Bladder cancer (BCa) is one of the most common malignancies affecting the urinary tract. It requires careful histological grading, as treatment strategies differ significantly between low- and high-grade cases. High-grade BCa often carries a greater risk of recurrence and progression to muscle-invasive disease, while low-grade tumours tend to have a more favourable prognosis and may require less aggressive interventions. Effective tumour grading is essential for tailoring treatment plans, guiding surgical decisions and determining the necessity for long-term therapies such as Bacillus Calmette–Guérin (BCG) instillation or radical cystectomy.
Conventional imaging techniques, including ultrasound, standard computed tomography (CT) and magnetic resonance imaging (MRI), have long been used in BCa detection and diagnosis. However, their ability to differentiate tumour grades based on tissue characteristics remains limited. The introduction of dual-layer spectral detector CT (DLCT) offers enhanced tissue characterisation capabilities through advanced spectral imaging, providing quantitative parameters that can aid in the differentiation of tumour histological grades. A recent article published in Insights into Imaging explores the diagnostic potential of DLCT parameters, with a focus on the arterial enhancement fraction (AEF), in distinguishing high- from low-grade bladder cancer.
Dual-Layer Spectral Detector CT Parameters in Bladder Cancer Grading
DLCT technology enables simultaneous acquisition of both high- and low-energy X-ray data, allowing for detailed tissue analysis through multiple spectral parameters. These parameters include iodine density (ID), normalised iodine density (NID), arterial enhancement fraction (AEF), extracellular volume (ECV) fraction, virtual non-contrast (VNC) imaging and the effective atomic number (Zeff).
Among these, the arterial enhancement fraction (AEF) has shown significant potential in distinguishing tumour grades. AEF measures the degree of contrast enhancement within tumour tissue, reflecting differences in vascularity and perfusion, which are more pronounced in high-grade malignancies due to their aggressive nature and increased angiogenesis. In the study, AEF exhibited a strong correlation with high-grade BCa, achieving an area under the curve (AUC) of 0.924. Its sensitivity reached 95.5% with a specificity of 81.0%, making it a reliable parameter for identifying aggressive bladder tumours.
Other spectral parameters, such as iodine density and ECV fraction, were also assessed for their diagnostic potential. However, multivariable regression analysis indicated that AEF was the most reliable single indicator for predicting tumour grade. Parameters like NID, ID and Zeff showed some predictive value but lacked the consistency and statistical significance demonstrated by AEF. This suggests that while multiple spectral CT metrics may provide supportive information, AEF stands out due to its direct association with tumour vascular characteristics.
Comparative Analysis and Clinical Impact
The study further explored the comparative diagnostic accuracy of AEF against other spectral CT parameters. While ID and NID offered insights into contrast absorption within the tumour, their diagnostic power was less consistent than AEF. The effective atomic number (Zeff) and virtual non-contrast images (VNC) provided additional metrics but were not as strongly correlated with histological grade.
A critical factor contributing to AEF's diagnostic superiority is its ability to reflect tumour angiogenesis more effectively than other spectral markers. Angiogenesis, the formation of new blood vessels, often correlates with tumour aggressiveness and invasive potential. High-grade BCa is characterised by increased microvascular density, which can be detected using AEF measurements derived from contrast-enhanced CT imaging.
The findings have significant implications for clinical practice. An accurate, non-invasive imaging tool like DLCT could assist clinicians in determining the histological grade of bladder tumours before surgical intervention. Identifying high-grade BCa at an earlier stage allows for more aggressive treatment planning, including extended BCG therapy or radical cystectomy. Conversely, patients with low-grade BCa could benefit from more conservative management strategies, reducing unnecessary treatment exposure and associated side effects.
However, the study also highlights the importance of further validation. The single-centre design and limited sample size of 64 patients suggest the need for larger, multicentre trials to confirm these results across diverse populations. Nonetheless, the high diagnostic performance observed with AEF in this study underscores its potential value in clinical oncology.
Limitations and Future Considerations
While the results are promising, the study had some limitations. The relatively small sample size and single-centre design may limit the broader applicability of the findings. Additionally, the exclusion of certain patients due to poor image quality and the limited number of spectral parameters evaluated suggest that further research is required to refine the methodology.
Another limitation is the exclusion of conventional CT features from the analysis. Integrating standard morphological characteristics with spectral parameters may further enhance diagnostic accuracy. Furthermore, while AEF was identified as a leading indicator, the study noted strong collinearity between AEF based on iodine density (AEF-ID) and AEF based on CT values (AEF-HU). This suggests that either metric could be used effectively, potentially allowing broader application in clinics without access to advanced DLCT technology.
Future research should focus on larger sample sizes and multicentre collaboration to validate these findings more robustly. Additionally, combining DLCT metrics with conventional imaging features and exploring their prognostic value could offer a more comprehensive diagnostic framework for BCa management.
Dual-layer spectral detector CT, particularly the AEF parameter, has demonstrated significant diagnostic value in differentiating high- from low-grade bladder cancer. Its ability to reflect tumour angiogenesis with high sensitivity and specificity makes it a promising tool for non-invasive tumour grading. The findings from this study suggest that integrating DLCT into clinical workflows could improve decision-making, allowing more tailored treatment strategies and better patient outcomes. Further research with larger cohorts is essential to confirm these results and optimise the use of spectral CT parameters in routine oncological practice.
Source: Insights into Imaging
Image Credit: iStock
