Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review

Abstract

Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients’ clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients’ clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.

FPN-SE-ResNet Model for Accurate Diagnosis of Kidney Tumors Using CT Images

“…Renal cancer ranks tenth globally, with a significant increase in individuals. In the US, 2023 projections predict 81,800 new RC diagnoses, resulting in 14,890 deaths [45]. Renal cancer is a diverse group of tumors originating from different kidney cell types, with renal cell carcinoma (RCC) being the most aggressive form, accounting for 70%.…”

“…Conventional tests like CBC, urinalysis, and blood assays can hint at RC but do not provide precise diagnoses or differentiate between subtypes, grades, or stages. Therefore, biopsy remains the ultimate benchmark for definitive RC diagnosis, as it accurately quantifies red blood cells, identifies blood, bacteria, or malignant cells, and gauges renal function [45]. Deep learning is a powerful machine learning technology that can autonomously acquire various features and patterns without human intervention [51,52].…”

An Exaggeration? Reality?

“…The nurse's input of patient data into the ChatGPT chatbot allows the system to process and analyze information, facilitating appropriate diagnoses, treatment planning, and imaging methods. This facilitates the management of the recovery process in newborns8 and removes obstacles to proper care 9. The deep learning approach, a technique supported by AI, has demonstrated high accuracy in pain assessment and classification 2…”