Prediction of prostate carcinoma stage by quantitative biopsy pathology

Veltri, Robert W.; Miller, Michael Craig; Partin, Alan W.; Poole, Edward C.; O’Dowd, Gerard J.

doi:10.1002/1097-0142(20010615)91:12<2322::aid-cncr1264>3.0.co;2-h

Cited by 31 publications

(19 citation statements)

References 30 publications

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“…PSA results from all patients were obtained on two different equimolar total PSA assays and were categorized into increments of 2 ng/mL, as described previously. 5 The categorized PSA levels were entered as ordinal levels (level 1, PSA 0 -2 ng/mL; level 2, PSA Ͼ 2-4 ng/mL; level 3, PSA Ͼ 4 -6 ng/mL; etc. ).…”

Section: Demographicsmentioning

confidence: 99%

“…The current network utilized an error back-propagation learning algorithm with a genetic preselection component and had a design similar to that of the original GENN model. 5 The new NN used the same 9 input variables (see Table 2), 2-3 hidden layers, and between 1 and 124 nodes per layer. The final, optimized architecture was determined by the software as a result of a best-fit network.…”

Section: Nn Modelmentioning

confidence: 99%

“…4 Veltri et al have developed computational models based on ordinal logistic regression (OLOGIT) and genetically engineered neural networks (GENNs) for the prediction of OC disease, nonorgan-confined (NOC)-CP disease, and locally advanced or metastatic (NOC-AD) disease. 5 This model was trained with 817 patients and demonstrated the importance of using quantitative prostate biopsy assessment to increase the accuracy of predictive tools. The predictive accuracy for OC disease was 91% and 95% using the OLOGIT and GENN models, respectively, upon validation of the model with an additional 116 patients.…”

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confidence: 99%

“…5,[12][13][14] Likewise, PSA levels and standard biopsy pathology have consolidated and become more similar (Gleason scores of 6 and 7) among patients. 15 Therefore, it is a more challenging task for the urologist to predict the pathologic stage of tumors that clinically present so uniformly.…”

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confidence: 99%

“…25,26 Patients who have locally disseminated disease at the time of diagnosis are managed best with more aggressive therapy that may involve combinations of surgery with adjuvant treatments or, in patients with distant metastatic disease, with ablative hormonal therapy. 25 In our previous study, 5 we demonstrated the substantial gain in diagnostic accuracy when a more detailed evaluation of prostate biopsies, termed quantitative prostate biopsy pathology, is used to develop and challenge a statistical (logistic regression) or NN model to predict prostate carcinoma stage based on preoperative parameters. These models were developed previously and underwent internal validation with a subset of 116 new patients, and their high accuracy (Ͼ 90%) was validated for identifying patients with OC prostate carcinoma.…”

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confidence: 99%

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Quantitative biopsy pathology for the prediction of pathologically organ‐confined prostate carcinoma

Haese

Chaudhari

Miller

et al. 2003

Cancer

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BACKGROUNDQuantitative biopsy pathology with prostate specific antigen significantly improves the prediction of pathologic stage in patients with clinically localized prostate carcinoma (PCa). The authors recently reported a computational model for predicting patient specific likelihood of organ confinement of PCa using biopsy pathology and clinical data. The current study validates the initial models and presents an new, improved tool for clinical decision making.METHODSThe authors assessed 10 biopsy pathologic parameters and 2 clinical parameters using data from two institutions. Of 1287 patients, 798 men had pathologically organ confined (OC) PCa, 282 men had nonorgan‐confined disease with capsular penetration (NOC‐CP) only, and 207 men showed seminal vesicle or lymph node invasion (NOC‐AD) after undergoing pelvic lymphadenectomy and radical prostatectomy. Patient input data were evaluated by ordinal logistic (OLOGIT) and neural network (NN) models; and the likelihood of developing OC, NOC‐CP, or NOC‐AD disease was calculated for the combined and separate data sets and was compared with the results from original presentation. In addition, a new two‐output model was constructed (OC/NOC‐CP vs. NOC‐AD).RESULTSThe three‐output OLOGIT and NN models predicted OC disease with 95.0% and 98.6% accuracy, respectively, for the combined data set and with 93.0% and 98.6% accuracy, respectively, on subset analysis. The combined accuracy for predicting OC, NOC‐CP, and NOC‐AD disease in the entire validation set was 66.7% for OLOGIT model and 66.0% for the NN model. The two‐output OLOGIT and NN models correctly predicted 94.9% and 100.0% of all OC/NOC‐CP disease, respectively.CONCLUSIONSBoth computation models predicted OC PCa with an accuracy of 93.0–98.6% when they were validated with two different data sets. The OLOGIT and NN‐based, two‐output model permitted an appropriate treatment decision for 85.2–90.2% of patients. These data support the use of quantitative pathology and clinical data‐based decision modeling to manage patients with clinically localized PCa. Cancer 2003;97:969–78. © 2003 American Cancer Society.DOI 10.1002/cncr.11153

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Section: Demographicsmentioning

confidence: 99%

Section: Nn Modelmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Quantitative biopsy pathology for the prediction of pathologically organ‐confined prostate carcinoma

Haese

Chaudhari

Miller

et al. 2003

Cancer

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An updated catalog of prostate cancer predictive tools

Shariat

Karakiewicz

Roehrborn

et al. 2008

Cancer

240

197

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Accurate estimates of risk are essential for physicians if they are to recommend a specific management to patients with prostate cancer. Accurate risk estimates also are required for clinical trial design to ensure that homogeneous, high-risk patient groups are used to investigate new cancer therapeutics. Using the MED-LINE database, a literature search was performed on prostate cancer predictive tools from January 1966 to July 2007. The authors recorded input variables, the prediction form, the number of patients used to develop prediction tools, the outcome being predicted, prediction tool-specific features, predictive accuracy, and whether validation was performed. Each prediction tool was classified into patient clinical disease state and the outcome being predicted. First, the authors described the criteria for evaluation (predictive accuracy, calibration, generalizability, head-to-head comparison, and level of complexity) and the limitations of current predictive tools. The literature search generated 109 published prediction tools, including only 68 that had undergone validation. An increasing number of predictive tools addressed important endpoints, such as disease recurrence, metastasis, and survival. Despite their limitations and the limitations of data, predictive tools are essential for individualized, evidence-based medical decision making. Moreover, the authors recommend wider adoption of risk-prediction models in the design and implementation of clinical trials. Among prediction tools, nomograms provide superior, individualized, disease-related risk estimations that facilitate management-related decisions. Nevertheless, many more predictive tools, comparisons between them, and improvements to existing tools are needed.

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Pathology of prostate cancer

Che

Grignon

Prostate Cancer: New Horizons in Research and Treatment

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Prediction of prostate carcinoma stage by quantitative biopsy pathology

Cited by 31 publications

References 30 publications

Quantitative biopsy pathology for the prediction of pathologically organ‐confined prostate carcinoma

Quantitative biopsy pathology for the prediction of pathologically organ‐confined prostate carcinoma

An updated catalog of prostate cancer predictive tools

Pathology of prostate cancer

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