our study, we observed that the DEI values of colonic luminal air in DE-CTC images (>0.10) were substantially higher than the theoretical value of 0.0063. In addition, the observed DEI values of colonic luminal air were significantly higher than those of soft tissue. These findings have an important impact on electronic cleansing: it may provide an effective means of differentiating colonic soft-tissue structures from the air-tagging mixture caused by the partial volume effect and thus of minimizing the cleansing artifacts.

OBJECTIVES: The Response Evaluation in Neurofibromatosis and Schwannomatosis (REiNS) International Collaboration Whole-Body MRI (WB-MRI) Working Group reviewed the existing literature on WB-MRI, an emerging technology for assessing disease in patients with neurofibromatosis type 1 (NF1), neurofibromatosis type 2 (NF2), and schwannomatosis (SWN), to recommend optimal image acquisition and analysis methods to enable WB-MRI as an endpoint in NF clinical trials.METHODS: A systematic process was used to review all published data about WB-MRI in NF syndromes to assess diagnostic accuracy, feasibility and reproducibility, and data about specific techniques for assessment of tumor burden, characterization of neoplasms, and response to therapy.RESULTS: WB-MRI at 1.5T or 3.0T is feasible for image acquisition. Short tau inversion recovery (STIR) sequence is used in all investigations to date, suggesting consensus about the utility of this sequence for detection of WB tumor burden in people with NF. There are insufficient data to support a consensus statement about the optimal imaging planes (axial vs coronal) or 2D vs 3D approaches. Functional imaging, although used in some NF studies, has not been systematically applied or evaluated. There are no comparative studies between regional vs WB-MRI or evaluations of WB-MRI reproducibility.CONCLUSIONS: WB-MRI is feasible for identifying tumors using both 1.5T and 3.0T systems. The STIR sequence is a core sequence. Additional investigation is needed to define the optimal approach for volumetric analysis, the reproducibility of WB-MRI in NF, and the diagnostic performance of WB-MRI vs regional MRI.

To evaluate the feasibility and sensitivity of the 3D-reading of fecal-tagging CT colonography (CTC) with a novel structure-analysis electronic cleansing (SAEC) in detecting colorectal flat lesions in comparison with a cleansed 3D reading with Viatronix V3D Colon system (V3D) and primary uncleansed 2D reading (2D).Forty CTC cases with flat lesions were retrospectively observed. The Subjects from a multicenter clinical trial underwent cathartic bowel preparation with orally administrated barium-based fecal-tagging. Sixty-nine flat lesions were confirmed using colonoscopy and histopathology as a reference standard. The results from SAEC reading were compared with those of prospective V3D and 2D readings.Overall detection sensitivity with SAEC was 52% (36/69), which was statistically higher than that of 32% (22/69) and 29% (20/69) with V3D and 2D readings, respectively (p0.05). The sensitivities in detecting not-on-fold flat lesions were 63% (24/38), 45% (17/38), and 42% (16/38) with SAEC, V3D, and 2D readings, respectively; whereas those of on-fold flat lesions were 39% (12/31), 16% (5/31), and 13% (4/31), respectively. None of the eight flat lesions (2-9mm) at cecum was detected by any of the three reading methods. Excluding the flat lesions at cecum, the sensitivity with SAEC for detecting flat lesion ≥4mm increased to 84% (31/37).The fecal-tagging CTC with structure-analysis electronic cleansing could yield a high sensitivity for detecting flat lesions ≥4mm. The not-on-fold flat lesions were detected with higher sensitivity than on-fold flat lesions.

