New staging system can inform therapy selections for non-metastatic prostate most cancers

Doctors and biostatisticians at the University of Michigan’s Rogel Cancer Center led the development and validation of a staging system to better predict outcomes and make treatment decisions for men with non-metastatic prostate cancer.

Despite being one of the most common cancers in the world, prostate cancer remains one of the few major cancers for which the well-known numerical staging system -; from level 1 to level 4 -; has not been included in national guidelines for the treatment or testing of new drugs in clinical trials.

The new proposed system -; called STAR-CAP -; JAMA Oncology uses patient, tumor, and outcome data from nearly 20,000 patients from 55 centers in the United States, Canada, and Europe to create a robust model with strong predictive power.

“Localized prostate cancer is sometimes less aggressive, sometimes more – and whether we are patients, doctors, or researchers, we all want to know as much as possible how aggressive a particular cancer is likely to be,” says study co-lead author Robert Dess, MD , Assistant Professor of Radiation Oncology at Michigan Medicine.

“This information aids in our discussions with patients, it helps design clinical trials, and is especially valuable if you can make these estimates based on standard information you would collect the first time you saw a patient about them Discuss treatment options. “

The system assigns patients to a specific stage using a point system based on several key variables. These include the patient’s age, tumor category, Gleason grade of the cell abnormality, and prostate-specific antigen levels, also known as PSA levels. And STAR-CAP uses more granularity in these categories than many of the previous models, according to the authors.

The model divides patients into nine stages of non-metastatic prostate cancer based on their scores -; from level 1 to level 3, each level being divided into sub-levels of A, B and C.

STAR-CAP’s predictions exceeded or matched previous unvalidated models, including the current staging system of the United States’ Joint Committee on Cancer, the study said.

And for a significant number of patients, the new model would classify them as patients with less advanced disease -; For example, 22% of patients who would be rated Level 3A by the 8th Edition AJCC criteria would be rated Level 1C using the STAR-CAP system, a downgrade of four grading steps.

“This type of information can give patients and doctors more confidence when discussing treatment options and expected results,” says Dess.

Several years ago, the AJCC established criteria for evaluating predictive models for prostate cancer staging. However, since no models met the criteria, the most recent staging names were based on consensus among experts in the field, says Dr. Daniel Spratt, co-senior study author, Laurie Snow Endowed Professor of Radiation Oncology at Michigan Medicine.

“None of the previously evaluated models met the criteria, so none of them could be used,” says Spratt. “So we said, ‘Well, let’s make one.’ We wanted it to be transparent, robust, and validated so that we could approach communicating with a common staging system, similar to other cancers. Currently we mainly categorize people as low risk, moderate or high risk quite blunt and imprecise system. “

In addition, the new rating system is designed to be used worldwide with information that is often collected about a patient and their cancer.

We use a backbone of more than three decades of research. And we wanted to do this in a formal way and provide the most validated forecasting system we could develop. It was simple, easy to use, and relied on readily available information. “

Robert Dess, MD, study co-author and assistant professor of radiation oncology, Michigan Medicine

The team made the rating system available to doctors and researchers around the world via a web-based app on STAR-CAP.org.

“We know that some of the latest tools we have that are going online right now, like genomics or molecular imaging, might improve that system, but we wanted to build the best and most accessible model based on the data we were about currently have – understanding that new tools could help us develop even better models in the future, “says Dess.

Both Dess and Spratt emphasized that the effort would not be without co-first author Krithika Suresh, Ph.D., a former PhD student in biostatistics, and co-senior author Matthew Schipper, Ph.D., a research professor of biostatistics would have been possible at the School of Public Health and Research Associate Professor of Radiation Oncology at Michigan Medicine, who led the complex statistical analyzes of the work.

Elizabeth Chase, a PhD student in biostatistics, was also instrumental in designing and developing the online web application.

Even without the participation of numerous national and international employees, the work would not have been possible.

“This job only gets done if you have the collaborative spirit of investigators across the country and around the world,” adds Dess.

Source:

Michigan Medicine – University of Michigan

Journal reference:

Dess, RT et al. (2020) Development and validation of a clinical-prognostic stage group system for non-metastatic prostate cancer using disease-specific mortality results from the international staging cooperation for prostate cancer. JAMA oncology. doi.org/10.1001/jamaoncol.2020.4922.

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