Tag Archive for: #ProstateCancer

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Editorial: The benefits of regular exercise

January is the month when we wish each other happiness and success for the year ahead. It is also the month when many are recovering from the excesses of the festive season. This is the time when gyms and diets become popular again with offers of reduced rates to attract customers. For Londoners the spring marathon is not far away and you often see runners training in different parks despite the cold weather and icy routes.

If you think this year is the one where you are about to start going to the gym, then we recommend you the best shake for post workout to add extra point to your routine.

Is this just a temporary fad? Or is there truly some benefit to be had by exercising regularly?

Over the past few years, we have published several papers showing clear associations between metabolic syndrome and LUTS, and the benefits of preoperative optimisation with diet and exercise prior to major urological surgery. In this issue of the BJUI, we present a small but well‐designed randomised controlled trial on the benefits of exercise in attenuating the treatment side‐effects in patients with newly diagnosed prostate cancer starting on androgen‐deprivation therapy [1]. It is an example of collaborative working between Urologists and experts on Sport, Exercise and Rehabilitation therapy. The authors clearly demonstrate that a short‐term programme of supervised exercise results in improvements in quality of life and cardiovascular risk profile in patients on hormonal therapy. Even after the supervised exercise was withdrawn and followed by self‐directed exercise, the benefits continued as compared to the control group.

As Urologists, we can help our patients in this journey by adopting a more active lifestyle ourselves. Inspired by Fiona Godlee’s article in the BMJ [2], I have started printing it and actually handing it/e‐mailing it to my patients. The paper describes physical activity as ‘The miracle cure’ with very few side‐effects. Any level of activity is better than none and a gentle start usually avoids an unexpected injury.

There is no better time to lead by example this New Year!

by Prokar Dasgupta

References

  1. Ndjevera WOrange STO’Doherty AF et al. Exercise‐induced attenuation of treatment side‐effects in patients with newly diagnosed prostate cancer beginning androgen‐deprivation therapy: a randomised controlled trial. BJU Int 2019: 125; 28-37.
  2. Godlee FThe miracle cureBMJ 2019366l5605.

Video: Exercise‐induced attenuation of treatment side‐effects in patients with newly diagnosed PCa beginning androgen‐deprivation therapy

Exercise‐induced attenuation of treatment side‐effects in patients with newly diagnosed prostate cancer beginning androgen‐deprivation therapy: a randomised controlled trial

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Abstract

Objectives

(i) To assess whether exercise training attenuates the adverse effects of treatment in patients with newly diagnosed prostate cancer beginning androgen‐deprivation therapy (ADT), and (ii) to examine whether exercise‐induced improvements are sustained after the withdrawal of supervised exercise.

Patients and Methods

In all, 50 patients with prostate cancer scheduled for ADT were randomised to an exercise group (n = 24) or a control group (n = 26). The exercise group completed 3 months of supervised aerobic and resistance exercise training (twice a week for 60 min), followed by 3 months of self‐directed exercise. Outcomes were assessed at baseline, 3‐ and 6‐months. The primary outcome was difference in fat mass at 3‐months. Secondary outcomes included: fat‐free mass, cardiopulmonary exercise testing variables, QRISK®2 (ClinRisk Ltd, Leeds, UK) score, anthropometry, blood‐borne biomarkers, fatigue, and quality of life (QoL).

Results

At 3‐months, exercise training prevented adverse changes in peak O2 uptake (1.9 mL/kg/min, P = 0.038), ventilatory threshold (1.7 mL/kg/min, P = 0.013), O2 uptake efficiency slope (0.21, P = 0.005), and fatigue (between‐group difference in Functional Assessment of Chronic Illness Therapy‐Fatigue score of 4.5 points, P = 0.024) compared with controls. After the supervised exercise was withdrawn, the differences in cardiopulmonary fitness and fatigue were not sustained, but the exercise group showed significantly better QoL (Functional Assessment of Cancer Therapy‐Prostate difference of 8.5 points, P = 0.034) and a reduced QRISK2 score (−2.9%, P = 0.041) compared to controls.

