Analysis of dual energy spectral CT and pathological grading of clear cell renal cell carcinoma (ccRCC)

Nomogram combining pre-operative clinical characteristics and spectral CT parameters for predicting the WHO/ISUP pathological grading in clear cell renal cell carcinoma

PURPOSE

To develop a novel clinical-spectral-computed tomography (CT) nomogram incorporating clinical characteristics and spectral CT parameters for the preoperative prediction of the WHO/ISUP pathological grade in clear cell renal cell carcinoma (ccRCC).

METHODS

Seventy-three ccRCC patients who underwent spectral CT were included in this retrospective analysis from December 2020 to June 2023. The subjects were pathologically divided into low- and high-grade groups (WHO/ISUP 1/2, n = 52 and WHO/ISUP 3/4, n = 21, respectively). Information on clinical characteristics, conventional CT imaging features, and spectral CT parameters was collected. Multivariate logistic regression analyses were conducted to create a nomogram combing clinical data and image data for preoperatively predicting the pathological grade of ccRCC, and the area under the curve (AUC) was utilized to assess the predictive performance of the model.

RESULTS

Multivariate logistic regression analyses revealed that age, systemic immune-inflammation index (SII), and the slope of the spectrum curve in the cortex phase (CP-K) were independent predictors for predicting high-grade ccRCC. The clinical-spectral-CT model exhibited high evaluation efficacy, with an AUC of 0.933 (95% confidence interval [CI]: 0.878-0.998; sensitivity: 0.810; specificity: 0.923). The calibration curve revealed that the predicted probability of the clinical-spectral-CT nomogram could better fit the actual probability, with high calibration. The Hosmer-Lemeshow test showed that the model had a good fitness (χ2 = 5.574, p = 0.695).

CONCLUSION

The clinical-spectral-CT nomogram has the potential to predict WHO/ISUP grading of ccRCC preoperatively.

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.

Prediction of World Health Organization /International Society of Urological Pathology (WHO/ISUP) Pathological Grading of Clear Cell Renal Cell Carcinoma by Dual-Layer Spectral CT

RATIONALE AND OBJECTIVES

To evaluate whether the dual-layer spectral computed tomography urography (DL-CTU) images could predict WHO/ISUP pathological grading of clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

We retrospectively included patients (n = 50) with pathologically confirmed ccRCC who underwent preoperative DL-CTU (from October 2017 to February 2021). They were divided into low-grade (WHO/ISUP 1/2, n = 30) and high-grade groups (WHO/ISUP 3/4, n = 20). The lesion size, attenuation (HU), iodine concentration (IC), normalized IC(NIC), and other quantitative characteristics were compared between the two groups. HU, IC, and NIC were obtained by plotting ROI with two different methods (circular ROI in the solid component or irregular ROI along the tumor edge containing tumor necrotic components). Receiver operating characteristic curves and multivariable model were used to evaluate the ability of parameters to predict WHO/ISUP grade.

RESULTS

Transverse diameter (TD) of low-grade tumors was smaller, and HU in the non-contrast phase of the second method (HU-U-2) was lower than that of high-grade tumors (34.21±15.14 mm vs. 46.50 ± 20.68 mm, 27.33 ± 6.65 HU vs. 31.36 ± 6.09 HU, p< 0.05). The NIC in the nephrographic phase by the two methods (NIC-N-1 and NIC-N-2) of low-grade was higher than that of the high-grade group (0.78± 0.19 vs.0.58 ± 0.22, 0.73 ± 0.42 vs. 0.46 ± 0.22, p< 0.05). The final multivariable model composed of TD, HU-U-2, and NIC-N-1 could predict ccRCC grade with the area under the curve, sensitivity, specificity, and accuracy of 0.852, 70%, 90%, and 82%.

CONCLUSION

Quantitative indicators in DL-CTU images could help predict the WHO/ISUP grade of ccRCC.

Spectral CT - a new supplementary method for preoperative assessment of pathological grades of esophageal squamous cell carcinoma

BACKGROUND

Spectral CT imaging parameters have been reported to be useful in the differentiation of pathological grades in different malignancies. This study aims to investigate the value of spectral CT in the quantitative assessment of esophageal squamous cell carcinoma (ESCC) with different degrees of differentiation.

METHODS

There were 191 patients with proven ESCC who underwent enhanced spectral CT from June 2018 to March 2020 retrospectively enrolled. These patients were divided into three groups based on pathological results: well differentiated ESCC, moderately differentiated ESCC, and poorly differentiated ESCC. Virtual monoenergetic 40 keV-equivalent image (VMI40keV), iodine concentration (IC), water concentration (WC), effective atomic number (Eff-Z), and the slope of the spectral curve(λHU) of the arterial phase (AP) and venous phase (VP) were measured or calculated. The quantitative parameters of the three groups were compared by using one-way ANOVA and pairwise comparisons were performed with LSD. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of these parameters in poorly differentiated groups and non-poorly differentiated groups.

RESULTS

There were significant differences in VMI40keV, IC, Eff-Z, and λHU in AP and VP among the three groups (all p < 0.05) except for WC (p > 0.05). The VMI40keV, IC, Eff-Z, and λHU in the poorly differentiated group were significantly higher than those in the other groups both in AP and VP (all p < 0.05). In the ROC analysis, IC performed the best in the identification of the poorly differentiated group and non-poorly differentiated group in VP (AUC = 0.729, Sensitivity = 0.829, and Specificity = 0.569 under the threshold of 21.08 mg/ml).