It is commonly believed that the size of a pneumothorax is an important determinant of treatment decision, in particular regarding whether chest tube drainage (CTD) is required. However, the volumetric quantification of pneumothoraces has not routinely been performed in clinics. In this paper, we introduced an automated computer-aided volumetry (CAV) scheme for quantification of volume of pneumothoraces in chest multi-detect CT (MDCT) images. Moreover, we investigated the impact of accurate volume of pneumothoraces in the improvement of the performance in decision-making regarding CTD in the management of traumatic pneumothoraces. For this purpose, an occurrence frequency map was calculated for quantitative analysis of the importance of each clinical parameter in the decision-making regarding CTD by a computer simulation of decision-making using a genetic algorithm (GA) and a support vector machine (SVM). A total of 14 clinical parameters, including volume of pneumothorax calculated by our CAV scheme, was collected as parameters available for decision-making. The results showed that volume was the dominant parameter in decision-making regarding CTD, with an occurrence frequency value of 1.00. The results also indicated that the inclusion of volume provided the best performance that was statistically significant compared to the other tests in which volume was excluded from the clinical parameters. This study provides the scientific evidence for the application of CAV scheme in MDCT volumetric quantification of pneumothoraces in the management of clinically stable chest trauma patients with traumatic pneumothorax.

Rationale and Objectives Our purpose in this study was to develop an automated computer-aided volumetry (CAV) scheme for quantifying pneumothorax in multidetector computed tomography (MDCT) images for pediatric patients and to investigate the imaging parameters that may affect its accuracy. Materials and Methods Fifty-eight consecutive pediatric patients (mean age 12 ± 6 years) with pneumothorax who underwent MDCT for evaluation were collected retrospectively for this study. All cases were imaged by a 16- or 64-MDCT scanner with weight-based kilovoltage, low-dose tube current, 1.0−1.5 pitch, 0.6−5.0 mm slice thickness, and a B70f (sharp) or B31f (soft) reconstruction kernel. Sixty-three pneumothoraces ≥1 mL were visually identified in the left (n = 30) and right (n = 33) lungs. Each identified pneumothorax was contoured manually on an Amira workstation V4.1.1 (Mercury Computer Systems, Chelmsford, MA) by two radiologists in consensus. The computerized volumes of the pneumothoraces were determined by application of our CAV scheme. The accuracy of our automated CAV scheme was evaluated by comparison between computerized volumetry and manual volumetry, for the total volume of pneumothoraces in the left and right lungs. Results The mean difference between the computerized volumetry and the manual volumetry for all 63 pneumothoraces ≥1 mL was 8.2%. For pneumothoraces ≥10 mL, ≥50 mL, and ≥200 mL, the mean differences were 7.7% (n = 57), 7.3% (n = 33), and 6.4% (n = 13), respectively. The correlation coefficient was 0.99 between the computerized volume and the manual volume of pneumothoraces. Bland-Altman analysis showed that computerized volumetry has a mean difference of -5.1% compared to manual volumetry. For all pneumothoraces ≥10 mL, the mean differences for slice thickness ≤1.25 mm, = 1.5 mm, and = 5.0 mm were 6.1% (n = 28), 3.5% (n = 10), and 12.2% (n = 19), respectively. For the two reconstruction kernels, B70f and B31f, the mean differences were 6.3% (n = 42, B70f) and 11.7% (n = 15, B31f), respectively. Conclusion Our automated CAV scheme provides an accurate measurement of pneumothorax volume in MDCT images of pediatric patients. For accurate volumetric quantification of pneumothorax in children in MDCT images by use of the automated CAV scheme, we recommended reconstruction parameters based on a slice thickness ≤1.5 mm and the reconstruction kernel B70f.

The authors introduce a structure-analysis cleansing scheme, which uses cutting-edge image processing techniques to reduce the major artifacts associated with electronic cleansing and produce diagnostic-quality noncathartic CT colonographic images.

Rationale and Objectives. The purpose of this evaluation was to compare the tagging quality of a barium-based regimen with that of iodine-based regimens for computed tomographic (CT) colonography. Materials and Methods. Tagging quality was assessed retrospectively in three different types of fecal-tagging CT colonographic cases: 24 barium-based cases, 22 nonionic iodine-based cases, and 24 ionic iodine-based cases. For the purpose of evaluation,the largeintestinewas divided intosix segments,and thetagginghomogeneity ofa total of420segments (70patients) was graded by three blinded readers from 0 (heterogeneous) to 4 (homogeneous). Results. For barium-based cases, the average score for the three readers was 2.4, whereas it was 3.4 for nonionic iodine and 3.6 for ionic iodine. The percentages of segments that were assigned scores of 4 (excellent tagging [100%]) were 11.6%, 61.9%, and 72.9% for the barium-based, nonionic iodine-based, and ionic iodine-based regimens, respectively. The homogeneity scores of iodine-based fecal-tagging regimens were significantly higher than those of the barium-based fecal-tagging regimen (P < .001). The CT attenuation values of tagging in the cases were also assessed: the minimum and maximum values were significantly higher for the iodine-based regimens than for the barium-based regimen (P < .001).