Conclusion

A short‐term programme of supervised exercise in patients with prostate cancer beginning ADT results in sustained improvements in QoL and cardiovascular events risk profile.

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Visual abstract: Exercise‐induced attenuation of treatment side‐effects in patients with newly diagnosed prostate cancer beginning androgen‐deprivation therapy: a randomised controlled trial

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January 2020 – About the cover

The first Article of the Month for 2020 is from work carried out at Northumbria University in Newcastle, Norfolk and Norwich University Hospital, both in the UK, and James Cook University in Queensland, Australia (Exercise-induced reduction of ADT side-effects in newly diagnosed PCa patients beginning androgen‐deprivation therapy: a randomised controlled trial). The article discusses the benefits of exercise in improving quality of life and reducing cardiovascular events following treatment for prostate cancer.

The cover image shows the city of Newcastle during the Great North Run – the city’s iconic half marathon – which takes place every year in September.  It was created by former Olympic Bronze 10, 000m medalist, Brendan Foster, in 1981 and in 2014 the one millionth runner crossed the finish line.

©shutterstock

Article of the week: A machine learning‐assisted decision‐support model to better identify patients with PCa requiring an extended pelvic lymph node dissection

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 editorial written by a prominent member of the urology community and a video prepared by the authors; 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. Merry Christmas!

A machine learning‐assisted decision‐support model to better identify patients with prostate cancer requiring an extended pelvic lymph node dissection

Ying Hou*, Mei-Ling Bao, Chen-Jiang Wu*, Jing Zhang*, Yu-Dong Zhang* and Hai-Bin Shi*

*Department of Radiology and Department of Pathology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, Jiangsu Province, China

Read the full article

Abstract

Objectives

To develop a machine learning (ML)‐assisted model to identify candidates for extended pelvic lymph node dissection (ePLND) in prostate cancer by integrating clinical, biopsy, and precisely defined magnetic resonance imaging (MRI) findings.

Patients and Methods

In all, 248 patients treated with radical prostatectomy and ePLND or PLND were included. ML‐assisted models were developed from 18 integrated features using logistic regression (LR), support vector machine (SVM), and random forests (RFs). The models were compared to the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram using receiver operating characteristic‐derived area under the curve (AUC) calibration plots and decision curve analysis (DCA).

Results

A total of 59/248 (23.8%) lymph node invasions (LNIs) were identified at surgery. The predictive accuracy of the ML‐based models, with (+) or without (−) MRI‐reported LNI, yielded similar AUCs (RFs+/RFs: 0.906/0.885; SVM+/SVM: 0.891/0.868; LR+/LR: 0.886/0.882) and were higher than the MSKCC nomogram (0.816; P < 0.001). The calibration of the MSKCC nomogram tended to underestimate LNI risk across the entire range of predicted probabilities compared to the ML‐assisted models. The DCA showed that the ML‐assisted models significantly improved risk prediction at a risk threshold of ≤80% compared to the MSKCC nomogram. If ePLNDs missed was controlled at <3%, both RFs+ and RFs resulted in a higher positive predictive value (51.4%/49.6% vs 40.3%), similar negative predictive value (97.2%/97.8% vs 97.2%), and higher number of ePLNDs spared (56.9%/54.4% vs 43.9%) compared to the MSKCC nomogram.

Conclusions

Our ML‐based model, with a 5–15% cutoff, is superior to the MSKCC nomogram, sparing ≥50% of ePLNDs with a risk of missing <3% of LNIs.

 

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Editorial: A better way to predict lymph node involvement using machine learning?

