The Incidence of Prostate Cancer Progression with Undetectable Serum Prostate Specific Antigen in a Series of 394 Radical Prostatectomies:

Radionuclide bone scintigraphy in patients with biochemical recurrence after radical prostatectomy: when is it indicated?

OBJECTIVE

To evaluate the use of radionuclide bone scintigraphy following biochemical recurrence after radical retropubic prostatectomy (RRP) for localized prostate cancer.

PATIENTS AND METHODS

Of 1197 patients undergoing RRP we identified those with biochemical recurrence and who had also had a bone scan. Biochemical recurrence was defined as a prostate specific antigen (PSA) level of > or = 0.4 ng/mL. Patients with indeterminate bone scan findings and those in whom the interval between the PSA test and the bone scan was >3 months were excluded. Patient age, PSA level and other relevant pathological details were recorded. Clinical symptoms at the time of bone scan, androgen deprivation after RRP, bone scintigram details and time to recurrence were documented.

RESULTS

Of the 1197 patients, 153 (12.8%) had a biochemical recurrence and 35 (23%) of these had a total of 44 bone scans taken over a mean (sd) follow-up of 70.4 (35.6) months; 34 (77%) bone scans were negative (group 1) and 10 (33%) positive (group 2). In group 1 the mean PSA at the bone scan was 5.2 ng/mL; 76% of the patients had a PSA of < 7 ng/mL. In group 2 the mean PSA at the bone scan was 30.7 ng/mL and all patients had a PSA of >7 ng/mL. The only significant difference between the groups was the PSA at the time of the bone scan (P < 0.001).

CONCLUSION

Bone scintigraphy is a sensitive diagnostic tool for detecting prostate cancer metastases to bone. A bone scan in patients with a serum PSA of <7 ng/mL on biochemical recurrence after RRP is unlikely to be positive, whereas a PSA of > or = 20 ng/mL is. The presence of skeletal symptoms or a PSA level of >7 ng/mL should prompt the clinician to obtain a bone scintigram.

Genetic adaptive neural network to predict biochemical failure after radical prostatectomy: a multi-institutional study

BACKGROUND AND PURPOSE

Despite many new procedures, radical prostatectomy remains one of the commonest methods of treating clinically localized prostate cancer. Both from the physician's and the patient's point of view, it is important to have objective estimation of the likelihood of recurrence, which forms the foundation for treatment selection for an individual patient. Currently, it is difficult to predict the probability of biochemical recurrence (rising serum prostate specific antigen [PSA] concentration) in an individual patient, and approximately 30% of the patients do experience recurrence. Tools predicting the recurrence will be of immense practical utility in the treatment selection and planning follow up. We have utilized preoperative parameters through a computer based genetic adaptive neural network model to predict recurrence in such patients, which can help primary care physicians and urologists in making management recommendations.

PATIENTS AND METHODS

Fourteen hundred patients who underwent radical prostatectomy at participating institutions form the subjects of this study. Demographic data such as age, race, preoperative PSA, systemic biopsy based staging and Gleason scores were used to construct a neural network model. This model simulated the functioning of a trained human mind and learned from the database. Once trained, it was used to predict the outcomes in new patients.

RESULTS

The patients in this comprehensive database were representative of the average prostate cancer patients as seen in USA. Their mean age was 68.4 years, the mean PSA concentration before surgery was 11.6 ng/mL, and 67% patients had a Gleason sum of 5 to 7. The mean length of follow-up was 41.5 months. Eighty percent of the cancers were clinical stage T2 and 5% T3. In our series, 64% of patients had pathologically organ-confined cancer, 33% positive margins, and 14% had seminal vesicle invasion. Lymph node positive patients were not included in this series. Progression as judged by serum PSA was noted in 30.6%. With entry of a few routinely used parameters, the model could correctly predict recurrence in 76% of the patients in the validation set. The area under the curve was 0.831. The sensitivity was 85%, the specificity 74%, the positive predictive value 77%, and the negative predictive value of 83%.

CONCLUSION

It was possible to predict PSA recurrence with a high accuracy (76%). Physicians desiring objective treatment counseling can use this model, and significant cost savings are anticipated because of appropriate treatment selection and patient-specific follow-up protocols. This technology can be extended to other treatments such as watchful waiting, external-beam radiation, and brachytherapy.