Doctors have long known that prostate-specific antigen (PSA) levels can help track how well treatment is working in men with metastatic prostate cancer.
If PSA levels drop very low soon after treatment starts, it often means the patient will live longer. But there’s a problem.
Doctors usually have to wait up to six months after treatment begins to see if a patient is responding well. For patients who are not doing well, this delay gives the cancer time to grow and become harder to treat.
A new study published in Nature Communications suggests a better way. Researchers have built a tool that may help predict, at the very start of treatment, which patients will respond well and which may not. This could allow doctors to act sooner and give extra treatment to those who need it.
The study was led by Dr. Soumyajit Roy, a radiation doctor at UH Seidman Cancer Center. He explained that many of the tools doctors currently use to judge how serious a person’s cancer is—like how many cancer spots they have—are not very precise.
These tools don’t always help predict who will respond well to treatment. So there has been a big need for something better.
The team focused on men with metastatic hormone-sensitive prostate cancer (mHSPC). These men have cancer that has spread but still responds to hormone treatment. Right now, the main treatment for them is a group of medicines called ARPIs (androgen receptor pathway inhibitors), used all over the world.
The study looked at whether it’s possible to predict early treatment success—specifically, how fast PSA levels drop—at the time of diagnosis.
What makes this research special is that the tool they created is one of the first that has been carefully tested and shown to work before doctors know the results of treatment. In simple terms, this tool could let doctors look into the future and see how likely it is that a patient will do well.
Dr. Daniel Spratt, the senior author, says this changes the way we treat prostate cancer. Instead of waiting to see who isn’t getting better, doctors could now try a more active and personal approach.
If the tool shows that a patient is not likely to have a strong early drop in PSA, the doctor could try other treatments sooner or recommend that the patient join a clinical trial.
Importantly, the new tool worked better than using just one piece of information, like PSA level or number of tumors. It combines different clinical details, making it much more accurate.
This model could help in many ways. First, it can spot high-risk patients right at the start. Second, it can help doctors and patients talk about whether stronger treatment or more frequent checkups are needed.
Third, it helps patients understand what to expect. And finally, it can help researchers design better clinical trials by including patients who are more likely to benefit from early treatment changes.
The next step is to test this tool in everyday hospitals and clinics, and in current trials, to see how it can fit into real-world care. Researchers also want to improve the model by adding new kinds of data, like results from gene tests or medical scans.
In the end, this study could open the door to faster, more personalized prostate cancer treatment. If doctors can act early based on the model’s predictions, they may be able to improve survival in patients with aggressive disease.
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