The size of a pneumothorax is an important index to guide the emergency treatment of trauma patients--chest tube drainage. The purpose of this study was to develop and validate an automated computer-aided volumetry scheme for detection and measurement of pneumothoraces for trauma patients imaged with MDCT.Three pigs and 68 trauma patients with at least one diagnosed occult pneumothorax (23 women and 45 men; age range, 14-89 years; mean age, 41 +/- 19 years) were selected for the development and validation of our computer-aided volumetry scheme for pneumothorax. Computer-aided volumetry of pneumothorax consisted of five automated steps: extraction of pleural region, detection of pneumothorax candidates, delineation of the detected pneumothorax candidates, reduction of false-positive findings, and report of the volumetric measurement of pneumothoraces.In the animal study, our computer-aided volumetry scheme yielded a mean value of 24.27 +/- 0.64 mL (SD) compared with 25 mL of air volume manually injected in each scan. The correlation coefficients were 0.999 and 0.997 for the in vivo and ex vivo comparison, respectively. In the patient study, the sensitivity of our computer-aided volumetry scheme was 100% with a false-positive rate of 0.15 per case for 32 occult pneumothoracesor = 25 mL. The correlation coefficient was 0.999 for manual volumetry comparison. This automated computer-aided volumetry scheme took approximately 3 minutes to finish the detection and measurement per case.The results show that our computer-aided volumetry scheme provides an automated method for accurate and efficient detection and measurement of pneumothoraces in MDCT images of trauma patients.

Objective: Given the frequency of transition from anorexia nervosa to bulimia nervosa, this study investigated whether a history of activity-based anorexia (ABA) during adolescence would promote binge eating during adulthood in female rats. Method: Adolescent rats were given 1-h unlimited access to chow and ad libitum access to a running wheel until body weight reached

Computed tomographic colonography (CTC) is less expensive, less time-consuming and better tolerated for patients than is optical colonoscopy. At present, CTC has a diagnostic performance similar to that of optical colonoscopy for detection of colorectal polyps. The American Cancer Society, the US Multi-Society Task Force on Colorectal Cancer, and the American College of Radiology now list CTC as a viable colorectal cancer screening option in its new joint guideline. In addition, CTC can provide accurate preoperative staging and thus can be a useful tool for navigation surgery.In this review, the authors discuss the essential aspects of minimum-invasive early diagnosis of colorectal cancer based on CTC, including the current techniques for patient preparation, data acquisition, image display and interpretation, the CTC performance for screening, preoperative staging, and navigation surgery.Increased screening by CTC is expected to result in a decrease in the incidence of and mortality due to CRC. Increased preoperative staging and navigation surgery by use of CTC is expected to secure an accurate choice of the precise therapeutic plan and a safer performance of surgery. Together, a comprehensive program for screening, preoperative staging, and navigation surgery based on CTC will ultimately reduce the mortality due to CRC.

