Advertisement

PSA-Based Computational Model Predicts Time to Relapse After Prostate Cancer Surgery

Advertisement

Key Points

  • The combination of PSA values and a biologically sensible computational model may predict the timing of tumor recurrence in patients with prostate cancer.
  • The PSA-based computational model may help oncologists plan a more effective and personalized therapy for patients with prostate cancer.
  • Utilizing a mathematical tool to predict cancer recurrence may potentially improve patients’ quality of life and help both physicians and patients make better-informed treatment choices.

Approximately one in four patients who undergo radical prostatectomy experience a cancer recurrence. Now a study by Stura et al investigating a prostate-specific antigen (PSA)-based computational model that uses four consecutive postsurgical PSA values has found the mathematical model to be highly predictive for prostate cancer relapse. The collection of PSA values and a biologically sensible computational model may be a valuable tool in predicting the timing of tumor recurrence and helping physicians to plan more effective and personalized therapy for their patients. The study was published in Cancer Research.

Study Methodology

The researchers analyzed data from 3,538 patients who were treated by prostatectomy as their primary therapy. Of those patients, 707 received adjuvant androgen-deprivation therapy, and 728 had a relapse.

Using multiple postsurgically available PSA values from each patient, including Gleason score, pathologic stage, type of surgery, margins, and lymph node metastasis, and data on survival outcomes, the researchers used a formula to estimate the parameter alpha (α) for 211 patients who did not receive androgen-deprivation therapy and 40 patients who did.  

Study Results

The researchers found that four consecutive PSA values collected after surgery, α4, were enough to consistently predict the time to relapse and that the higher the value of α4, the greater the probability of relapse. For example, for a patient who underwent prostate surgery and had not received adjuvant androgen-deprivation therapy, a α4 value below 0.01 means there is an 82% probability that the patient’s cancer will not relapse within 3 years and a 54% probability that his cancer will not relapse in 4 years. Conversely, an α4 value between 0.02 and 0.04 means there is a 71% probability that his cancer will recur in 2 years and a 95% probability that his cancer will recur in 4 years. An α4 value greater than 0.04 means there is an 87% probability that his cancer will relapse in 1 year and a 93% probability that it will relapse in 2 years.

The researchers also developed an algorithm for patients who receive adjuvant androgen-deprivation therapy as well as an algorithm for the development of resistance to androgen-deprivation therapy.

“Our work is another small step toward personalized medicine and shows how mathematics can be important to better understand tumor evolution,” said Ilaria Stura, a mathematician and doctoral candidate in the Complex Systems for Life Sciences program at the University of Turin in Italy, in a statement. “We are working to improve the reliability of the model by testing it on data from new patients and making the algorithm available for clinicians and patients for free.”

Funding for this study was provided by the European Union Seventh Framework Program in the Computational Horizon in Cancer Project.

The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.


Advertisement

Advertisement



Advertisement