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Editorial: Growth spurts of small renal masses correlate with pathology

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A rapid growth rate has long been known to be a harbinger of aggressive tumour pathology and clinical behaviour of small renal masses (SRMs) 1. However, this association has not been confirmed in prospective studies of patients with SRMs on active surveillance (AS) 2. Jewett et al. 2, in a multicentre prospective phase 2 clinical trial of 209 patients with a SRM, found that despite an average growth rate of 0.13 cm/year, pathology did not impact growth. Clinically, the growth rates of SRMs are most commonly variable, rather than the assumed steady slow growing rate that is often reported 3. Due to the discrepancies in the literature, the use of average growth rate for a SRM can be misleading. Finding a clinically effective tool to identify potentially aggressive cancers remains an important (and unmet) need for patients undergoing AS.

Jang et al. 4 provide insight into this need by evaluating the number of growth periods over time as a risk factor for renal masses under surveillance. They retrospectively reviewed renal masses that were initially <4 cm at diagnosis and were followed for a variable time period before undergoing surgical therapy. Two cohorts were grouped into ‘favourable‐’ (benign tumours, chromophobe RCC and low grade, pT1–2 RCC) and ‘unfavourable’‐risk tumours (high‐grade RCC of any stage and low‐grade, pT3–4 RCC), finding no difference in the amount of interval imaging between the two groups. There was a significant difference in the number of positive growth periods between the ‘favourable’ and ‘unfavourable’ pathology groups (P = 0.02) for the entire cohort (all pT stages) and a similar finding when examining only pT1a tumours (P < 0.05). Additionally, there was a positive association between increased number of positive growth periods and unfavourable pathology (odds ratio 0.15, 95% CI 0.02–0.29).

Active surveillance is considered an acceptable initial option for the management of all patients with SRMs, not just those with limited life expectancy or poor performance, as highlighted in the 2017 AUA guidelines 5. Optimal management of patients on AS would appear to require a good understanding of growth kinetics, pathological details, and risk–benefit analysis. The previously held dogma of using linear growth rate as a guide for the aggressiveness of a tumour has been challenged recently 6 and again put into question by this study 4. The Delayed Intervention and Surveillance for Small Renal Masses (DISSRM) study showed that there was a higher growth rate in patients who had delayed intervention compared with those on AS, but there was no significant association (P = 0.15) 6. However, in the same study, the proportion of patients who had a positive growth rate (>0 cm/year) or a recorded ‘growth spurt’ were significantly more likely to have delayed intervention than AS patients (P < 0.01) 6.

Growth spurts, rather than growth rate over time, as a marker for malignancy, correlates best with clinical experience. Average growth rate can be artificially high with one large growth and multiple stable imaging intervals. One of the important elements of AS is regular interval imaging. The development of protocols that can be evaluated to determine the optimal type, interval, and duration of follow‐up imaging is a clinical need in this space. Whilst the cohort from Jang et al. 4 did not have regular intervals for imaging, there were an equal number of studies performed on patients with ‘favourable’ and ‘unfavourable’ pathology. Without clear protocols, using growth spurts rather than growth rate, also provides a simplified message for patients and will aid in counselling.

The identification of growth spurts as a risk factor for potentially aggressive features of SRMs under surveillance provides a useful tool when determining the need for intervention of a SRM. The uniform reporting of pathological data for patients under study is a strength, but several weaknesses limit the generalisability of the findings including the retrospective study design and inconsistent manner in which AS was performed. The clinical impact of growth spurts in the AS population warrants further investigation before definitive conclusions can be drawn.

Conrad M. Tobert* and Brian R. Lane†‡
*Department of Urology, University of Iowa, Iowa City, IA, USA, †Division of Urology, Spectrum Health Medi cal Group, Grand Rapids, MI, USA and ‡Michigan State University College of Human Medicine, Grand Rapids, MI, USA

 

References
  • 1 Lee SW, Sung HH, Jeon HG et al. Size and volumetric growth kinetics of renal masses in patients with renal cell carcinoma. Urology 2016; 90: 119–24

 

 

  • 3 Chawla SN, Crispen PL, Hanlon AL, Greenberg RE, Chen DY, Uzzo RG. The natural history of observed enhancing renal masses: meta‐analysis and review of the world literature. J Urol 2006; 175: 425–31

 

  • 4 Jang A, Patel HD, Riffon M et al. Multiple growth periods predict unfavourable pathology in patients with small renal masses. BJU Int 2018; 121: 732–6

 

 

 

Video: Multiple Growth Periods of SRMs Predict Unfavourable Pathology

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Multiple growth periods predict unfavourable pathology in patients with small renal masses

 

Abstract

Objective

To use the number of positive growth periods as a characterization of the growth of small renal masses in order to determine potential predictors of malignancy.