Electronic cleansing (EC) is an emerging method for segmentation of fecal material in CT colonography (CTC) that is used for reducing or eliminating the requirement for cathartic bowel preparation and hence for improving patients’ adherence to recommendations for colon cancer screening. In EC, feces tagged by an x-ray-opaque oral contrast agent are removed from the CTC images, effectively cleansing the colon after image acquisition. Existing EC approaches tend to suffer from the following cleansing artifacts: degradation of soft-tissue structures because of pseudo-enhancement caused by the surrounding tagged fecal materials, and pseudo soft-tissue structures and false fistulas caused by partial volume effects at the boundary between the air lumen and the tagged regions, called the air-tagging boundary (AT boundary). In this study, we developed a novel EC method, called structure-analysis cleansing, which effectively avoids these cleansing artifacts. In our method, submerged soft-tissue structures are recognized by their local morphologic signatures that are characterized based on the eigenvalues of a three-dimensional Hessian matrix. A structure-enhancement function is formulated for enhancing of the soft-tissue structures. In addition, thin folds sandwiched between the air lumen and tagged regions are enhanced by analysis of the local roughness based on multi-scale volumetric curvedness. Both values of the structure-enhancement function and the local roughness are integrated into the speed function of a level set method for delineating the tagged fecal materials. Thus, submerged soft-tissue structures as well as soft-tissue structures adhering to the tagged regions are preserved, whereas the tagged regions are removed along with the associated AT boundaries from CTC images. Evaluation of the quality of the cleansing based on polyps and folds in a colon phantom, as well as on polyps in clinical cathartic and noncathartic CTC cases with fluid and stool tagging, showed that our structure-analysis cleansing method is significantly superior to that of our previous thresholding-based EC method. It provides a cleansed colon with substantially reduced subtraction artifacts.

Objectives Neurofibromatosis 1 (NF1), NF2, and schwannomatosis are characterized by a predisposition to develop multiple neurofibromas and schwannomas. Currently, there is no blood test to estimate tumor burden in patients with these disorders. We explored whether S100β would act as a biomarker of tumor burden in NF since S100β is a classic immunohistochemical marker of astrocytes, oligodendrocytes and Schwann cells and a small study showed S100β concentrations correlate with the volume of vestibular schwannomas. Design and methods We calculated whole-body tumor burden in subjects with NF1, NF2, and schwannomatosis using whole-body MRI (WBMRI) and measured the concentration of S100β in plasma using ELISA. We used chi-square tests and Spearman rank correlations to test the relationship between S100β levels and whole-body tumor burden. Results 127 consecutive patients were enrolled in the study (69 NF1 patients, 28 NF2 patients, and 30 schwannomatosis patients). The median age was 40 years, 43% were male, and median whole-body tumor volume was 26.9 mL. There was no relationship between the presence of internal tumors and the presence of detectable S100β in blood for the overall group or for individual diagnoses (p > 0.05 by chi-square for all comparisons). Similarly, there was no correlation between whole-body tumor volume and S100β concentration for the overall group or for individual diagnoses (p > 0.05 by Spearman for all comparisons). Conclusions Plasma S100β is not a useful biomarker for tumor burden in the neurofibromatoses. Further work is needed to identify a reliable biomarker of tumor burden in NF patients.

Analyses of sea surface height anomalies (SSHA) from Jan. 1993 to Dec. 2001 measured by TOPEX/ Poseidon (T/P) satellite altimetry indicate that there are two energy highs of SSHA fluctuations on both sides of the Luzon Strait. The highs centered at (19.5° N, 119.5° E) and (22.0° N, 124.0° E) respectively, are separated by a low energy band in the Strait. Empirical Orthogonal Function (EOF) analysis on the “Far Field” suggests that fluctuations of these two areas are mutually independent. EOF analysis on the “Near Field” shows that SSHA oscillation in the northeastern South China Sea (SCS) is dominated by seasonal variations accompanied with significant intra-seasonal and inter-annual fluctuations. Among which, EOF1 and EOF2 reflect 66.7% of the total variance. They are both seasonal but showing three months difference in phase. The basic pattern of EOF1 appears to be the seasonal SSHA oscillation centered at (18.0° N, 119.0° E) northwest off Luzon Island, which shows maximums (minimums) in August/September (January/February) and mainly reflects the seasonal variation of steric heights of the deep SCS basin and its corresponding circulation adjustments. The basic pattern of EOF2 is featured by seasonal seesaw of sea surface elevation in the SE-NW direction, which imaged the Ekman adjustment of sea surface height (SSH) in the northeastern South China Sea (NESCS) driven by the monsoons. Our analyses also reveal that intra-seasonal variation is an important component of SSHA fluctuation of the NESCS, which reflects the frequent occurrence of mesoscale processes west to the Luzon Strait. Moreover, inter-annual variations appear in all major EOF modes.