CONCLUSIONS

Quantitative parameters of spectral CT could offer supplemental information for the preoperative differential diagnosis of ESCC with different degrees of differentiation.

Virtual monochromatic spectral attenuation curve analysis for evaluation of incidentally detected small renal lesions using rapid kilovoltage-switching dual-energy computed tomography

PURPOSE

To determine whether the spectral attenuation curve on a rapid kilovoltage-switching dual-energy computed tomography (DECT) scan can distinguish enhancing from nonenhancing incidental small (1-4 cm) renal lesions compared with conventional single-energy attenuation changes.

METHODS

This retrospective study enrolled 46 patients with 78 renal lesions (24 enhancing; 54 nonenhancing) who underwent DECT with DE mode performed during the portovenous or nephrographic phase. Final diagnosis of enhancing and nonenhancing masses was confirmed by pathology or imaging following the established criteria. Virtual monochromatic images (VMI) were reconstructed, and the slopes between the VMI dataset at 40-70 keV (Slope HU_40-70), 40-100 keV (Slope HU_40-100), and 40-140 keV (Slope HU_40-140) were measured. Visual assessment of the curve pattern was recorded. Diagnostic accuracies were calculated with a cross-validated Mann-Whitney U test, and correlations of quantitative spectral parameters and intraclass correlation coefficient (ICC) were calculated using Spearman's rho correlation.

RESULTS

All quantitative and qualitative spectral analysis parameters significantly differentiated the enhancing and nonenhancing lesions (P < 0.001). The optimal slope thresholds calculated by cross-validation for Slope HU_40-70, Slope HU_40-100, and Slope HU_40-140 were 3.0, 1.8 and 1.2, respectively for reader 1 and 3.0, 1.9 and 1.15, respectively for reader 2. Using a slope threshold at all datasets yielded a high diagnostic accuracy of 96 for reader 1 and 95 for reader 2. Using a ∆HU threshold of 20 HU yielded an accuracy of 100. Visual analysis of the curve pattern also yielded high accuracy of 94.

CONCLUSIONS

The spectral attenuation curve on rapid kilovoltage-switching DECT gives excellent diagnostic accuracy differentiating between incidental enhancing and nonenhancing renal lesions. This benefit of DECT will be most helpful when the true unenhanced phase is not performed.

A Clinical Radiomics Nomogram Was Developed by Integrating Radiomics Signatures and Clinical Variables to Distinguish High-Grade ccRCC from Type 2 pRCC

Purpose

A nomogram was constructed by combining clinical factors and a CT-based radiomics signature to discriminate between high-grade clear cell renal cell carcinoma (ccRCC) and type 2 papillary renal cell carcinoma (pRCC).

Methods

A total of 142 patients with 71 in high-grade ccRCC and seventy-one in type 2 pRCC were enrolled and split into a training cohort (n = 98) and a testing cohort (n = 44). A clinical factor model containing patient demographics and CT imaging characteristics was designed. By extracting the radiomics features from the precontrast phase, corticomedullary phase (CMP), and nephrographic phase (NP) CT images, a radiomics signature was established, and a Rad-score was computed. By combining the Rad-score and significant clinical factors using multivariate logistic regression analysis, a clinical radiomics nomogram was subsequently developed. The diagnostic performance of these three models was evaluated by using data from both the training and testing groups using a receiver operating characteristic (ROC) curve analysis.

Results

The radiomics signature contained eight validated features from the CT images. The relative enhancement value of CMP (REV1) was an independent risk factor in the clinical factor model. The area under the curve (AUC) value of the clinical radiomics nomogram was 0.974 and 0.952 in the training and testing cohorts, respectively. In the training cohort, the decision curves of the nomogram demonstrated an added overall net advantage compared to the clinical factor model.

Conclusion

A noninvasive prediction tool termed radiomics nomogram, combining clinical criteria and the radiomics signature, may accurately predict high-grade ccRCC and type 2 pRCC before surgery. It also has some importance in assisting clinicians in determining future treatment strategies.

Preoperative Prediction of the Aggressiveness of Oral Tongue Squamous Cell Carcinoma with Quantitative Parameters from Dual-Energy Computed Tomography

Objectives

To determine whether quantitative parameters derived from dual-energy computed tomography (DECT) were predictive of the aggressiveness of oral tongue squamous cell carcinoma (OTSCC) including the pathologic stages, histologic differentiation, lymph node status, and perineural invasion (PNI).

Methods

Between August 2019 and March 2021, 93 patients (mean age, 54.6 ± 13.8 years; 66 men) with pathologically diagnosed OTSCC were enrolled in this prospective study. Preoperative DECT was performed and quantitative parameters (e.g., slope of the spectral Hounsfield unit curve [λ_Hu], normalized iodine concentration [nIC], normalized effective atomic number [nZ_eff], and normalized electron density [nRho]) were measured on arterial phase (AP) and venous phase (VP) DECT imaging. Quantitative parameters from DECT were compared between patients with different pathologic stages, histologic differentiation, lymph node statuses, and perineural invasion statuses. Logistic regression analysis was utilized to assess independent parameters and the diagnostic performance was analyzed by the receiver operating characteristic curves (ROC).

