CT-based radiomics for differentiating renal tumours: a systematic review
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REVIEW
CT‑based radiomics for differentiating renal tumours: a systematic review Abhishta Bhandari1 · Muhammad Ibrahim1 · Chinmay Sharma1 · Rebecca Liong2 · Sonja Gustafson2 · Marita Prior2 Received: 8 February 2020 / Revised: 6 October 2020 / Accepted: 12 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Purpose Differentiating renal tumours into grades and tumour subtype from medical imaging is important for patient management; however, there is an element of subjectivity when performed qualitatively. Quantitative analysis such as radiomics may provide a more objective approach. The purpose of this article is to systematically review the literature on computed tomography (CT) radiomics for grading and differentiating renal tumour subtypes. An educational perspective will also be provided. Methods The Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist was followed. PubMed, Scopus and Web of Science were searched for relevant articles. The quality of each study was assessed using the Radiomic Quality Score (RQS). Results 13 studies were found. The main outcomes were prediction of pathological grade and differentiating between renal tumour types, measured as area under the curve (AUC) for either the receiver operator curve or precision recall curve. Features extracted to predict pathological grade or tumour subtype included shape, intensity, texture and wavelet (a type of higher order feature). Four studies differentiated between low-grade and high-grade clear cell renal cell cancer (RCC) with good performance (AUC = 0.82–0.978). One other study differentiated low- and high-grade chromophobe with AUC = 0.84. Finally, eight studies used radiomics to differentiate between tumour types such as clear cell RCC, fat-poor angiomyolipoma, papillary RCC, chromophobe RCC and renal oncocytoma with high levels of performance (AUC 0.82–0.96). Conclusion Renal tumours can be pathologically classified using CT-based radiomics with good performance. The main radiomic feature used for tumour differentiation was texture. Fuhrman was the most common pathologic grading system used in the reviewed studies. Renal tumour grading studies should be extended beyond clear cell RCC and chromophobe RCC. Further research with larger prospective studies, performed in the clinical setting, across multiple institutions would help with clinical translation to the radiologist’s workstation. Keywords Computed tomography · Machine learning · Artificial intelligence · Renal tumours · Radiomics · Grade
Introduction The rapid development of computational analysis for radiological images represents a major change from conventional visual interpretation. Radiological images are a rich source of quantitative data [1]. Quantitative analysis of images has given rise to the field of radiomics which has resulted in an * Abhishta Bhandari [email protected] 1
Townsville University Hospital, 100 Angus Smith Drive, Douglas, QLD 4814, Australia
Department of Medical Imagi
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