Purpose To investigate image quality and radiation dose of CT coronary angiography (CTCA) scanned using automatic tube current modulation (ATCM) and reconstructed by strong adaptive iterative dose reduction three-dimensional (AIDR3D). Methods Eighty-four consecutive CTCA patients were collected for the study. All patients were scanned using ATCM and reconstructed with strong AIDR3D, standard AIDR3D and filtered back-projection (FBP) respectively. Two radiologists who were blinded to the patients' clinical data and reconstruction methods evaluated image quality. Quantitative image quality evaluation included image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). To evaluate image quality qualitatively, coronary artery is classified into 15 segments based on the modified guidelines of the American Heart Association. Qualitative image quality was evaluated using a 4-point scale. Radiation dose was calculated based on dose-length product. Results Compared with standard AIDR3D, strong AIDR3D had lower image noise, higher SNR and CNR, their differences were all statistically significant (P

Dual-energy CT provides a promising solution to identify tagged fecal materials in electronic cleansing (EC) for fecal-tagging CT colonography (CTC). In this study, we developed a new EC method based on virtual colon tagging (VCT) for minimizing EC artifacts by use of the material decomposition ability in dual-energy CTC images. In our approach, a localized three-material decomposition model decomposes each voxel into a material mixture vector and the first partial derivatives of three base materials: luminal air, soft tissue, and iodine-tagged fecal material. A Poisson-based derivative smoothing algorithm smoothes the derivatives and implicitly smoothes the associated material mixture fields. VCT is a means for marking the entire colonic lumen by virtually elevating the CT value of luminal air as high as that of the tagged fecal materials to differentiate effectively soft-tissue structures from air-tagging mixtures. A dual-energy EC scheme based on VCT method, denoted as VCT-EC, was developed, in which the colonic lumen was first virtually tagged and then segmented by its high values in VCT images. The performance of the VCT-EC scheme was evaluated in a phantom study and a clinical study. Our results demonstrated that our VCT-EC scheme may provide a significant reduction of EC artifacts.

Patients with neurofibromatosis 1 (NF1), NF2, and schwannomatosis share a predisposition to develop multiple nerve sheath tumors. Previous studies have demonstrated that patients with NF1 and NF2 have reduced quality of life (QOL), but no studies have examined the relationship between whole-body tumor burden and QOL in these patients. We administered a QOL questionnaire (the SF-36) and a visual analog pain scale (VAS) to a previously described cohort of adult neurofibromatosis patients undergoing whole-body MRI. One-sample t-tests were used to compare norm-based SF-36 scores to weighted population means. Spearman correlation coefficients and multiple linear regression analyses controlling for demographic and disease-specific clinical variable were used to relate whole-body tumor volume to QOL scales. Two hundred forty-five patients (142 NF1, 53 NF2, 50 schwannomatosis) completed the study. Subjects showed deficits in selected subscales of the SF-36 compared to adjusted general population means. In bivariate analysis, increased tumor volume was significantly associated with pain in schwannomatosis patients, as measured by the SF-36 bodily pain subscale (rho = −0.287, P = 0.04) and VAS (rho = 0.34, P = 0.02). Regression models for NF2 patients showed a positive relationship between tumor burden and increased pain, as measured by the SF-36 (P = 0.008). Patients with NF1, NF2, and schwannomatosis suffer from reduced QOL, although only pain shows a clear relationship to patient's overall tumor burden. These findings suggest that internal tumor volume is not a primary contributor to QOL and emphasize the need for comprehensive treatment approaches that go beyond tumor-focused therapies such as surgery by including psychosocial interventions. © 2014 Wiley Periodicals, Inc.

