Quantitative spectral CT evaluation of kidney tumors with the stretched-exponential nonlinear regression analysis model

Spectral CT imaging versus conventional CT post-processing technique in differentiating malignant and benign renal tumors

OBJECTIVE

Quantitative comparison of the diagnostic value of spectral CT imaging and conventional CT post-processing technique in differentiating malignant and benign renal tumors.

METHODS

A total of 209 patients with renal tumors who had undergone CT enhancement were assigned to three groups-clear cell renal cell carcinoma (ccRCC, n = 106), non-ccRCC (n = 60), and benign renal tumor (n = 43) groups. Parametric CT enhancement of each tumor based on spectral CT and conventional CT was performed using in-house software, and the iodine concentration, water content, slope, and density values among the three groups were compared. The receiver operating characteristic (ROC) curve analysis was performed to determine the optimum diagnostic thresholds, the area under the ROC curve (AUC), sensitivity, specificity, and accuracy of the above parameters.

RESULTS

The iodine concentration, slope, and density values were higher in the ccRCCs group compared to the non-ccRCCs and benign renal tumor groups (p < 0.05). Moreover, the iodine concentration, slope, and density values were higher in benign renal tumors compared to non-ccRCCs (p < 0.05). According to the ROC curve analysis, iodine concentration presented the highest diagnostic efficacy in differentiating ccRCCs/non-ccRCCs from benign renal tumors. The pairwise comparisons of the ROC curves and the diagnostic efficacies revealed that ROI-based CT enhancement was worse than the spectral CT imaging analysis in terms of density (p < 0.05).

CONCLUSION

Iodine concentration presented the highest diagnostic efficacy in differentiating ccRCCs/non-ccRCCs from benign renal tumors.

ADVANCES IN KNOWLEDGE

1. The iodine concentration, slope, and density values were higher for the ccRCCs compared to non-ccRCCs and benign renal tumors.2. Iodine concentration presented the highest diagnostic efficacy in differentiating ccRCCs/non-ccRCCs from benign renal tumors.3. Spectral CT imaging analysis performed better than conventional CT in differentiating malignant and benign renal tumors.

Pre-treatment spectral CT combined with CT perfusion can predict hemorrhagic transformation after thrombolysis in patients with acute ischemic stroke

OBJECTIVE

To investigate the value of pre-treatment spectral CT angiography (CTA) in predicting hemorrhagic transformation (HT) after intravenous thrombolysis (IVT) treatment in acute ischemic stroke (AIS) patients.

MATERIALS AND METHODS

AIS patients who underwent IVT with recombinant tissue plasminogen activator and pre-treatment head and neck spectral CTA and head CT perfusion (CTP) from January 2018 to June 2020 were reviewed retrospectively. Finally, 20 patients were included in the HT group and 22 age-matched patients were included in the non-HT group. Spectral and CTP parameters of the region of interest on pre-treatment CTA axial raw images and CTP images, including the infarct core (IC) and ischemic penumbral (IP) regions, were recorded. The differences in clinical variables, CTP, collateral scores and spectral parameters between the two groups were analyzed. Three multivariate logistic regression models were then developed, where model 1 included clinical and spectral parameters, model 2 included clinical and CTP parameters and a combined model included clinical, CTP, and spectral parameters. Receiver operating characteristic analysis was used to evaluate the performance of the multivariate model.

RESULTS

Patients with HT had higher Safe Implementation of Treatments in Stroke (SITS) score (p = 0.023), the volume of perfusion lesions (p = 0.005), the volume of IP (p = 0.003), the mean transit time (MIT) in the IC area (p = 0.012), as well as the TTP in IP area (p = 0.015) compared with patients without HT. The HT group showed significantly lower CBF in the IC area (p = 0.019), iodine concentration (p = 0.017) and the effective atomic number (p = 0.024) in the IP area than non-HT group. And the slope of the spectral curve of the HT group in the IP region was larger than that of the non-HT group (p = 0.023). Gender, age, SITS score, the volume of entire perfusion lesion, CBF and MIT in the IC area, TTP in the IP area, as well as iodine concentration in the IP area were included in the final multivariate model for predicting HT. And CBF in the IC area (OR = 0.779, 95 % CI:0.609-0.996, p = 0.046) as well as the iodine concentration of IP area (OR = 0.343, 95 % CI: 0.131-0.901, p = 0.030) were proved to be independent predictors for HT. The combined model including clinical, spectral, and CTP parameters, showed improved accuracy compared to the other two models, while the Delong test did not suggest a statistically significant difference (both p values > 0.05).

CONCLUSIONS

The iodine concentration of IP area derived from pre-treatment spectral CTA was an independent predictor of HT after IVT treatment for AIS patients. Moreover, multivariate models combined with clinical, spectral, and CTP parameters may be able to predict HT.

