Iodine-based Material Decomposition Images Research Articles

Multi-DECT image-based intratumoral and peritumoral radiomics for preoperative prediction of muscle invasion in bladder cancer

ObjectivesTo assess the predictive value of intratumoral and peritumoral radiomics based on Dual-energy CT urography (DECTU) multi-images for preoperatively predicting the muscle invasion status of bladder cancer (BCa). Material and methodsThis retrospective analysis involved 202 BCa patients who underwent DECTU. DECTU-derived quantitative parameters were identified as risk factors through stepwise regression analysis to construct a DECT model. The radiomic features from the intratumoral and 3mm outward peritumoral regions were extracted from the 120 kVp-like, 40 keV, 100 keV, and iodine-based material-decomposition (IMD) images in the venous-phase and were screened using Mann-Whitney U test, Spearman correlation analysis, and LASSO. Radiomics models were developed using the Multilayer Perceptron for the intratumoral, peritumoral and intra- and peritumoral (IntraPeri) regions. Subsequently, a nomogram was created by integrating the multi-image IntraPeri radiomics and DECT model. Model performance was evaluated using area-under-the-curve (AUC), accuracy, sensitivity, and specificity. ResultsNormalized iodine concentration (NIC) was identified as an independent predictor for the DECT model. The IntraPeri model demonstrated superior performance compared to the intratumoral and peritumoral models both in 40 keV (0.830 vs. 0.766 vs. 0.763) and IMD images (0.881 vs. 0.840 vs. 0.821) in the test cohort. In the test cohort, the nomogram exhibited the best predictability (AUC=0.886, accuracy=0.836, sensitivity=0.737, and specificity=0.881), outperformed the DECT model (AUC=0.763, accuracy=0.754, sensitivity=0.632, and specificity=0.810) in predicting muscle invasion status of BCa with a statistically significant difference (p< 0.05). ConclusionThe nomogram, incorporating IntraPeri radiomics and NIC, serves as a valuable and non-invasive tool for preoperatively assessing the muscle invasion status of BCa.

Radiomics Features Using Dual-energy CT for Lymph Nodes After Preoperative Chemotherapy for Esophageal Cancer.

Progress has been made in a triplet preoperative chemotherapy regimen for advanced esophageal cancer. We performed a preliminary investigation of the radiomics features of pathological lymph node metastasis after neoadjuvant chemotherapy using dual-energy computed tomography (DECT). From January to December 2022, 36 lymph nodes from 10 patients with advanced esophageal cancer who underwent contrast-enhanced DECT after neoadjuvant chemotherapy and radical surgery in our department were studied. Radiomics features were extracted from iodine-based material decomposition images at the portal venous phase constructed by DECT using MATLAB analysis software. Receiver operating characteristic (ROC) analysis and cut-off values were determined for the presence or absence of pathological metastasis. ROC for the short axis of the pathologically positive lymph nodes showed an AUC of 0.713. Long run emphasis (LRE) within gray-level run-length matrix (GLRLM) was confirmed with a high AUC of 0.812. Sensitivity and specificity for lymph nodes with a short axis >10 mm were 0.222 and 1, respectively. Sensitivity and specificity for LRE within GLRLM were 0.722 and 0.833, respectively. Sensitivity and specificity for small zone emphasis (SZE) within gray-level size zone matrix (GLSZM) were 0.889 and 0.667, and zone percentage (ZP) values within GLSZM were 0.722 and 0.778, respectively. Discrimination of existing metastases using radiomics showed significantly higher sensitivity compared to lymph node short axis >10 mm (odds ratios of LRE, SZE, and ZP: 9.1, 28, and 9.1, respectively). Evaluation of radiomics analysis using DECT may enable a more detailed evaluation of lymph node metastasis after neoadjuvant chemotherapy.

The Value of Dual-Energy Computed Tomography-Based Radiomics in the Evaluation of Interstitial Fibers of Clear Cell Renal Carcinoma.

