Differentiation of renal masses with multi-parametric MRI: the de Silva St George classification scheme

[89Zr]Zr-girentuximab for PET-CT imaging of clear-cell renal cell carcinoma: a prospective, open-label, multicentre, phase 3 trial

BACKGROUND

With limitations of conventional imaging and biopsy, accurate, non-invasive techniques to detect clear-cell renal cell carcinoma in patients with renal masses remain an unmet need. 89Zr-labelled monoclonal antibody ([89Zr]Zr-girentuximab) has high affinity for carbonic anhydrase 9, a tumour antigen highly expressed in clear-cell renal cell carcinoma. We aimed to evaluate [89Zr]Zr-girentuximab PET-CT imaging for detection and characterisation of clear-cell renal cell carcinoma.

METHODS

ZIRCON was a prospective, open-label, multicentre, phase 3 trial conducted at 36 research hospitals and practices across nine countries (the USA, Australia, Canada, the UK, Türkiye, Belgium, the Netherlands, Spain, and France). Patients aged 18 years or older with an indeterminate renal mass 7 cm or smaller (cT1) suspicious for clear-cell renal cell carcinoma and scheduled for nephrectomy received a single dose of [89Zr]Zr-girentuximab (37 MBq ±10%; 10 mg girentuximab) intravenously followed by abdominal PET-CT imaging 5 days (±2 days) later. Surgery was performed no later than 90 days after administration of [89Zr]Zr-girentuximab. Blinded central review, conducted by three independent readers, determined the histology from surgical samples. The coprimary endpoints, determined for each individual reader, were the sensitivity and specificity of [89Zr]Zr-girentuximab PET-CT imaging to detect clear-cell renal cell carcinoma, with histopathological confirmation as standard of truth. Analyses were on the full analysis set of patients, defined as patients who had evaluable PET-CT imaging and a confirmed histopathological diagnosis. The trial is registered with ClinicalTrials.gov, NCT03849118, and EUDRA Clinical Trials Register, 2018-002773-21, and is closed to enrolment.

FINDINGS

Between Aug 14, 2019, and July 8, 2022, 371 patients were screened for eligibility, 332 of whom were enrolled. 300 patients received [89Zr]Zr-girentuximab (214 [71%] male and 86 [29%] female). 284 (95%) evaluable patients were included in the primary analysis. The mean sensitivity was 85·5% (95% CI 81·5-89·6) and mean specificity was 87·0% (81·0-93·1). No safety signals were observed. Most adverse events were not or were unlikely to be related to [89Zr]Zr-girentuximab, with most (193 [74%] of 261 events) occurring during or after surgery. The most common grade 3 or worse adverse events were post-procedural haemorrhage (in six [2%] of 261 patients), urinary retention (three [1%]), and hypertension (three [1%]). In 25 (8%) of 300 patients, 52 serious adverse events were reported, of which 51 (98%) occurred after surgery. There were no treatment-related deaths.

INTERPRETATION

Our results suggest that [89Zr]Zr-girentuximab PET-CT has a favourable safety profile and is a highly accurate, non-invasive imaging modality for the detection and characterisation of clear-cell renal cell carcinoma, which has the potential to be practice changing.

FUNDING

Telix Pharmaceuticals.

Feasibility and Reproducibility of T2 Mapping Compared with Diffusion-Weighted Imaging in Solid Renal Masses

Accurate prediction of renal mass subtypes, along with the WHO/ISUP grade and pathological T (pT) stage of clear cell renal cell carcinoma (ccRCC), is crucial for optimal decision making. Our study aimed to investigate the feasibility and reproducibility of motion-robust radial T2 mapping in differentiating lipid-poor angiomyolipoma (MFAML) from RCC and characterizing the WHO/ISUP grade and pT stage of ccRCC. Finally, 92 patients undergoing renal radial T2 mapping and ZOOMit DWI were recruited. The T2 values and apparent diffusion coefficient (ADC) were analyzed. Correlation coefficients were calculated between ADC and T2 values. Notably, ccRCC exhibited higher T2 and ADC values than MFAML (p < 0.05). T2 values were lower in the higher WHO/ISUP grade and pT stage of ccRCC (all p < 0.05). ADC showed no significant difference for pT stage (p = 0.056). T2 values revealed a higher area under the curve (AUC) in evaluating the WHO/ISUP grade compared to ADC (0.936 vs. 0.817, p = 0.027). T2 values moderately positively correlated with ADC (r = 0.675, p < 0.001). In conclusion, quantitative motion-robust radial T2 mapping is feasible for characterizing solid renal masses and could provide additional value for multiparametric imaging in predicting WHO/ISUP grade and pT stage of ccRCC.

