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In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses
Published on: March 30, 2015
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CT-based radiomics for differentiating renal tumours: a systematic review
Abhishta Bhandari1, Muhammad Ibrahim2, Chinmay Sharma2
1Townsville University Hospital, 100 Angus Smith Drive, Douglas, QLD, 4814, Australia. Abhishta.bhandari@my.jcu.edu.au.
Abdominal Radiology (New York)
|November 2, 2020
Summary
Computed tomography (CT) radiomics can objectively differentiate renal tumour grades and subtypes. Texture analysis, a key radiomic feature, showed high performance in classifying various renal tumours, improving patient management.
Area of Science:
- Medical Imaging
- Radiology
- Oncology
Background:
- Qualitative assessment of renal tumours from medical imaging introduces subjectivity in grading and subtype differentiation.
- Quantitative analysis, particularly radiomics, offers a more objective approach to renal tumour characterization.
- Accurate differentiation is crucial for effective patient management and treatment planning.
Purpose of the Study:
- To systematically review the literature on computed tomography (CT) radiomics for grading and differentiating renal tumour subtypes.
- To provide an educational perspective on the application of CT radiomics in renal tumour classification.
- To assess the performance of radiomic features in distinguishing tumour grades and types.
Main Methods:
- A systematic literature search was conducted using PubMed, Scopus, and Web of Science.
- Studies were selected based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist.
- The quality of included studies was assessed using the Radiomic Quality Score (RQS).
Main Results:
- Thirteen studies were included, evaluating radiomics for renal tumour grading and subtype differentiation.
- Radiomics demonstrated good to high performance (AUC 0.82-0.978) in differentiating low-grade from high-grade clear cell renal cell carcinoma (RCC) and chromophobe RCC.
- Eight studies successfully differentiated between various renal tumour types (e.g., clear cell RCC, angiomyolipoma, papillary RCC, oncocytoma) with high accuracy (AUC 0.82-0.96).
Conclusions:
- CT-based radiomics can effectively classify renal tumours by pathological grade and subtype, with texture features being particularly important.
- While promising, current research primarily focuses on clear cell and chromophobe RCC; expansion to other subtypes is recommended.
- Further large-scale, multi-institutional prospective studies are needed for clinical translation and integration into radiologist workflows.
