The problem of cutoff levels in a screened population

Horninger, Wolfgang; Bartsch, Georg; Snow, Peter B.; Brandt, Jeffrey M.; Partin, Alan W.

doi:10.1002/1097-0142(20010415)91:8+<1667::aid-cncr1181>3.0.co;2-l

Cited by 25 publications

(1 citation statement)

References 24 publications

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“…In a screening study, a substantial increase in specificity (150–200%) at the 90% and 95% sensitivity level for patients with tPSA below and above 4 ng/ml using an ANN could be obtained 33. However, the advantage of this ANN with 8 input data including %fPSA, DRE status and different prostate volume parameters was less remarkable at low tPSA values 33…”

Section: Discussionmentioning

confidence: 99%

An artificial neural network considerably improves the diagnostic power of percent free prostate‐specific antigen in prostate cancer diagnosis: Results of a 5‐year investigation

Stephan

Jung

Cammann

et al. 2002

Intl Journal of Cancer

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Our study was performed to evaluate the diagnostic usefulness of %fPSA alone and combined with an ANN at different PSA concentration ranges, including the low range 2-4 ng/ml, to improve the risk assessment of prostate cancer. A total of 928 men with prostate cancer and BPH without any pretreatment of the prostate in the PSA range 2-20 ng/ml were enrolled in the study between 1996 and 2001. An ANN with input data of PSA, %fPSA, patient's age, prostate volume and DRE status was developed to calculate the individual's risk before performing a prostate biopsy within the different PSA ranges 2-4, 4.1-10 and 10.1-20 ng/ml. ROC analysis and cut-off calculations were used to estimate the diagnostic improvement of %fPSA and ANN in comparison to PSA. At the 90% sensitivity level, %fPSA and ANN performed better than PSA in all ranges, enhancing the specificity by 15-28% and 32-44%, respectively. For the low PSA range 2-4 ng/mL, we recommend a first-time biopsy at an ANN specificity level of 90%. For PSA 4 -10 ng/mL, we recommend a first-time biopsy based on the ANN at the 90% sensitivity level. Use of an ANN enhances the %fPSA performance to further reduce the number of unnecessary biopsies within the PSA range 2-10 ng/ml. © 2002 Wiley-Liss, Inc. Key words: prostate cancer; prostate-specific antigen; receiver operating characteristic; neural network; prostate biopsyProstate cancer is the most common neoplasia among men in the Western world and PSA is recognized as the best marker for its early detection. 1 Due to lack of specificity, various techniques, such as PSA density, velocity and age-specific ranges, have been developed but are reported to be only partially successful. 1 Measurements of the molecular forms of PSA improve specificity over tPSA alone. 2,3 Approximately 20 -25% of all biopsies could be avoided using %fPSA in the tPSA range 4 -10 ng/ml 4,5 as well as for tPSA values lower than 4 ng/ml. 6 -8 For further enhancement of %fPSA specificity to differentiate between prostate cancer and benign prostate diseases, logistic regression 9,10 and ANNs have been successfully used. 11,12 Briefly, these models can predict diagnostic outcome using a variety of factors. Based on the data of 28 studies, both methods perform equally, especially for large cohorts. 13 However, ANNs can predict an outcome for an individual patient, which cannot currently be done with traditional statistics and they can better handle a greater number of variables with more nonlinear relations. 14 In a study of 656 men within the 4 -10 ng/ml tPSA range, Finne et al. 11 demonstrated an advantage of ANNs compared to logistic regression, avoiding approximately one-third of unnecessary biopsies at 95% sensitivity. For the lower tPSA range 2.6 -4 ng/ml, Babaian et al. 12 used a combination of 3 different ANNs and saved 63.6% of all unnecessary biopsies. To date, no study has evaluated 1 valid, clinically practicable ANN on %fPSA data for the whole tPSA range 2-10 or 2-20 ng/ml.We used data from a 5-year period of %fPSA measurements with only 1 assa...

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Section: Discussionmentioning

confidence: 99%

An artificial neural network considerably improves the diagnostic power of percent free prostate‐specific antigen in prostate cancer diagnosis: Results of a 5‐year investigation

Stephan

Jung

Cammann

et al. 2002

Intl Journal of 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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Techniques and predictive models to improve prostate cancer detection

Herman

Dorsey

John

et al. 2009

Cancer

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The use of prostate-specific antigen (PSA) as a screening test remains controversial. There have been several attempts to refine PSA measurements to improve its predictive value. These modifications, including PSA density, PSA kinetics, and the measurement of PSA isoforms, have met with limited success. Therefore, complex statistical and computational models have been created to assess an individual's risk of prostate cancer more accurately. In this review, the authors examined the methods used to modify PSA as well as various predictive models used in prostate cancer detection. They described the mathematical underpinnings of these techniques along with their intrinsic strengths and weaknesses, and they assessed the accuracy of these methods, which have been shown to be better than physicians' judgment at predicting a man's risk of cancer. Without understanding the design and limitations of these methods, they can be applied inappropriately, leading to incorrect conclusions. These models are important components in counseling patients on their risk of prostate cancer and also help in the design of clinical trials by stratifying patients into different risk categories. Thus, it is incumbent on both clinicians and researchers to become familiar with these tools. Despite intensive work over the last several decades, prostate cancer continues to be the most common cancer in men and their second leading cause of cancer death. In 2008, it is estimated that 186,320 men received a new diagnosis of this disease, and nearly 29,000 died from it.1 However, there is no universal agreement on screening for prostate cancer. The American Urological Association and the American Cancer Society both recommend using a combination of digital rectal examination (DRE) and serum prostatespecific antigen (PSA) level to screen low-risk white men starting at age 50 years and to screen high-risk populations (including African Americans) starting at age 40 years. Much of the dilemma has to do with limitations of the PSA test itself, which has poor sensitivity and specificity. Various attempts to improve the predictive capacity of PSA likewise have met with only partial success. Because of these deficiencies, statistical and computational models have been created to more accurately predict a patient's risk of prostate cancer at biopsy. This, in turn, helps patients make a more informed decision concerning the choice to proceed with biopsy, a decision that should not be made lightly given the costs involved, the possibility of complications, and the chance of diagnosing clinically insignificant cancer. In this review, we provide a brief summary of PSA and its permutations and examine the methods that generate the predictive models as well as their inherent strengths and weaknesses.Overview of PSA and PSA-specific antigen modifications Catalona et al initially explored the use of PSA as a screening tool. In 2 large studies of healthy men aged !50 years, a higher screening PSA level was correlated with a greater likelihood of cancer. 5,6 Furth...

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The problem of cutoff levels in a screened population

Cited by 25 publications

References 24 publications

An artificial neural network considerably improves the diagnostic power of percent free prostate‐specific antigen in prostate cancer diagnosis: Results of a 5‐year investigation

An artificial neural network considerably improves the diagnostic power of percent free prostate‐specific antigen in prostate cancer diagnosis: Results of a 5‐year investigation

An updated catalog of prostate cancer predictive tools

Techniques and predictive models to improve prostate cancer detection

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