Prostate cancer diagnosis: Unveiling disease aggressiveness with metabolic imaging

The BioMR group at Radboudumc's Medical Imaging department has pioneered an innovative advancement in prostate cancer diagnosis. Their recent study, featured in NMR in Biomedicine, introduces a novel automated tool capable of accurately distinguishing between tumor and non-tumor prostate tissue, while also significantly improving the classification of tumor aggressiveness.

Current prostate cancer diagnosis heavily relies on Magnetic Resonance Imaging (MRI) to evaluate suspicious tissue. When a suspicious lesion is identified, patients typically undergo a biopsy. The aggressiveness of the lesion is assessed from the biopsy analysis. Immediate treatment is administered to patients with aggressive tumors, while those with low aggressive tumors are advised to enter active surveillance programs involving regular screenings and biopsies.

The BioMR research group has applied a so-called Multivariate Curve Resolution (MCR) algorithm to metabolic imaging data acquired by MR from 106 prostate cancer patients across 9 hospitals worldwide. This approach not only identified tumor lesions among non-tumor prostate tissue but, more importantly, was able to unveil the aggressiveness of the tumor lesions.

The real game-changer? Their tool's ability to correlate the intensity of specific metabolic components with tumor aggressiveness in a significantly improved way compared to previous studies. Key advancements are its automation, quantitative nature and ability to extract these components even from low quality data. This capability has the potential to have a major impact on prostate cancer diagnosis and monitoring, particularly for individuals under active surveillance. It offers the possibility of obtaining essential diagnostic information non-invasively, that otherwise would only be provided by invasive biopsies.

What's next? The team aims to further enhance this tool by evaluating its performance on larger patient groups, seeking to uncover more information within the MR acquired metabolic data, holding promise for new and exciting dimensions in prostate cancer diagnostics.

Read the publication here

Angeliki Stamatelatou, Carlo Giuseppe Bertinetto, Jeroen J Jansen, Geert Postma, Kirsten Margrete Selnaes, Tone F Bathen, Arend Heerschap, Tom W J Scheenen; PCa-MAP consortium (2023). A multivariate curve resolution analysis of multicenter proton spectroscopic imaging of the prostate for cancer localization and assessment of aggressiveness. NMR in BioMedicine. https://doi.org/10.1002/nbm.5062