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

Keywords: computer-assisted; diagnosis; neoplasm staging; radiomics; renal neoplasms; urinary bladder neoplasms.

Figure 1

A) Radiomics image processing. B) Inherent ability of radiomics to integrate key data.signatures from large image bases. CT – computed tomography; MRI – magnetic resonance imaging; PET – positron emission tomography; ML – machine learning, DL – deep learning

Figure 2

Flow diagram of the literature screening. n – number