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Could STAR-CAP be the New Prostate Cancer Staging System?

PROSTATE cancer is one of the most common cancers worldwide, yet it remains one of the few cancers not to adopt the familiar staging system of Stage I–IV. Researchers from the University of Michigan, Ann Arbor, Michigan, USA, have developed a new prostate cancer staging system that could help clinicians determine prognosis and appropriate treatment.

Patient, tumour, and outcomes data from nearly 20,000 patients across 55 centres in the USA, Canada, and Europe were used to develop the staging system. The system, named STAR-CAP, uses patient’s age, tumour category, Gleason grade, and prostate-specific antigen levels to assign a stage to the cancer. Using the points scored from the above variables, the model splits the patients into nine stages of nonmetastatic prostate cancer: Stage I–III, with each stage divided into substages A–C.

When comparing to existing, non-validated models, including the American Joint Committee on Cancer (AJCC) staging system, STAR-CAP outperformed or equalled the models and had strong prognostic power. Reclassification of the stages of numerous patients was seen with the STAR-CAP model; for example, 22% of patients changed from AJCC Stage 3A to STAR-CAP Stage 1C.

“Localised prostate cancer is sometimes less aggressive, sometimes more, and whether we’re patients, physicians, or researchers, we all want to know as best we can how aggressive a particular cancer is likely to be,” said study co-first author Assoc Prof Robert Dess, Michigan Medicine, Ann Arbor. He added: “This is the kind of information that can give patients and doctors more confidence when discussing treatment options and expected outcomes.”

When speaking about the reasons behind developing the new staging system, the authors noted that “none of the previous models evaluated met the criteria,” and therefore they decided to develop a new model that was transparent, robust, and validated. Having a universally accepted staging system that is robust and accurate will help with clinical trial design and support clinicians and patients when deciding treatment options.