Use of patient outcome endpoints to identify the best functional CT imaging parameters in metastatic renal cell carcinoma patients

Dual-Energy Computed Tomography Applications in the Genitourinary Tract

By virtue of material differentiation capabilities afforded through dedicated postprocessing algorithms, dual-energy CT (DECT) has been shown to provide benefit in the evaluation of various diseases. In this article, we review the diagnostic use of DECT in the assessment of genitourinary diseases, with emphasis on its role in renal stone characterization, incidental renal and adrenal lesion characterization, retroperitoneal trauma, reduction of radiation, and contrast dose and cost-effectiveness potential. We also discuss future perspectives of the DECT scanning mode, including the use of novel contrast injection strategies and photon-counting detector computed tomography.

Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects

Renal cancer management is challenging from diagnosis to treatment and follow-up. In cases of small renal masses and cystic lesions the differential diagnosis of benign or malignant tissues has potential pitfalls when imaging or even renal biopsy is applied. The recent artificial intelligence, imaging techniques, and genomics advancements have the ability to help clinicians set the stratification risk, treatment selection, follow-up strategy, and prognosis of the disease. The combination of radiomics features and genomics data has achieved good results but is currently limited by the retrospective design and the small number of patients included in clinical trials. The road ahead for radiogenomics is open to new, well-designed prospective studies, with large cohorts of patients required to validate previously obtained results and enter clinical practice.

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.

Early reduction in spectral dual-layer detector CT parameters as favorable imaging biomarkers in patients with metastatic renal cell carcinoma

OBJECTIVES

To associate the early change in DL-CT parameters and HU with survival outcomes and treatment response in patients with metastatic renal cell carcinoma (mRCC).

METHODS

DL-CT scans were performed at baseline and after 1 month of checkpoint immunotherapy or tyrosine kinase inhibitor therapy. Scans were reconstructed to conventional CT and DL-CT series, and used for assessment of HU, iodine concentration (IC), and the effective atomic number (Z_effective) in the combined RECISTv.1.1 target lesions. The relative changes, defined as ΔIC(combined), ΔZ_effective(combined), and ΔHU(combined), were associated with progression-free survival (PFS), overall survival (OS), and objective response rate (ORR). The reduction in the sum of diameters of target lesions ≥ 30% after 1 month was associated with OS, PFS, and ORR.

RESULTS

Overall, 115 and 104 mRCC patients were included at baseline and 1 month, respectively. Median IC(combined) decreased from 2.3 to 1.2 mg/ml (p < 0.001), Z_effective(combined) from 8.5 to 8.0 (p < 0.001), and HU(combined) from 86.0 to 64.00 HU (p < 0.001). After multivariate adjustments, the largest reductions in ΔIC(combined) (HR 0.47, 95% CI: 0.24-0.94, p = 0.033) and ΔZ_effective(combined) (HR = 0.43, 95% CI: 0.21-0.87, p = 0.019) were associated with favorable OS; the largest reduction in ΔZ_effective(combined) was associated with higher response (OR = 2.79, 95% CI: 1.12-6.94, p = 0.027). The largest reduction in ΔHU(combined) was solely associated with OS in univariate analysis (HR 0.45, 95% CI: 0.23-0.91). Reduction in SOD ≥ 30% at 1 month was not associated with outcomes (p > 0.075).

CONCLUSIONS

Early reductions at 1 month in ΔIC(combined) and ΔZ_effective(combined) are associated with favorable outcomes in patients with mRCC. This information may reassure physicians and patients about treatment strategy.

KEY POINTS

• Early reductions following 1 month of therapy in spectral dual-layer detector CT-derived iodine concentration and the effective atomic number (Z_effective) are independent biomarkers for better overall survival in patients with metastatic renal cell carcinoma. • Early reduction after 1 month of therapy in the effective atomic number (Z_effective) is an independent imaging biomarker for better treatment response metastatic renal cell carcinoma.