In their study in this issue of BJUIHou et al. [1] use machine‐learning algorithms to evaluate several preoperative clinical variables (highlighting specific MRI findings of locally advanced prostate cancer) to determine whether lymph node involvement (LNI) could be present during radical prostatectomy, which would justify an extended pelvic lymph node dissection (PLND). This is a well‐designed study with scientific rigour, providing evidence‐based justifications and definitions (i.e. of relevant MRI findings). The authors successfully illustrate a practical application of using artificial intelligence (AI) methods to augment clinical decision‐making prior to and during surgery compared to today’s ‘gold standard’ (nomograms).

For many years, the Memorial Sloan Kettering Cancer Centre (MSKCC) nomogram, among a number of predictive models, has been used to determine the probability of LNI. The output of these tools has assisted surgeons in determining whether to perform a PLND, and if so, to what extent [2,3,4]. The authors hypothesize that, with additional MRI parameters not previously used, machine‐learning algorithms can better select which patients are more likely to have LNI and will therefore require extended PLND. In fact, the authors report that the MSKCC nomogram and conventional MRI reporting of LNI consistently underestimated LNI risk compared to the machine‐learning‐assisted models presented in their study. The outputs of the present models would allow a higher number of extended PLNDs to be spared compared to reliance on the MSKCC nomogram alone. It was appropriate to use several existing AI models in this study, as it is never readily apparent initially which existing predictive model may perform best with a given dataset. In fact, all the models used – logistic regression (LR), support vector machine (SVM) and random forest (RF) – while similar in performance to each other, outperformed the MSKCC nomogram (P < 0.001). Many adjustments were probably performed for each model to tailor it to the dataset and optimize prediction performance.

Criticisms of the study are that: (i) cases for which PLND was not performed were excluded, which could have created a selection bias; (ii) the model would only be applicable when the patient has undergone MRI; (iii) the study was conducted at a single institution in a small sample (AI methods thrive on big and diverse datasets).

This study by Hou et al. is a great example of a machine‐learning application that may positively impact clinical practice. For many years, we have relied on nomograms, but with increasing use of MRI, additional factors should also be included, as Hou et al. have done. Machine‐learning is particularly adept at simultaneously examining numerous variables to elicit which ones may contribute best to a particular outcome. As BJUI has evaluated many manuscripts examining machine‐learning methods for clinical decision‐making in the past year, we have encouraged authors to use present‐day gold standard methods, such as the MSKCC nomogram, as controls [5]. As we embrace AI methods, we must keep one eye on the tried and tested conventional ways. This ensures that we do not take backward steps but rather take forward steps responsibly. Similarly to recent AI studies published in the BJUI, the sample size in this study was relatively small. External validation in a multicentre study on larger datasets is highly recommended.

by Andrew J. Hung

References

  1. Hou YBao MWu CJZhang JZhang YDShi HBA machine learning‐assisted decision support model with mri can better spare the extended pelvic lymph node dissection at cost of less missing in prostate cancerBJU Int 2019124972– 83
  2. Briganti ALarcher AAbdollah F et al. Updated nomogram predicting lymph node invasion in patients with prostate cancer undergoing extended pelvic lymph node dissection: the essential importance of percentage of positive cores. Eur Urol 201261480– 7
  3. Memorial Sloan Kettering Cancer CenterDynamic prostate cancer nomogram: coefficients. Accessed April 2018
  4. Tosoian JJChappidi MFeng Z et al. Prediction of pathological stage based on clinical stage, serum prostate-specific antigen, and biopsy Gleason score: Partin Tables in the contemporary era. BJU Int 2017119676– 83
  5. Hung AJCan machine‐learning algorithms replace conventional statistics? BJU Int 20181231

 

Video: Machine learning‐assisted decision‐support model to identify PCa patients requiring an extended PLND

A machine learning‐assisted decision‐support model to better identify patients with prostate cancer requiring an extended pelvic lymph node dissection

Read the full article

Abstract

Objectives

To develop a machine learning (ML)‐assisted model to identify candidates for extended pelvic lymph node dissection (ePLND) in prostate cancer by integrating clinical, biopsy, and precisely defined magnetic resonance imaging (MRI) findings.

