Use of tumor dynamics to clarify the observed variability among biochemical recurrence nomograms for prostate cancer

Dimonte, Guy; Bergstralh, Eric J.; Bolander, Mark E.; Karnes, R.J.; Tindall, Donald J.

doi:10.1002/pros.21429

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…The model was used to study the author's and another patient's data. In a subsequent work, Dimonte and colleagues [78] simplify the model and use it to explain the variability in the recurrence time of prostate cancer patients. They reach a similar conclusion to use PSA doubling time to improve predictive power.…”

Section: Dimonte (2010) Dimonte Et Al (2012)mentioning

confidence: 99%

Review: Mathematical Modeling of Prostate Cancer and Clinical Application

et al. 2020

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We review and synthesize key findings and limitations of mathematical models for prostate cancer, both from theoretical work and data-validated approaches, especially concerning clinical applications. Our focus is on models of prostate cancer dynamics under treatment, particularly with a view toward optimizing hormone-based treatment schedules and estimating the onset of treatment resistance under various assumptions. Population models suggest that intermittent or adaptive therapy is more beneficial to delay cancer relapse as compared to the standard continuous therapy if treatment resistance comes at a competitive cost for cancer cells. Another consensus among existing work is that the standard biomarker for cancer growth, prostate-specific antigen, may not always correlate well with cancer progression. Instead, its doubling rate appears to be a better indicator of tumor growth. Much of the existing work utilizes simple ordinary differential equations due to difficulty in collecting spatial data and due to the early success of using prostate-specific antigen in mathematical modeling. However, a shift toward more complex and realistic models is taking place, which leaves many of the theoretical and mathematical questions unexplored. Furthermore, as adaptive therapy displays better potential than existing treatment protocols, an increasing number of studies incorporate this treatment into modeling efforts. Although existing modeling work has explored and yielded useful insights on the treatment of prostate cancer, the road to clinical application is still elusive. Among the pertinent issues needed to be addressed to bridge the gap from modeling work to clinical application are (1) real-time data validation and model identification, (2) sensitivity analysis and uncertainty quantification for model prediction, and (3) optimal treatment/schedule while considering drug properties, interactions, and toxicity. To address these issues, we suggest in-depth studies on various aspects of the parameters in dynamical models such as the evolution of parameters over time. We hope this review will assist future attempts at studying prostate cancer.Prostate cancer (PCa) is the leading cause of cancer in US men (behind non-melanoma skin cancers), as well as the second leading cause of cancer-related mortality in men. The American Cancer Society estimates that in 2020, 191,930 Americans will be diagnosed with prostate cancer, with approximately 21.5% of all new cancer cases in men. The average five-year survival rate for all cases of prostate cancer is about 99%, which is a 31% point higher than in the 1970s [1]. However, once cancer metastasizes, the five-year survival rate drops to 30%, an increase of 10 percentage-point compared to the 1970s [2]. While the incidence of prostate cancer in the US is decreasing (see Figure 1), one possible cause is doctors' discouragement of early PCa diagnosis due to the fear of overtreating cancer. Globally, the incidence of prostate cancer is increasing, especially among developing nations [3].

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Section: Dimonte (2010) Dimonte Et Al (2012)mentioning

confidence: 99%

Review: Mathematical Modeling of Prostate Cancer and Clinical Application

et al. 2020

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“…Basically, the proposed predictive nomograms to select patients that are more suitable for SRT can be considered static (large databases that analyze the pre and postoperative characteristics of the tumor) 10,15 or dynamic (models that study postsurgical tumor dynamics based on PSADT) 33‐ …”

Section: Discussionmentioning

confidence: 99%

Predictive value of Prostate Specific Antigen variations in the last week of salvage radiotherapy for biochemical recurrence of prostate cancer after surgery: A practical approach

et al. 2020

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Background: About a third of patients who underwent radical prostatectomy for prostate cancer (Pca) develop a biochemical failure (BF) within 10 years from surgery, and about a half of them receive salvage radiation therapy (SRT). Factors to predict risk to relapse after SRT are still lacking. Dynamic models, based on the assessment of changes in Prostate Specific Antigen (PSA) postsurgery seem to show good reliability. Aims: The goal of the study was to identify a simple analytical method for the postsalvage radiation therapy biochemical failure (post-SRTBF) prediction before the end of the SRT, regardless of the PSA value at the beginning of the treatment (PSA start), measuring the PSA values at the start and 1 week before the end of SRT. Methods: In a series of 83 patients treated with SRT for BF of Pca we measured PSA values at the first day and 1 week before the end of SRT. These values were used to define an analytical method for the post-SRTBF prediction. Results: PSA value in patients without post-SRTBF show a significant difference in term of difference during the SRT with respect to patients with post-SRTBF. Starting from this difference, we identified a simple and practical analytical method for the post-SRTBF prediction before the end of the SRT. The data corresponds with the model and the analytical method is highly predictive (Sensitivity = 81%, Specificity = 85%, Accuracy = 83%). Conclusion: This study offers a new tool to early predict Pca relapse overtime and to select patients who can benefit from an early additional systemic treatment.

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“…In this article, we propose to extend its application to investigate how the PSA production grows in recurrent prostate cancer, based on the fact that tumor volume is strictly related to PSA production in radically prostatectomized patients (10,21). In particular, we focus our attention on the growth parameter (see the Quick Guide to Equations and Assumptions for further details), which reflects the proliferation of surviving prostate cancer cells after RP and may be evaluated by the resulting overall PSA production.…”

Section: Quick Guide To Equations and Assumptionsmentioning

confidence: 99%

A Simple PSA-Based Computational Approach Predicts the Timing of Cancer Relapse in Prostatectomized Patients

2016

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Recurrences of prostate cancer affect approximately one quarter of patients who have undergone radical prostatectomy. Reliable factors to predict time to relapse in specific individuals are lacking. Here, we present a mathematical model that evaluates a biologically sensible parameter (a) that can be estimated by the available follow-up data, in particular by the PSA series. This parameter is robust and highly predictive for the time to relapse, also after administration of adjuvant androgen deprivation therapies. We present a practical computational method based on the collection of only four postsurgical PSA values. This study offers a simple tool to predict prostate cancer relapse. Cancer Res; 76(17); 4941-7. Ó2016 AACR.

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Use of tumor dynamics to clarify the observed variability among biochemical recurrence nomograms for prostate cancer

Cited by 6 publications

References 15 publications

Review: Mathematical Modeling of Prostate Cancer and Clinical Application

Review: Mathematical Modeling of Prostate Cancer and Clinical Application

Predictive value of Prostate Specific Antigen variations in the last week of salvage radiotherapy for biochemical recurrence of prostate cancer after surgery: A practical approach

A Simple PSA-Based Computational Approach Predicts the Timing of Cancer Relapse in Prostatectomized Patients

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