Patients and Methods

Patients who underwent axial imaging at multiple time points prior to surgical resection for a small renal mass were queried. Patients were categorized based on their pathological tumour grade and stage: favourable (benign, chromophobe and low‐grade pT1–2 renal cell carcinoma [RCC]) vs unfavourable (high‐grade of any stage and low‐grade pT3–4 RCC). A positive growth period was counted each time the difference in greatest tumour diameters between two images was positive. The Cochran–Armitage trend test and Somers’ D association were used to determine if the number of positive growth periods was correlated with unfavourable pathology.

Results

Of the 124 patients, 86 (69.4%) had favourable pathology and 38 (30.6%) had unfavourable pathology. Those who had favourable pathology were younger than those who had unfavourable pathology: median (interquartile range [IQR]) 61.0 (52.2–66.0) vs 68.5 (61.5–77.0); P < 0.001. The overall growth rate was higher in the unfavourable group, but was not statistically significant: mean (sd) 0.7 (1.7) vs 1.6 (2.8) cm/year; P = 0.07. There was a significant trend difference in the number of positive growth periods between favourability groups (P = 0.02). An association between increased number of positive growth periods and unfavourable pathology was observed: 0.15 (95% confidence interval 0.02, 0.29). The ratios of favourable to unfavourable pathology were 1.8, 1.0, 0.66, 0.59 and 0 as the number of positive growth periods increased from 0 to 4, respectively.

Conclusion

While overall growth rate was not predictive of pathology favourability, there was a positive association between the number of positive growth periods and unfavourable pathology. The number of positive growth periods may be a potential parameter for malignant potential in patients undergoing active surveillance for small renal masses.

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Article of the Month: MEAL Study – Effects of Diet in PCa Patients on AS

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Every Month, the Editor-in-Chief selects an Article of the Month from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.

In addition to the article itself, there is an accompanying editorial written by a prominent member of the urological community. This blog is intended to provoke comment and discussion and we invite you to use the comment tools at the bottom of each post to join the conversation.

If you only have time to read one article this month, it should be this one.

Men’s Eating and Living (MEAL) study (CALGB 70807 [Alliance]): recruitment feasibility and baseline demographics of a randomized trial of diet in men on active surveillance for prostate cancer

J. Kellogg Parsons*†‡ , John P. Pierce§, James Mohler¶, Electra Paskett**, Sin-Ho Jung††, Michael J. Morris‡‡, Eric Small§§, Olwen Hahn¶¶, Peter Humphrey***, John Taylor††† and James Marshall†††

*Division of Urologic Oncology, UC San Diego Moores Comprehensive Cancer Center, La Jolla, CA, USA, †Department of Urology, UC San Diego Health System, La Jolla, CA, USA, ‡VA San Diego Healthcare System, La Jolla, CA, USA, §Department of Family Medicine and Public Health and Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA, ¶Department of Urology, Roswell Park Cancer Institute, Buffalo, NY, USA, **Department of Medicine, College of Medicine, Comprehensive Cancer Center, Ohio State University, Columbus, OH, USA, ††Alliance Statistics and Data Center, Duke University, Durham, NC, USA, ‡‡Memorial Sloan Kettering Cancer Center, New York, NY, USA, §§UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, USA, ¶¶Alliance Central Protocol Operations, University of Chicago, Chicago, IL, USA, ***Department of Pathology, Yale University Medical School, New Haven, CT, USA, and †††Department of Prevention and Population Sciences, Roswell Park Cancer Institute, Buffalo, NY, USA J. Protocol Operations, University of Chicago, Chicago, IL, USA, ***Department of Pathology, Yale University Medical School, New Haven, CT, USA, and †††Department of Prevention and Population Sciences, Roswell Park Cancer Institute, Buffalo, NY, USA

 

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Abstract

Objective

To assess the most recommended books on keto and the feasibility of performing national, randomized trials of dietary interventions for localized prostate cancer.

Methods

The Men’s Eating and Living (MEAL) study (CALGB 70807 [Alliance]) is a phase III clinical trial testing the efficacy of a high‐vegetable diet to prevent progression in patients with prostate cancer on active surveillance (AS). Participants were randomized to a validated diet counselling intervention or to a control condition. Chi‐squared and Kruskal–Wallis analyses were used to assess between‐group differences at baseline.