Patients and Methods

In all, 248 patients treated with radical prostatectomy and ePLND or PLND were included. ML‐assisted models were developed from 18 integrated features using logistic regression (LR), support vector machine (SVM), and random forests (RFs). The models were compared to the Memorial SloanKettering Cancer Center (MSKCC) nomogram using receiver operating characteristic‐derived area under the curve (AUC) calibration plots and decision curve analysis (DCA).

Results

A total of 59/248 (23.8%) lymph node invasions (LNIs) were identified at surgery. The predictive accuracy of the ML‐based models, with (+) or without (−) MRI‐reported LNI, yielded similar AUCs (RFs+/RFs: 0.906/0.885; SVM+/SVM: 0.891/0.868; LR+/LR: 0.886/0.882) and were higher than the MSKCC nomogram (0.816; P < 0.001). The calibration of the MSKCC nomogram tended to underestimate LNI risk across the entire range of predicted probabilities compared to the ML‐assisted models. The DCA showed that the ML‐assisted models significantly improved risk prediction at a risk threshold of ≤80% compared to the MSKCC nomogram. If ePLNDs missed was controlled at <3%, both RFs+ and RFs resulted in a higher positive predictive value (51.4%/49.6% vs 40.3%), similar negative predictive value (97.2%/97.8% vs 97.2%), and higher number of ePLNDs spared (56.9%/54.4% vs 43.9%) compared to the MSKCC nomogram.

Conclusions

Our ML‐based model, with a 5–15% cutoff, is superior to the MSKCC nomogram, sparing ≥50% of ePLNDs with a risk of missing <3% of LNIs.

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IP4-CHRONOS is launched

IP4- CHRONOS is open! CHRONOS is a phase II randomised control trial, that will review the outcomes (including oncological, functional, quality of life and cost-effectiveness) of focal therapy against those from radical therapy, in men with newly diagnosed localised clinically significant prostate cancer.

 

 

All men newly diagnosed with low-intermediate risk prostate cancer, confined to the prostate, with a life expectancy of at least 10 years will be screened for eligibility. Men must be well enough to undergo the interventions outlined in the trial prior to being enrolled.

Men will then have a choice of enrolling into CHRONOS A or CHRONOS B. CHRONOS A will randomise men to having radical whole gland treatment (radiotherapy, brachytherapy or prostatectomy), or focal therapy (HIFU or cryotherapy). CHRONOS A will answer the question, ‘is focal therapy equivalent in cancer control as radical therapy?’ CHRONOS B will randomise men to having focal therapy with or without additional neoadjuvant treatment and will answer the question: ‘can the success of focal therapy be improved by using neoadjuvant treatment?’ Randomisation will be stratified by disease characteristics.

All men will undergo intervention as they would within the NHS, however by doing so in a trial setting, we can directly compare the results of such treatments against each other. As the follow up mimics that of standard of care, the extra burden of treatment within the trial is minimal.

60 men will be recruited into both CHRONOS A and CHRONOS B (total 120) over a 1-year period, during the pilot, and if recruitment is successful the aim is to continue to a larger study assessing 2450 patients over 5 years, with a minimum follow up of 3 years. The primary outcome measures will be progression free survival in CHRONOS A, and failure free survival in CHRONOS B. The CHRONOS pilot will open in 12 UK hospital sites, aiming to open across the UK and Europe within the larger study.

CHRONOS is entirely funded by the Prostate Cancer UK charity, and available on the NIHR CRN portfolio. If you would like to join the main phase of CHRONOS as a site, please contact Miss Deepika Reddy ([email protected]) or visit our website for further information www.imperialprostate.org.uk/CHRONOS

Prof Hashim U. Ahmed (CHRONOS PI&CI)

Mr Taimur T. Shah (CHRONOS sub-investigator, Urology SpR & Research Fellow)

Miss Deepika Reddy (CHRONOS Clinical Research Fellow)

 

Editorial: Translating cost-utility modelling into the real world – the case of focal high-intensity focussed ultrasound and active surveillance

Health economic modelling is always a challenge. The inputs are never quite what we want them to be. The literature that we have at our disposal suffers from the inevitable deficiencies of lack of maturity, ever diminishing relevance, and questionable applicability as practice evolves. The modelling can never quite reflect the nuances and vagaries of clinical practice. However, the process is an important and in some cases (evaluation by the UK’s National Institute of Clinical and Care Excellence) a necessary one. Knowing the cost of achieving a given health status over a defined time frame is an important consideration in the allocation resource in any finite system of care.