Prognostic Utility of Parameters Derived From Pretreatment Dual-Layer Spectral-Detector CT in Patients With Metastatic Renal Cell Carcinoma

Background: New therapies have emerged for metastatic renal cell carcinoma (mRCC), though corresponding imaging markers are lacking. Dual-layer spectral-detector CT (DLCT) can quantify iodine concentration (IC) and effective atomic number (Zeffective), providing information beyond attenuation that may indicate mRCC prognosis. Objective: To assess the utility of the DLCT-derived parameters IC and Zeffective for predicting mRCC treatment response and survival. Methods: This prospective study (ClinicalTrials.gov identifier: NCT03616951) enrolled 120 participants with mRCC from January 2018 to January 2020 who underwent DLCT before treatment initiation, with reconstruction of IC and Zeffective maps. Final analysis included 115 participants (86 men, 29 women; median age, 65.1 years), incorporating 313 target lesions that were clinically selected using RECIST version 1.1 on arterial-phase acquisitions of the chest and abdomen. Semiautomatic volumetric segmentation was performed of the target lesions. Pixels from all lesions were combined to a single histogram per patient. Median IC and Zeffective of the combined histograms were recorded. Measurements above and below the cohort median values were considered high and low, respectively. Univariable associations were explored between IC and Zeffective, with objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Multivariable associations were explored between IC and ORR, PFS, and OS, adjusting for treatment (tyrosine kinase inhibitor versus checkpoint immunotherapy) and significant univariable predictors [including tumor histology and International mRCC Database Consortium (IMDC) risk factors]. Results: At baseline, median IC was 2.26 mg/ml, and median Zeffective was 8.49. In univariable analysis, high IC and high Zeffective were associated with better ORR (both OR=4.35, p=.001), better PFS (both HR=0.51, p=.004), and better OS (both HR=0.38, p<.001). In multivariable models, high IC independently predicted better ORR (OR=4.35, p=.001), better PFS (HR=0.51, p=.004), and better OS (HR=0.37, p<.001); neutrophilia independently predicted worse PFS (HR=2.10, p=.004) and worse OS (HR=2.28, p=.003). The estimated c-index for predicting OS using IMDC risk factors was 0.650, versus 0.687 when incorporating high attention and 0.692 when incorporating high IC or high Zeffective. Conclusions: High IC and high Zeffective are significant predictors of better treatment response and survival in mRCC. Clinical impact: Baseline DLCT parameters may improve current mRCC prognostic models.

Blood Volume as a new functional image-based biomarker of progression in metastatic renal cell carcinoma

RECIST v1.1 has limitations in evaluating progression. We assessed Dynamic Constrast Enhanced Computed Tomography (DCE-CT) identified Blood Volume (BV) for the evaluation of progressive disease (PD) in patients with metastatic renal cell carcinoma (mRCC). BV was quantified prospectively at baseline, after one month, then every three months until PD. Relative changes (ΔBV) were assessed at each timepoint compared with baseline values. The primary endpoint was Time to PD (TTP), the secondary endpoint was Time to the scan prior to PD (PDminus1). Cox proportional hazard models adjusted ΔBV for treatments and International mRCC Database Consortium factors. A total of 62 patients had analyzable scans at the PD timepoint. Median BV was 23.92 mL × 100 g^-1 (range 4.40-399.04) at PD and 26.39 mL × 100 g^-1 (range 8.70-77.44) at PDminus1. In the final multivariate analysis higher ΔBV was statistically significantly associated with shorter Time to PD, HR 1.11 (95% CI 1.07-1.15, P < 0.001). Also assessed at PDminus1, higher ΔBV was significantly associated with shorter time to PD, HR 1.14 (95% CI 1.01-1.28, P = 0.031). In conclusion, DCE-CT identified BV is a new image-based biomarker of therapy progression in patients with mRCC.

Prognostic value of DCE-CT-derived blood volume and flow compared to core biopsy microvessel density in patients with metastatic renal cell carcinoma

BACKGROUND

Angiogenesis is prominent in metastatic renal cell carcinoma (mRCC). We compared two angiogenesis assessment methods: dynamic contrast-enhanced computed tomography (DCE-CT)-derived blood volume (BV) and blood flow (BF) and core biopsy microvessel density (MVD).

METHODS

As planned in DaRenCa Study-1 study, DCE-CT and core biopsy were performed from the same tumour/metastasis at baseline. MVD was assessed by CD34 immunostaining in tumour (CD34-index_T) or tumour including necrosis (CD34-index_TN). BV and BF were assessed using the DCE-CT software. Overall survival (OS) and progression-free survival (PFS) were assessed by Kaplan-Meier analysis. Spearman coefficient (rho) tested the correlation between MVD and BV, BF, or CT density (HU).

