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Alan W Partin

Publications and source records attributed to Alan W Partin.

116 records · Page 7Linked to original sources
PubMed2002

Prediction of pathological stage in patients with clinical stage T1c prostate cancer: the new challenge.

PURPOSE: We developed an algorithm for predicting the likelihood of organ confined disease in patients with clinical stage T1c prostate cancer using biopsy pathology, computer assisted image analysis and serum prostate specific antigen (PSA). MATERIALS AND METHODS: Of the 557 consecutive men enrolled in this study between October 1998 and January 2000 scheduled for radical prostatectomy at a single institution 386 (69%) presented with clinical stage T1c disease. Study exclusion criteria included neoadjuvant hormonal treatment with luteinizing hormone-releasing hormone, antiandrogen or 5alpha-reductase inhibitors. Preoperative serum, biopsy histology slides, clinical demographic information, prostatectomy pathology and prostate weight data were obtained. Biomarkers assessed included total PSA, complexed PSA, free PSA, the free-to-total PSA ratio, quantitative nuclear grade determined by image analysis, complexed PSA density, and biopsy Gleason grade and score. To determine patient specific quantitative nuclear grade values, images from approximately 125 cancer nuclei were captured per patient from the area of the biopsy section with the highest Gleason score. The variance in 60 nuclear size, shape and chromatin texture descriptors was calculated for each gallery of nuclei. Logistic regression was done to determine the most accurate combination of variables for predicting organ confined prostate cancer. RESULTS: Complete results and data were available on 255 of the 386 men (66%) with an average age plus or minus standard deviation of 58.8 +/- 6 years who had stage T1c disease, including 49 (19%) with pathologically nonorgan confined disease. Logistic regression analysis revealed that quantitative nuclear grade, biopsy Gleason score, total PSA, the calculated free-to-total PSA ratio, complexed PSA and complexed PSA density were univariately significant for predicting organ confined disease (p <0.05). On backward stepwise logistic regression only quantitative nuclear grade, complexed PSA density and Gleason score remained in a model yielding an area under the receiver operating characteristics curve of 82.4%. CONCLUSIONS: The quantitative nuclear grade biomarker was the strongest independent predictor of pathological stage in men with clinical stage T1c prostate cancer when combined with biopsy Gleason score and complexed PSA density data.

Aged↗
PubMed2002

Percent free prostate specific antigen in the total prostate specific antigen 2 to 4 ng./ml. range does not substantially increase the number of biopsies needed to detect clinically significant prostate cancer compared to the 4 to 10 ng./ml. range.

PURPOSE: Percent free prostate specific antigen (PSA) is useful to select patients for prostate biopsy with total PSA 4 to 10 ng./ml. However, 20% of men with PSA between 2.6 and 4 ng./ml. harbor significant prostate cancer and percent free PSA has been suggested to aid in the decision to biopsy in this total PSA range as well. Concerns exist that the number of biopsies needed to detect 1 cancer in this range may be inappropriately high. In a prospective referral population we evaluated sensitivity and specificity of various percent free PSA cutoffs and determined the biopsy-per-cancer ratio in the PSA 2 to 4 ng./ml. range in men with a benign digital rectal examination, and report on the biological nature of the detected cancers based on Gleason score. Results were compared to those obtained from a reference group of patients (PSA 4 to 10 ng./ml., benign digital rectal examination) from the same prospective referral cohort. MATERIALS AND METHODS: Total PSA and free PSA were measured and percent free PSA was calculated. Of the initial 1,602 men 756 had a benign digital rectal examination and PSA 4 to 10 ng./ml., and 219 had a benign digital rectal examination and PSA 2 to 4 ng./ml. Sensitivity, specificity, the number of true positive (evidence of cancer) and false-positive (no evidence of cancer) biopsies were determined. The ratio of true positive biopsies-to-all biopsies performed was used to determine the biopsy-per-cancer ratio. Gleason score of the detected cancers was evaluated. The procedure was repeated for the PSA 4 to 10 ng./ml. range. RESULTS: In the PSA 4 to 10 ng./ml. range a sensitivity of 63.7% to 92.5% with a specificity of 57.5% to 18.7% was found when percent free PSA was 18% to 25%. On average 3 biopsies were needed to detect 1 cancer. When PSA was 2 to 4 ng./ml. sensitivity was 46.3% to 75.6% and specificity was 73.6% to 37.6% when the same percent free PSA cutoff was examined. Calculation of the biopsy-per-cancer ratio for various percent free PSA cutoffs revealed that 3 to 5 biopsies were needed to find 1 cancer. Of 41 cancers detected in the PSA 2 to 4 ng./ml. range 6 had a Gleason score 5. The majority (28 of 41) of cases had a Gleason score of 6. Gleason score was 7 in 5 patients and 8 in 1. CONCLUSIONS: In the PSA 4 to 10 ng./ml. range high sensitivity for prostate cancer detection is critical and 3 biopsies are needed to detect 1 cancer. In the PSA 2 to 4 ng./ml. range a percent free PSA cutoff of 18% to 20% detected about 50% of cancers while sparing up to 73% of unnecessary biopsies with a biopsy-to-cancer ratio of 3 to 4:1. Percent free PSA can be applied to the PSA 2 to 4 ng./ml. range to detect prostate cancer and only moderately increases the number of biopsies needed to detect 1 significant cancer compared to the greater than 4 to 10 ng./ml. range.

