PURPOSE: Men diagnosed with clinically localized prostate cancer have a number of treatment options available, including watchful waiting, radical prostatectomy and radiation therapy. With the widespread use of serum prostate specific antigen (PSA) testing, prostate cancers are being diagnosed earlier in their natural history, with many tumors being small and of little health risk to the patient, at least in the short term. To better counsel men diagnosed with prostate cancer, we developed a statistical model that accurately predicts the presence of small moderately differentiated, confined cancer based on clinical variables (serum PSA, clinical stage, prostate biopsy Gleason grade and ultrasound volume) and variables derived from the analysis of systematic biopsies. MATERIALS AND METHODS: The analysis included 409 patients diagnosed by systematic needle biopsy with clinical stages T1c or T2a N0 or NX and M0 or MX prostate cancer who were treated solely with radical prostatectomy at 1 of 2 institutions. Additional biopsy features included number and percentage of biopsy cores involved with cancer and high grade cancer, in addition to total length of biopsy cores involved. Indolent cancer was defined as pathologically organ confined cancer 0.5 cc or less in volume and without poorly differentiated elements. Logistic regression was used to construct several prediction models and the resulting nomograms. RESULTS: Overall 80 (20%) of the patients had indolent cancer. The nomogram predicted the presence of an indolent cancer with discrimination (area under the receiver operating characteristics curves) for various models ranging from 0.64 to 0.79. Calibration of the models appeared good. CONCLUSIONS: Nomograms incorporating pretreatment variables (clinical stage, Gleason grade, PSA and the amount of cancer in a systematic biopsy specimen) can predict the probability that a man with prostate cancer has an indolent tumor. These nomograms have good discriminatory ability and calibration, and may benefit the patient and clinician when the various treatment options for prostate cancer are being considered.
PURPOSE:Men diagnosed with clinically localized prostate cancer have a number of treatment options available, including watchful waiting, radical prostatectomy and radiation therapy. With the widespread use of serum prostate specific antigen (PSA) testing, prostate cancers are being diagnosed earlier in their natural history, with many tumors being small and of little health risk to the patient, at least in the short term. To better counsel men diagnosed with prostate cancer, we developed a statistical model that accurately predicts the presence of small moderately differentiated, confined cancer based on clinical variables (serum PSA, clinical stage, prostate biopsy Gleason grade and ultrasound volume) and variables derived from the analysis of systematic biopsies. MATERIALS AND METHODS: The analysis included 409 patients diagnosed by systematic needle biopsy with clinical stages T1c or T2a N0 or NX and M0 or MX prostate cancer who were treated solely with radical prostatectomy at 1 of 2 institutions. Additional biopsy features included number and percentage of biopsy cores involved with cancer and high grade cancer, in addition to total length of biopsy cores involved. Indolent cancer was defined as pathologically organ confined cancer 0.5 cc or less in volume and without poorly differentiated elements. Logistic regression was used to construct several prediction models and the resulting nomograms. RESULTS: Overall 80 (20%) of the patients had indolent cancer. The nomogram predicted the presence of an indolent cancer with discrimination (area under the receiver operating characteristics curves) for various models ranging from 0.64 to 0.79. Calibration of the models appeared good. CONCLUSIONS: Nomograms incorporating pretreatment variables (clinical stage, Gleason grade, PSA and the amount of cancer in a systematic biopsy specimen) can predict the probability that a man with prostate cancer has an indolent tumor. These nomograms have good discriminatory ability and calibration, and may benefit the patient and clinician when the various treatment options for prostate cancer are being considered.
Authors: Amita Shukla-Dave; Hedvig Hricak; Oguz Akin; Changhong Yu; Kristen L Zakian; Kazuma Udo; Peter T Scardino; James Eastham; Michael W Kattan Journal: BJU Int Date: 2011-09-20 Impact factor: 5.588
Authors: M Suzanne Stratton; Amit M Algotar; James Ranger-Moore; Steven P Stratton; Elizabeth H Slate; Chiu-Hsieh Hsu; Patricia A Thompson; Larry C Clark; Frederick R Ahmann Journal: Cancer Prev Res (Phila) Date: 2010-07-20
Authors: Stephen B Williams; Simpa Salami; Meredith M Regan; Donna P Ankerst; John T Wei; Mark A Rubin; Ian M Thompson; Martin G Sanda Journal: Cancer Date: 2011-10-17 Impact factor: 6.860
Authors: Danil V Makarov; Bruce J Trock; Elizabeth B Humphreys; Leslie A Mangold; Patrick C Walsh; Jonathan I Epstein; Alan W Partin Journal: Urology Date: 2007-06 Impact factor: 2.649
Authors: Shahrokh F Shariat; Michael W Kattan; Andrew J Vickers; Pierre I Karakiewicz; Peter T Scardino Journal: Future Oncol Date: 2009-12 Impact factor: 3.404