Results

Between 2011 and 2015, 478 (103%) of a targeted 464 patients were randomized at 91 study sites. At baseline, the mean (sd) age was 64 (6) years and mean (sd) PSA concentration was 4.9 (2.1) ng/mL. Fifty‐six (12%) participants were African‐American, 17 (4%) were Hispanic/Latino, and 16 (3%) were Asian‐American. There were no significant between‐group differences for age (P = 0.98), race/ethnicity (P = 0.52), geographic region (P = 0.60), time since prostate cancer diagnosis (P = 0.85), PSA concentration (P = 0.96), clinical stage (T1c or T2a; P = 0.27), or Gleason sum (Gleason 6 or 3+4 = 7; P = 0.76). In a pre‐planned analysis, the baseline prostate biopsy samples of the first 50 participants underwent central pathology review to confirm eligibility, with an expectation that <10% would become ineligible. One of 50 participants (2%) became ineligible.

Conclusion

The MEAL study shows the feasibility of implementing national, multi‐institutional phase III clinical trials of diet for prostate cancer and of testing interventions to prevent disease progression in AS.

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Editorial: PCa Prevention – Proof is Elusive

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Prevention is so much better than cure because it saves the labor of being sick. Thomas Adams, 1618

Inferior doctors treat the full blown disease; mediocre doctors treat the disease before evident; superior doctors prevent disease.   Nai Ching, 1st Chinese Medical Text, 2600 BC. 

Enthusiasm for prevention is hundreds, even thousands of years old.  In the field of prostate cancer, profound differences in the regional variation of prostate cancer around the world (highest in Americans and Scandinavians, lowest in Asians) despite the similar incidence of histologic occult prostate cancer, and shifts in the incidence in mortality amongst immigrant populations moving from low to high prostate cancer regions, led to a firm belief that clinical disease was preventable.   This belief was supported by the known long initiation phase for prostate cancer, providing an opportunity over decades for diet and micronutrient intake to influence the likelihood of disease progression.

In addition, many epidemiologic studies pointed to the benefits of fruits and vegetable intake high in Vitamin E, Selenium, Beta Carotene, Lycopene, and other micronutrients, and a diet low in animal fat.

However, recently several pivotal studies have taken the bloom off the rose of prevention.  In particular, the SELECT study demonstrated a 17% increased rate of prostate cancer in men on Vitamin E, and an increase in DM in men on Selenium (1).  The study was resoundingly negative.  In addition, both high intake of multivitamins, and high dairy and calcium intake, have been associated with an increased risk of fatal prostate cancer (2).   Folic acid intake results in an increased incidence of prostate cancer.  Despite the positive PCPC and Reduce trials, the 5 ARIs were not approved for prevention by the FDA due to concerns about an increased risk of high grade prostate cancer, despite the reduction in positive biopsies in men on the drug (mostly due to  a decrease in low grade cancer).

Further, studies of the association between dietary intake of fruits and vegetables and PCa are inconsistent.  For example, one large study of 130,544 men found no significant association between fruit or vegetable intake, including cruciferous vegetables, and prostate cancer. (3)  Another study showed dietary modification, reducing fat and increasing fruits, vegetables, and fiber, had no impact on PSA.  (4).

And yet, despite the negative intervention studies, a lingering spark of hope exists that the many positive population, epidemiologic, and pre- clinical studies supporting dietary prevention will be vindicated.  The study in the current issue of BJU Int on the MEAL study is therefore a laudable and ambitious initiative (5).   Remarkably, 478 men have been randomized to validated dietary counseling intervention vs no intervention.  This paper reports the initial demographics and eligibility data.  It is undoubtedly the first of many publications that will arise from this important trial.

Will this study prove its’ ambitious goal, to demonstrate that prostate cancer progression can be influenced by dietary modification?   While the initiative is laudable, I suspect the hurdles are insurmountable given the sample size and conceptual basis for the study.  The study is being performed in men on active surveillance, and the primary end point will be the risk of disease ‘progression’.  The study references the Redeem study, which showed a 44% reduction in disease ‘progression’ with dutasteride compared to placebo (6).