MRI-Based Radiomics and Urine Creatinine for the Differentiation of Renal Angiomyolipoma With Minimal Fat From Renal Cell Carcinoma: A Preliminary Study

Objectives

Standard magnetic resonance imaging (MRI) techniques are different to distinguish minimal fat angiomyolipoma (mf-AML) with minimal fat from renal cell carcinoma (RCC). Here we aimed to evaluate the diagnostic performance of MRI-based radiomics in the differentiation of fat-poor AMLs from other renal neoplasms.

Methods

A total of 69 patients with solid renal tumors without macroscopic fat and with a pathologic diagnosis of RCC (n=50) or mf-AML (n=19) who underwent conventional MRI and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) were included. Clinical data including age, sex, tumor location, urine creatinine, and urea nitrogen were collected from medical records. The apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were measured from renal tumors. We used the ITK-SNAP software to manually delineate the regions of interest on T2-weighted imaging (T2WI) and IVIM-DWI from the largest cross-sectional area of the tumor. We extracted 396 radiomics features by the Analysis Kit software for each MR sequence. The hand-crafted features were selected by using the Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO). Diagnostic models were built by logistic regression analysis. Receiver operating characteristic curve analysis was performed using five-fold cross-validation and the mean area under the curve (AUC) values were calculated and compared between the models to obtain the optimal model for the differentiation of mf-AML and RCC. Decision curve analysis (DCA) was used to evaluate the clinical utility of the models.

Results

Clinical model based on urine creatinine achieved an AUC of 0.802 (95%CI: 0.761-0.843). IVIM-based model based on f value achieved an AUC of 0.692 (95%CI: 0.627-0.757). T2WI-radiomics model achieved an AUC of 0.883 (95%CI: 0.852-0.914). IVIM-radiomics model achieved an AUC of 0.874 (95%CI: 0.841-0.907). Combined radiomics model achieved an AUC of 0.919 (95%CI: 0.894-0.944). Clinical-radiomics model yielded the best performance, with an AUC of 0.931 (95%CI: 0.907-0.955). The calibration curve and DCA confirmed that the clinical-radiomics model had a good consistency and clinical usefulness.

Conclusion

The clinical-radiomics model may be served as a noninvasive diagnostic tool to differentiate mf-AML with RCC, which might facilitate the clinical decision-making process.

One half-scan dual-energy CT imaging using the Dual-domain Dual-way Estimated Network (DoDa-Net) model

BACKGROUND

Compared with single-energy computed tomography (CT), dual-energy CT (DECT) can distinguish materials better. However, most DECT reconstruction theories require two full-scan projection datasets of different energies, and this requirement is hard to meet, especially for cases where a physical blockage disables a full circular rotation. Thus, it is critical to relax the requirements of data acquisition to promote the application of DECT.

METHODS

A flexible one half-scan DECT scheme is proposed, which acquires two projection datasets on two-quarter arcs (one for each energy). The limited-angle problem of the one half-scan DECT scheme can be solved by a reconstruction method. Thus, a dual-domain dual-way estimation network called DoDa-Net is proposed by utilizing the ability of deep learning in non-linear mapping. Specifically, the dual-way mapping Generative Adversarial Network (DM-GAN) was first designed to mine the relationship between two different energy projection data. Two half-scan projection datasets were obtained, the data of which was twice that of the original projection dataset. Furthermore, the data transformation from the projection domain to the image domain was realized by the total variation (TV)-based method. In addition, the image processing network (Im-Net) was employed to optimize the image domain data.

RESULTS

The proposed method was applied to a digital phantom and real anthropomorphic head phantom data to verify its effectiveness. The reconstruction results of the real data are encouraging and prove the proposed method's ability to suppress noise while preserving image details. Also, the experiments conducted on simulated data show that the proposed method obtains the closest results to the ground truth among the comparison methods. For low- and high-energy reconstruction, the peak signal-to-noise ratio (PSNR) of the proposed method is as high as 40.3899 and 40.5573 dB, while the PSNR of other methods is lower than 36.5200 dB. Compared with FBP, TV, and other GAN-based methods, the proposed method reduces root mean square error (RMSE) by, respectively, 0.0124, 0.0037, and 0.0016 for low-energy reconstruction, and 0.0102, 0.0028, and 0.0015 for high-energy reconstruction.

CONCLUSIONS

The developed DoDa-Net model for the proposed one half-scan DECT scheme consists of two stages. In stage one, DM-GAN is used to realize the dual map of projection data. In stage two, the TV-based method is employed to transform the data from the projection domain to the image domain. Furthermore, the reconstructed image is processed by the Im-Net. According to the experimental results of qualitative and quantitative evaluation, the proposed method has advantages in detail preservation, indicating the potential of the proposed method in one half-scan DECT reconstruction.