The paper by Bénard et al. [1] is most useful in helping us to understand what the issues are and how our decision-making might impact on cost in the context of low-to-moderate risk prostate cancer.

The issue with these types of analyses is the degree to which the inevitable assumptions made by the investigators are consistent with current practice. Below I have tried to identify some of the areas in which the assumptions diverge from current knowledge and ‘know-how’, in order to illustrate just how difficult the task that Bénard et al. [1] have undertaken.

The first relates to the assumption that both strategies can be applied to the same population. They cannot, or perhaps more correctly – should not. For instance, nobody I know would offer a man focal treatment who had well-characterised micro-focal low-volume Gleason 3+3 (or Gleason Grade Group 1) [2]. We know, from what now constitutes a considerable body of level-1 evidence, that there is no benefit to be derived from intervening in disease that confers little, if any, risk of premature death [3]. Today, focal therapy tends to be applied to men with well-characterised, visually localised Gleason Grade Group ≥2, who want to avoid radical whole gland therapy and the genitourinary side-effects associated with them [4].

The second relates to the synergies between the two treatments. Increasingly men who opt for active surveillance (AS) upfront have an increasing tendency to opt for focal treatment on radiological progression of any lesion under scrutiny. This makes quite a bit of intuitive sense. These are men who appear comfortable with the process of observation, are likely to place high utility on genitourinary function, may have exhibited a very stable background prostate (apart from the expanding lesion depicted on MRI), are likely to be very well informed, and will, by now, be very well-characterised histologically. These, as it happens, are the ideal attributes for a candidate for focal therapy.

The third is a reflection on the relevance of the literature to inform the question being posed. It is no fault of the authors that AS has changed beyond recognition in the last few years. This change has been driven by the use of MRI in the risk stratification process for candidate selection, the substation of temporal biopsy assessment by imaging and the reduction, and at times elimination, of the re-classification vs progression error that confounds most of the literature on
surveillance. Modelling events on historical single-institution cohorts (as AS has never been evaluated in a randomised setting apart from one comparison against focal therapy) is probably unhelpful in helping us to understand and inform our future [5].

The fourth concerns scope. Why limit this analysis to focal high-intensity focussed ultrasound? All focal therapies, irrespective of energy source, seem to produce very similar outcomes, both in terms of freedom from failure (time to radical treatment and/or metastasis) and in relation to preservation of genitourinary function. Broadening the scope, by including vascular targeted photo-therapy and cryotherapy, would have meant that randomised trials could have been
included as inputs, with the effect of possibly reducing the high levels of uncertainty that bedevil the current analysis [5,6].

The fifth recognises the dynamic nature of the progression risk in AS cohorts. This is an important, but poorly recognised, attribute of the mature AS cohorts that we tend to rely upon. These cohorts are dynamic entities that have as entrants men of increasingly lower risk (due to a recent improvement in risk stratification) and, at the same time, continually exit the very men with the highest risk, i.e., the ‘progressors’. Thus, over time, the cohort undergoes a gradual, but inevitable, reduction in risk. The more mature the cohort, the greater the reduction. By referencing mature cohorts (when trying to predict the fate of future patients) we
will, therefore, have a tendency to over-estimate the benefit/safety of AS in a contemporary setting.