What we have learned since the Redeem study was initiated more than a decade ago was that the major limitation of conservative management in men diagnosed with low grade prostate cancer on systematic biopsy is not disease progression as it is usually defined (ie, developing worse disease over time); it is grade misattribution, based on sampling and pathologic miss of co-existent higher grade cancer (7).  Higher grade cancer is present in about 30% of men with Gleason 6 cancer on systematic biopsy.  Finding this on subsequent systematic biopsy is largely a matter of luck, location of the cancer, and biopsy strategy and number.    In contrast, true grade progression (from Gleason pattern 3 to pattern 4 or 5) is uncommon, estimated to occur in only 1-2% of patients per year (8).  The adoption of MRI and targeted biopsy into the surveillance algorithm has reduced the misattribution problem.   Thus, the true ‘event rate’ (exclusive of misattribution) is likely to be in the 15% range at 10 years.   A study with the power to detect a 20% relative difference in these events, ie a 3% absolute difference, would require more than a thousand patients followed for 10 years.

In the Redeem study, the reduction in ‘progression’ was entirely related to a decrease in the volume of low grade cancer.  Indeed, the rate of upgrading was 13% in both arms in Redeem.  Therefore the decrease in progression in that study likely reflected the cytoreduction effect of 5 ARIs, and not a real biological effect on cancer progression.

Thus, to be meaningful, prevention studies in men on surveillance should identify, at the very least, a real reduction in grade progression, based on state of the art evaluation at baseline with MRI and targeted biopsies as warranted, and long term follow up.    A decrease in the rate of volume progression of Gleason 6, a major end point of this study, is not meaningful.   In the study as described, which does not explicitly incorporate MRI, an imbalance in the number of patients having off protocol MRI and targeted biopsies between the two arms could significantly bias the outcome.

A further problem with long term studies of dietary intervention relates to the well-known methodological limitations in this area—ensuring long term compliance, recall bias of food intake, and contamination of the control arm.

Nonetheless, the authors deserve strong congratulations for pursuing this major initiative.  We will follow the course of this study with interest.

 

Dr. Laurence Klotz C.M.

Division of Urology, Sunnybrook Health Sciences Centre, 2075 Bayview Ave. #MG408 

Toronto, Ontario M4N 3M5

 

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References

  1. Klein EA, Thompson IM Jr, Tangen CM, et al.: Vitamin E and the risk of prostate cancer: the Selenium and Vitamin E Cancer Prevention Trial (SELECT). JAMA 306 (14): 1549-56, 2011
  2. Lawson KA, Wright ME, Subar A, et al.: Multivitamin use and risk of prostate cancer in the National Institutes of Health-AARP Diet and Health Study. J Natl Cancer Inst 99 (10): 754-64, 2007
  3. Key TJ, Allen N, Appleby P, et al.: Fruits and vegetables and prostate cancer: no association among 1104 cases in a prospective study of 130544 men in the European Prospective Investigation into Cancer and Nutrition (EPIC). Int J Cancer 109 (1): 119-24, 2004
  4. Shike M, Latkany L, Riedel E, et al.: Lack of effect of a low-fat, high-fruit, -vegetable, and -fiber diet on serum prostate-specific antigen of men without prostate cancer: results from a randomized trial. J Clin Oncol 20 (17): 3592-8, 2002.
  5. BJU-2016-1793.R2 – The Men’s Eating and Living (MEAL) Study (CALGB 70807 [Alliance]): Recruitment Feasibility and Baseline Demographics of a Randomized Trial of Diet in Men on Active Surveillance for Prostate Cancer
  6. Fleshner NE, Lucia MS, Egerdie B, et al. Dutasteride in localised prostate cancer management: the REDEEM randomised, double-blind, placebo-controlled trial. 2012;379(9821):1103-1111.
  7. Cooperberg MR, Carroll PR, Klotz L: Active surveillance for prostate cancer: progress and promise. J Clin Oncol 29 (27): 3669-76, 2011. [PubMed]
  8. Lurdes Y.T. Inoue, Bruce J. Trock, Alan W. Partin, H. Ballentine Carter, Ruth Etzioni Modeling Grade Progression In An Active Surveillance Study Stat Med. Author manuscript; available in PMC 2015 Mar 15. Published in final edited form as: Stat Med. 2014 Mar 15; 33(6): 930–939.

 

Article of the Week: Risk prediction tool for grade re-classification in men with favourable-risk prostate cancer on active surveillance

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Every Week the Editor-in-Chief selects an Article of the Week from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.