Aged↗
PubMed2002

Prostate-specific antigen as a marker of disease activity in prostate cancer.

Despite the impact of prostate-specific antigen (PSA) testing on the detection and management of prostate cancer, controversy about its usefulness as a marker of disease activity continues. This review, based on a recent roundtable discussion, examines whether PSA measurements can be used rationally in several clinical settings. Following radical prostatectomy and radiation therapy, prediction of survival by PSA level is most reliable in high-risk patients. PSA doubling time after radiation therapy is the strongest predictor of biochemical failure. PSA measurements have been associated with inconsistent results following hormonal treatment; reduced PSA levels may result from antiandrogen treatment, which decreases expression of the PSA gene, and therefore, the level of PSA production. In the setting of primary and secondary cancer prevention, PSA is important in risk stratification when selecting patients for studies. Part 1 of this two-part article, which concludes in the September issue, focuses on the physiology of PSA, its measurement and use in clinical practice, and its predictive value following radical prostatectomy and radiation therapy.

Biomarkers, Tumor↗
PubMed2002

Comparison of logistic regression and neural net modeling for prediction of prostate cancer pathologic stage.

BACKGROUND: Prostate cancer (PCa) pathologic staging remains a challenge for the physician using individual pretreatment variables. We have previously reported that UroScore, a logistic regression (LR)-derived algorithm, can correctly predict organ-confined (OC) disease state with >90% accuracy. This study compares statistical and neural network (NN) approaches to predict PCa stage. METHODS: A subset (756 of 817) of radical prostatectomy patients was assessed: 434 with OC disease, 173 with capsular penetration (NOC-CP), and 149 with metastases (NOC-AD) in the training sample. Additionally, an OC + NOC-CP (n = 607) vs NOC-AD (n = 149) two-outcome model was prepared. Validation sets included 120 or 397 cases not used for modeling. Input variables included clinical and several quantitative biopsy pathology variables. The classification accuracies achieved with a NN with an error back-propagation architecture were compared with those of LR statistical modeling. RESULTS: We demonstrated >95% detection of OC PCa in three-outcome models, using both computational approaches. For training patient samples that were equally distributed for the three-outcome models, NNs gave a significantly higher overall classification accuracy than the LR approach (40% vs 96%, respectively). In the two-outcome models using either unequal or equal case distribution, the NNs had only a marginal advantage in classification accuracy over LR. CONCLUSIONS: The strength of a mathematics-based disease-outcome model depends on the quality of the input variables, quantity of cases, case sample input distribution, and computational methods of data processing of inputs and outputs. We identified specific advantages for NNs, especially in the prediction of multiple-outcome models, related to the ability to pre- and postprocess inputs and outputs.

Aged↗
PubMed2002

Prostate-specific antigen as a marker of disease activity in prostate cancer.

Despite the impact of prostate-specific antigen (PSA) testing on the detection and management of prostate cancer, controversy about its usefulness as a marker of disease activity continues. This review, based on a recent roundtable discussion, examines whether PSA measurements can be used rationally in several clinical settings. Following radical prostatectomy and radiation therapy, prediction of survival by PSA level is most reliable in high-risk patients. PSA doubling time after radiation therapy is the strongest predictor of biochemical failure. PSA measurements have been associated with inconsistent results following hormonal treatment; reduced PSA levels may result from antiandrogen treatment, which decreases expression of the PSA gene, and therefore, the level of PSA production. In the setting of primary and secondary cancer prevention, PSA is important in risk stratification when selecting patients for studies. Part 2 of this two-part article, which began in the August issue, discusses the role of PSA in hormonal and drug therapies and in primary and secondary chemoprevention.

Biomarkers, Tumor↗
PubMedDate not supplied

Correlation of postoperative pain to quality of recovery in the immediate postoperative period.

BACKGROUND AND OBJECTIVES: It is unclear whether the severity of postoperative pain may affect patients' quality of recovery in the immediate postoperative period (within 2 weeks of surgery). METHODS: This was a prospective, observational study in patients undergoing elective radical retropubic prostatectomy. All patients received a standardized intraoperative general or spinal anesthetic followed by intravenous patient-controlled analgesia. Visual analog scores for pain at rest, pain with activity, and nausea along with the QoR, an instrument validated to assess quality of recovery in the postoperative period, and Brief Fatigue Inventory were assessed on postoperative days 1 to 3, 7, and 30. The Epworth Sleepiness Scale was assessed on postoperative days 7 and 30. RESULTS: We found that the severity of pain both at rest and with activity correlated with a decrease in quality of recovery as assessed by the QoR. CONCLUSIONS: Our findings suggest that an increase in postoperative pain is correlated with a decrease in a patient's quality of recovery in the immediate postoperative period.

Analgesia, Patient-Controlled↗