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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.

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.

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

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.

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.

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.

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.

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.

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

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.

CT colonography (CTC), also known as virtual colonoscopy, is a promising alternative technique for providing mass screening for colorectal carcinoma. In this study, we developed a high-performance CAD scheme that automatically detects suspicious polyps in CTC data, and retrospectively compared its performance with that of prospective unaided human CTC reading in the detection of clinically significant adenomas/carcinomas by use of CTC database consisting of cases from two large multicenter clinical trials.

Purpose To evaluate the diagnostic value of multidetector CT (MDCT) and its multiplanar reformation (MPR), volume rendering (VR) and virtual bronchoscopy (VB) postprocessing techniques for primary trachea and main bronchus tumors. Methods Detection results of 31 primary trachea and main bronchus tumors with MDCT and its MPR, VR and VB postprocessing techniques, were analyzed retrospectively with regard to tumor locations, tumor morphologies, extramural invasions of tumors, longitudinal involvements of tumors, morphologies and extents of luminal stenoses, distances between main bronchus tumors and trachea carinae, and internal features of tumors. The detection results were compared with that of surgery and pathology. Results Detection results with MDCT and its MPR, VR and VB were consistent with that of surgery and pathology, included tumor locations (tracheae, n = 19; right main bronchi, n = 6; left main bronchi, n = 6), tumor morphologies (endoluminal nodes with narrow bases, n = 2; endoluminal nodes with wide bases, n = 13; both intraluminal and extraluminal masses, n = 16), extramural invasions of tumors (brokethrough only serous membrane, n = 1; 4.0 mm—56.0 mm, n = 14; no clear border with right atelectasis, n = 1), longitudinal involvements of tumors (3.0 mm, n = 1; 5.0 mm—68.0 mm, n = 29; whole right main bronchus wall and trachea carina, n = 1), morphologies of luminal stenoses (irregular, n = 26; circular, n = 3; eccentric, n = 1; conical, n = 1) and extents (mild, n = 5; moderate, n = 7; severe, n = 19), distances between main bronchus tumors and trachea carinae (16.0 mm, n = 1; invaded trachea carina, n = 1; >20.0 mm, n = 10), and internal features of tumors (fairly homogeneous densities with rather obvious enhancements, n = 26; homogeneous density with obvious enhancement, n = 1; homogeneous density without obvious enhancement, n = 1; not enough homogeneous density with obvious enhancement, n = 1; punctate calcification with obvious enhancement, n = 1; low density without obvious enhancement, n = 1). Conclusion MDCT and its MPR, VR and VB images have respective advantages and disadvantages. Their combination could complement to each other to accurately detect locations, natures (benignancy, malignancy or low malignancy), and quantities (extramural invasions, longitudinal involvements, extents of luminal stenoses, distances between main bronchus tumors and trachea carinae) of primary trachea and main bronchus tumors with crucial information for surgical treatment, are highly useful diagnostic methods for primary trachea and main bronchus tumors.

Digital bowel cleansing (DBC) is an emerging method for segmentation of fecal materials, which are tagged by an X-ray-opaque oral contrast agent in CT colonography (CTC) images, effectively removing them for digital cleansing of the colon. Existing DBC approaches tend to use simple thresholding-based methods for the removal of tagged fecal materials; however, because of the pseudo-enhancement of polyps caused by the surrounding tagged fecal materials, such methods tend erroneously to remove a part of or the entire polyps submerged in these materials. In this study, we developed a novel DBC method that preserves the soft-tissue structures submerged in or partially covered by tagged fecal materials. In our approach, submerged soft-tissue structures are characterized by their local shape signatures that are calculated based on the eigenvalues of a Hessian matrix. A structure-enhancement function is formulated for enhancing of the soft-tissue structures, and the values of the function are integrated into the speed function of a level set method to delineate the submerged soft-tissue structures while removing the tagged fecal materials. In an analysis of 10 submerged polyps, our new DBC method was shown to delineate polyps better than was possible with our previously reported cleansing method based on thresholding. Application of our computer-aided detection (CAD) scheme showed that the use of the new DBS method substantially reduced the number of false-positive detections compared with those of our previous, thresholding-based method.

With the rapid development of multi-detector computed tomography (MDCT), resulting in increasing temporal and spatial resolution of data sets, clinical use of computed tomographic angiography (CTA) is rapidly increasing. Analysis of vascular structures is much needed in CTA images; however, the basis of the analysis, vessel segmentation, can still be a challenging problem. In this paper, we present a fast interactive method for CTA vessel segmentation, called vesselness propagation. This method is a two-step procedure, with a pre-processing step and an interactive step. During the pre-processing step, a vesselness volume is computed by application of a CTA transfer function followed by a multi-scale Hessian filtering. At the interactive stage, the propagation is controlled interactively in terms of the priority of the vesselness. This method was used successfully in many CTA applications such as the carotid artery, coronary artery, and peripheral arteries. It takes less than one minute for a user to segment the entire vascular structure. Thus, the proposed method provides an effective way of obtaining an overview of vascular structures.