We investigated the potential of dual-energy computed tomography (DECT) radiomics in assessing cancer-associated fibroblasts in clear cell renal carcinoma (ccRCC). A retrospective analysis was conducted on 132 patients with ccRCC. The arterial and venous phase iodine-based material decomposition images (IMDIs), virtual non-contrast images, 70 keV, 100 keV, and 150 keV virtual monoenergetic images, and mixed energy images (MEIs) were obtained from the DECT datasets. On the Radcloud platform, radiomics feature extraction, feature selection, and model establishment were performed. Seven radiomics models were established using the support vector machine. The predictive performance was evaluated by utilizing receiver operating characteristic and the area under the curve (AUC) was calculated. Nomograms were constructed. The combined model demonstrated high efficiency in evaluating pseudocapsule thickness with AUC, specificity, and sensitivity of 0.833, 0.870, and 0.750, respectively in the validation set, surpassing those of other models. The precision, F1-score, and Youden index were also higher for the combined model. For evaluating the number of collagen fibers, the combined model exhibited the highest AUC (0.741) among all models, with a specificity of 0.830 and a sensitivity of 0.330. The AUC in the 150 kv model and IMDI model were slightly lower than those in the combined model (0.728 and 0.710, respectively), with corresponding sensitivity and specificity of 0.560/0.780 and 0.670/0.830. The nomogram exhibited that Rad-score had good prediction efficiency. DECT radiomics features have significant value in evaluating the interstitial fibers of ccRCC. The combined model of IMDI + MEI exhibits superior performance in assessing the thickness of the pseudocapsule, while the combined, 150 keV, and IMDI models demonstrate higher efficacy in evaluating collagen fiber number. Radiomics, combined with imaging features and clinical features, has excellent predictive performance. These findings offer crucial support for the clinical diagnosis, treatment, and prognosis of ccRCC and provide valuable insights into the application of DECT.

Radiomics Analysis to Predict Lymphovascular Invasion of Gastric Cancer Based on Iodine-Based Material Decomposition Images and Virtual Monoenergetic Images.

This study aimed to investigate the utility of virtual monoenergetic images (VMIs) and iodine-based material decomposition images (IMDIs) in the assessment of lymphovascular invasion (LVI) in gastric cancer (GC) patients. A total of 103 GC patients who underwent dual-energy spectral computed tomography preoperatively were enrolled. The LVI status was confirmed by pathological analysis. The radiomics features obtained from the 70 keV VMI and IMDI were used to build radiomics models. Independent clinical factors for LVI were identified and used to build the clinical model. Then, combined models were constructed by fusing clinical factors and radiomics signatures. The predictive performance of these models was evaluated. The computed tomography-reported N stage was an independent predictor of LVI, and the areas under the curve (AUCs) of the clinical model in the training group and testing group were 0.750 and 0.765, respectively. The radiomics models using the VMI signature and IMDI signature and combining these 2 signatures outperformed the clinical model, with AUCs of 0.835, 0.855, and 0.924 in the training set and 0.838, 0.825, and 0.899 in the testing set, respectively. The model combined with the computed tomography-reported N stage and the 2 radiomics signatures achieved the best performance in the training (AUC, 0.925) and testing (AUC, 0.961) sets, with a good degree of calibration and clinical utility for LVI prediction. The preoperative assessment of LVI in GC is improved by radiomics features based on VMI and IMDI. The combination of clinical, VMI-, and IMDI-based radiomics features effectively predicts LVI and provides support for clinical treatment decisions.

Differentiating gastric schwannoma from gastric stromal tumor (≤5 cm) by histogram analysis based on iodine-based material decomposition images: a preliminary study.

This study aims to investigate the value of histogram analysis based on iodine-based material decomposition (IMD) images obtained through dual-energy computed tomography (DECT) to differentiate gastric schwannoma (GS) from gastric stromal tumor (GST) (≤5 cm) preoperatively. From January 2015 to January 2023, 15 patients with GS and 30 patients with GST (≤5 cm) who underwent biphasic contrast-enhanced scans using DECT were enrolled in this study. For each tumor, we reconstructed IMD images at the arterial phase (AP) and venous phase (VP). Nine histogram parameters were automatically extracted and selected using MaZda software based on the IMD of AP and VP, respectively, including mean, 1st, 10th, 50th, 90th, and 99th percentile of the iodine concentration value (Perc.01, Perc.10, Perc.50, Perc.90, and Perc.99), variance, skewness, and kurtosis. The extracted IMD histogram parameters were compared using the Mann-Whitney U-test. The optimal IMD histogram parameters were selected using receiver operating characteristic (ROC) curves. Among the IMD histogram parameters of AP, the mean, Perc.50, Perc.90, Perc.99, variance, and skewness of the GS group were lower than that of the GST group (all P < 0.05). Among the IMD histogram parameters of VP, Perc.90, Perc.99, and the variance of the GS group was lower than those of the GST group (all P < 0.05). The ROC analysis showed that Perc.99 (AP) generated the best diagnostic performance with the area under the curve, sensitivity, and specificity being 0.960, 86.67%, and 93.33%, respectively, when using 71.00 as the optimal threshold. Histogram analysis based on IMD images obtained through DECT holds promise as a valuable tool for the preoperative distinction between GS and GST (≤5 cm).