Results

λ_Hu and nIC in AP and λ_Hu, nZ_eff, and nIC in VP were significantly lower in stage III–IV lesions than in stage I–II lesions (p < 0.001 to 0.024). λ_Hu in VP was an independent predictor of tumor stage with an odds ratio (OR) of 0.29, and area under the curve (AUC) of 0.80. λ_Hu and nIC were higher in well-differentiated lesions than in poorly differentiated lesions (p < 0.001 to 0.021). The nIC in VP was an independent predictor of histologic differentiation with OR of 0.31, and AUC of 0.78. λ_Hu and nIC in VP were lower in OTSCCs with lymph node metastasis than those without metastasis (p < 0.001 to 0.005). λ_Hu in VP was the independent predictor of lymph node status with OR of 0.42, and AUC of 0.74. No significant difference was found between OTSCCs without PNI and those with PNI in terms of the quantitative DECT parameters.

Conclusion

DECT can be a complementary means for the preoperative prediction of the aggressiveness of OTSCC.

Value of energy spectrum CT parameters in the differential diagnosis of high-grade clear cell renal cell carcinoma and type II papillary renal cell carcinoma

BACKGROUND

Energy spectrum computed tomography (CT) has become a promising approach for the differential diagnosis of tumor subtypes.

PURPOSE

To explore the value of energy spectrum CT parameters in the differential diagnosis of high-grade clear cell renal cell carcinoma (ccRCC) and type II papillary renal cell carcinoma (pRCC).

MATERIAL AND METHODS

Forty-two cases of high-grade ccRCC and 28 cases of type II pRCC were retrospectively reviewed. All region of interest (ROI) measurements were maintained consistently between the two-phase contrast-enhanced examinations. The ROIs encompassed as much of the enhancing areas of the lesions as possible. Energy spectrum CT parameters of all cases, including the 70 keV (HU) value, normalized iodine concentration (NIC), and energy spectrum curve slope were recorded by two radiologists with over 10 years of experience in abdominal CT diagnosis.

RESULTS

In the cortical phase (CP) and parenchymal phase (PP), the 70 keV (HU) value, NIC, and slope value of the energy spectrum curve of high-grade ccRCC were significantly higher than those of type II pRCC. In the CP, NIC showed the highest differential diagnosis efficiency for the two group tumors, with a sensitivity of 78.9% and a specificity of 77.0%. There was no statistical difference in tumor hemorrhage, tumor envelope, tumor morphology, tumor border, lymph node metastasis, embolism, renal pelvis invasion, or tumor calcification between the two tumor types. However, there was significant difference in the number of tumors (P = 0.019).

CONCLUSION

Energy spectrum CT parameters are valuable for the differential diagnosis of high-grade ccRCC and type II pRCC.

A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma

Objective

This study aims to develop and validate a CT-based radiomics nomogram integrated with clinic-radiological factors for preoperatively differentiating high-grade from low-grade clear cell renal cell carcinomas (CCRCCs).

Methods

370 patients with complete clinical, pathological, and CT image data were enrolled in this retrospective study, and were randomly divided into training and testing sets with a 7:3 ratio. Radiomics features were extracted from nephrographic phase (NP) contrast-enhanced images, and then a radiomics model was constructed by the selected radiomics features using a multivariable logistic regression combined with the most suitable feature selection algorithm determined by the comparison among least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) and ReliefF. A clinical model was established using clinical and radiological features. A radiomics nomogram was constructed by integrating the radiomics signature and independent clinic-radiological features. Performance of these three models was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).

Results

Using multivariate logistic regression analysis, three clinic-radiological features including intratumoral necrosis (OR=3.00, 95% CI=1.30-6.90, p=0.049), intratumoral angiogenesis (OR=3.28, 95% CI=1.22-8.78, p=0.018), and perinephric metastasis (OR=2.90, 95% CI=1.03-8.17, p=0.044) were found to be independent predictors of WHO/ISUP grade in CCRCC. Incorporating the above clinic-radiological predictors and radiomics signature constructed by LASSO, a CT-based radiomics nomogram was developed, and presented better predictive performance than clinic-radiological model and radiomics signature model, with an AUC of 0.891 (95% CI=0.832-0.962) and 0.843 (95% CI=0.718-0.975) in the training and testing sets, respectively. DCA indicated that the nomogram has potential clinical usefulness.

Conclusion

The CT-based radiomics nomogram is a promising tool to predict WHO/ISUP grade of CCRCC preoperatively and noninvasively.

Effect of spectral CT on tumor microvascular angiogenesis in renal cell carcinoma

Background

To examine the value of energetic-spectrum computed tomography (spectral CT) quantitative parameters in renal cell carcinoma (RCC) microvascular angiogenesis.

Methods

The authors evaluated 32 patients with pathologically confirmed RCC who underwent triple-phase contrast-enhanced CT with spectral CT imaging mode from January 2017 to December 2019. Quantitative parameters include parameters derived from iodine concentration (IC) and water concentration (WC) of 120 keV monochromatic images. All specimens were evaluated including the microvascular density (MVD), microvascular area (MVA) and so on. The correlation between IC and WC (including average values and random values) with microvascular parameters were analyzed with Pearson or Spearman rank correlation coefficients.