This is not to say that we should not endeavour to estimate the cost of achieving a given health state. We need this, perhaps more than ever. What we need to strive towards are models that represent both the reality of practice and the very latest, and most subtle, distillation of the current evidence.

by Mark Emberton

 

References

  1. Bénard A, Duroux T, Robert G. Cost-utility analysis of focal high-intensity focussed ultrasound vs active surveillance for low- to intermediate-risk prostate cancer using a Markov multi-state model. BJU Int 2019; 124: 962–71
  2. Klotz L, Emberton M. Management of low risk prostate cancer-active surveillance and focal therapy. Nat Rev Clin Oncol 2014; 11: 324–34
  3. Hamdy FC, Donovan JL, Lane JA et al. 10-year outcomes after monitoring, surgery, or radiotherapy for localized prostate cancer. N Engl J Med 2016; 375: 1415–24
  4. Elliott D, Hamdy FC, Leslie TA et al. Overcoming difficulties with equipoise to enable recruitment to a randomised controlled trial of partial ablation vs radical prostatectomy for unilateral localised prostate cancer. JU Int 2018; 122: 970–7
  5. Azzouzi AR, Vincendeau S, Barret E et al. Padeliporfin vascular-targeted photodynamic therapy versus active surveillance in men with low-risk prostate cancer (CLIN1001 PCM301): an open-label, phase 3, randomised controlled trial. Lancet Oncol 2017; 18: 181–91
  6. Donnelly BJ, Saliken JC, Brasher PM et al. A randomized trial of external beam radiotherapy versus cryoablation in patients with localized prostate cancer. Cancer 2010; 116: 323–30

 

 

Video: Cost–utility analysis of focal HIFU vs AS for low‐ to intermediate‐risk prostate cancer using a Markov multi‐state model

Cost–utility analysis of focal high‐intensity focussed ultrasound vs active surveillance for low‐ to intermediate‐risk prostate cancer using a Markov multi‐state model

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Abstract

Objectives

To estimate the relative cost‐effectiveness of focal high‐intensity focussed ultrasound (F‐HIFU) compared to active surveillance (AS) in patients with low‐ to intermediate‐risk prostate cancer, in France.

Patients and Methods

A Markov multi‐state model was elaborated for this purpose. Our analyses were conducted from the French National Health Insurance perspective, with a time horizon of 10 years and a 4% discount rate for cost and effectiveness. A secondary analysis used a 30‐year time horizon. Costs are presented in 2016 Euros (€), and effectiveness is expressed as quality‐adjusted life years (QALYs). Model parameters’ value (probabilities for transitions between health states, and cost and utility of health states) is supported by systematic literature reviews (PubMed) and random effect meta‐analyses. The cost of F‐HIFU in our model was the temporary tariff attributed by the French Ministry of Health to the overall treatment of prostate cancer by HIFU (€6047).

Our model was analysed using Microsoft Excel 2010 (Microsoft Corp., Redmond, WA, USA). Uncertainty about the value of the model parameters was handled through probabilistic analyses.

Results

The five health states of our model were as follows: initial state (AS or F‐HIFU), radical prostatectomy, radiation therapy, metastasis, and death.

Transition probabilities from the initial F‐HIFU state relied on four articles eligible for our meta‐analyses. All were non‐comparative studies. Utilities relied on a single cohort in San Diego, CA, USA.

For a fictive cohort of 1000 individuals followed for 10 years, F‐HIFU would be €207 520 more costly and would yield 382 less QALYs than AS, which means that AS is cost‐effective when compared to F‐HIFU. For a threshold value varying from €0 to 100 000/QALY, the probability of AS being cost‐effective compared to F‐HIFU varied from 56.5% to 60%. This level of uncertainty was in the same range with a 30‐year time horizon.

Conclusion

Given existing published data, our results suggest that AS is cost‐effective compared to F‐HIFU in patients with low‐ and intermediate‐risk prostate cancer, but with high uncertainty. This uncertainty must be scaled down by continuing to supply the model with new published data and ideally through a randomised clinical trial that includes cost‐effectiveness analyses.

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