Venous Phase Contrast-enhanced Computed Tomography Research Articles

Identification of D842V mutation in gastrointestinal stromal tumors based on CT radiomics: a multi-center study

BackgroundGastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract. Recent advent of tyrosine kinase inhibitors (TKIs) has significantly improved the prognosis of GIST patients. However, responses to TKI therapy can vary depending on the specific gene mutation. D842V, which is the most common mutation in platelet-derived growth factor receptor alpha exon 18, shows no response to imatinib and sunitinib. Radiomics features based on venous-phase contrast-enhanced computed tomography (CECT) have shown potential in non-invasive prediction of GIST genotypes. This study sought to determine whether radiomics features could help distinguish GISTs with D842V mutations.MethodsA total of 872 pathologically confirmed GIST patients with CECT data available from three independent centers were included and divided into the training cohort (n=487) and the external validation cohort (n=385). Clinical features including age, sex, tumor size and location were collected. Radiomics features on the largest axial image of venous-phase CECT were analyzed and a total of two radiomics features were selected after feature selection. Random forest models trained on non-radiomics features only (the non-radiomics model) and on both non-radiomics and radiomics features (the combined model) were compared.ResultsThe combined model showed better average precision (0.250 vs. 0.102, p = 0.039) and F1 score (0.253 vs. 0.155, p = 0.012) than the non-radiomics model. There was no significant difference in ROC-AUC (0.728 vs. 0.737, p = 0.836) and geometric mean (0.737 vs. 0.681, p = 0.352).ConclusionsThis study demonstrated the potential of radiomics features based on venous-phase CECT images to identify D842V mutation in GISTs. Our model may provide an alternative approach to guide TKI therapy for patients inaccessible to sequence variant testing, potentially improving treatment outcomes for GIST patients especially in resource-limited settings.

Noninvasive identification of SOX9 status using radiomics signatures may help construct personalized treatment strategy in hepatocellular carcinoma.

To develop and validate a radiomics-based model for predicting SOX9-positive hepatocellular carcinoma (HCC) using preoperative contrast-enhanced computed tomography (CT) images. From January 2013 to April 2017, patients with histologically proven HCC who received systemic sorafenib treatment after curative resection were retrospectively enrolled. Radiomic features were extracted from portal venous phase CT images and selected to build a radiomics score using logistic regression analysis. The factors associated with SOX9 expression were selected and combined by univariate and multivariate analyses to establish clinico-liver imaging (CL) model and clinico-liver imaging-radiomics (CLR) model. Diagnostic performance was measured by area under curve (AUC). Overall survival (OS) and recurrence-free survival (RFS) rates were compared using Kaplan-Meier method. A total of 108 patients (training cohort: n = 80; validation cohort: n = 28) were enrolled. Multivariate analyses revealed that the albumin-bilirubin grade and tumor size were significant independent factors for predicting SOX9-positive HCCs and were included in the CL model. The CLR model integrating the radiomics score with albumin-bilirubin grade and tumor size showed better discriminative performance than the CL model with AUCs of 0.912 and 0.790 in the training and validation cohorts. Survival curves for RFS and OS showed that SOX9 expression was closely related to the prognosis of HCC patients. RFS and OS rates were significantly lower in patients with SOX9-positive than SOX9-negative (51.02% vs. 75.00% at 1-year RFS rates; 76.92% vs. 94.94% at 2-year OS rates). Radiomics signatures may serve as noninvasive predictors for SOX9 status evaluation in patients with HCC and may aid in constructing individualized treatment strategies.

A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study

SummaryBackgroundAccurate prediction of treatment response to neoadjuvant chemotherapy (NACT) in individual patients with locally advanced gastric cancer (LAGC) is essential for personalized medicine. We aimed to develop and validate a deep learning radiomics nomogram (DLRN) based on pretreatment contrast-enhanced computed tomography (CT) images and clinical features to predict the response to NACT in patients with LAGC.Methods719 patients with LAGC were retrospectively recruited from four Chinese hospitals between Dec 1st, 2014 and Nov 30th, 2020. The training cohort and internal validation cohort (IVC), comprising 243 and 103 patients, respectively, were randomly selected from center I; the external validation cohort1 (EVC1) comprised 207 patients from center II; and EVC2 comprised 166 patients from another two hospitals. Two imaging signatures, reflecting the phenotypes of the deep learning and handcrafted radiomics features, were constructed from the pretreatment portal venous-phase CT images. A four-step procedure, including reproducibility evaluation, the univariable analysis, the LASSO method, and the multivariable logistic regression analysis, was applied for feature selection and signature building. The integrated DLRN was then developed for the added value of the imaging signatures to independent clinicopathological factors for predicting the response to NACT. The prediction performance was assessed with respect to discrimination, calibration, and clinical usefulness. Kaplan-Meier survival curves based on the DLRN were used to estimate the disease-free survival (DFS) in the follow-up cohort (n = 300).FindingsThe DLRN showed satisfactory discrimination of good response to NACT and yielded the areas under the receiver operating curve (AUCs) of 0.829 (95% CI, 0.739–0.920), 0.804 (95% CI, 0.732–0.877), and 0.827 (95% CI, 0.755–0.900) in the internal and two external validation cohorts, respectively, with good calibration in all cohorts (p > 0.05). Furthermore, the DLRN performed significantly better than the clinical model (p < 0.001). Decision curve analysis confirmed that the DLRN was clinically useful. Besides, DLRN was significantly associated with the DFS of patients with LAGC (p < 0.05).InterpretationA deep learning-based radiomics nomogram exhibited a promising performance for predicting therapeutic response and clinical outcomes in patients with LAGC, which could provide valuable information for individualized treatment.