Results

The MVD of all tumors was 26.00 (15.00–43.75) vessels per field at × 400 magnification. The MVD of RCC correlated positively with the mean IC, mean WC, mean NWC, mean NIC, random IC, random NIC in renal cortical phase, WCD₁, WCD₂, NWCD₂ and ICD₁ (Spearman rank correlation coefficients, r range, 0.362–0.533; all p < 0.05). The MVA of all tumors was (16.16 ± 8.98) % per field at × 400 magnification. The MVA of RCC correlated positively with the mean IC, mean WC, mean NWC, mean NIC, random IC, random NIC in renal cortical, mean WC and mean NWC in renal parenchymal phase, WCD₁, WCD₂, WCD₃, NWCD₂, and NWCD₃ (Pearson or Spearman rank correlation coefficients, r range, 0.357–0.576; all p < 0.05). Microvascular grading correlated positively with the mean NWC, mean NIC and random NIC in renal cortical phase, mean NWC in renal parenchymal phase, NWCD₂, WCD₃, NWCD₃, NICD₂ and NICD₃ (Spearman rank correlation coefficients, r range, 0.367–0.520; all p < 0.05). As for tumor diameter (55.19 ± 19.15), μm, only NWCD₃ was associated with it (Spearman rank correlation coefficients, r = 0.388; p < 0.05).

Conclusions

ICD and WCD of spectral CT have a potential for evaluating RCC microvascular angiogenesis. MVD, MVA and microvascular grade showed moderate positive correlation with ICD and WCD. ICD displayed more relevant than that of WCD. The parameters of renal cortical phase were the best in three phases. NICD and NWCD manifested stronger correlation with microvascular parameters than that of ICD and WCD.

Low monoenergetic DECT detection of pyelonephritis extent

OBJECTIVES

To determine whether contrast enhanced DECT low monoenergetic can improve diagnostic conspicuity of inflamed kidney foci in acute pyelonephritis compared to conventional images.

MATERIALS AND METHODS

A retrospective study of 45 patients with clinical signs of acute pyelonephritis undergoing contrast-enhanced exams on a single source-DECT was conducted. Representative conventional and monoenergetic images were randomized and presented to four abdominal radiologists to determine their preference for inflamed kidney foci detection, and to determine the number of foci identified. Clinical impact of monoenergetic images was assessed using multivariant analysis. Contrast and signal to noise ratios were compared between the images using paired t-tests.

RESULTS

A greater number of foci were detected on the low energetic images for each patient (6.4 ± 5.3 vs. 4.2 ± 3.8, p < 0.02). Additionally, a consistent linear increase in the number of detected foci on the monoenergetic compared to the conventional images was seen (y = 0.10X + 0.36 R² = 0.76). Most notably, in 16% of kidneys a clearly definable focus was detected only on monoenergetic images. SNR and CNR were increased by 2 and 1.5 fold for monoenergetic compared to conventional images (p < 0.001). Monoenergetic images were preferred by all readers for detecting inflamed foci (162/180 reads, P < 0.05), with 79% interreader reliability.

CONCLUSION

Low monoenergetic images enable increased detection of inflamed kidney parenchyma, and permit identification of pathologic foci some of which were not seen on the conventional images. Along with the strong preference of radiologists, these images should be considered beneficial for evaluating acute pyelonephritis.

Application of contrast-enhanced dual-energy spectral CT for differentiating borderline from malignant epithelial ovarian tumours

AIM

To investigate the value of contrast-enhanced dual-energy spectral computed tomography (CT) in differentiating borderline epithelial ovarian tumours (BEOTs) from malignant epithelial ovarian tumours (MEOTs).

MATERIALS AND METHODS

Sixty patients who underwent pelvic contrast-enhanced spectral CT were divided into two groups for analysis based on the tumour types confirmed at histopathological examination (26 BEOTs and 34 MEOTs). The regions of interest (ROIs) were selected on solid tumour components to measure attenuation values on monochromatic image sets (40-140 keV) in all imaging phases and tumour iodine concentrations (IC) on material decomposition images. Differences in the attenuation value between the unenhanced and contrast-enhanced phases (enhancement degree) and between energy strengths (slope k, k = [attenuation at 40 keV- attenuation at 140 keV]/100) were calculated. All measurements between the two groups were compared with independent t-test. Receiver operating characteristic (ROC) curves were generated to calculate the sensitivity, specificity and area under the ROC curve (AUC). Logistic regression analysis was used to evaluate the diagnostic efficacy of using combined parameters in two-phase contrast-enhanced images.

RESULTS

In the arterial phase (AP) and venous phase (VP), the BEOTs had significantly lower enhancement than MEOTs from 40 to 100 keV (p<0.05). The k values and IC values both showed significant differences in the AP and VP (p<0.05). Combining parameters in two contrast-enhanced phases provided 80.8% sensitivity and 82.4% specificity in differentiating MEOTs from BEOTs with an AUC of 0.844.

CONCLUSION

Dual-energy spectral CT provides a multiparametric approach in differentiating BEOTs from MEOTs with the best diagnostic efficacy using combined parameters in the AP and VP images.

Development of a dual-energy spectral computed tomography-based nomogram for the preoperative discrimination of histological grade in colorectal adenocarcinoma patients

Background

The usefulness of a dual-energy spectral computed tomography (DESCT)-based nomogram in discriminating between histological grades of colorectal adenocarcinoma (CRAC) is unclear. This study aimed to develop such a nomogram and assess its ability to preoperatively discriminate between histological grades in CRAC patients.