The developmental dysplasia of the hip (DDH) is a congenital malformation affecting the proximal femurs and acetabulum that are subluxatable, dislocatable, and dislocated. Early diagnosis and treatment is important because failure to diagnose and improper treatment can result in significant morbidity. In this paper, we designed and implemented a computer aided system for the diagnosis and treatment planning of this disease. With the design, the patient received CT (computed tomography) or MRI (magnetic resonance imaging) scan first. A mixture-based PV partial-volume algorithm was applied to perform bone segmentation on CT image, followed by three-dimensional (3D) reconstruction and display of the segmented image, demonstrating the special relationship between the acetabulum and femurs for visual judgment. Several standard procedures, such as Salter procedure, Pemberton procedure and Femoral Shortening osteotomy, were simulated on the screen to rehearse a virtual treatment plan. Quantitative measurement of Acetabular Index (AI) and Femoral Neck Anteversion (FNA) were performed on the 3D image for evaluation of DDH and treatment plans. PC graphics-card GPU architecture was exploited to accelerate the 3D rendering and geometric manipulation. The prototype system was implemented on PC/Windows environment and is currently under clinical trial on patient datasets.

Background Colon screening by optical colonoscopy (OC) or computed tomographic colonography (CTC) requires a laxative bowel preparation, which inhibits screening participation. Objective To assess the performance of detecting adenomas 6 mm or larger and patient experience of laxative-free, computer-aided CTC. Design Prospective test comparison of laxative-free CTC and OC. The CTC included electronic cleansing and computer-aided detection. Optical colonoscopy examinations were initially blinded to CTC results, which were subsequently revealed during colonoscope withdrawal; this method permitted reexamination to resolve discrepant findings. Unblinded OC served as a reference standard. (ClinicalTrials.gov registration number: NCT01200303) Setting Multicenter ambulatory imaging and endoscopy centers. Participants 605 adults aged 50 to 85 years at average to moderate risk for colon cancer. Measurements Per-patient sensitivity and specificity of CTC and first-pass OC for detecting adenomas at thresholds of 10 mm or greater, 8 mm or greater, and 6 mm or greater; per-lesion sensitivity and survey data describing patient experience with preparations and examinations. Results For adenomas 10 mm or larger, per-patient sensitivity of CTC was 0.91 (95% CI, 0.71 to 0.99) and specificity was 0.85 (CI, 0.82 to 0.88); sensitivity of OC was 0.95 (CI, 0.77 to 1.00) and specificity was 0.89 (CI, 0.86 to 0.91). Sensitivity of CTC was 0.70 (CI, 0.53 to 0.83) for adenomas 8 mm or larger and 0.59 (CI, 0.47 to 0.70) for those 6 mm or larger; sensitivity of OC for adenomas 8 mm or larger was 0.88 (CI, 0.73 to 0.96) and 0.76 (CI, 0.64 to 0.85) for those 6 mm or larger. The specificity of OC at the threshold of 8 mm or larger was 0.91 and at 6 mm or larger was 0.94. Specificity for OC was greater than that for CTC, which was 0.86 at the threshold of 8 mm or larger and 0.88 at 6 mm or larger (P= 0.02). Reported participant experience for comfort and difficulty of examination preparation was better with CTC than OC. Limitations There were 3 CTC readers. The survey instrument was not independently validated. Conclusion Computed tomographic colonography was accurate in detecting adenomas 10 mm or larger but less so for smaller lesions. Patient experience was better with laxative-free CTC. These results suggest a possible role for laxative-free CTC as an alternate screening method.

Purpose Patients with neurofibromatosis 1 (NF1), NF2, and schwannomatosis are at risk for multiple nerve sheath tumors and premature mortality. Traditional magnetic resonance imaging (MRI) has limited ability to assess disease burden accurately. The aim of this study was to establish an international cohort of patients with quantified whole-body internal tumor burden and to correlate tumor burden with clinical features of disease. Methods We determined the number, volume, and distribution of internal nerve sheath tumors in patients using whole-body MRI (WBMRI) and three-dimensional computerized volumetry. We quantified the distribution of tumor volume across body regions and used unsupervised cluster analysis to group patients based on tumor distribution. We correlated the presence and volume of internal tumors with disease-related and demographic factors. Results WBMRI identified 1286 tumors in 145/247 patients (59%). Schwannomatosis patients had the highest prevalence of tumors (P = 0.03), but NF1 patients had the highest median tumor volume (P = 0.02). Tumor volume was unevenly distributed across body regions with overrepresentation of the head/neck and pelvis. Risk factors for internal nerve sheath tumors included decreasing numbers of café-au-lait macules in NF1 patients (P = 0.003) and history of skeletal abnormalities in NF2 patients (P = 0.09). Risk factors for higher tumor volume included female gender (P = 0.05) and increasing subcutaneous neurofibromas (P = 0.03) in NF1 patients, absence of cutaneous schwannomas in NF2 patients (P = 0.06), and increasing age in schwannomatosis patients (p = 0.10). Conclusion WBMRI provides a comprehensive phenotype of neurofibromatosis patients, identifies distinct anatomic subgroups, and provides the basis for investigating molecular biomarkers that correlate with unique disease manifestations.