Computed tomography-based radiomics for predicting lymphovascular invasion in rectal cancer

PurposeTo develop and externally validate a computed tomography (CT)-based radiomics model for predicting lymphovascular invasion (LVI) before treatment in patients with rectal cancer (RC). MethodThis retrospective study enrolled 351 patients with RC from three hospitals between March 2018 and March 2021. These patients were assigned to one of the following three groups: training set (n = 239, from hospital 1), internal validation set (n = 60, from hospital 1), and external validation set (n = 52, from hospitals 2 and 3). Large amounts of radiomics features were extracted from the intratumoral and peritumoral regions in the portal venous phase contrast-enhanced CT images. The score of radiomics features (Rad-score) was calculated by performing logistic regression analysis following the L1-based method. A combined model (Rad-score + clinical factors) was developed in the training cohort and validated internally and externally. The models were compared using the area under the receiver operating characteristic curve (AUC). ResultsOf the 351 patients, 106 (30.2%) had an LVI + tumor. Rad-score (comprised of 22 features) was significantly higher in the LVI + group than in the LVI- group (0.60 ± 0.17 vs. 0.42 ± 0.19, P = 0.001). The combined model obtained good predictive performance in the training cohort (AUC = 0.813 [95% CI: 0.758–0.861]), with robust results in internal and external validations (AUC = 0.843 [95% CI: 0.726–0.924] and 0.807 [95% CI: 0.674–0.903]). ConclusionsThe proposed combined model demonstrated the potential to predict LVI preoperatively in patients with RC.

Predictors of malignancy in incidental adnexal lesions identified on CT in patients with prior non-ovarian cancer.

To identify imaging features in incidental adnexal lesions which are associated with malignancy on portal venous phase contrast-enhanced CT in patients with known non-ovarian cancer. This IRB-approved, HIPAA-compliant retrospective study was performed at a tertiary cancer center. Portal venous phase contrast-enhanced CT from January 2010 to December 2015 was reviewed to identify women with non-ovarian malignancy and incidental adnexal lesion, with mean 18months (range 1-80months) to definitive diagnosis or last imaging follow-up. Imaging features of adnexal lesions were recorded (size, laterality, shape, attenuation, and composition) and correlated with outcome (benign or malignant) using univariate and multivariate logistic regression analysis. A point-based system was used to predict likelihood of malignancy. Of 276 women (mean age 45years), 216 (78.3%) had benign lesions, 58 (21.0%) ovarian metastasis, and 2 (0.7%) had primary ovarian malignancy. On logistic regression model, lesion size > 5cm (p-value, OR, 95% CI 0.01, 9.11, 1.70-48.87), bilaterality (< 0.0001, 28.34, 7.46-107.67), irregular shape (0.01, 12.31, 1.61-94.05), higher-than-simple-fluid attenuation (< 0.0001, 28.27, 5.65-141.59), and heterogeneous composition (0.0017, 10.75, 2.45-47.23) were associated with malignant outcome (AUC 0.97). A point-based system incorporating these five features (possible 0-5 points) had AUC of 0.97. Rate of malignancy was 0% (0/147) if none of the features of malignancy were present, 12.7% (8/63) if one feature was present, 51.7% (15/29) if two features were present, and 100% (37/37) if three or more features present. Risk of malignancy of incidental adnexal lesions in women with prior non-ovarian cancer can be estimated based on lesion features seen on portal venous phase contrast-enhanced CT.