Methods

Primary tumors monochromatic CT value, iodine concentration (IC) value, and effective atomic number (Eff-Z) in the arterial (AP) and venous phases (VP) were retrospectively compared between patients with high-grade (n=65) and low-grade (n=108) CRAC who underwent preoperative abdominal DESCT. Univariate analysis was used to compare the DESCT parameters and clinical factors between these two patient groups. Statistically significant features in the univariate analysis were included in the multivariate logistic regression model to identify the indicators for building a nomogram that could discriminate between histological grades in CRAC patients. The clinical usefulness of the nomogram and its value for predicting overall survival were statistically evaluated.

Results

The logistic regression analysis showed that age, clinical T stage, clinical N stage, and IC values in AP and VP were significant independent predictors for high-grade CRAC. A quantitative nomogram developed based on these predictors showed excellent performance for discriminating between the histological grades, with an area under the curve (AUC) of 0.886 and excellent agreement in the calibration curve. The Kaplan-Meier curve for overall survival showed that our nomogram identified a significant difference between the high- and low-risk groups [hazard ratio (HR), 2.188; 95% CI, 1.072-4.465; P=0.027).

Conclusions

This study presents a nomogram that incorporates DESCT parameters and clinical factors and can potentially be used as a clinical tool for individual preoperative prediction of CRAC histological grade.

Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma

OBJECTIVE

To investigate the predictive performance of different machine learning models for the discrimination of low and high nuclear grade clear cell renal cell carcinoma (ccRCC) by using multiphase computed tomography (CT)-based radiomic features.

MATERIALS AND METHODS

A total of 137 consecutive patients with pathologically proven ccRCC (including 96 low-grade [grade 1 or 2] and 41 high-grade [grade 3 or 4] ccRCC) from January 2011 to January 2019 were enrolled in this retrospective study. Target region of interest (ROI) delineation followed by texture extraction was performed on a representative slice with the largest section of the tumor on the four-phase (unenhanced phase [UP], corticomedullary phase [CMP], nephrographic phase [NP] and excretory phase [EP]) CT images. Fifteen concatenations of the four-phase features were fed into 176 classification models (built with 8 classifiers and 22 feature selection methods), the classification performances of the 2640 resultant discriminative models were compared, and the top-ranked features were analyzed.

RESULTS

Image features extracted from the unenhanced phase (UP) CT images demonstrated a dominant classification performance over features from the other three phases. The discriminative model "Bagging + CMIM" achieved the highest classification AUC of 0.75. The top-ranked features from the UP included one shape-based feature and five first-order statistical features.

CONCLUSION

Image features extracted from the UP are more effective than other CT phases in differentiating low and high nuclear grade ccRCC based on machine learning-based classification modeling.

Liq_ccRCC: Identification of Clear Cell Renal Cell Carcinoma Based on the Integration of Clinical Liquid Indices

Currently, preoperative diagnosis and differentiation of renal clear cell carcinoma and other subtypes remain a serious challenge for doctors. The liquid biopsy technique and artificial intelligence have inspired the pursuit of distinguishing clear cell renal cell carcinoma using clinically available test data. In this work, a method called liq_ccRCC based on the integration of clinical blood and urine indices through machine learning approaches was successfully designed to achieve this goal. Clinically available biochemical blood data and urine indices were collected from 306 patients with renal cell carcinoma. Finally, the integration of 18 top-ranked clinical liquid indices (13 blood samples and 5 urine samples) was proven to be able to distinguish renal clear cell carcinoma from other subtypes of renal carcinoma by cross-valuation with an AUC of 0.9372. The successful introduction of this identification method suggests that subtype differentiation of renal cell carcinoma can be accomplished based on clinical liquid test data, which is noninvasive and easy to perform. It has huge potential to be developed as a promising innovation strategy for preoperative subtype differentiation of renal cell carcinoma with the advantages of convenience and real-time testing. liq_ccRCC is available online for the free test of readers at http://lishuyan.lzu.edu.cn/liq_ccRCC.

Noninvasive Staging of Liver Fibrosis Using 5-Minute Delayed Dual-Energy CT: Comparison with US Elastography and Correlation with Histologic Findings

Background Normalized iodine concentration (NIC) (ratio of iodine concentration of liver to that of aorta) of liver at delayed dual-energy CT (DECT) may reflect the amount of fibrosis based on the extent of iodine uptake. Purpose To stage liver fibrosis by using 5-minute delayed DECT and compare findings with those of transient elastography (TE), shear-wave elastography (SWE), and histologic examination. Materials and Methods This prospective study included patients with chronic liver disease who were scheduled to undergo multiphase abdominal CT and liver biopsy from January 2017 to September 2018. Fifty individuals being screened as renal donors comprised the control group. Study participants underwent TE, SWE, multiphasic DECT (including 5-minute delayed dual-energy scanning), and liver biopsy. Multiphasic DECT and SWE were performed in the control group. The NIC of the right lobe of the liver (RNIC) was compared with liver stiffness (LS) as measured with TE and SWE and with the METAVIR fibrosis stage (ranging from F0 to F4). Diagnostic performance was assessed by using areas under the receiver operating characteristic curve (AUCs). Results A total of 107 participants (mean age, 35 years ± 12 [standard deviation]; 57 men) and 50 control subjects (mean age, 47 years ± 11; 29 women) were evaluated. The RNIC showed strong correlation with METAVIR stage (Spearman ρ = 0.81, P < .001). The AUC for RNIC with each METAVIR stage ranged between 0.86 (95% CI: 0.76, 0.97) and 0.96 (95% CI: 0.92, 0.99). The cut-off value of RNIC was 0.24 (sensitivity: 85% [86 of 101 participants; 95% CI: 77%, 91%]; specificity: 83% [84 of 101 participants; 95% CI: 42%, 98%]) for stage F1 fibrosis and 0.29 (sensitivity: 84% [67 of 80 participants; 95% CI: 74%, 90%]; specificity: 81% [65 of 80 participants; 95% CI: 63%, 92%]) for stage F2 fibrosis. RNIC correlated well with LS as measured with TE and SWE (Spearman ρ = 0.60 and 0.64, respectively; P < .001). Conclusion Normalized iodine concentration of liver at 5-minute delayed dual-energy CT showed strong correlation with the histologic stages of liver fibrosis and good diagnostic performance in estimating liver fibrosis. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Chandarana and Shanbhogue in this issue.

