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.

Comparison of Perioperative, Functional, and Oncologic Outcomes of Open vs. Robot-Assisted Off-Clamp Partial Nephrectomy: A Propensity Scored Match Analysis

“…The field of robotic urology is on the edge of a transformative era, thanks to the synergistic integration of advanced robotics [45], artificial intelligence (AI) [46], and cuttingedge pre-and intraoperative imaging technologies [47,48]. AI algorithms are able to analyze patient data, medical history, and imaging to predict the potential for intra-and postoperative complications [49].…”

MRI-Based Radiomics in Bladder Cancer: A Systematic Review and Radiomics Quality Score Assessment

“…Recently, radiomic features have been integrated into the BCa diagnosis workflow by offering preoperative staging and tumoral grading predictions based on bladder mpMRI acquisitions. It has been shown that the addition of textural features increased the specificity of BCa diagnosis by 12% and reduced the lymph nodes understaging rate by 36% when compared to the radiologist’s mpMRI interpretation alone [ 9 ].…”