Radiomic signature of the FOWARC trial predicts pathological response to neoadjuvant treatment in rectal cancer

BackgroundWe aimed to develop a radiomic model based on pre-treatment computed tomography (CT) to predict the pathological complete response (pCR) in patients with rectal cancer after neoadjuvant treatment and tried to integrate our model with magnetic resonance imaging (MRI)-based radiomic signature.MethodsThis was a secondary analysis of the FOWARC randomized controlled trial. Radiomic features were extracted from pre-treatment portal venous-phase contrast-enhanced CT images of 177 patients with rectal cancer. Patients were randomly allocated to the primary and validation cohort. The least absolute shrinkage and selection operator regression was applied to select predictive features to build a radiomic signature for pCR prediction (rad-score). This CT-based rad-score was integrated with clinicopathological variables using gradient boosting machine (GBM) or MRI-based rad-score to construct comprehensive models for pCR prediction. The performance of CT-based model was evaluated and compared by receiver operator characteristic (ROC) curve analysis. The LR (likelihood ratio) test and AIC (Akaike information criterion) were applied to compare CT-based rad-score, MRI-based rad-score and the combined rad-score.ResultsWe developed a CT-based rad-score for pCR prediction and a gradient boosting machine (GBM) model was built after clinicopathological variables were incorporated, with improved AUCs of 0.997 [95% CI 0.990–1.000] and 0.822 [95% CI 0.649–0.995] in the primary and validation cohort, respectively. Moreover, we constructed a combined model of CT- and MRI-based radiomic signatures that achieve better AIC (75.49 vs. 81.34 vs.82.39) than CT-based rad-score (P = 0.005) and MRI-based rad-score (P = 0.003) alone did.ConclusionsThe CT-based radiomic models we constructed may provide a useful and reliable tool to predict pCR after neoadjuvant treatment, identify patients that are appropriate for a 'watch and wait' approach, and thus avoid overtreatment. Moreover, the CT-based radiomic signature may add predictive value to the MRI-based models for clinical decision making.

Retrospective evaluation of venous phase contrast-enhanced computed tomography images in patients who developed pancreatic adenocarcinomas after treatment for nonpancreatic primary cancer.

Objectives:To clarify venous phase contrast-enhanced CT findings in early pancreatic adenocarcinomas by retrospectively evaluating CT images of pancreatic adenocarcinomas that developed during follow-up after treatment for non-pancreatic cancers.Methods:The study cohort comprised six patients who developed pancreatic adenocarcinomas between April 2005 and April 2020 during follow-up after treatment for non-pancreatic primary cancers. Two radiologists retrospectively evaluated CT images and reached consensus on previously reported CT findings that were suggestive of small pancreatic adenocarcinomas; namely pancreatic duct interruption and dilatation, pancreatic parenchymal atrophy, focal hypoattenuated areas, and appearance of cystic lesions. Time intervals between the first CT with these suggestive findings and the latest pre-operative CT were recorded. Doubling times were calculated in patients with hypoattenuated areas on initial CT scans.Results:Small (<10 mm) focal hypoattenuated areas with (n = 2) or without rim enhancement (n = 1) were identified on initial CT images of three patients. Pancreatic duct interruption and dilatation, pancreatic parenchymal atrophy, and cystic lesion were identified in two, one and one patient, respectively. Time intervals between initial and latest preoperative CT examination were 6–19 months (median, 14.5 months). Tumor doubling time according to CT findings was calculated as 46–407 days (median 106 days).Conclusion:Venous phase contrast-enhanced CT can provide findings that are suggestive of early pancreatic adenocarcinoma. Pancreatic phase contrast-enhanced CT should therefore be performed in patients with such findings with the aim of early detection of pancreatic adenocarcinoma.Advances in knowledge:Pancreatic adenocarcinoma can develop subsequently in patients with non-pancreatic malignancies. Patients with non-pancreatic cancers are often followed up with monophasic contrast-enhanced CT in venous phase timing. Venous phase contrast-enhanced CT can provide some findings suggestive of early pancreatic adenocarcinoma. Knowledge of these findings is important for early detection of pancreatic adenocarcinoma.

CT-based radiomics features in the prediction of thyroid cartilage invasion from laryngeal and hypopharyngeal squamous cell carcinoma

BackgroundLaryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) with thyroid cartilage invasion are considered T4 and need total laryngectomy. However, the accuracy of preoperative diagnosis of thyroid cartilage invasion remains lower. Therefore, the purpose of this study was to assess the potential of computed tomography (CT)-based radiomics features in the prediction of thyroid cartilage invasion from LHSCC.MethodsA total of 265 patients with pathologically proven LHSCC were enrolled in this retrospective study (86 with thyroid cartilage invasion and 179 without invasion). Two head and neck radiologists evaluated the thyroid cartilage invasion on CT images. Radiomics features were extracted from venous phase contrast-enhanced CT images. The least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) method were used for dimension reduction and model construction. In addition, the support vector machine-based synthetic minority oversampling (SVMSMOTE) algorithm was adopted to balance the dataset and a new LR-SVMSMOTE model was constructed. The performance of the radiologist and the two models were evaluated with receiver operating characteristic (ROC) curves and compared using the DeLong test.ResultsThe areas under the ROC curves (AUCs) in the prediction of thyroid cartilage invasion from LHSCC for the LR-SVMSMOTE model, LR model, and radiologist were 0.905 [95% confidence interval (CI): 0.863 to 0.937)], 0.876 (95%CI: 0.830 to 0.913), and 0.721 (95%CI: 0.663–0.774), respectively. The AUCs of both models were higher than that of the radiologist assessment (all P < 0.001). There was no significant difference in predictive performance between the LR-SVMSMOTE and LR models (P = 0.05).ConclusionsModels based on CT radiomic features can improve the accuracy of predicting thyroid cartilage invasion from LHSCC and provide a new potentially noninvasive method for preoperative prediction of thyroid cartilage invasion from LHSCC.

Radiomics Based on CECT in Differentiating Kimura Disease From Lymph Node Metastases in Head and Neck: A Non-Invasive and Reliable Method.

Background: Kimura disease may be easily misdiagnosed as malignant tumors such as lymph node metastases based on imaging and clinical symptoms. The aim of this article is to investigate whether the radiomic features and the model based on the features on venous-phase contrast-enhanced CT (CECT) images can distinguish Kimura disease from lymph node metastases in the head and neck.Methods: A retrospective analysis of 14 patients of head and neck Kimura disease (a total of 38 enlarged lymph nodes) and 39 patients with head and neck lymph node metastases (a total of 39 enlarged lymph nodes), confirmed by biopsy or surgery resection, was conducted. All patients accepted CECT within 10 days before biopsy or surgery resection. Radiomic features based on venous-phase CECT were generated automatically from Artificial-Intelligence Kit (AK) software. All lymph nodes were randomly divided into the training set (n = 54) and testing set (n = 23) in a ratio of 7:3. ANOVA + Mann–Whitney, Spearman correlation, least absolute shrinkage and selection operator, and Gradient Descent were introduced for the reduction of the highly redundant features. Binary logistic regression model was constructed based on the selected features. Receiver operating characteristic was used to evaluate the diagnostic performance of the features and the model. Finally, a nomogram was established for model application.Results: Seven features were screened out at the end. Significant difference was found between the two groups for all the features with area under the curves (AUCs) ranging from 0.759 to 0.915. The AUC of the model's identification performance was 0.970 in the training group and 0.977 in the testing group. The disease discrimination efficiency of the model was better than that of any single feature.Conclusions: The radiomic features and the model based on these features on venous-phase CECT images had very good performance for the discrimination between Kimura disease and lymph node metastases in the head and neck.

A radiomics model of liver CT to predict risk of hepatic encephalopathy secondary to hepatitis B related cirrhosis

PurposeTo build a radiomics model of liver contrast-enhanced computed tomography (CT) to predict hepatic encephalopathy secondary to Hepatitis B related cirrhosis. Materials and MethodsThis study consisted of 304 consecutive patients with first-diagnosed hepatitis B related cirrhosis. 212 and 92 patients were randomly computer-generated into training and testing cohorts, among which 38 and 21 patients endured HE, respectively. 356 radiomics features of liver were extracted from portal venous-phase CT data, and 3 clinical features were collected from medical record. After data were standardized by Z-score, we used least absolute shrinkage and selection operator to choose useful radiomics features. Ultimately, three predictive models including a radiomics model, a clinical model and an integrated model of radiomics and clinical features were built by analysis of R-software. Predictive performance was tested by multivariable logistic regression, and evaluated by area under receiver-operating characteristic curve (AUC), and accuracy. Results19 radiomics features of liver CT were selected. The selected radiomics features and 3 relevant clinical features were applied to develop a radiomics model, a clinical model, and an integrated model of both radiomics and clinical features. The integrated model showed better performance than the radiomics model or clinical model to predict HE (AUC = 0.94 vs. 0.91 or 0.76, and 0.87 vs. 0.86 or 0.73; accuracy = 0.93 vs. 0.89 or 0.83, and 0.83 vs. 0.84 or 0.77) in the training and testing cohorts, respectively. ConclusionThe integrated model of radiomics and clinical features could well predict HE secondary to hepatitis B related cirrhosis.

Utility of radiomics based on contrast-enhanced CT and clinical data in the differentiation of benign and malignant gallbladder polypoid lesions.