In addition to the article itself, there is an accompanying editorial written by a prominent member of the urological community. This blog is intended to provoke comment and discussion and we invite you to use the comment tools at the bottom of each post to join the conversation.

If you only have time to read one article this week, it should be this one.

Risk prediction tool for grade re-classification in men with favourable-risk prostate cancer on active surveillance

Mufaddal M. Mamawala, Karthik Rao, Patricia Landis, Jonathan I. Epstein, Bruce J. Trock, Jeffrey J. Tosoian, Kenneth J. Pienta and H. Ballentine Carter

 

The James Buchanan Brady Urological Institute, Johns Hopkins Medical Institutions, Baltimore, MD, USA
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How to Cite

Mamawala, M. M., Rao, K., Landis, P., Epstein, J. I., Trock, B. J., Tosoian, J. J., Pienta, K. J. and Carter, H. B. (2017), Risk prediction tool for grade re-classification in men with favourable-risk prostate cancer on active surveillance. BJU International, 120: 25–31. doi: 10.1111/bju.13608

Objective

To create a nomogram for men on active surveillance (AS) for prediction of grade re-classification (GR) above Gleason score 6 (Grade group >2) at surveillance biopsy.

Patients and Methods

From a cohort of men enrolled in an AS programme, a multivariable model was used to identify clinical and pathological parameters predictive of GR. Nomogram performance was assessed using receiver operating characteristic curves, calibration, and decision curve analysis.

aotw-jul-2017-5-results

Results

Of 1 374 men, 254 (18.50%) were re-classified to Gleason ≥7 on surveillance prostate biopsy. Variables predictive of GR were earlier year of diagnosis [≤2004 vs ≥2005; odds ratio (OR) 2.16, P < 0.001], older age (OR 1.05, P < 0.001), higher prostate-specific antigen density [OR 1.19 (per 0.1 unit increase), P = 0.04], bilateral disease (OR 2.86, P < 0.001), risk strata (low-risk vs very-low-risk, OR 1.79, P < 0.001), and total number of biopsies without GR (OR 0.68, P < 0.001). On internal validation, a nomogram created using the multivariable model had an area under the curve of 0.757 (95% confidence interval 0.730–0.797) for predicting GR at the time of next surveillance biopsy.

Conclusion

The nomogram described is currently being used at each return visit to assess the need for a surveillance biopsy, and could increase retention in AS.

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Editorial: Shift from protocol-based to personalized medicine in active surveillance: beginning of a new era

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The use of active surveillance (AS) is rapidly expanding worldwide, with rates as high as 74% among patients with low-risk prostate cancer in the nationwide registry of Sweden [1]. Despite increasing uptake of this strategy by patients, there is no consensus among the medical community as to the ideal criteria for selection and monitoring [2]. For example, the Johns Hopkins AS programme restricts enrolment to men with low-risk disease and performs annual biopsies for monitoring. Other protocols also include men with intermediate-risk disease and perform prostate biopsy at less frequent intervals.

Is it really optimal to use the same follow-up protocol for all patients? Many factors influence the risk of reclassification, including patient characteristics (e.g. race, body mass index) and disease features (e.g. PSA density, Gleason score and extent of disease on biopsy) [3]. Moreover, previous studies have shown that the risk of reclassification during AS is a conditional probability, where the risk decreases with each additional negative biopsy [4]. Given that individual patients have vastly different risks of reclassification, and that the risk changes over time, AS represents an ideal context for personalized medicine.

There has already been a significant paradigm shift in prostate cancer screening from a one-size-fits-all to a multivariable, risk-adapted approach [5]. Why would we use the same screening intervals and biopsy cutoff for patients with vastly different risk profiles? Multiple guidelines already recommend using PSA levels to guide screening protocols, and there are several validated multivariable tools to provide more personalized estimates of prostate cancer risk. Both the Prostate Cancer Prevention Trial (PCPT) and the European Randomised Study of Screening for Prostate Cancer (ERSPC) risk calculators have been extensively studied and are readily available online for use in clinical practice [6].

To date, the concept of risk-adapted AS has received relatively little attention, and few nomograms have been created specifically for the AS population. Using data from the Canary Prostate Active Surveillance Study (PASS), Ankerst et al. [7] designed a nomogram to predict biopsy reclassification using age at biopsy, months since the last biopsy, last PSA level, percentage of cores positive for cancer on the last biopsy, and number of previous negative biopsies. This tool had an area under the curve (AUC) of 0.724 on internal validation, and is available online at https://prostate-cancer-risk-calculator.org to facilitate additional validation and clinical use.