The added value of radiomics from dual-energy spectral CT derived iodine-based material decomposition images in predicting histological grade of gastric cancer

BackgroundThe histological differentiation grades of gastric cancer (GC) are closely related to treatment choices and prognostic evaluation. Radiomics from dual-energy spectral CT (DESCT) derived iodine-based material decomposition (IMD) images may have the potential to reflect histological grades.MethodsA total of 103 patients with pathologically proven GC (low-grade in 40 patients and high-grade in 63 patients) who underwent preoperative DESCT were enrolled in our study. Radiomic features were extracted from conventional polychromatic (CP) images and IMD images, respectively. Three radiomic predictive models (model-CP, model-IMD, and model-CP–IMD) based on solely CP selected features, IMD selected features and CP coupled with IMD selected features were constructed. The clinicopathological data of the enrolled patients were analyzed. Then, we built a combined model (model-Combine) developed with CP–IMD and clinical features. The performance of these models was evaluated and compared.ResultsModel-CP–IMD achieved better AUC results than both model-CP and model-IMD in both cohorts. Model-Combine, which combined CP–IMD radiomic features, pT stage, and pN stage, yielded the highest AUC values of 0.910 and 0.912 in the training and testing cohorts, respectively. Model-CP–IMD and model-Combine outperformed model-CP according to decision curve analysis.ConclusionDESCT-based radiomics models showed reliable diagnostic performance in predicting GC histologic differentiation grade. The radiomic features extracted from IMD images showed great promise in terms of enhancing diagnostic performance.

Dual-energy CT-based radiomics nomogram in predicting histological differentiation of head and neck squamous carcinoma: a multicenter study.

To develop and validate a dual-energy CT (DECT)-based radiomics nomogram from multicenter trials for predicting the histological differentiation of head and neck squamous cell carcinoma (HNSCC). A total of 178 patients (112 in the training and 66 in the validation cohorts) from eight institutions with histologically proven HNSCCs were included in this retrospective study. Radiomics-signature models were constructed from features extracted from virtual monoenergetic images (VMI) and iodine-based material decomposition images (IMDI), reconstructed from venous-phase DECT images. Clinical factors were also assessed to build a clinical model. Multivariate logistic regression analysis was used to develop a nomogram combining the radiomics signature models and clinical model for predicting poorly differentiated HNSCC and moderately well-differentiated HNSCC. The predictive performance of the clinical model, radiomics signature models, and nomogram was compared. The calibration degree of the nomogram was also assessed. The tumor location, VMI-signature, and IMDI-signature were associated with the degree of HNSCC differentiation, and areas under the ROC curves (AUCs) were 0.729, 0.890, and 0.833 in the training cohort and 0.627, 0.859, and 0.843 in the validation cohort, respectively. The nomogram incorporating tumor location and two radiomics-signature models yielded the best performance in training (AUC = 0.987) and validation (AUC = 0.968) cohorts with a good calibration degree. The nomogram that integrated the DECT-based radiomics-signature models and tumor location showed good performance in predicting histological differentiation degree of HNSCC, providing a novel combination for predicting HNSCC differentiation.

The associations among quantitative spectral CT parameters, Ki-67 expression levels and EGFR mutation status in NSCLC

Dual-energy spectral computed tomography (DESCT) is based on fast switching between high and low voltages from view to view to obtain dual-energy imaging data, and it can generate monochromatic image sets, iodine-based material decomposition images and spectral CT curves. Quantitative spectral CT parameters may be valuable for reflecting Ki-67 expression and EGFR mutation status in non-small-cell lung cancer (NSCLC). We investigated the associations among the quantitative parameters generated in DESCT and Ki-67 expression and EGFR mutation in NSCLC. We studied sixty-five NSCLC patients with preoperative DESCT scans, and their specimens underwent Ki-67 and EGFR evaluations. Statistical analyses were performed to identify the spectral CT parameters for the diagnosis of Ki-67 expression and EGFR mutation status. We found that tumour grade and the slope of the spectral CT curve in the venous phase were the independent factors influencing the Ki-67 expression level, and the area under the curve (AUC) of the slope of the spectral CT curve in the venous phase in the receiver operating characteristic analysis for distinguishing different Ki-67 expression levels was 0.901. Smoking status and the normalized iodine concentration in the venous phase were independent factors influencing EGFR mutation, and the AUC of the two-factor combination for predicting the presence of EGFR mutation was 0.807. These results show that spectral CT parameters may be useful for predicting Ki-67 expression and the presence of EGFR mutation in NSCLC.

Diagnostic accuracy of single-source dual-energy computed tomography and ultrasonography for detection of lateral cervical lymph node metastases of papillary thyroid carcinoma.