To develop and validate a novel method based on radiomics for the preoperative differentiation of benign and malignant gallbladder polypoid lesions (PLG). A total of 145 patients with pathological proven gallbladder polypoid lesions ≥ 1cm were included in this retrospective study. All the patients underwent abdominal contrast-enhanced computed tomography (CT) examinations 3weeks before cholecystectomy from January 2013 to January 2019. Seventy percent of the cases were randomly selected for the training dataset, and 30% of the cases were independently used for testing. Radiomics features extracted from portal venous-phase CT of the PLG and clinical features were analyzed, and the LASSO regression algorithm was used for data dimension reduction. Multivariable logistic regression was used to generate radiomics signatures, clinical signatures, and combination signatures. The receiver operating characteristic (ROC) curve and decision curve were plotted to assess the differentiating performance of the three signatures. The area under the ROC curve (AUC) of the radiomics signature and clinical signature was 0.924 and 0.861 in the testing dataset, respectively. For the radiomics signature, the accuracy was 88.6%, with 88.0% specificity and 89.5% sensitivity. When combined, the AUC was 0.931, the specificity was 84.0%, and the sensitivity was 89.5%. The differences between the AUC values of the two sole models and the combination model were statistically nonsignificant. Radiomics based on CT images can be helpful to differentiate benign and malignant gallbladder polyps ≥ 1cm in size.

Abdominal CT protocol’s influence on postoperative follow-up of lesions detection associated with gastrointestinal tumours

Background: Postoperative abdominal computed tomography (CT) follow-up for gastrointestinal tumours is used to monitor therapeutic effects and detect relevant lesions, while the effective dose (ED) associated with dual-phase enhanced CT (DPCT) can be more than 20 mSv. In this study, we investigated the diagnostic utility of abdominal CT protocols based upon radiation dose control. Methods: In this institutional ethics review board-approved retrospective study, the electronic pathology database in authors’ hospital was searched for all cases of gastrointestinal tumours from June 2016 to June 2017, yielding 200 cases with complete information of clinical history and CT examination. CT scans were separated into six groups, including non-enhanced CT (NECT), arterial phase contrast-enhanced CT (APCT), venous phase contrast-enhanced CT (VPCT), NECT + APCT, NECT + VPCT and NECT + DPCT. ED was calculated for each group, and the detectability of relevant lesions was compared. Results: The ED of NECT, APCT, VPCT, NECT + APCT, NECT + VPCT and NECT + DPCT were 7.27±1.64, 7.32±1.65, 9.17±2.07, 14.55±3.29, 16.41±3.71 and 23.70±5.36 mSv, respectively. The six groups according to CT protocols showed no signi cant differences in the detection of anastomosis. For the detectability of metastatic lesions, VPCT is equivalent to NECT + DPCT. In addition, VPCT is comparable to NECT + APCT in nding lymphadenopathy. Conclusions: Postoperative VPCT follow-up for gastrointestinal tumours is comparable to conventional NECT + DPCT in the detection of relevant lesions postoperatively. The radiation dose of single VPCT is 62% less than NECT + DPCT.

Prognostic significance of contrast-enhanced CT attenuation value in extrahepatic cholangiocarcinoma

To determine whether washout characteristics of dynamic contrast-enhanced computed tomography (CT) could predict survival in patients with extrahepatic cholangiocarcinoma (EHC). This study collected 46 resected cases. All cases were examined by dynamic contrast study on multidetector-row CT. Region-of-interest measurements were obtained at the non-enhanced, portal venous phase and delayed phase in the tumour and were used to calculate the washout ratio as follows: [(attenuation value at portal venous phase CT - attenuation value at delayed enhanced CT)/(attenuation value at portal venous phase CT - attenuation value at unenhanced CT)] × 100. On the basis of the median washout ratio, we classified the cases into two groups, a high-washout group and low-washout group. Associations between overall survival and various factors including washout rates were analysed. The median washout ratio was 29.4%. Univariate analysis revealed that a lower washout ratio, venous invasion, lymphatic permeation and lymph node metastasis were associated with shorter survival. Multivariate analysis identified the lower washout ratio as an independent prognostic factor (hazard ratio, 3.768; p value, 0.027). The washout ratio obtained from the contrast-enhanced CT may be a useful imaging biomarker for the prediction of survival of patients with EHC. • Dynamic contrast study can evaluate the aggressiveness of extrahepatic cholangiocarcinoma. • A lower washout ratio was an independent prognostic factor for overall survival. • CT can predict survival and inform decisions on surgical options or chemotherapy.

