Renal Lesion Characterization with Spectral CT: Determining the Optimal Energy for Virtual Monoenergetic Reconstruction

Schabel, Christoph; Patel, Bhavik N.; Harring, Scott; Duvnjak, Petar; Ramírez-Giraldo, Juan Carlos; Nikolaou, Konstantin; Nelson, Rendon C.; Farjat, Alfredo E.; Marin, Daniele

doi:10.1148/radiol.2018171657

Cited by 26 publications

(15 citation statements)

References 32 publications

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“…Our findings showed that 40 keV VMI (+) provided the optimal visualization of NPC T1 and BH, and combined image features with quantitative parameters yielded the best performance for differentiating the 2 entities, with an AUC of 0.963, a sensitivity of 89.4%, and a specificity of 93.2%. DECT could yield VMIs at low-energy levels, increasing image contrast and reducing artifacts, enabling improvement of tumor discrimination (25)(26)(27). In our study, 40 keV VMI (+) in the contrast-enhanced phase yielded the highest subjective image quality scores, SNR and CNR, to detect NPC T1 .…”

Section: Discussionmentioning

confidence: 52%

Multiparametric dual-energy CT to differentiate stage T1 nasopharyngeal carcinoma from benign hyperplasia

Shen¹,

Yuan²,

Liu³

et al. 2021

Quant Imaging Med Surg

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Background: Stage T1 nasopharyngeal carcinoma (NPC T1 ) and benign hyperplasia (BH) are 2 common causes of nasopharyngeal mucosa/submucosa thickening without specific clinical symptoms. The treatment management of these 2 entities is significantly different. Reliable differentiation between the 2 entities is critical for the treatment decision and prognosis of patients. Therefore, our study aims to explore the optimal energy level of noise-optimized virtual monoenergetic images [VMI (+)] derived from dual-energy computed tomography (DECT) to display NPC T1 and BH and to explore the clinical value of DECT for differentiating these 2 diseases.Methods: A total of 91 patients (44 NPC T1 , 47 BH) were enrolled. The demarcation of the lesion margins and overall image quality, noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were evaluated for 40-80 kiloelectron volts (keV) VMIs (+) and polyenergetic images in the contrast-enhanced phase. Image features were assessed in the contrast-enhanced images with optimal visualization of NPC T1 and BH. The demarcation of NPC T1 and BH in iodine-water maps was also assessed. The contrast-enhanced images were used to calculate the slope of the spectral Hounsfield unit curve (λ HU ) and normalized iodine concentration (NIC). The nonenhanced phase images were used to calculate the normalized effective atomic number (NZ eff ).The attenuation values on 40-80 keV VMIs (+) in the contrast-enhanced phase were recorded. The diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis.Results: The 40 keV VMI (+) in the enhanced phase yielded higher demarcation of the lesion margins scores, overall image quality scores, noise, SNR, and CNR values than 50-80 keV VMIs (+) and polyenergetic images. NPC T1 yielded higher attenuation values on VMI (+) at 40 keV (A 40 ), NIC, λ HU , and NZ eff values than BH. The multivariate logistic regression model combining image features (tumor symmetry) with quantitative parameters (A 40 , NIC, λ HU , and NZ eff ) yielded the best performance for differentiating the 2 diseases (AUC: 0.963, sensitivity: 89.4%, specificity: 93.2%). Conclusions:The combination of DECT-derived image features and quantitative parameters contributed to the differentiation between NPC T1 and BH.

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Section: Discussionmentioning

confidence: 52%

Multiparametric dual-energy CT to differentiate stage T1 nasopharyngeal carcinoma from benign hyperplasia

Shen¹,

Yuan²,

Liu³

et al. 2021

Quant Imaging Med Surg

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“…Two radiologists independently drew circular regions of interest (ROIs) at the maximum slice of the tumor with an average area of 97.27 mm 2 on 70 keV monochromatic images in the AP and VP. Previous studies have shown that 70 keV monochromatic images may provide an optimal trade-off between sensitivity and specificity for abdominal lesion analysis ( 14 ). ROIs were placed on solid areas to avoid vascular, necrotic, and cystic changes as much as possible.…”

