Sequential Testing Approach As an Efficient and Easier Alternative for the Validation of New Predictive Technologies in the Clinic

Beam, Craig A.; Gao, Weihua; Macias, Virgilia; Li, Weimin; Balla, Andre

doi:10.1200/jco.2008.18.3061

Cited by 4 publications

(6 citation statements)

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“…In our study, the highest biopsy Gleason score in ≥8-core biopsies provided a significant independent predictor comparable to serum cysteine and homocysteine. However, routine ultrasound directed first biopsies are reported to miss nearly a quarter of the prostate cancers [19] and often underestimate tumor grade [20], [21]. The combination of serum PSA with cystathionine, cysteine, and homocysteine as markers could improve decision-making for primary treatment and earlier subsequent adjuvant therapy.…”

Section: Discussionmentioning

confidence: 99%

Serum Methionine Metabolites Are Risk Factors for Metastatic Prostate Cancer Progression

et al. 2011

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BackgroundClinical decision for primary treatment for prostate cancer is dictated by variables with insufficient specificity. Early detection of prostate cancer likely to develop rapid recurrence could support neo-adjuvant therapeutics and adjuvant options prior to frank biochemical recurrence. This study compared markers in serum and urine of patients with rapidly recurrent prostate cancer to recurrence-free patients after radical prostatectomy. Based on previous identification of urinary sarcosine as a metastatic marker, we tested whether methionine metabolites in urine and serum could serve as pre-surgical markers for aggressive disease.Methodology/Principal FindingsUrine and serum samples (n = 54 and 58, respectively), collected at the time of prostatectomy were divided into subjects who developed biochemical recurrence within 2 years and those who remained recurrence-free after 5 years. Multiple methionine metabolites were measured in urine and serum by GC-MS. The role of serum metabolites and clinical variables (biopsy Gleason grade, clinical stage, serum prostate specific antigen [PSA]) on biochemical recurrence prediction were evaluated. Urinary sarcosine and cysteine levels were significantly higher (p = 0.03 and p = 0.007 respectively) in the recurrent group. However, in serum, concentrations of homocysteine (p = 0.003), cystathionine (p = 0.007) and cysteine (p<0.001) were more abundant in the recurrent population. The inclusion of serum cysteine to a model with PSA and biopsy Gleason grade improved prediction over the clinical variables alone (p<0.001).ConclusionsHigher serum homocysteine, cystathionine, and cysteine concentrations independently predicted risk of early biochemical recurrence and aggressiveness of disease in a nested case control study. The methionine metabolites further supplemented known clinical variables to provide superior sensitivity and specificity in multivariable prediction models for rapid biochemical recurrence following prostatectomy.

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Section: Discussionmentioning

confidence: 99%

Serum Methionine Metabolites Are Risk Factors for Metastatic Prostate Cancer Progression

et al. 2011

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“…This sequential updating process continues until the line crosses either of two decision boundaries. These boundaries are set by Beam et al using statistical theory to limit the probability of a type I error to be no more than 5% and type II error to be no more than 20% [1] In patients with brain metastases, the four-tiered score developed by Rades et al [6] has been shown to predict survival. The score is based on performance status, age, presence of extracranial metastases and interval from first cancer diagnosis to treatment for brain metastases.…”

Section: Resultsmentioning

confidence: 99%

“…The effort to provide a tool for local validation of new prognostic and predictive models is thus timely and helpful. In the present article, we have demonstrated how the validation approach published by Beam et al [1] can be applied to patients receiving palliative radiotherapy outside of clinical trials. We have focused on prediction of poor prognosis (survival < 6 months) by strictly following the methods proposed by Beam et al, including their PPV of 80% and boundaries set to limit the probability of type I error (error in hypothesis testing when the null hypothesis is rejected when it is true) and type II error (error in hypothesis testing when the null hypothesis is not rejected when it is false).…”

Section: Discussionmentioning

confidence: 99%

“…For practitioners, the question arises how their own patient population differs from that used in these large-scale analyses and whether or not a new score actually is valid at a local level and thus can be implemented. Beam et al have recently discussed the benefits to be conferred by such local assessment, which might lead to greater confidence in a truly effective tool or to the prudent decision not to use an ineffective model [1]. Of course, most practitioners do not have large databases, statisticians and other resources needed to perform classic validation analyses.…”

Section: Introductionmentioning

confidence: 99%

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Validation of New Prognostic and Predictive Scores by Sequential Testing Approach

et al. 2010

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“…An interesting alternative involves sequential analysis of a series of patients. This may reduce the number needed to validate the model while simultaneously making it more specific for a particular clinic's population 36…”

Section: Predictive Models In Prostate Cancer Detectionmentioning

confidence: 99%

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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Sequential Testing Approach As an Efficient and Easier Alternative for the Validation of New Predictive Technologies in the Clinic

Abstract: In-clinic validation of predictive technologies will help the clinician adopt truly beneficial technologies and avoid the adoption of technologies which provide no significant benefit to their local patient population. For this task, sequential methods offer clear advantages.

Cited by 4 publications

References 8 publications

Serum Methionine Metabolites Are Risk Factors for Metastatic Prostate Cancer Progression

Serum Methionine Metabolites Are Risk Factors for Metastatic Prostate Cancer Progression

Validation of New Prognostic and Predictive Scores by Sequential Testing Approach

Techniques and predictive models to improve prostate cancer detection

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