Effect of automatic spectral imaging mode selection and adaptive statistical iterative reconstruction at abdominal CT with low contrast agent dose

Objective To investigate the image quality and radiation dose of automatic spectral imaging mode selection and adaptive statistical iterative reconstruction (ASIR) at abdominal CT with low contrast agent dose. Methods One hundred cases with the arterial-phase (AP) and portal venous phase (PVP) contrast-enhanced abdominal CT scanning were analyzed prospectively. Patients were randomly assigned to the study group and control group (n=50 each). In the study group, automatic spectral imaging mode selection and contrast agent dose of 300 mg/kg were used and spectral monochromatic images(40 to 60 keV) were reconstructed using either filtered back-projection (FBP) (group A) or ASIR (group B). In the control group, the fixed tube potential of 120 kVp and contrast agent dose of 450 mg/kg were used with images reconstructed using FBP (group C). Quantitative parameters (image noise and contrast-to-noise ratio of the liver, pancreas, aorta and portal vein) and qualitative visual parameters (overall image quality as graded on a 5-point scale) were compared among the groups by using One-way ANOVA or Kruskal-Wallis H test. Two sample t tests were used compare the radiation dose difference. Results There had no significant difference in CTDIvol[both (12±5) mGy] and DLP[(364±142) mGy·cm versus (377±131) mGy·cm] between the study group and control group(t= -0.408 and -0.428,P>0.05). During the AP and PVP, at the energy level of 40 keV, group B showed higher CNRs than group A and group C, lower image noise[(29±6) HU in AP, (24±6) HU in PVP] than group A[(43±11) HU, (44±10) HU] but higher image noise than group C[(18± 4) HU, (18±4) HU], lower overall image quality scores[(3.0±0.2) point, (2.9±0.3) point] than group C[(3.6± 0.4) point , (3.6±0.5) point] but similar scores to group A[(2.9±0.4) point,(2.8±0.4)point]. At the energy level of 50 keV, group B showed higher CNRs than group A but higher than or similar CNRs to group C, lower image noise[(20±5) HU, (20±4) HU] than group A[(31±8) HU, (31±7) HU] but similar image noise to group C, higher overall image quality scores[(3.6±0.4) point, (3.5±0.4) point]than group A[(3.3±0.3) point,(3.3±0.3) point] but similar scores to group C. At the energy level of 60 keV, group B showed lower image noise[(14±4) HU, (14±3) HU], higher CNRs and overall image quality scores[(3.9±0.4) point,(3.9±0.3) point] than group A[(19±5) and (20±5)HU in image noise, (3.7±0.4) and (3.7±0.3) point in overall image quality scores ]and group C. Except for monochromatic images at 40 keV, the overall image quality scores in group B were all greater than 3 point and met the clinical diagnostic level. Conclusions The radiation dose of CT spectral imaging and conventional 120 kVp CT scan is equivalent with the use of automatic spectral imaging mode selection. By combining ASIR technique, monochromatic images at 50 and 60 keV can improve CNR and reduce contrast agent dose while maintain or improve overall image quality. Key words: Tomography,X-ray computed; Comparative study; Radiation dosage; Image quality

Determination of a cutoff attenuation value on single-phase contrast-enhanced CT for characterizing adrenal nodules via chemical shift MRI.

This study aims to determine the optimal cutoff attenuation value on single-phase contrast-enhanced CT(CECT) at which chemical shift MRI (CSMRI) yields sufficient accuracy to replace the standard CT adrenal protocol for the diagnosis of adrenal adenomas. Between January 2010 and December 2014, a total of 49 patients (age: 20-81years; 23 men and 26 women) with 60 adrenal tumors (48 adenomas and 12 non-adenomas) who underwent both CECT in portal venous phase and CSMRI were included in the study. Attenuation on portal venous phase CECT, adrenal-to-spleen chemical shift ratio (ASR), and signal-intensity index (SII) were obtained for each adrenal mass. Among different cutoff values on CECT (from <70 to <120HU), the diagnostic accuracies for those lesions measuring <80HU were the highest and most similar to dedicated adrenal CT. The sensitivities and specificities of SII were up to 96% (25/26) and 100% (7/7) for those measuring <80HU, but reduced to 73% (16/22) and 80% (4/5) for those ≥80HU. The overall sensitivities and specificities for diagnosing adrenal adenoma using SII vs. ASR were 85% (41/48) and 92% (11/12) vs. 71% (34/48) and 100% (12/12), respectively. CSMRI may replace CT adrenals in the work-up of patients with adrenal nodules below 80HU on single-phase CECT, hence reducing radiation exposure and iodinated contrast administration. Adrenal nodules greater than 80HU cannot be accurately diagnosed by CSMRI. CT adrenal protocol remains the appropriate investigative modality in those cases.

