Imaging and Management of Renal Tumors
Summary
Renal tumours encompass a spectrum from benign cysts and angiomyolipomas to aggressive renal cell carcinomas. Accurate characterisation and staging are critical to guide treatment, which ranges from active surveillance and percutaneous ablation to nephron‐sparing surgery and systemic therapies. Cross‐sectional imaging with computed tomography (CT) remains first-line for lesion detection and staging of locoregional or metastatic disease, while magnetic resonance imaging (MRI) offers superior soft-tissue contrast and functional information via diffusion and perfusion sequences. Contrast-enhanced ultrasonography permits real-time evaluation of vascular patterns without ionising radiation. Emerging quantitative methods such as radiomics and deep learning extract high-dimensional features from CT or MRI to predict histology, tumour grade and therapeutic response. Molecular imaging and hybrid modalities further refine lesion characterisation. A multidisciplinary approach integrates imaging biomarkers with clinical factors to individualise management, minimise overtreatment and improve global outcomes.
Research from Nature Portfolio
Quantitative analysis of contrast-enhanced ultrasonography in a large cohort of patients has demonstrated that parameters such as time-to-peak and relative enhancement percentage reliably differentiate clear-cell renal cell carcinoma from benign lesions. By combining enhancement kinetics with homogeneity and pseudocapsule features, investigators achieved diagnostic accuracy exceeding 90%. This work establishes a reproducible, low-cost tool for real-time lesion characterisation, supporting more conservative management of indolent tumours and reducing unnecessary biopsies or surgeries.
Imaging and Management of Renal Tumors publication trend
The graph below shows the total number of articles in imaging and management of renal tumors across all publications each year (not limited to Nature Index journals).
Technical terms
Contrast-enhanced ultrasonography (CEUS): Ultrasound technique using injected microbubble agents to visualise tumour vascularity in real time.
Radiomics: Extraction and analysis of large numbers of quantitative imaging features to characterise tumour phenotype and predict clinical outcomes.
Deep learning: Subfield of machine learning employing multi-layer neural networks to identify complex patterns in imaging data for classification or segmentation tasks.
Multiparametric MRI: MRI protocol combining anatomical, diffusion-weighted and perfusion-weighted sequences to assess tissue structure and function.
Bosniak classification: CT-based system for categorising cystic renal lesions by imaging features to estimate risk of malignancy.
References
- Deep Learning Approaches Applied to Image Classification of Renal Tumors: A Systematic Review. Archives of Computational Methods in Engineering (2023).
- Radiomics in Renal Cell Carcinoma—A Systematic Review and Meta-Analysis. Cancers (2021).
- Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature. Cancers (2020).
- Contrast-Enhanced Ultrasonography with Quantitative Analysis allows Differentiation of Renal Tumor Histotypes. Scientific Reports (2016).
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