Dual-energy computed tomography (DECT) imaging can generate iodine-based material decomposition (MD) images and spectral HU curve. This study aimed to investigate the diagnostic accuracy of single-source dual-energy CT (DECT) and ultrasonography (US) for detecting lateral cervical lymph node metastases of papillary thyroid carcinoma (PTC). Thirty patients with PTC were enrolled in the study and underwent DECT and US examination before thyroidectomy and cervical lymph node dissection. The spectral parameters included iodine concentration (IC), normalized iodine concentration (NIC) and slope (λHU) of lymph nodes. The CT morphological parameters included maximal short diameter, shape, margin, calcification and cystic degeneration of lymph nodes. The US morphological parameters included maximal short diameter, calcification and cystic degeneration of lymph nodes. The diagnostic value of every single spectral parameter, combined gemstone spectral image (GSI) parameters, CT morphological parameters and US morphological parameters between metastatic and non-metastatic lymph nodes were statistically compared. Receiver operating characteristic (ROC) curves, sensitivity, and specificity were used to determine the diagnostic value. Ninety-nine lymph nodes from thirty patients were pathologically confirmed. Among them, 70 (70.7%) were metastatic. For single GSI parameters, ROC analysis showed that the area under the curve (AUC) for IC was the highest (AUC =0.937) but the difference was not statistically significant when compared with NIC or slope (λHU) (P>0.05). The optimal diagnostic threshold for IC was 2.56 mg/mL, with a sensitivity, specificity and accuracy of 87.1%, 93.1%, and 88.9%, respectively. The AUC for combined GSI parameter (AUC =0.942) was higher compared with the US morphological parameters (AUC =0.771, P<0.001), with a sensitivity, specificity, and accuracy of 92.9%, 86.2%, and 90.9%, respectively. However AUC did not differ significantly among combined GSI parameters, combined CT morphological parameters and a single application for spectral CT parameters IC (P>0.05). Combined GSI parameters showed better diagnostic accuracy in lateral cervical lymph node metastasis of PTC compared with that of combined US morphological parameters. IC alone showed excellent diagnostic stability and could be performed easily.

Radiomics Analysis of Iodine-Based Material Decomposition Images With Dual-Energy Computed Tomography Imaging for Preoperatively Predicting Microsatellite Instability Status in Colorectal Cancer.

Purpose: The aim of this study was to investigate the value of radiomics analysis of iodine-based material decomposition (MD) images with dual-energy computed tomography (DECT) imaging for preoperatively predicting microsatellite instability (MSI) status in colorectal cancer (CRC).Methods: This study included 102 CRC patients proved by postoperative pathology, and their MSI status was confirmed by immunohistochemistry staining. All patients underwent preoperative DECT imaging scanned on either a Revolution CT or Discovery CT 750HD scanner, and the iodine-based MD images in the venous phase were reconstructed. The clinical, CT-reported, and radiomics features were obtained and analyzed. Data from the Revolution CT scanner were used to establish a radiomics model to predict MSI status (70% samples were randomly selected as the training set, and the remaining samples were used to validate); and data from the Discovery CT 750HD scanner were used to test the radiomics model. The stable radiomics features with both inter-user and intra-user stability were selected for the next analysis. The feature dimension reduction was performed by using Student's t-test or Mann–Whitney U-test, Spearman's rank correlation test, min–max standardization, one-hot encoding, and least absolute shrinkage and selection operator selection method. The multiparameter logistic regression model was established to predict MSI status. The model performances were evaluated: The discrimination performance was accessed by receiver operating characteristic (ROC) curve analysis; the calibration performance was tested by calibration curve accompanied by Hosmer–Lemeshow test; the clinical utility was assessed by decision curve analysis.Results: Nine top-ranked features were finally selected to construct the radiomics model. In the training set, the area under the ROC curve (AUC) was 0.961 (accuracy: 0.875; sensitivity: 1.000; specificity: 0.812); in the validation set, the AUC was 0.918 (accuracy: 0.875; sensitivity: 0.875; specificity: 0.857). In the testing set, the diagnostic performance was slightly lower with AUC of 0.875 (accuracy: 0.788; sensitivity: 0.909; specificity: 0.727). A nomogram based on clinical factors and radiomics score was generated via the proposed logistic regression model. Good calibration and clinical utility were observed using the calibration and decision curve analyses, respectively.Conclusion: Radiomics analysis of iodine-based MD images with DECT imaging holds great potential to predict MSI status in CRC patients.

T and N Staging of Gastric Cancer Using Dual-Source Computed Tomography.

