MP86-10 CLINICAL PROSTATE CANCER RISK PREDICTION DERIVED FROM TARGETED BIOPSY

Abstract Background Online clinical risk prediction tools built on data from multiple cohorts are increasingly being utilized for contemporary doctor-patient decision-making and validation. This report outlines a comprehensive data science strategy for building such tools with application to the Prostate Biopsy Collaborative Group prostate cancer risk prediction tool. Methods We created models for high-grade prostate cancer risk using six established risk factors. The data comprised 8492 prostate biopsies collected from ten institutions, 2 in Europe and 8 across North America. We calculated area under the receiver operating characteristic curve (AUC) for discrimination, the Hosmer-Lemeshow test statistic (HLS) for calibration and the clinical net benefit at risk threshold 15%. We implemented several internal cross-validation schemes to assess the influence of modeling method and individual cohort on validation performance. Results High-grade disease prevalence ranged from 18% in Zurich (1863 biopsies) to 39% in UT Health San Antonio (899 biopsies). Visualization revealed outliers in terms of risk factors, including San Juan VA (51% abnormal digital rectal exam), Durham VA (63% African American), and Zurich (2.8% family history). Exclusion of any cohort did not significantly affect the AUC or HLS, nor did the choice of prediction model (pooled, random-effects, meta-analysis). Excluding the lowest-prevalence Zurich cohort from training sets did not statistically significantly change the validation metrics for any of the individual cohorts, except for Sunnybrook, where the effect on the AUC was minimal. Therefore the final multivariable logistic model was built by pooling the data from all cohorts using logistic regression. Higher prostate-specific antigen and age, abnormal digital rectal exam, African ancestry and a family history of prostate cancer increased risk of high-grade prostate cancer, while a history of a prior negative prostate biopsy decreased risk (all p-values < 0.004). Conclusions We have outlined a multi-cohort model-building internal validation strategy for developing globally accessible and scalable risk prediction tools.

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Development and Validation of a Multiparameterized Artificial Neural Network for Prostate Cancer Risk Prediction and Stratification

JCO Clinical Cancer Informatics ◽

10.1200/cci.17.00119 ◽

2018 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

David A. Roffman ◽

Gregory R. Hart ◽

Michael S. Leapman ◽

James B. Yu ◽

Fangliang L. Guo ◽

...

Keyword(s):

Neural Network ◽

Prostate Cancer ◽

Cancer Risk ◽

Risk Prediction ◽

Prostate Cancer Risk ◽

Area Under The Curve ◽

Personal Health Information ◽

Personal Health ◽

Training Set ◽

Data Set

Purpose To develop and validate a multiparameterized artificial neural network (ANN) on the basis of personal health information for prostate cancer risk prediction and stratification. Methods The 1997 to 2015 National Health Interview Survey adult survey data were used to train and validate a multiparameterized ANN, with parameters including age, body mass index, diabetes status, smoking status, emphysema, asthma, race, ethnicity, hypertension, heart disease, exercise habits, and history of stroke. We developed a training set of patients ≥ 45 years of age with a first primary prostate cancer diagnosed within 4 years of the survey. After training, the sensitivity and specificity were obtained as functions of the cutoff values of the continuous output of the ANN. We also evaluated the ANN with the 2016 data set for cancer risk stratification. Results We identified 1,672 patients with prostate cancer and 100,033 respondents without cancer in the 1997 to 2015 data sets. The training set had a sensitivity of 21.5% (95% CI, 19.2% to 23.9%), specificity of 91% (95% CI, 90.8% to 91.2%), area under the curve of 0.73 (95% CI, 0.71 to 0.75), and positive predictive value of 28.5% (95% CI, 25.5% to 31.5%). The validation set had a sensitivity of 23.2% (95% CI, 19.5% to 26.9%), specificity of 89.4% (95% CI, 89% to 89.7%), area under the curve of 0.72 (95% CI, 0.70 to 0.75), and positive predictive value of 26.5% (95% CI, 22.4% to 30.6%). For the 2016 data set, the ANN classified all 13,031 patients into low-, medium-, and high-risk subgroups and identified 5% of the cancer population as high risk. Conclusion A multiparameterized ANN that is based on personal health information could be used for prostate cancer risk prediction with high specificity and low sensitivity. The ANN can further stratify the population into three subgroups that may be helpful in refining prescreening estimates of cancer risk.

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