Artificial intelligence support in MR imaging of incidental renal masses: an early health technology assessment

OBJECTIVE

This study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)-assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up.

MATERIALS AND METHODS

For estimation of quality-adjusted life years (QALYs) and lifetime costs, a decision model was created, including the MRI strategy and MRI + AI strategy. Model input parameters were derived from recent literature. Willingness to pay (WTP) was set to $100,000/QALY. Costs of $0 for the AI were assumed in the base-case scenario. Model uncertainty and costs of the AI system were assessed using deterministic and probabilistic sensitivity analysis.

RESULTS

Average total costs were at $8054 for the MRI strategy and $7939 for additional use of an AI-based algorithm. The model yielded a cumulative effectiveness of 8.76 QALYs for the MRI strategy and of 8.77 for the MRI + AI strategy. The economically dominant strategy was MRI + AI. Deterministic and probabilistic sensitivity analysis showed high robustness of the model with the incremental cost-effectiveness ratio (ICER), which represents the incremental cost associated with one additional QALY gained, remaining below the WTP for variation of the input parameters. If increasing costs for the algorithm, the ICER of $0/QALY was exceeded at $115, and the defined WTP was exceeded at $667 for the use of the AI.

CONCLUSIONS

This analysis, rooted in assumptions, suggests that the additional use of an AI-based algorithm may be a potentially cost-effective alternative in the differentiation of incidental renal lesions using MRI and needs to be confirmed in the future.

CLINICAL RELEVANCE STATEMENT

These results hint at AI's the potential impact on diagnosing renal masses. While the current study urges careful interpretation, ongoing research is essential to confirm and seamlessly integrate AI into clinical practice, ensuring its efficacy in routine diagnostics.

KEY POINTS

• This is a model-based study using data from literature where AI has been applied in the diagnostic workup of incidental renal lesions. • MRI + AI has the potential to be a cost-effective alternative in the differentiation of incidental renal lesions. • The additional use of AI can reduce costs in the diagnostic workup of incidental renal lesions.

Imaging modalities for characterising T1 renal tumours: A systematic review and meta-analysis of diagnostic accuracy

Objectives

International guidelines recommend resection of suspected localised renal cell carcinoma (RCC), with surgical series showing benign pathology in 30%. Non-invasive diagnostic tests to differentiate benign from malignant tumours are an unmet need. Our objective was to determine diagnostic accuracy of imaging modalities for detecting cancer in T1 renal tumours.

Methods

A systematic review was performed for reports of diagnostic accuracy of any imaging test compared to a reference standard of histopathology for T1 renal masses, from inception until January 2023. Twenty-seven publications (including 2277 tumours in 2044 participants) were included in the systematic review, and nine in the meta-analysis.

Results

Forest plots of sensitivity and specificity were produced for CT (seven records, 1118 participants), contrast-enhanced ultrasound (seven records, 197 participants), [99mTc]Tc-sestamibi SPECT/CT (five records, 263 participants), MRI (three records, 220 participants), [18F]FDG PET (four records, 43 participants), [68Ga]Ga-PSMA-11 PET (one record, 27 participants) and [111In]In-girentuximab SPECT/CT (one record, eight participants). Meta-analysis returned summary estimates of sensitivity and specificity for [99mTc]Tc-sestamibi SPECT/CT of 88.6% (95% CI 82.7%-92.6%) and 77.0% (95% CI 63.0%-86.9%) and for [18F]FDG PET 53.5% (95% CI 1.6%-98.8%) and 62.5% (95% CI 14.0%-94.5%), respectively. A comparison hierarchical summary receiver operating characteristic (HSROC) model did not converge. Meta-analysis was not performed for other imaging due to different thresholds for test positivity.

Conclusion

The optimal imaging strategy for T1 renal masses is not clear. [99mTc]Tc-sestamibi SPECT/CT is an emerging tool, but further studies are required to inform its role in clinical practice. The field would benefit from standardisation of diagnostic thresholds for CT, MRI and contrast-enhanced ultrasound to facilitate future meta-analyses.

Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics

Background: Small renal masses (SRMs) are defined as contrast-enhanced renal lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a renal cell carcinomas (RCCs). Currently, 50-61% of all renal tumors are found incidentally. Methods: The characteristics of the lesion influence the choice of the type of management, which include several methods SRM of management, including nephrectomy, partial nephrectomy, ablation, observation, and also stereotactic body radiotherapy. Typical imaging methods available for differentiating benign from malignant renal lesions include ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI). Results: Although ultrasound is the first imaging technique used to detect small renal lesions, it has several limitations. CT is the main and most widely used imaging technique for SRM characterization. The main advantages of MRI compared to CT are the better contrast resolution and tissue characterization, the use of functional imaging sequences, the possibility of performing the examination in patients allergic to iodine-containing contrast medium, and the absence of exposure to ionizing radiation. For a correct evaluation during imaging follow-up, it is necessary to use a reliable method for the assessment of renal lesions, represented by the Bosniak classification system. This classification was initially developed based on contrast-enhanced CT imaging findings, and the 2019 revision proposed the inclusion of MRI features; however, the latest classification has not yet received widespread validation. Conclusions: The use of radiomics in the evaluation of renal masses is an emerging and increasingly central field with several applications such as characterizing renal masses, distinguishing RCC subtypes, monitoring response to targeted therapeutic agents, and prognosis in a metastatic context.

Renal Mass Imaging with MRI Clear Cell Likelihood Score: A User's Guide

Small solid renal masses (SRMs) are frequently detected at imaging. Nearly 20% are benign, making careful evaluation with MRI an important consideration before deciding on management. Clear cell renal cell carcinoma (ccRCC) is the most common renal cell carcinoma subtype with potentially aggressive behavior. Thus, confident identification of ccRCC imaging features is a critical task for the radiologist. Imaging features distinguishing ccRCC from other benign and malignant renal masses are based on major features (T2 signal intensity, corticomedullary phase enhancement, and the presence of microscopic fat) and ancillary features (segmental enhancement inversion, arterial-to-delayed enhancement ratio, and diffusion restriction). The clear cell likelihood score (ccLS) system was recently devised to provide a standardized framework for categorizing SRMs, offering a Likert score of the likelihood of ccRCC ranging from 1 (very unlikely) to 5 (very likely). Alternative diagnoses based on imaging appearance are also suggested by the algorithm. Furthermore, the ccLS system aims to stratify which patients may or may not benefit from biopsy. The authors use case examples to guide the reader through the evaluation of major and ancillary MRI features of the ccLS algorithm for assigning a likelihood score to an SRM. The authors also discuss patient selection, imaging parameters, pitfalls, and areas for future development. The goal is for radiologists to be better equipped to guide management and improve shared decision making between the patient and treating physician. © RSNA, 2023 Quiz questions for this article are available in the supplemental material. See the invited commentary by Pedrosa in this issue.

Using the "2 standard deviations" rule with Dixon MRI to differentiate renal cell carcinoma types

BACKGROUND

Clear cell and non-clear cell renal cell carcinoma (RCC) are distinguishable based on microscopic fat, detectable by chemical shift MRI. However, these assessments are often subjective. Conversely, Dixon MRIs and the "2 standard deviations" rule (2SDR) are quantitative methods that may decrease diagnostic subjectivity. Therefore, this study assessed the value of the 2SDR for detecting microscopic fat and thus differentiating clear cell and non-clear cell RCC using Dixon MRI.

METHODS

This retrospective study included patients with RCC who underwent preoperative Dixon MRI. The patients were grouped based on tumor type: clear cell RCC and non-clear cell RCC. The 2SDR value was calculated based on in-phase and opposed-phase images and then compared between the two groups. 2SDR values >0 indicated clear cell RCCs, whereas values <0 indicated non-clear cell RCC.

RESULTS

We included 151 patients; 114 patients had clear cell RCC, of which 106 had a 2SDR value >0. Furthermore, 37 patients had non-clear cell RCC, of which 3 had a 2SDR value >0. The 2SDR value was significantly higher in the clear cell RCC group than in the non-clear cell RCC group (p = 0.000). Overall, 93.0% (106/114) and 8.1% (3/37) of patients with clear cell and non-clear cell RCC, respectively, had microscopic fat. The evaluation indices for this 2SDR method were: accuracy: 92.72%, sensitivity: 92.98%, specificity: 91.89%, positive predictive value: 97.25%, and negative predictive value: 80.95%.

CONCLUSIONS

2SDR values calculated from Dixon magnetic resonance images can differentiate clear cell from non-clear cell RCCs by detecting microscopic fat.

PRECIS

The "2 standard deviations" rule value calculated from Dixon MR images differentiates clear cell from non-clear cell renal cell carcinoma with high efficiency by detecting microscopic fat.