Aim This study is aimed at comparing gastric cancer T and N staging between virtual monochromatic energy images and fusion images generated by dual-source computed tomography (DSCT) dual-energy mode data acquisition prospectively while measuring the iodine concentration of gastric cancer and lymph nodes at different T and N stages from iodine map retrospectively. Methods A total of 71 patients (50 males and 21 females; mean age: 59 ± 11 years) confirmed with gastric cancer by endoscopic biopsy with no neoadjuvant chemotherapy were enrolled for the CT examination before surgeries. The preoperative T and N staging results were compared between groups with pathological results as the gold standard. The iodine concentrations of the gastric lesions and LNs were measured on the iodine-based material decomposition images. All iodine concentration values were normalized against those in the abdominal aorta and defined as normalized iodine concentration (nIC) values. The short axis length of LNs and nIC values were statistically analyzed. Results Group A was better than group B for T3 and T4 staging. No statistically significant difference in the overall accuracies for N staging was found between groups. For the late arterial and delayed phases, T3 and T4 nIC values of the extraserosal adipose tissue showed statistically significant differences. The nIC values between N0 and Nm (N1–N3) showed statistically significant differences in the portal phase only. Conclusions T3 and T4 nIC values of the extraserosal adipose tissue showed statistically significant differences. Hence, dual-source CT may be helpful in the differential diagnosis between T3 and T4.

Dual Energy Spectral CT Imaging in the assessment of Gastric Cancer and cell proliferation: A Preliminary Study

Gastric cancer is one of the main diseases leading to cancer-related death. The recently introduced dual-energy spectral CT (DEsCT), allows to obtain many quantitative measurements from iodine-based material decomposition (MD) images, which contribute to improve the accuracy of staging of GC comparing to multidetector spiral CT. And Ki-67 is a well-recognized nuclear antigen-specific biomarker reflecting cellular proliferation for estimating growth fractions of various tumor types. In the present study we analyzed the features of quantitative measurements (the curve slope (λHU), IC, normalized iodine concentrations (NIC)) obtained from DEsCT and levels of Ki-67 protein expression. We demonstrated that the values between advanced gastric cancer (AGC) and early gastric cancer (EGC) were significantly different both in venous phase (VP) and delayed phase (DP). The values of different level of Ki-67 expression grade were significantly different both in VP and DP. The rank correlation analysis between Ki-67 grade and IC, NIC and λHU values showed significantly positive correlation in VP and DP. These results suggested that quantitative parameters (IC, NIC and λHU) in dual-energy CT imaging can be used to differentiate EGC from AGC, and have significantly positive correlation with Ki-67 antigen expression levels in gastric cancer for indicating tumor cellular proliferation.

Assessing Chemotherapeutic Response in Pancreatic Ductal Adenocarcinoma: Histogram Analysis of Iodine Concentration and CT Number in Single-Source Dual-Energy CT.

The objective of this study was to evaluate the feasibility of histographic analysis of iodine concentration (IC) and CT number on single-source dual-energy CT (DECT) to assess response to first-line chemotherapy in patients with pancreatic ductal adenocarcinoma (PDAC) who received first-line chemotherapy but not radiation therapy. This prospective study was approved by our institutional review board, and patients gave written informed consent. Sixty consecutive patients with PDAC undergoing first-line chemotherapy underwent DECT during the pancreatic parenchymatous phase (PPP) and the equilibrium phase (EP). The IC and CT number of PDAC were measured using PPP and EP iodine-based material decomposition and monochromatic images (65 keV), respectively. Histographic parameters for the IC and CT number of PDACs were obtained, and differences in mean IC (ΔIC) and CT number (ΔHU) between the PPP and the EP were calculated. These parameters were then compared between the response (partial response or stable disease) and nonresponse (progressive disease) groups. Among the histographic parameters, the kurtosis of IC during the PPP (p = 0.018) and ΔIC (p = 0.0004) were identified as significant for differentiating between the two groups. IC diagnostic factor was calculated using the following coefficients of logistic regression analysis: 0.52 - (1.45 × kurtosis of IC during PPP) + (0.69 × ΔIC). The sensitivity, specificity, and area under the ROC curve for differentiating between the two groups were 97.7%, 70.6%, and 0.889, respectively. The IC diagnostic factor is a potential biomarker for assessing chemotherapeutic response in patients with PDAC.

