Pietro Tramanzoli, Daniele Castellani, Virgilio De Stefano, Carlo Brocca, Carlotta Nedbal, Giuseppe Chiacchio, Andrea Benedetto Galosi, Rodrigo Donalisio Da Silva, Jeremy Yuen-Chun Teoh, Ho Yee Tiong, Nithesh Naik, Bhaskar K Somani, Vineet Gauhar
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Studies were included if radiomics were compared with radiological reports only.</p><p><strong>Results: </strong>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. 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引用次数: 2
摘要
简介:放射组学在泌尿肿瘤学中是一门快速发展的科学,被证明是一种新的方法,可以优化医学图像的大量数据分析,为临床问题提供辅助指导。本综述旨在确定放射组学可以潜在提高肾癌和膀胱癌诊断、分期和分级准确性的关键方面。材料和方法:于2022年6月使用PubMed、Embase和Cochrane Central Controlled Register of Trials进行文献检索。如果仅将放射组学与放射学报告进行比较,则纳入研究。结果:共纳入22篇论文,其中膀胱癌4篇,肾癌18篇。放射组学在预测肌肉侵袭方面优于放射科医生在对比增强计算机断层扫描(CECT)中的视觉评估,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)放射组学优于淋巴结转移的放射学报告。放射组学的表现优于放射科医生报告肾细胞癌的可能性,提高了解读者的一致性和表现。放射组学还有助于确定肾脏病理类型的差异,以及恶性病变与良性病变之间的差异。放射组学可以帮助建立一种仅通过增强CT扫描就能高精度区分低级别和高级别透明细胞肾癌的模型。结论:我们的综述表明,放射学模型在合并许多更复杂的放射学特征方面的能力优于放射科医生的个人报告。
Radiomics vs radiologist in bladder and renal cancer. Results from a systematic review.
Introduction: 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.
Material and methods: 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.
Results: 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.
Conclusions: Our review shows that radiomic models outperform individual reports by radiologists by their ability to incorporate many more complex radiological features.