Prostate cancer on computed tomography: A direct comparison with multi-parametric magnetic resonance imaging and tissue pathology

ObjectivesMulti-parametric prostate magnetic resonance imaging (MRI) is considered the current imaging standard for detection and staging of prostate cancer. The combination of anatomical and functional imaging provided in this exam significantly increases the accuracy of prostate cancer detection. Computed tomography (CT) imaging has so far been found to be lacking in this regard, however observations at our academic institution as well as evidence present in the literature support the proposition that CT could indeed be helpful in detecting prostate abnormalities that correspond to neoplasm. The purpose of this study was to prove that areas of focal mass-like enhancement on CT imaging directly correlate with prostate neoplasms as revealed on multi-parametric MRI and follow-up targeted biopsy. Materials and methodsThis was a single institution retrospective study with 27 male subjects. Inclusion criteria required subjects to have a multi-parametric MRI of the prostate between January 1, 2014 and June 1, 2015 and a pelvic venous phase contrast-enhanced CT study between January 1, 2000 and June 1, 2015. Two blinded Radiologists read subjects' CT scans for any abnormalities of the prostate. CT and multi-parametric MRI results were compared and were considered concordant if focal or mass like enhancement to a greater degree than the background parenchyma was detected in the same areas of the prostate on CT scan as areas of decreased T2 signal, perfusion abnormalities, and restricted diffusion on multi-parametric MRI. ResultsCT results were directly compared to multi-parametric MRI findings and biopsy results. The overall agreement of MRI and CT is 85.19% (95% CI: 67.52–94.08%). The positive percent agreement is 78.95% (95% CI: 54.43–93.95%) and the negative percent agreement is 100.0% (95% CL: 63.06–100.0%). When CT results are directly compared to biopsy results, sensitivity and specificity of CT are 63.64% (95% CI: 30.79–89.07%) and 100.0% (95% CI: 47.82–100.0%). The positive predictive value (PPV) is 100.0% (95% CI: 59.04–100.0%) and the negative predictive value (NPV) is 55.56% (95% CI: 21.2–86.3%). When compared to MRI, CT has a lower sensitivity and a higher specificity, as well as a higher PPV and NPV. Logistic regression analysis did not show a significant relationship between concordance of MRI and CT and Gleason score, time between studies, age, and Prostate-specific antigen (PSA) level. ConclusionIncidental focal areas of mass-like enhancement in the peripheral prostate detected on venous phase contrast-enhanced CT imaging may indeed correlate with prostate neoplasm and it would be prudent to suggest further work-up with PSA and perhaps multi-parametric MRI, especially in high-risk patients.

Renal stones on portal venous phase contrast-enhanced CT: does intravenous contrast interfere with detection?

To determine the sensitivity of portal venous phase contrast-enhanced CT for the detection of renal stones. This retrospective study included 97 CT examinations of the abdomen without and with intravenous contrast, including 85 (87.6%) examinations with at least one renal stone on the "gold standard" noncontrast images, as scored by a single radiologist. Three other radiologists each independently reviewed only the contrast-enhanced images from all 97 examinations and recorded all renal stones. Reviewer sensitivity for stones was categorized by stone diameter. Reviewer sensitivity and specificity for stone disease were also calculated on a per-kidney basis. The 97 cases included a total of 238 stones ≥1 mm, with a mean (±SD) of 1.2 ± 1.9 stones per kidney and a stone diameter of 3.5 ± 3.0 mm. Pooling data for the three reviewers, sensitivity for all stones was 81%; sensitivity for stones ≥2, ≥3, ≥4, and ≥5 mm was 88%, 95%, 99%, and 98%, respectively. Sensitivity for stone disease on a per-kidney basis was 94% when considering all stones; when considering only stones ≥2, ≥3, and ≥4 mm, sensitivity was 96%, 99%, and 100%, respectively. Specificity for stone disease on a per-kidney basis was 98% overall, 99% when considering only stones ≥2 mm, and 100% when considering only stones ≥3 mm. Contrast-enhanced CT is highly sensitive for the detection of renal stones ≥3 mm in diameter and less sensitive for smaller stones. In cases where the clinical diagnosis is uncertain and performance of a CT examination is being contemplated, intravenous contrast utilization would allow assessment for stone disease while also optimizing evaluation for other conditions.