In the current issue of BJUI, Mamawala et al. [8] report on the development of another new AS nomogram using data from the Johns Hopkins programme. Specifically, the tool predicts the risk of biopsy reclassification using six variables: age; PSA density; year of diagnosis; laterality; risk strata; and total number of biopsies. The nomogram was well calibrated and had an AUC of 0.757 on internal validation. Notably, the same authors have also recently developed a different tool to predict pathological Gleason score for men on AS using a Bayesian joint model [9]. Following external validation, these tools may help provide more customized decision support for the AS population by integrating longitudinal data.

It is noteworthy that none of these nomograms incorporate new markers or imaging, and it is likely that such data could further refine their estimates. For example, longitudinal measurements of the Prostate Health Index were previously shown to predict biopsy reclassification during AS [10], and the use of multiparametric MRI continues to expand. As more data on these tests become available, the AS risk calculators should be updated, as has been done with the PCPT and ERSPC risk calculators used in the screening context. In the future, continued research on genetics may allow further tailoring of AS. In the meantime, these risk calculators are an important first step (‘version 1.0’) toward a more personalized approach to AS.

Stacy Loeb

 

Department of Urology, Population Health, Laura & Isaac Perlmutter Cancer Center, New York University, New York, NY, US
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References

 

1 Loeb S, Folkvaljon Y, Curnyn C, Robinson D, Bratt O, Stattin P. Almost complete uptake of active surveillance for very low-risk prostate cancer in Sweden. JAMA Oncol 2016; [Epub ahead of print]. doi: 10.1001/ jamaoncol.2016.3600

 

2 Tosoian JJ, Carter HB, Lepor A, Loeb S. Active surveillance for prostate cancer: current evidence and contemporary state of practice. Nat Rev Urol 2016; 13: 205–15

 

3 Loeb S, Bruinsma SM, Nicholson J et al. Active surveillance for prostate cancer: a systematic review of clinicopathologic variables and biomarkers for risk stratification. Eur Urol 2015; 67: 619–26

 

4 Alam R, Carter HB, Landis P, Epstein JI, Mamawala M. Conditional probability of reclassification in an active surveillance program for prostate cancer. J Urol 2015; 193: 1950–5

 

5 Loeb S. Evidence-based versus personalized prostate cancer screening: using baseline prostate-specific antigen measurements to individualize screening. J Clin Oncol 2016; 34: 2684–6

 

6 PoyetC, Niebo er D, Bhindi B et al. Prostate cancer risk prediction using the novel versions of the European Randomised Study for Screening of Prostate Cancer (ERSPC) and Prostate Cancer Prevention Trial (PCPT) risk calculators: independent validation and comparison in a contemporary European cohort. BJU Int 2016; 117: 401– 8

 

7 Ankerst DP, Xia J, Thompson IM Jr et al. Precision medicine in active surveillance for prostate cancer: development of the canary-early detection research network active surveillance biopsy risk calculator. Eur Urol 2015; 68: 1083–8

 

8 Mamawala MM, Rao K, Landis P et al. Risk prediction tool for grade re- classification in men with favourable-risk prostate cancer on active surveillance. BJU Int 2017; 120: 25–31

 

9 ColeyRY, Zeger S L, Mamawala M, Pienta KJ, Carter HB. Prediction of the pathologic gleason score to inform a personalized management program for prostate cancer. Eur Urol 2016; [Epub ahead of print]. doi: 10.1016/j.eururo.2016.08.005

 

10 Tosoian JJ, Loeb S , Feng Z et al. Association of [2]proPSA with biopsy reclassification during active surveillance for prostate cancer. J Urol 2012; 188: 1131–6

 

Video: Risk prediction tool for grade re-classification in men with favourable-risk prostate cancer on active surveillance

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Risk prediction tool for grade re-classification in men with favourable-risk prostate cancer on active surveillance

Read the full article

Abstract

Objective

To create a nomogram for men on active surveillance (AS) for prediction of grade re-classification (GR) above Gleason score 6 (Grade group >2) at surveillance biopsy.

Patients and Methods

From a cohort of men enrolled in an AS programme, a multivariable model was used to identify clinical and pathological parameters predictive of GR. Nomogram performance was assessed using receiver operating characteristic curves, calibration, and decision curve analysis.