Iodine density Changes in Hepatic and Splenic Parenchyma in Liver Cirrhosis with Dual Energy CT (DECT): A Preliminary Study

To investigate the hemodynamic changes in liver cirrhosis by comparing iodine density in hepatic and splenic parenchyma with 8 cm detector dual energy CT (DECT). Forty-six consecutive patients with liver cirrhosis and 22 healthy volunteers were recruited in this study, and they were all performed contrast enhanced examination with 8 cm detector DECT. All raw data were reconstructed with 1.25 mm slice thickness, Iodine density (in milligrams per milliliter) were measured on iodine-based material decomposition images. Quantitative indices of iodine density (ID), including normalized ID of liver parenchyma for arterial phase (NIDLAP), ID of liver parenchyma for venous phase (IDLVP), ID of splenic parenchyma for arterial phase (IDSAP), ID of splenic parenchyma for venous phase (IDSVP), ID of portal vein in venous phase (IDPVP) and Liver arterial iodine density fraction (AIF) were measured and compared between two groups. The correlation between Child-Pugh grade and other quantitative indices were calculated, with statistical significance as P<0.05. For all 46 liver cirrhosis patients, 10 were classified in grade A, 24 in Grade B and 12 in Grade C. Compared with control group, patients with liver cirrhosis showed (1) no statistical difference in general data (age, gender, height and weight) (all P>0.05), (2) higher iodine density of NIDLAP, IDSVP, IDPVP and AIF, and lower NIDSAP (all P<0.01), (3) NIDLAP, AIF, IDSVP and IDPVP in grade A were all lower than Grade B and C (all P<0.01). (4) AIF and NIDLAP showed positive correlation with Child-Pugh grade, with coefficient of R = 0.71 and R = 0.46, respectively. Based on iodine density measurement in DECT, it is possible to evaluate the hemodynamic changes in liver and spleen parenchyma in liver cirrhosis. Quantitative indices of AIF and NIDLAP demonstrate positive correlation with Child-Pugh grade, which accommodates potential possibility for DECT as a noninvasive tool in assessing the severity of liver cirrhosis.

Lower energy levels and iodine-based material decomposition images increase pancreatic ductal adenocarcinoma conspicuity on rapid kV-switching dual-energy CT.

Multidetector computed tomography (MDCT) is used in the diagnosis of pancreatic ductal adenocarcinoma (PDAC), but it may be inadequate in some cases. Tumor detection can be improved using rapid kV-switching dual-energy CT (rsDECT) and iodine maps. Our aim this study is to evaluate tumor conspicuity in PDAC cases using rsDECT and iodine maps. Ninety cases with PDAC were evaluated rsDECT. Tumor contrast (HU) differences, tumor size, CNR (contrast-noise ratio), and noise were measured at 70keV, individual CNR-energy level, and 45keV, respectively. Quantitative differences in contrast gain ∆70-CNR and ∆CNR-45 were compared. On iodine maps, the iodine concentration measured in the tumor and parenchyma was normalized to the aorta as normalized iodine concentration (NIC) and compared. The median optimized viewing energy level was 51keV. The mean ± SD tumor contrast values were 62 ± 20, 115 ± 48, and 152 ± 48 HU (p < 0.001); the largest axial diameters were 36.6 ± 5.1, 37.9 ± 4.2, and 38.3 ± 3.7mm (p = 0.015); the CNRs were 1.83 ± 0.72, 3.37 ± 0.93, and 2.36 ± 0.56; and the image noise levels were 23.7 ± 6.8, 39.3 ± 11.6, and 59.5 ± 17.2 (p < 0.001) (p < 0.001) for 70keV, optimized energy level, and 45keV, respectively. The mean ± SD contrast gain ∆70-CNR was 63 ± 12; and ∆CNR-45 was 31 ± 26 HU (p < 0.001). NICtumor and NICparenchyma values were 0.62 ± 0.03 and 1.36 ± 0.05mg/mL, respectively (p = 0.004). The use of low energy levels on rsDECT and iodine maps improves tumor conspicuity. This situation may be help better detection of pancreatic tumors.

Correlation between dual-energy spectral CT imaging parameters and pathological grades of non-small cell lung cancer

To investigate the correlation between pathological grades of non-small cell lung cancers (NSCLCs) and quantitative parameters generated in dual-energy spectral computed tomography (CT). Fifty-three patients with NSCLCs who underwent preoperative dual-energy spectral CT imaging and surgical resection were evaluated retrospectively. These patients were divided into a low-grade group and a high-grade group based on their histopathological differentiation. In the arterial phase (AP) and venous phase (VP), iodine concentration (IC) in cancers was measured in iodine-based material decomposition images, and normalised to the IC in the aorta to calculate the normalised iodine concentration (NIC), the spectral CT curve was generated from the monochromatic images to calculate the slope of the spectral curve (λHU). Differences in quantitative parameters (NIC and λHU) were compared using the two-sample t-test. The correlations between spectral CT parameters and tumour grades were evaluated using the Spearman rank correlation analysis. Receiver operating characteristic (ROC) curves were generated to calculate their diagnostic efficacies. The NIC and λHU in the low-grade NSCLC group were significantly higher than those in the high-grade NSCLC group both in AP and VP (all p<0.001). There was a significant negative correlation between spectral CT parameters and pathological grades by the Spearman rank correlation (all p<0.001). ROC analysis indicated that λHU in VP provided the best diagnostic performance in distinguishing high-grade cancers from low-grade cancers (area under the ROC curve [AUC], 0.914; sensitivity, 85.7%; specificity, 84.4%). The quantitative parameters in dual-energy spectral CT imaging provide useful information to differentiate the pathological grades of NSCLCs.

