Work

A clinically validated AI framework for kidney cancer detection and characterization

Year: 2025

Type: article

Abstract: Renal cell carcinoma is one of the most common cancers of the urinary tract and is usually diagnosed by interpreting contrast-enhanced computed tomography scans. Rising demand for radiological services, combined with a shortage of radiologists, makes timely and accurate diagnosis increasingly challenging. Automated approaches may help radiologists improve efficiency and accuracy. We developed BMVision, a deep learning-based tool for detecting and characterizing kidney cancer. The tool integrates with a web-based viewer designed to provide an intuitive interface for radiologists. Its performance was evaluated in a two-stage retrospective reader study. Six radiologists independently reviewed 200 scans across both AI-assisted and unaided workflows, allowing comparison of diagnostic performance and workflow efficiency with and without support from the tool. Statistical analysis compared AI-aided and unaided workflows across predefined clinical criteria, including diagnostic sensitivity, lesion measurement, reporting efficiency, and inter-radiologist agreement, using non-parametric tests and bootstrapping. Here we show that BMVision reduces radiologists’ reporting time by an average of 33%, up to 52%. The tool provides structured auto-generated reports, minimizing the need for manual dictation or typing. In addition, BMVision improves sensitivity for detecting benign renal lesions (from 79.9 to 86.3%) and leads to a significant increase in inter-radiologist agreement. To the best of our knowledge, BMVision is the first clinically validated commercial artificial intelligence tool for kidney cancer detection and characterization. By improving diagnostic accuracy and reporting efficiency, it has the potential to enhance patient care and help radiologists meet the growing demand for high-quality cancer diagnostics. Kidney cancer is one of the most common cancers of the urinary system. It is typically identified using specialized medical images called computed tomography (CT) scans, which are carefully reviewed by radiologists. However, there are not enough radiologists, and the demand for scans is growing. This makes it more challenging to provide patients with fast and accurate results. In this study, we developed a computer tool called BMVision, which utilizes artificial intelligence to analyze CT scans and assist radiologists in diagnosing kidney cancer. We tested BMVision with six radiologists, who reviewed a large number of scans with and without help from the tool. We found that BMVision enables radiologists to work more efficiently and consistently. Tools like BMVision can help patients by making cancer diagnosis faster, more reliable, and more widely available. Petryshak et al developed and validated BMVision, an artificial intelligence tool designed to assist radiologists in diagnosing kidney cancer from CT scans. In a reader study with six radiologists, the tool reduces reporting time by 32%, improves detection of benign lesions, and increases consistency across readers.

Source: Communications Medicine

DOI: 10.1038/s43856-025-01264-0