The purpose of this study is to determine whether benign whole body tumor volume of plexiform neurofibromas (PNs) is a risk factor for malignant peripheral nerve sheath tumors (MPNST) in individuals with neurofibromatosis type 1 (NF1). Thirty-one NF1 patients with MPNSTs and 62 age- and sex-matched NF1 patients without MPNSTs, who had undergone whole body magnetic resonance imaging (MRI) were analyzed for benign whole body tumor volume. Mann-Whitney U test, Wilcoxon signed ranks test, Fisher's exact test (two-tailed), and logistic regression analysis were used for statistical analysis. Sixteen percent of all patients with MPNST did not have internal PN. The median whole body benign tumor volume in patients with PN was 352.0 mL among the MPNST group and 3.8 mL in the comparison group (p < 0.001). When the patients were stratified by age as younger or older than 30 years (median age of MPNST diagnosis), the median benign whole body tumor volume was 693.0 mL in MPNST patients and 0.0 mL in control patients younger than 30 years (p < 0.001). The mean number of PNs in MPNST patients was 2.8 (range 0-13, median 2.0) and 1.4 (range 0-13, median 1.0) in patients without MPNST (p = 0.001). The risk of MPNST development increased 0.2 % with each additional mL of benign PN volume (adjusted odds ratio [OR] = 1.002, 95 % confidence interval [CI] 1.001-1.003, p = 0.005) and was higher in patients younger than 30 years (adjusted OR = 1.007, 95 % CI 1.002-1.012, p = 0.003). Higher numbers of PNs, larger whole body benign tumor volume, and younger age are important risk factors for MPNST. We identified a subgroup of patients with MPNST without internal PN on MRI and the lack of correlation of MPNST development with tumor burden in older patients. These findings may alter our belief that all MPNSTs arise from pre-existing PNs and suggest that surveillance MRI based on clinical suspicion may be warranted in older patients, respectively.

Dual-energy electronic cleansing (DE-EC) provides a promising means for cleansing the tagged fecal materials in fecaltagging CT colonography (CTC). However, the increased radiation dose due to the double exposures in dual-energy CTC (DE-CTC) scanning is a major limitation for the use of DE-EC in clinical practice. The purpose of this study was to develop and evaluate a low-dose DE-EC scheme in fecal-tagging DE-CTC. In this study, a custom-made anthropomorphic colon phantom, which was filled with simulated tagged materials by non-ionic iodinated contrast agent (Omnipaque iohexol, GE Healthcare), was scanned by a dual-source CT scanner (SOMATON Definition Flash, Siemens Healthcare) at two photon energies: 80 kVp and 140 kVp with nine different tube current settings ranging from 12 to 74 mAs for 140 kVp, and then reconstructed by soft-tissue reconstruction kernel (B30f). The DE-CTC images were subjected to a low-dose DE-EC scheme. First, our image-space DE-CTC denoising filter was applied for reduction of image noise. Then, the noise-reduced images were processed by a virtual lumen tagging method for reduction of partial volume effect and tagging inhomogeneity. The results were compared with the registered CTC images of native phantom without fillings. Preliminary results showed that our low-dose DE-EC scheme achieved the cleansing ratios, defined by the proportion of the cleansed voxels in the tagging mask, between 93.18% (12 mAs) and 96.62% (74 mAs). Also, the soft-tissue preservation ratios, defined by the proportion of the persevered voxels in the soft-tissue mask, were maintained in the range between 94.67% and 96.41%.

Partial volume effect (PVE) and tagging inhomogeneity are two major causes of artifacts in electronic cleansing (EC) for fecal-tagging CT colonography (CTC). Our purpose was to develop a novel method called “virtual tagging” for electronic cleansing in dual-energy fecal-tagging CTC. A three-material decomposition scheme was first applied in dual-energy CTC to decompose each voxel into a mixture of air, soft tissue, and iodine-tagged fecal material. The entire colonic lumen was then marked by virtually tagging the mixture portion of luminal air at each voxel. As a result, colon lumen including air and tagged materials was segmented and subtracted by their high values in virtually tagged images. Our virtual tagging scheme provides a cleansed colon that is free from artifacts caused by the PVE at air-tagging mixture and inhomogeneous tagging.