Detection of Hepatic Steatosis on Contrast-Enhanced CT Images: Diagnostic Accuracy of Identification of Areas of Presumed Focal Fatty Sparing

The purpose of this article is to determine the diagnostic accuracy of identifying focal areas of increased density along the gallbladder fossa or in the periphery of segment IV for diagnosing hepatic steatosis. Five hundred consecutive three-phase CT examinations were retrospectively evaluated. Two reference standards for hepatic steatosis were determined using the unenhanced CT examination: a liver-spleen attenuation difference of greater than 10 HU and the absolute attenuation of the liver less than 40 HU. The portal venous phase was independently analyzed by two radiologists. Hepatic steatosis was diagnosed on the contrast-enhanced images if there was increased attenuation in the liver, either at the gallbladder fossa or in the posterior medial aspect of segment IV, when compared with background liver parenchyma. The criterion of relative liver-spleen attenuation difference diagnosed 38 cases. The criterion of absolute liver attenuation less than 40 HU diagnosed 44 cases. Of these cases, hepatic steatosis was diagnosed on the portal venous phase in 23 cases (κ = 1.0), with no false-positive cases. The criterion of relative liver-spleen attenuation difference yielded sensitivity, specificity, positive predictive value, and negative predictive value of 60.5%, 100%, 100%, and 96.9%, respectively. The criterion of absolute liver attenuation less than 40 HU yielded sensitivity, specificity, positive predictive value, and negative predictive value of 52.5%, 100%, 100%, and 95.7%, respectively. Qualitative evaluation of the liver on a portal venous phase contrast-enhanced CT is highly specific for the diagnosis of hepatic steatosis; the sensitivity of the method, however, is rather low.

Are contrast media required for (68)Ga-DOTATOC PET/CT in patients with neuroendocrine tumours of the abdomen?

To determine the value of intravenous contrast medium in (68)Ga-DOTA-Phe(1)-Tyr(3)-octreotide - (68)Ga-DOTATOC - PET/CT for the detection of abdominal neuroendocrine tumours (NET). In fifty-five patients with known or suspected NETs of the abdomen PET/CT was performed on a 64-row multi-detector hybrid system. For PET, 150 MBq of (68)Ga-DOTATOC were injected intravenously. Full-dose unenhanced, and arterial- and venous-phase contrast-enhanced CT images were obtained. Unenhanced and contrast-enhanced PET/CT images were evaluated separately for the presence of NETs on a per-region basis, by two separate teams with different experience levels. On unenhanced PET/CT, sensitivity and specificity ranged from 89.3% (junior team) to 92% (senior team), and 99.1% (junior team) to 99.2% (senior team), respectively. On contrast-enhanced PET/CT, sensitivity and specificity ranged from 92.3% (junior team) to 98.5% (senior team), and 99.4% (junior team) to 99.5% (senior team), respectively. These increases in sensitivity and specificity, due to the use of contrast-enhanced images, were statistically significant (P < 0.05). Intravenous contrast medium only moderately, aleit significantly, improves the sensitivity of (68)Ga-DOTATOC PET/CT for the detection of abdominal NETs, and hardly affects specificity. Thus, while contrast enhancement is justified to achieve maximum sensitivity, unenhanced images may be sufficient for routine PET/CT in NET patients. Contrast media moderately improve the sensitivity of (68)Ga-DOTATOC PET/CT for neuroendocrine tumours. Contrast media hardly affect the specificity of (68)Ga-DOTATOC PET/CT for neuroendocrine tumours. Unenhanced PET/CT is sufficient for routine imaging of patients with neuroendocrine tumours.

The Corona Mortis, a Frequent Vascular Variant Susceptible to Blunt Pelvic Trauma: Identification at Routine Multidetector CT

To determine if contrast-enhanced multidetector computed tomography (CT) can demonstrate the "corona mortis" (translated as "crown of death"), a common variant obturator artery originating from the external iliac artery system that is susceptible to pelvic trauma. A representative case study is described. The authors evaluated 50 consecutive patients undergoing routine clinically indicated, standard protocol, venous phase contrast-enhanced multidetector CT of the pelvis, 25 with a 16-slice scanner and 25 with a 64-slice scanner. Three data sets were created for each study, as follows: (a) 5 x 5-mm axial, (b) 1.25 x 1.0-mm axial, and (c) 1.25 x 1.0-mm coronal. Three radiologists independently reviewed the images for the presence or absence of corona morti. One hundred hemipelves in 50 patients were evaluated. In total, 29 corona morti were identified, including 10 on the 5-mm axial images, 25 on the 1.25-mm coronal images, and 29 on the 1.25-mm axial images. By consensus agreement, interpretation of challenging cases was easiest with the 64-slice images. The corona mortis variant can be identified on routine contrast-enhanced multidetector CT scans in about one-third of patients. Thin (1.25-mm) CT reconstructions demonstrate this variation much more frequently than 5-mm-thick images. This suggests that the corona mortis may be prospectively identified at contrast-enhanced multidetector CT in pelvic trauma patients and help guide subsequent endovascular embolization. However, further study in the trauma population is necessary to confirm this.