Clinical value of energy spectrum curves of dual-energy computer tomography may help to predict pathological grading of gastric adenocarcinoma

BACKGROUND

To explore the clinical value of energy spectrum curves of dual-energy computer tomography (CT) in quantitative evaluation of different pathological grades of gastric adenocarcinoma.

METHODS

A total of 62 patients with 36 poorly, 25 moderately and 1 well differentiated gastric adenocarcinomas confirmed pathologically were collected. Dual-energy CT plain and enhanced scanning were undergone before operation. Dual-Energy software was used to measure the slope of the energy spectrum curves (λ) in arterial and venous phases (VPs) after image reconstruction. Patients were divided into two groups according to the pathological results, including well and moderately differentiated gastric adenocarcinoma group and poorly differentiated gastric adenocarcinoma group. Data of each group were analyzed by independent sample t-test. Receiver operating characteristic curve (ROC) was used to evaluate the diagnostic efficiency of the corresponding parameters.

RESULTS

There were significant differences in λ values of 40-50, 40-60, 40-80, 40-90, 40-100, 40-120, 40-130, 40-140 and 40-150 keV energy ranges in VP between the well and moderately differentiated group and poorly differentiated group (P<0.05), but no significant differences in λ values of different energy ranges in arterial phase (AP) between the two groups (P>0.05). And the area under curve in 40-120 keV energy range was the largest in VP. λ_40-120keV=2.69 was selected as the diagnostic threshold with the maximum Youden index, the sensitivity and specificity were 61.1% and 76%, respectively.

CONCLUSIONS

The energy spectrum curve of dual-energy CT had certain diagnostic value in the quantitative evaluation of pathological grading of gastric adenocarcinoma.

Quantitative evaluation of bone marrow infiltration using dual-energy spectral computed tomography in patients with multiple myeloma

OBJECTIVE

To explore the potential value of quantitative parameters derived from dual-energy spectral computed tomography (DESCT) as comparing to the parameters derived from magnetic resonance imaging (MRI) in detecting bone marrow (BM) infiltration and distinguishing different patterns of BM infiltration in patients diagnosed with Multiple myeloma (MM).

METHODS

This study involved 35MM patients and 15 healthy control subjects who had undergone spinal DESCT and MRI. Pattern assignment was based on visual assessment of MR images, and the regions of interest were defined on both DESCT and apparent diffusion coefficient maps. Quantitative values of DESCT parameters were measured and compared between infiltrated and healthy bone marrow. Receiver operating characteristic (ROC) analysis was performed to determine potential utility of DESCT parameters in identifying BM infiltration and different patterns defined by MRI. Sensitivity and specificity under the optimal thresholds determined by the Youden Index were also calculated.

RSULTS

Statistical differences were observed between the DESCT parameters including Ca(Water), Water(Ca), HAP(Fat), Fat(HAP) and Effective atomic number (Eff-Z) but not for the 70-keV CT value between the infiltrated and healthy BM (all P < 0.001). The 70keV CT value and Ca(Water), HAP(Fat) and Eff-Z values were also found to be statistically different in comparing different infiltration patterns (all P < 0.05). Performance of the model-based parameter Ca/Water was superior in differentiating between infiltrated and healthy BM in which the area under ROC curve, AUC = 0.856 [95% CI, 81.4-89.1%] with sensitivity = 0.841 and specificity = 0.768, as well as between MM patients and control subjects (AUC = 0.910 [95% CI, 79.5-97.3%], sensitivity = 0.829 and specificity = 1.000).

CONCLUSIONS

Analysis of DESCT offers potential as a quantitative method to detect infiltrated BM and evaluate infiltration patterns of BM in patients diagnosed with MM.

Use of dual-energy CT for renal mass assessment

Although dual-energy CT (DECT) may prove useful in a variety of abdominal imaging tasks, renal mass evaluation represents the area where this technology can be most impactful in abdominal imaging compared to routinely performed contrast-enhanced-only single-energy CT exams. DECT post-processing techniques, such as creation of virtual unenhanced and iodine density images, can help in the characterization of incidentally discovered renal masses that would otherwise remain indeterminate based on post-contrast imaging only. The purpose of this article is to review the use of DECT for renal mass assessment, including its benefits and existing limitations. KEY POINTS: • If DECT is selected as the scanning mode for most common abdominal protocols, many incidentally found renal masses can be fully triaged within the same exam. • Virtual unenhanced and iodine density DECT images can provide additional information when renal masses are discovered in the post-contrast-only setting. • For renal mass evaluation, virtual unenhanced and iodine density DECT images should be interpreted side-by-side to troubleshoot pitfalls that can potentially lead to erroneous interpretation.