Preliminary study on the diagnostic value of single-source dual-energy CT in diagnosing cervical lymph node metastasis of thyroid carcinoma.

To investigate the value of single-source dual-energy spectral CT imaging in improving the accuracy of preoperative diagnosis of lymph node metastasis of thyroid carcinoma. Thirty-four thyroid carcinoma patients were enrolled and received spectral CT scanning before thyroidectomy and cervical lymph node dissection surgery. Iodine-based material decomposition (MD) images and 101 sets of monochromatic images from 40 to 140 keV were reconstructed after CT scans. The iodine concentrations (IC) of lymph nodes were measured on the MD images and was normalized to that of common carotid artery to obtain the normalized iodine concentration (NIC). The CT number of lymph nodes as function of photon energy was measured on the 101 sets of images to generate a spectral HU curve and to calculate its slope λHU. The measurements between the metastatic and non-metastatic lymph nodes were statistically compared and receiver operating characteristic (ROC) curves were used to determine the optimal thresholds of these measurements for diagnosing lymph nodes metastasis. There were 136 lymph nodes that were pathologically confirmed. Among them, 102 (75%) were metastatic and 34 (25%) were non-metastatic. The IC, NIC and the slope λHU of the metastatic lymph nodes were 3.93±1.58 mg/mL, 0.70±0.55 and 4.63±1.91, respectively. These values were statistically higher than the respective values of 1.77±0.71 mg/mL, 0.29±0.16 and 2.19±0.91 for the non-metastatic lymph nodes (all P<0.001). ROC analysis determined the optimal diagnostic threshold for IC as 2.56 mg/mL, with the sensitivity, specificity and accuracy of 83.3%, 91.2% and 85.3%, respectively. The optimal threshold for NIC was 0.289, with the sensitivity, specificity and accuracy of 96.1%, 76.5% and 91.2%, respectively. The optimal threshold for the spectral curve slope λHU was 2.692, with the sensitivity, specificity and accuracy of 88.2%, 82.4% and 86.8%, respectively. The measurements obtained in dual-energy spectral CT improve the sensitivity and accuracy for preoperatively diagnosing lymph node metastasis in thyroid carcinoma.

Differentiating malignant from benign gastric mucosal lesions with quantitative analysis in dual energy spectral computed tomography: Initial experience.

To investigate the value of quantitative analysis in dual energy spectral computed tomography (DESCT) for differentiating malignant gastric mucosal lesions from benign gastric mucosal lesions (including gastric inflammation [GI] and normal gastric mucosa [NGM]). This study was approved by the ethics committee, and all patients provided written informed consent. A total of 161 consecutive patients (63 with gastric cancer [GC], 48 with GI, and 50 with NGM) who underwent dual-phase contrast enhanced DESCT scans in the arterial phase (AP) and portal venous phase (PVP) were included in this study. Iodine concentration (IC) in lesions was derived from the iodine-based material-decomposition images and normalized to that in the aorta to obtain normalized IC (nIC). The ratios of IC and nIC between the AP and PVP were calculated. Diagnostic confidence for GC and GI was evaluated with reviewing the features including gastric wall thickness, focal, and eccentric on the conventional polychromatic images. All statistical analyses were performed by using statistical software SPSS 17.0 (SPSS, Chicago, IL). IC and nIC in GC differed significantly from those in GI and NGM, except for nICAP in comparing GC with GI. Mean nIC values of GC (0.18 ± 0.06 in AP and 0.62 ± 0.16 in PVP) were significantly higher than that of NGM (0.12 ± 0.03 in AP and 0.37 ± 0.08 in PVP) (all P < 0.05). There was also significant difference for IC values in GC, GI, and NGM (24.19 ± 8.27, 19.07 ± 5.82, and 13.61 ± 2.52 mg/mL, respectively, in AP and 28.00 ± 7.01, 24.66 ± 6.55, and 16.94 ± 3.06 mg/mL, respectively, in PVP). Based on Receiver Operating Characteristic Curve analysis, nIC and IC in PVP had high sensitivities of 88.89% and 90.48%, respectively, in differentiating GC from NGM, while the sensitivities were 71.43% and 88.89% during AP. Ratios IC and nIC ratios did not provide adequate diagnostic accuracy with their area under curves less than 0.65. With the conventional features, the diagnostic accuracies for GC and GI were 75.0% and 98.0%, respectively. Quantitative analysis of DESCT imaging parameters for gastric mucosa, such as nIC and IC, is useful for differentiating malignant from benign gastric mucosal lesions.

