Tramanzoli Pietro, Castellani Daniele, De Stefano Virgilio, Brocca Carlo, Nedbal Carlotta, Chiacchio Giuseppe, Galosi Andrea Benedetto, Da Silva Rodrigo Donalisio, Teoh Jeremy Yuen-Chun, Tiong Ho Yee, Naik Nithesh, Somani Bhaskar K, Gauhar Vineet
Urology Unit, Azienda Ospedaliero-Universitaria delle Marche, Università Politecnica delle Marche, Ancona, Italy.
Division of Urology, Denver Health Medical Center, University of Colorado, Denver, USA.
Cent European J Urol. 2023;76(1):12-19. doi: 10.5173/ceju.2023.252. Epub 2023 Jan 21.
Radiomics in uro-oncology is a rapidly evolving science proving to be a novel approach for optimizing the analysis of massive data from medical images to provide auxiliary guidance in clinical issues. This scoping review aimed to identify key aspects wherein radiomics can potentially improve the accuracy of diagnosis, staging, and grading of renal and bladder cancer.
A literature search was performed in June 2022 using PubMed, Embase, and Cochrane Central Controlled Register of Trials. Studies were included if radiomics were compared with radiological reports only.
Twenty-two papers were included, 4 were pertinent to bladder cancer, and 18 to renal cancer. Radiomics outperforms the visual assessment by radiologists in contrast-enhanced computed tomography (CECT) to predict muscle invasion but are equivalent to CT reporting by radiologists in predicting lymph node metastasis. Magnetic resonance imaging (MRI) radiomics outperforms radiological reporting for lymph node metastasis. Radiomics perform better than radiologists reporting the probability of renal cell carcinoma, improving interreader concordance and performance. Radiomics also helps to determine differences in types of renal pathology and between malignant lesions from their benign counterparts. Radiomics can be helpful to establish a model for differentiating low-grade from high-grade clear cell renal cancer with high accuracy just from contrast-enhanced CT scans.
Our review shows that radiomic models outperform individual reports by radiologists by their ability to incorporate many more complex radiological features.
泌尿肿瘤学中的放射组学是一门快速发展的科学,已被证明是一种优化医学图像海量数据分析的新方法,可为临床问题提供辅助指导。本范围综述旨在确定放射组学可能提高肾癌和膀胱癌诊断、分期及分级准确性的关键方面。
2022年6月使用PubMed、Embase和Cochrane中央对照试验注册库进行文献检索。纳入的研究仅限将放射组学与放射学报告进行比较的研究。
共纳入22篇论文,其中4篇与膀胱癌相关,18篇与肾癌相关。在预测肌肉浸润方面,放射组学在对比增强计算机断层扫描(CECT)中优于放射科医生的视觉评估,但在预测淋巴结转移方面与放射科医生的CT报告相当。磁共振成像(MRI)放射组学在预测淋巴结转移方面优于放射学报告。放射组学在报告肾细胞癌的可能性方面比放射科医生表现更好,提高了阅片者间的一致性和性能。放射组学还有助于确定肾脏病理类型之间以及恶性病变与其良性对应物之间的差异。仅通过对比增强CT扫描,放射组学有助于建立一个高精度区分低级别与高级别透明细胞肾癌的模型。
我们的综述表明,放射组学模型通过纳入更多复杂放射学特征的能力,优于放射科医生的个体报告。