Role of Quantitative Spectral CT Analysis for Differentiation of Orbital Lymphoma and Other Orbital Lymphoproliferative Disease

PURPOSE

To investigate the value of quantitative parameters from spectral computed tomography for the differentiation of orbital lymphoma from other lymphoproliferative disease, including idiopathic orbital inflammatory disease (IOID) and IgG4-related disease (IgG4-RD).

METHODS

Patients with orbital masses who underwent pre-treatment contrast-enhanced spectral CT were enrolled in this retrospective study. The subjects were divided into lymphoma and other orbital lymphoproliferative disease groups. Qualitative imaging features (margin, location, enhancement pattern, cranial nerves, soft tissue, and bone involvement) were reviewed. Quantitative parameters (iodine density and spectral attenuation curve slope) derived from spectral CT were measured.

RESULTS

Eleven patients had orbital lymphoma and 11 had other orbital lymphoproliferative diseases (idiopathic orbital inflammatory disease (IOID), n = 5; IgG4-related disease (IgG4-RD), n = 6). Qualitative analysis showed no significant difference between the two groups. There was significantly higher iodine density in orbital lymphoma (1.24 ± 0.24 mg/ml) than in IOID/IgG4-RD (0.83 ± 0.23 mg/ml; P = 0.001). An iodine density threshold of 1.0 mg/ml gave sensitivity, specificity, and accuracy of 81.8%, with an area under the curve of 0.876 (P = 0.0003). Orbital lymphoma had a significantly higher iodine spectral attenuation curve slope (2.44 ± 0.51 HU/keV) than IOID/IgG4-RD (1.66 ± 0.47 HU/keV; P = 0.001). A threshold of 1.99 HU/keV for the spectral attenuation curve slope of 40-70 keV gave sensitivity, specificity, and accuracy of 81.8%, with an area under the curve of 0.884 (P = 0.0002).

CONCLUSIONS

Quantitative spectral CT parameters can help differentiate orbital lymphoma from other orbital lymphoproliferative disease, with lymphoma having a significantly higher iodine density value and spectral attenuation curve slope than IOID/IgG4-RD.

Differential diagnosis of chromophobe renal cell carcinoma and papillary renal cell carcinoma with dual-energy spectral computed tomography

BACKGROUND

Computed tomography (CT) image features of chromophobe renal cell carcinoma (ChRCC) and papillary renal cell carcinoma (PRCC) are, occasionally, sometimes difficult to identify. However, spectral CT might provide quantitative parameters to differentiate them.

PURPOSE

To differentiate between ChRCC and PRCC with quantitative parameters using spectral CT.

MATERIAL AND METHODS

Forty cases of RCC confirmed with pathological tests were analyzed retrospectively (27 cases of PRCC and 13 cases of ChRCC). All patients underwent non-enhanced CT and dual-phase contrast-enhanced CT scans. For each lesion, the CT value of monochromatic images as well as iodine and water concentrations were measured, and the slope of spectrum curve was calculated. Data were analyzed using Student's t-test. Sensitivity and specificity of the quantitative parameters were analyzed using the receiver operating characteristic (ROC) curve.

RESULTS

During the cortex phase (CP) and parenchyma phase (PP), the CT value and slope of spectrum curve of ChRCC were higher than those of PRCC, and significant differences were observed at low energy levels (40-70 keV). Normalized iodine concentration of ChRCC and that of PRCC was significantly different during CP and PP (P < 0.05). The water (iodine) concentrations of ChRCC and PRCC in CP and PP were not statistically different (P > 0.05). All the ROCs for parameters were above the reference line.

CONCLUSION

Spectral CT may help increase the diagnostic accuracy of differentiating PRCC from ChRCC using a quantitative analysis.

Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma

BACKGROUND

Nuclear grade is of importance for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC).

PURPOSE

To develop and validate an MRI-based radiomic model for preoperative predicting WHO/ISUP nuclear grade in ccRCC.

STUDY TYPE

Retrospective.

POPULATION

In all, 379 patients with histologically confirmed ccRCC. Training cohort (n = 252) and validation cohort (n = 127) were randomly assigned.

FIELD STRENGTH/SEQUENCE

Pretreatment 3.0T renal MRI. Imaging sequences were fat-suppressed T₂ WI, contrast-enhanced T₁ WI, and diffusion weighted imaging.

ASSESSMENT

Three prediction models were developed using selected radiomic features, radiomic and clinicoradiologic characteristics, and a model containing only clinicoradiologic characteristics. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to assess the predictive performance of these models in predicting high-grade ccRCC.

STATISTICAL TESTS

The least absolute shrinkage and selection operator (LASSO) and minimum redundancy maximum relevance (mRMR) method were used for the selection of radiomic features and clinicoradiologic characteristics, respectively. Multivariable logistic regression analysis was used to develop the radiomic signature of radiomic features and clinicoradiologic model of clinicoradiologic characteristics.

