Personalizing androgen suppression for prostate cancer using mathematical modeling

Yoshito Hirata, Kai Morino, Koichiro Akakura, Celestia S. Higano, Kazuyuki Aihara

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


Using a dataset of 150 patients treated with intermittent androgen suppression (IAS) through a fixed treatment schedule, we retrospectively designed a personalized treatment schedule mathematically for each patient. We estimated 100 sets of parameter values for each patient by randomly resampling each patient's time points to take into account the uncertainty for observations of prostate specific antigen (PSA). Then, we identified 3 types and classified patients accordingly: In type (i), the relapse, namely the divergence of PSA, can be prevented by IAS; in type (ii), the relapse can be delayed by IAS later than by continuous androgen suppression (CAS); in type (iii) IAS was not beneficial and therefore CAS would have been more appropriate in the long run. Moreover, we obtained a treatment schedule of hormone therapy by minimizing the PSA of 3 years later in the worst case scenario among the 100 parameter sets by searching exhaustively all over the possible treatment schedules. If the most frequent type among 100 sets was type (i), the maximal PSA tended to be kept less than 100 ng/ml longer in IAS than in CAS, while there was no statistical difference for the other cases. Thus, mathematically personalized IAS should be studied prospectively.

Original languageEnglish
Article number2673
JournalScientific reports
Issue number1
Publication statusPublished - Dec 1 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General


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