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After skin cancer, prostate cancer (PC) is the most common cancer among men. The gold standard for PC diagnosis is based on the PSA (prostate-specific antigen) test. Based on this preliminary screening, the physician decides whether to proceed with further tests, typically prostate biopsy, to confirm cancer and evaluate its aggressiveness. Nevertheless, the specificity of the PSA test is suboptimal and, as a result, about 75% of men who undergo a prostate biopsy do not have cancer even if they have elevated PSA levels. Overdiagnosis leads to unnecessary overtreatment of prostate cancer with undesirable side effects, such as incontinence, erectile dysfunction, infections, and pain. Here, we used artificial neuronal networks to develop models that can diagnose PC efficiently. The model receives as an input a panel of 4 clinical variables (total PSA, free PSA, p2PSA, and PSA density) plus age. The output of the model is an estimate of the Gleason score of the patient. After training on a dataset of 190 samples and optimization of the variables, the model achieved values of sensitivity as high as 86% and 89% specificity. The efficiency of the method can be improved even further by training the model on larger datasets.

国際標準化とgray zone症例の意義について検討した.従来のデータを生かした標準値への転換式の提案とgray zoneの予後が良好であることが判明した.このため効率良い診断手段, とりわけmolecular form測定の意義に関する検討が必要であろうと考えた, An international standard of prostate specific antigen (PSA) assays was constructed and prognosis of the patients with prostate cancers showing gray zone PSA was studied. For lower levels of serum PSA (< 50 ng/ml), the conversion formula to that of Tandem-R PSA from other assays was presented. Furthermore, based on the standards of Stanford Reference and Markit-MPA, conversion rates to this international standard from the conventional PSA assays were also obtained. Patients' cancer-specific survival was found to be significantly better in the gray zone group. Further studies to obtain higher specificity such as using free or complex rate in total PSA is necessary.