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Radiomics vs radiologist in bladder and renal cancer. Results from a systematic review

  • 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
  • *Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish
Pages (from-to)12-19
Number of pages8
JournalCentral European Journal of Urology
Volume76
Issue number1
DOIs
Publication statusPublished - 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

  • Urology

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