Clinical application of dual energy spectral CT method in patients with hepatic alveolar echinococcosis

PurposeTo investigate the usefulness of dual energy spectral computed tomographic (DESCT) imaging parameters in diagnosing hepatic alveolar echinococcosis (AE) disease by comparing with traditional CT. Materials and methodsIn this prospective study, 35 consecutive patients (22 men, 13 women; mean age, 42.6 years) suffering from hepatic echinococcosis (HE) (HE cases were confirmed by surgery, pathology or multiple imaging methods) underwent abdominal DESCT scan, consisting of arterial phase (AP), portal venous phase (PVP) and delayed phase (DP), in dual-energy (80 kV/140 kV) mode. 15 patients had AE; 20 patients were pathologically confirmed cystic echinococcosis (CE). The CT patterns of the AE lesions were classified into solid, pseudocystic, or mixed type. The size, location, invasion findings were recorded. The serial function images (ie, iodine-based material-decomposition CT images, contrast-to-noise ratio images, monochromatic images, spectral curve images) from DESCT data were processed with a software algorithm designed to calculate optimal CNR value, quantitative iodine concentration (QIC), curve slope for evaluation of HE lesions. Sensitivity and specificity were compared between the qualitative and quantitative studies for DESCT and traditional CT in HE. The two-sample test and ROC curve analyses were performed to compare quantitative parameters between AE and CE. ResultsA total of 18 AE lesions (6 solid, 2 pseudocystic, and 10 mixed) with average 87.6 ± 50.7 mm lesion size were assessed. DECT detected more contrast enhanced lesions (71.6%) than traditional CT (50.9). There was a significant difference in the QICs between various layers of the HAE in each of the three scan phases (P < 0.001). The QICs in the marginal zone were significantly higher than that in solid or cystic layers and liver parenchyma. No significant difference was found among the various CT patterns of hepatic AE. 65 keV is the optimal monochromatic image with corresponding contrast noise radio (CNR) 10.24 ± 2.62. The spectrum curve showed “inversed saddle pattern” for HAE lesion. The differences of slopes between solid, margin zone and normal liver parenchyma of HAE lesion were of statistical significances in the region of 40–60 keV and 60–70 keV (P < 0.05). There were statistical significances in slopes of spectrum curve between AE and CE with different keV regions. The spectrum curve of CT was in gradual falling type. ROC showed the areas under the curves (AUC) for k values to differentiate between AE and CE were 1 in region of 40–60 keV and 0.847 in region of 70–140 keV. ConclusionsDESCT could provide more information by using monochromatic images, iodine-based material decomposition images and the quantitative analysis of IC than traditional CT for the diagnosis of HE.

Can Spectral CT Imaging Improve the Differentiation between Malignant and Benign Solitary Pulmonary Nodules?

PurposeTo quantitatively assess the value of dual-energy CT (DECT) in differentiating malignancy and benignity of solitary pulmonary nodules.Materials and MethodsSixty-three patients with solitary pulmonary nodules detected by CT plain scan underwent contrast enhanced CT scans in arterial phase (AP) and venous phase (VP) with spectral imaging mode for tumor type differentiation. The Gemstone Spectral Imaging (GSI) viewer was used for image display and data analysis. Region of interest was placed on the relatively homogeneous area of the nodule to measure iodine concentration (IC) on iodine-based material decomposition images and CT numbers on monochromatic image sets to generate spectral HU curve. Normalized IC (NIC), slope of the spectral HU curve (λHU) and net CT number enhancement on 70keV images were calculated. The two-sample t-test was used to compare quantitative parameters. Receiver operating characteristic curves were generated to calculate sensitivity and specificity.ResultsThere were 63 nodules, with 37 malignant nodules (59%) and 26 benign nodules (41%). NIC, λHU and net CT number enhancement on 70keV images for malignant nodules were all greater than those of benign nodules. NIC and λHU had intermediate to high performances to differentiate malignant nodules from benign ones with the areas under curve of 0.89 and 0.86 respectively in AP, 0.96 and 0.89 respectively in VP. Using 0.30 as a threshold value for NIC in VP, one could obtain sensitivity of 93.8% and specificity of 85.7% for differentiating malignant from benign solitary pulmonary nodules. These values were statistically higher than the corresponding values of 74.2% and 53.8% obtained with the conventional CT number enhancement.ConclusionsDECT imaging with GSI mode provides more promising value in quantitative way for distinguishing malignant nodules from benign ones than CT enhancement numbers.