Results

Of 1 374 men, 254 (18.50%) were re-classified to Gleason ≥7 on surveillance prostate biopsy. Variables predictive of GR were earlier year of diagnosis [≤2004 vs ≥2005; odds ratio (OR) 2.16, P < 0.001], older age (OR 1.05, P < 0.001), higher prostate-specific antigen density [OR 1.19 (per 0.1 unit increase), P = 0.04], bilateral disease (OR 2.86, P < 0.001), risk strata (low-risk vs very-low-risk, OR 1.79, P < 0.001), and total number of biopsies without GR (OR 0.68, P < 0.001). On internal validation, a nomogram created using the multivariable model had an area under the curve of 0.757 (95% confidence interval 0.730–0.797) for predicting GR at the time of next surveillance biopsy.

Conclusion

The nomogram described is currently being used at each return visit to assess the need for a surveillance biopsy, and could increase retention in AS.

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Article of the Week: Decision-Making by PCa Physicians During AS

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Every Week the Editor-in-Chief selects an Article of the Week from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.

In addition to the article itself, there is an accompanying editorial written by a prominent member of the urological community. This blog is intended to provoke comment and discussion and we invite you to use the comment tools at the bottom of each post to join the conversation.

If you only have time to read one article this week, it should be this one.

Qualitative study on decision-making by prostate cancer physicians during active surveillance

Stacy Loeb*,†,‡,§, Caitlin Curnyn†, Angela Fagerlin¶,**, Ronald Scott Braithwaite†
Mark D. Schwartz†,§, Herbert Lepor*‡, Herbert Ballentine Carter†† and Erica Sedlander†

 

Departments of *Urology, †Population Health, ‡Laura and Isaac Perlmutter Cancer Center, New York University, §Manhattan Veterans Affairs Medical Center, New York, NY, Department of Population Health Sciences, University of Utah, Salt Lake City, UT, **Informatics, Decision Enhancement, and Surveillance (IDEAS) Center, Salt Lake City VA, UT, and ††Brady Urological Institute, Johns Hopkins Hospital, Baltimore, MD, USA

 

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How to Cite

Loeb, S., Curnyn, C., Fagerlin, A., Braithwaite, R. S., Schwartz, M. D., Lepor, H., Carter, H. B. and Sedlander, E. (2017), Qualitative study on decision-making by prostate cancer physicians during active surveillance. BJU International, 120: 32–39. doi: 10.1111/bju.13651

Abstract

Objective

To explore and identify factors that influence physicians’ decisions while monitoring patients with prostate cancer on active surveillance (AS).

Subjects and Methods

A purposive sampling strategy was used to identify physicians treating prostate cancer from diverse clinical backgrounds and geographic areas across the USA. We conducted 24 in-depth interviews from July to December 2015, until thematic saturation was reached. The Applied Thematic Analysis framework was used to guide data collection and analysis. Interview transcripts were reviewed and coded independently by two researchers. Matrix analysis and NVivo software were used for organization and further analysis.

Results

Eight key themes emerged to explain variation in AS monitoring: (i) physician comfort with AS; (ii) protocol selection; (iii) beliefs about the utility and quality of testing; (iv) years of experience and exposure to AS during training; (v) concerns about inflicting ‘harm’; (vi) patient characteristics; (vii) patient preferences; and (viii) financial incentives.

Conclusion

These qualitative data reveal which factors influence physicians who manage patients on AS. There is tension between providing standardized care while also considering individual patients’ needs and health status. Additional education on AS is needed during urology training and continuing medical education. Future research is needed to empirically understand whether any specific protocol is superior to tailored, individualized care.

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Editorial: AS in PCa- New Efforts, New Voices, New Hope

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In January 2016, in his final State of the Union address, US President Barack Obama tasked Vice President Joseph Biden with heading up a new national mission, the Cancer Moonshot, to expedite advances in cancer prevention, diagnosis and treatment. One of the blue-ribbon panel recommendations was to minimize the side effects of cancer treatment.

There is no better target for that goal than prostate cancer, the cancer that leads all others in the toll of Americans annually diagnosed with cancer, and the fourth most common worldwide. Many men with low-risk prostate cancer undergo unnecessary treatments, including prostatectomy and radiation therapy, which are unlikely to affect their survival, even if their disease were left untreated. A case in point is the ProtecT study [1], which showed at a median of 10 years that there was no difference in prostate cancer-specific mortality between treatment with surgery or radiation therapy and no treatment [1]. Although there has been a paradigm shift in the management of low-risk prostate cancer with an increased uptake of active surveillance (AS) [2], the fact is that only ~40% of men with low-risk prostate cancer choose AS.