RESULTS

At baseline, 25 patients had analysable scans and tissue. BV_deconv, BV_Patlak, and BF_deconv > median were associated with favourable OS (43.2 versus 14.6 months, p = 0.002; 31.6 versus 20.2 months, p = 0.015; and 31.6 versus 24.5 months, p = 0.019). CD34-index_T and CD34-index_TN did not correlate with age (p = 0.543), sex (p = 0.225), treatment (p = 0.848), International mRCC Database Consortium category (p = 0.152), synchronous versus metachronous metastatic disease (p = 0.378), or tumour volume (p = 0.848). CD34-index_T or CD34-index_TN > median was not associated with PFS (p = 0.441 and p = 0.854, respectively) or OS (p = 0.987 and p =0.528, respectively). CD34-index_T or CD34-index_TN was not correlated with BV, BF, or HU (rho 0.20-0.26).

CONCLUSIONS

Differently from MVD, DCE-CT-derived BV and BF had prognostic impact and may better reflect angiogenesis in mRCC.

TRIAL REGISTRATION

NCT01274273.

Baseline blood volume identified by dynamic contrast-enhanced computed tomography as a new independent prognostic factor in metastatic renal cell carcinoma

Background

Preliminary data showed prognostic impact of contrast-enhanced computed tomography (DCE-CT) identified Blood Volume (BV) in patients with metastatic renal cell carcinoma (mRCC). BV as an independent prognostic factor remains to be assessed.

Materials and Methods

DCE-CT identified BV was prospectively quantified in patients with mRCC receiving first line therapies, adjusted for International mRCC Database Consortium (IMDC) individual features and treatments, and associated with overall survival (OS), progression-free survival (PFS) and objective response (ORR), using Cox and logistic regression, respectively.

Results

105 patients with mRCC were included. Median baseline BV was 32.87 mL × 100 g⁻¹ (range 9.52 to 92.87 mL × 100 g⁻¹). BV above median was associated with IMDC favorable risk category (P = 0.004), metastasis free interval ≥ 1 year (P = 0.007), male gender (P = 0.032), normal hemoglobin (P = 0.040) and normal neutrophils (P = 0.007), whereas low BV was associated with poor risk IMDC features (P < 0.05). Patients with high vs. low baseline BV had longer PFS (12.5 vs. 5.6 months, P = 0.015) and longer OS (42.2 vs. 22.4 months, P = 0.001), respectively. In multivariate analysis high baseline BV remained independent favorable for OS (HR 0.49, 95% CI 0.30–0.78, P = 0.003) and PFS (HR 0.64; 95% CI 0.42–0.97, P = 0.036). BV as a continuous variable was also associated with OS in the multivariate analysis (HR 0.98, 95% CI 0.96–1.00, P = 0.017). The estimated concordance index (c-index) was 0.688 using IMDC score and 0.701 when BV was added.

Conclusions

DCE-CT identified Blood Volume is a new, independent prognostic factor in mRCC, which may improve the prognostic accuracy of IMDC.

Early Changes in CT Perfusion Parameters: Primary Renal Carcinoma Versus Metastases After Treatment with Targeted Therapy

Computed tomography (CT) perfusion is a novel imaging method to determine tumor perfusion using a low-dose CT technique to measure iodine concentration at multiple time points. We determined if early changes in perfusion differ between primary renal tumors and metastatic tumor sites in patients with renal cell carcinoma (RCC) receiving targeted anti-angiogenic therapy. A total of 10 patients with advanced RCC underwent a CT perfusion scan at treatment baseline and at one week after initiating treatment. Perfusion measurements included blood volume (BV), blood flow (BF), and flow extraction product (FEP) in a total of 13 lesions (six primary RCC tumors, seven RCC metastases). Changes between baseline and week 1 were compared between tumor locations: primary kidney tumors vs metastases. Metastatic lesions had a greater decrease in BF (average BF difference ± standard deviation (SD): −75.0 mL/100 mL/min ± 81) compared to primary kidney masses (−25.5 mL/100 mL/min ± 35). Metastatic tumors had a wider variation of change in BF, BV and FEP measures compared to primary renal tumors. Tumor diameters showed little change after one week, but early perfusion changes are evident, especially in metastatic lesions compared to primary lesions. Future studies are needed to determine if these changes can predict which patients are benefiting from targeted therapy.