RESULTS

The radiomic signature showed good performance in discriminating high-grade (grades 3 and 4) from low-grade (grades 1 and 2) ccRCC, with sensitivity, specificity, and AUC of 77.3%, 80.0%, and 0.842, respectively, in the validation cohort. The radiomic model, combining radiomic signature and clinicoradiologic characteristics, displayed good predictive ability for high-grade with sensitivity, specificity, and accuracy of 63.6%, 93.3%, and 88.2%, respectively, in the validation cohort. The radiomic model showed a significantly better performance than the clinicoradiologic model (P < 0.05).

DATA CONCLUSION

Multiparametric MRI-based radiomic model can predict WHO/ISUP grade in patients with ccRCC with satisfying performance, and thus could help the physician to improve treatment decisions.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Anti-G250 nanobody-functionalized nanobubbles targeting renal cell carcinoma cells for ultrasound molecular imaging

Traditional imaging examinations have difficulty in identifying benign and malignant changes in renal masses. This difficulty may be solved by ultrasound molecular imaging based on targeted nanobubbles, which could specifically enhance the ultrasound imaging of renal cell carcinomas (RCC) so as to discriminate benign and malignant renal masses. In this study, we aimed to prepare anti-G250 nanobody-functionalized targeted nanobubbles (anti-G250 NTNs) by coupling anti-G250 nanobodies to lipid nanobubbles and to verify their target specificity and binding ability to RCC cells that express G250 antigen and their capacity to enhance ultrasound imaging of RCC xenografts. Anti-G250 nanobodies were coupled to the lipid nanobubbles using the biotin-streptavidin bridge method. The average particle diameter of the prepared anti-G250 NTNs was 446 nm. Immunofluorescence confirmed that anti-G250 nanobodies were uniformly distributed on the surfaces of nanobubbles. In vitro experiments showed that the anti-G250 NTNs specifically bound to G250-positive 786-O cells and HeLa cells with affinities of 88.13% ± 4.37% and 71.8% ± 5.7%, respectively, and that they did not bind to G250-negative ACHN cells. The anti-G250 NTNs could significantly enhance the ultrasound imaging of xenograft tumors arising from 786-O cells and HeLa cells compared with blank nanobubbles, while the enhancement was not significant for xenograft tumors arising from ACHN cells. Immunofluorescence of tumor tissue slices confirmed that the anti-G250 NTNs could enter the tissue space through tumor blood vessels and bind to tumor cells specifically. In conclusion, anti-G250 nanobody-functionalized targeted nanobubbles could specifically bind to G250-positive RCC cells and enhance the ultrasound imaging of G250-positive RCC xenografts. This study has high-potential clinical application value for the diagnosis and differential diagnosis of renal tumors.

A preoperative nomogram predicting the pseudocapsule status in localized renal cell carcinoma

Background

Tumor enucleation (TE) surgery for localized renal cell carcinoma (RCC) relies on a complete peritumoral pseudocapsule (PC). Study objective was to develop a preoperative model to predict PC status.

Methods

The prediction model was developed in a cohort that consisted of 170 patients with localized RCC, and data was gathered from 2010 to 2015. Multivariable logistic regression analysis and R were used to generate this prediction model. The statistical performance was assessed with respect to the calibration, discrimination, and clinical usefulness.

Results

The prediction model incorporated the systemic inflammatory markers [neutrophil-lymphocyte ratio (NLR); albumin-globulin ratio (AGR)], CT imaging features (tumor size and necrosis), and clinical risk factors (BMI). The model showed good discrimination, with a C-index of 0.85 (0.78–0.91), and good calibration (P=0.60). The sensitivity and specificity were 62% and 94% respectively. Decision curves and clinical impact curve demonstrated that the current model was clinically useful.

Conclusions

We constructed a model that incorporated both the systematic inflammatory markers and clinical risk factors. It can be conveniently used to preoperatively predict the individualized risk of PC invasion and identify the best candidates to receive TE surgery.

FORCE dual-energy CT in pathological grading of clear cell renal cell carcinoma

The aim of the present study was to examine the value of FORCE dual-energy CT in grading the clear cell renal cell carcinoma (ccRCC). A total of 35 cases of ccRCC were included. Hematoxylin and eosin staining was performed, and the cases were divided into low- (Fuhrman I-II) and high-grade (Fuhrman III-IV) groups. FORCE dual-energy CT parameters, including virtual network computing CT value (VNCV), iodine overlay value (IOV), mixed energy CT value (MEV), iodine concentration (IC), normalized iodine concentration (NIC), NIC based on aorta (NICA), NIC based on cortex (NICC) and NIC based on medulla (NICM), were analyzed and compared. Receiver operating characteristic analysis was also performed. There were significant differences in the arterial phase IOV, MEV and IC, and the venous phase IOV and IC between the low- and high-grade groups. No significant differences were observed in VNCV and MEV between the low -and high-grade groups in the venous phase. Significant differences were observed in the NICA and NICC between these two groups, however no difference was observed in NICM. There were significant differences in the tumor CT values for the arterial phase at the 40, 60, 80 and 100 kiloelectron volt (keV) between the low- and high-grade groups, while no significant differences were observed at the 120-140 keV levels. The k-slope for the low-grade group was significantly higher than the high-grade group. In addition, the area under curve for the arterial phase IOV, arterial phase MEV, arterial phase IC, aortic NIC, cortical NIC, venous phase IOV, venous phase IC and curve slope K of mono-energy CT value suggested high value in diagnosis of low- and high-grade ccRCC cases.