Because of equivalency in effectiveness of treatment options in low-risk prostate cancer, an explication of the steps involved in the clinical decision-making process were long overdue. In an innovative study in the present issue of BJUI, Loeb et al. [3] report a qualitative analysis using a purposive sampling strategy to explore the decision-making process of physicians caring for patients with prostate cancer undergoing AS. This study used qualitative interviews and investigators then analysed responses to identify factors influencing therapeutic decision-making. It is noteworthy that despite the fact that AS acceptance rates have increased and it is an established therapeutic approach, significant differences still remain with regard to when physicians enroll and how they monitor patients on AS. These findings align with those from a Surveillance Epidemiology and End Results (SEER) registry study of 12 068 men with low-risk prostate cancer whose urologists and radiation oncologists reported a spectrum of observation practices [4]. Neither study accounted for patients’ preference or perspectives.

Although there are many national guidelines for AS, no consensus on optimum AS management exists, but Movember–GAP3 (https://au.movember.com/report-cards/view/id/3372/gap3-prostate-cancer-active-surveillance), an international effort comprising 25 institutions with AS programmes, may change that. It seeks to establish standard guidelines for patient selection and monitoring and to find agreement on a trigger for treatment. The tumour heterogeneity and possible lack of linearity in early disease progression that we can glean from the next-generation sequencing studies in advanced prostate cancer [5] will not make that easy. Given the promise of precision medicine, we anticipate a decision-making process that by integrating clinical and pathological data, imaging, and biomarkers prognostic of risk of disease progression as well as patient comorbidity effectively removes guesswork from the calculation.

As this international effort and the vice president’s work proceed, we urge all to listen to the voices of patients and ensure they are heard as clearly as those of the experts. We know the paternalistic model of medicine, in which physicians are the exclusive decision-makers, has long been outmoded [6]. With so much at stake, let us now act like it.

Spyridon P. Basourakos* Karen Hoffman† and Jeri Kim*

 

*Department of Genitourinary Medical Oncology, and †Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, US

 

Read the full article

 

References

 

1 Hamdy F, Donovan J, Lane J et al. 10-year outcomes after monitoring, surgery, or radiotherapy for localized prostate cancer. N Engl J Med 2016; 375: 1415–24

 

2 Cooperberg M, Carroll P. Trends in management for patients with localized prostate cancer, 1990–2013. JAMA 2015; 314: 80–2

 

3 Loeb S, Curnyn C, Fagerlin A et al. Qualitative study on decision- making by prostate cancer physicians during active surveillance. BJU Int 2017; 120: 32–9

 

4 Hoffman K, Niu J, Shen Y et al. Physician variation in management of low-risk prostate cancer: a population-based cohort study. JAMA Intern Med 2014; 174: 1450–9

 

5 Robinson D, Van Allen E, Wu Y et al. Integrative clinical genomics of advanced prostate cancer. Cell 2015; 161: 1215–28

 

6 Lerner B. The Good Doctor: A Father, a Son, and the Evolution of Medical Ethics. Boston: Beacon Press, 2014

 

Video: Decision-Making by PCa Physicians During AS

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Qualitative study on decision-making by prostate cancer physicians during active surveillance

 

Read the full article

Abstract

Objective

To explore and identify factors that influence physicians’ decisions while monitoring patients with prostate cancer on active surveillance (AS).

Subjects and Methods

A purposive sampling strategy was used to identify physicians treating prostate cancer from diverse clinical backgrounds and geographic areas across the USA. We conducted 24 in-depth interviews from July to December 2015, until thematic saturation was reached. The Applied Thematic Analysis framework was used to guide data collection and analysis. Interview transcripts were reviewed and coded independently by two researchers. Matrix analysis and NVivo software were used for organization and further analysis.

Results

Eight key themes emerged to explain variation in AS monitoring: (i) physician comfort with AS; (ii) protocol selection; (iii) beliefs about the utility and quality of testing; (iv) years of experience and exposure to AS during training; (v) concerns about inflicting ‘harm’; (vi) patient characteristics; (vii) patient preferences; and (viii) financial incentives.

Conclusion

These qualitative data reveal which factors influence physicians who manage patients on AS. There is tension between providing standardized care while also considering individual patients’ needs and health status. Additional education on AS is needed during urology training and continuing medical education. Future research is needed to empirically understand whether any specific protocol is superior to tailored, individualized care.

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