Section: Methodsmentioning

confidence: 99%

Development of a Nomogram Combining Clinical Risk Factors and Dual-Energy Spectral CT Parameters for the Preoperative Prediction of Lymph Node Metastasis in Patients With Colorectal Cancer

et al. 2021

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ObjectiveThis study aimed to develop a dual-energy spectral computed tomography (DESCT) nomogram that incorporated both clinical factors and DESCT parameters for individual preoperative prediction of lymph node metastasis (LNM) in patients with colorectal cancer (CRC).Material and MethodsWe retrospectively reviewed 167 pathologically confirmed patients with CRC who underwent enhanced DESCT preoperatively, and these patients were categorized into training (n = 117) and validation cohorts (n = 50). The monochromatic CT value, iodine concentration value (IC), and effective atomic number (Eff-Z) of the primary tumors were measured independently in the arterial phase (AP) and venous phase (VP) by two radiologists. DESCT parameters together with clinical factors were input into the prediction model for predicting LNM in patients with CRC. Logistic regression analyses were performed to screen for significant predictors of LNM, and these predictors were presented as an easy-to-use nomogram. The receiver operating characteristic curve and decision curve analysis (DCA) were used to evaluate the clinical usefulness of the nomogram.ResultsThe logistic regression analysis showed that carcinoembryonic antigen, carbohydrate antigen 199, pericolorectal fat invasion, ICAP, ICVP, and Eff-ZVP were independent predictors in the predictive model. Based on these predictors, a quantitative nomogram was developed to predict individual LNM probability. The area under the curve (AUC) values of the nomogram were 0.876 in the training cohort and 0.852 in the validation cohort, respectively. DCA showed that our nomogram has outstanding clinical utility.ConclusionsThis study presents a clinical nomogram that incorporates clinical factors and DESCT parameters and can potentially be used as a clinical tool for individual preoperative prediction of LNM in patients with CRC.

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“…The original data were reconstructed into 1.25 mm virtual monochromatic image sets using the adaptive statistical iterative reconstruction (ASIR) algorithm at 40% strength. The corticomedullary phase images at 70 keV energy level 17 were imported into ITK-Snap v.3.4.0 software (An open source software for medical image segmentation, URL https://www. itksnap.…”

Section: Voi Delineation and Radiomics Features Calculationmentioning

confidence: 99%

Prediction models for clear cell renal cell carcinoma ISUP/WHO grade: comparison between CT radiomics and conventional contrast-enhanced CT

Han

Yan

et al. 2020

The British Journal of Radiology

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Objectives: Comparing the prediction models for the ISUP/WHO grade of clear cell renal cell carcinoma (ccRCC) based on CT radiomics and conventional contrast-enhanced CT (CECT). Methods: The corticomedullary Phase images of 119 cases of low-grade (I and II) and high-grade (III and IV) ccRCC based on 2016 ISUP/WHO pathological grading criteria were analyzed retrospectively. The patients were randomly divided into training and validation set by stratified sampling according to 7:3 ratio.Prediction models of ccRCC differentiation were constructed using CT radiomics and conventional CECT findings in the training setandwere validated using validation set.The discrimination, calibration, net reclassification index (NRI) and integrated discrimination improvement index (IDI) of the two prediction models were further compared.The decision curve was used to analyze the net benefit of patients under different probability thresholds of the two models. Results: In the training set, the C-statistics of radiomics prediction model was statistically higher than that of CECT (p < 0.05), with NRI of 9.52% and IDI of 21.6%, both with statistical significance (p < 0.01).In the validation set, the C-statistics of radiomics prediction model was also higher but did not show statistical significance (p = 0.07). The NRI and IDI was 14.29 and 33.7%, respectively, both statistically significant (p < 0.01). Validation setdecision curve analysis showed the net benefit improvement of CT radiomics prediction model in the range of 3–81% over CECT. Conclusion: The prediction model using CT radiomics in corticomedullary phaseis more effective for ccRCC ISUP/WHO grade than conventional CECT. Advances in knowledge: As a non-invasive analysis method, radiomics can predict the ISUP/WHO grade of ccRCC more effectively than traditional enhanced CT.

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Renal Lesion Characterization with Spectral CT: Determining the Optimal Energy for Virtual Monoenergetic Reconstruction

Cited by 26 publications

References 32 publications

Multiparametric dual-energy CT to differentiate stage T1 nasopharyngeal carcinoma from benign hyperplasia

Multiparametric dual-energy CT to differentiate stage T1 nasopharyngeal carcinoma from benign hyperplasia

Development of a Nomogram Combining Clinical Risk Factors and Dual-Energy Spectral CT Parameters for the Preoperative Prediction of Lymph Node Metastasis in Patients With Colorectal Cancer

Prediction models for clear cell renal cell carcinoma ISUP/WHO grade: comparison between CT radiomics and conventional contrast-enhanced CT

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