Please use this identifier to cite or link to this item: https://doi.org/10.5173/ceju.2023.252
Title: Radiomics vs radiologist in bladder and renal cancer. Results from a systematic review
Authors: 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
Keywords: Science & Technology
Life Sciences & Biomedicine
Urology & Nephrology
diagnosis
computer-assisted
neoplasm staging
urinary bladder neoplasms
renal neoplasms
radiomics
CT
METASTASIS
PREDICTION
Issue Date: 1-Jan-2023
Publisher: POLISH UROLOGICAL ASSOC
Citation: 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 (2023-01-01). Radiomics vs radiologist in bladder and renal cancer. Results from a systematic review. CENTRAL EUROPEAN JOURNAL OF UROLOGY 76 (1). ScholarBank@NUS Repository. https://doi.org/10.5173/ceju.2023.252
Abstract: 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 clinicaissues. 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 tomog-raphy (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.
Source Title: CENTRAL EUROPEAN JOURNAL OF UROLOGY
URI: https://scholarbank.nus.edu.sg/handle/10635/242060
ISSN: 2080-4806
2080-4873
DOI: 10.5173/ceju.2023.252
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