Healthcare professionals including EFPIA partners' survey - second stage

 

Thank you for taking the time to complete this second round of survey. During the first round 57 patients and 73 healthcare professionals took part in the survey. We are extremely grateful to all these participants, and would encourage them to also help us with the second round and kindly complete this survey which include additional questions proposed during the first round.

The EAU Prostate Cancer Guideline Panel and other experts have identified 44 questions they believe are most important to be answered using Big Data. In addition, 12 additional questions were proposed by the respondents (which are relevant for patients with prostate cancer and answerable through BigData) during the first round of this survey. There are currently 56 viable questions. It is important that prioritisation exercise in undertaken to narrow down the number of questions that are regarded as top priority by all stakeholders. Some questions are followed by their PICO (PICO stands for Participants, Intervention, Compactor and Outcome) to make them more clear and/or brief explanation "How could Big Data” help us to answer that question. 

Please score the importance of the following questions on a scale of 1 (not important) to 9 (critically important). When deciding what score to give, you should consider what impact answering the questions will have on better diagnosis and treatment outcomes for Prostate Cancer.  

The first 44 questions are the same as were used in the first round of the survey. We are following Delphi Method for this prioritisation process. Therefore, these 44 questions are contain percentage of patients and healthcare professionals who considered the question “critically important”. Question number 45 (item number 46) onwards do not contain these percentage as these are new questions proposed during the first round of prioritisation process. 

Before you begin the survey, please enter your name followed by email address, in the space provided. This will help us avoid sending you unnecessary reminders.

Please contact at m.i.omar@abdn.ac.uk if you require more information. 

Thank you for your help!

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* 1. Before you begin the survey, could you please type your name, organization's name and email address below?

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* 2. We do not know which of the proposed environmental risk factors are actually causative or protective for (significant) PCa?

How could Big Data help us? As PCa is a complex disease, genetically and environmentally, we can assume the effect size of an environmental risk factor is small. This combined with the limited exposure of certain epidemiological risk/protective factors such as Soy consumption in Western-European countries, an adequate sample size is of crucial importance. Therefore, big data could be an important game changer especially in this setting and identify clear epidemiological risk/protective factors which could then be applied for patient awareness programs, diet recommendations and the setup of novel trials.

ROUND 1 RESULT: This question was considered critically important by 30.4% of patients and 34.7% of healthcare professionals who participated in the first round of survey.

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* 3. We do not know whether there is a difference in (significant) PCa occurrence based on geographical location within Europe when corrected for differential PCa management (differences in PSA screening, treatment decisions etc). Of special interest would be comparing the Finish and Southern Italian population who are genetically different determined by a founder effect.

How could Big Data help us? Big data, combining existing PCa databases containing clinical data (age of PCa diagnosis, screening status, PCa staging, treatment strategies, follow-up schedules and outcome data) from different countries could help us design a very detailed geographical PCa risk map which could give us tremendous insight in PCa biology.

ROUND 1 RESULT: This question was considered critically important by 45.6% of patients and 44.4% of healthcare professionals who participated in the first round of survey. 



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* 4. We do not know whether the knowledge of the genetic risk profile based on known susceptibility loci could have a beneficial impact on PCa management, mainly on PCa screening. This could for example be performed by comparing patients with known germline PCa risk loci and known screening status. Four groups of patients could be compared: patients with a) high risk loci and PSA screened, b) low risk loci and PSA screened, c) high risk loci not PSA screened and d) low risk loci not PSA screened. These patients could be compared regarding staging, differential therapies received and patient outcomes.

PICO of this question is copied below:

PICO: High versus low genetic prostate cancer risk to determine outcome after prostate cancer diagnosis

How could big data help us?  With the use of big data (combining databases of PCa patients and available clinical data such as the PRACTICAL consortium and independent centres), it could become clear whether the knowledge of a genetic risk could have a beneficial effect on patient management. If patients with a high genetic risk not being screened have worse outcomes compared to patients who are being screened, this could be an indication that genetic testing could clearly help patients. Secondly, big data could help us as well in identifying (sub)cohorts of particular interest for further translational research which would have never been discovered due to sample size restrictions.

ROUND 1 RESULT: This question was considered critically important by 80.7% of patients and 60.6% of healthcare professionals who participated in the first round of survey.

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* 5. Although there is an excellent correlation between the newly introduced histological grading groups (ISUP groups) and prognosis, these results are all based on biochemical recurrence, which is a surrogate endpoint for PCa outcomes such as prostate cancer specific mortality and overall mortality. We do not know yet whether these grading groups are actually associated with hard end points such as prostate cancer specific survival.

How could big data help us? By using big data, this question could be answered. Since PCa specific mortality is relatively rare after radical treatment in a non-selected PCa population, cohort size is a critical determining factor to be able to make relevant statements. By combining international databases with clinical and pathological data of patients treated with radical prostatectomy, we could answer the question stated above by comparing PCa specific survival in patients with different ISUP groups. If they remain predictive, we could develop nomograms which would be applicable world-wide to guide more individualized patient management.

ROUND 1 RESULT: This question was considered critically important by 68.4% of patients and 45.2% of healthcare professionals who participated in the first round of survey.


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* 6. Although it is generally assumed that a Gleason pattern 5 (most dedifferentiated histological subtype) is a major determinant in PCa mortality, we do not know whether a tertiary Gleason 5 component <5% in ISUP group 2 or 3 on a RP specimen has an impact on patients’ outcome and whether there is a differential outcome in patients with ISUP group 4 with a Gleason 5 component >5% compared to only Gleason 4 pattern. It has been suggested before that this tertiary component is correlated with a more extensive tumor phenotype, mainly in lower grade tumors.

How could big data help us? Due to the use of surrogate endpoints and limited sample size a definite answer cannot be given. In this way, big data can help us similarly as described above. A comparison should be made in Gleason score 3+4 (+5) vs 4+3 (+5) vs 4+4 vs 3+5 vs 5+3 and compare outcomes of ISUP groups 1 and 5. Depending on the outcome, a new revision of the Gleason grading system might need to be introduced, which would be an important game changer in patient management.

ROUND 1 RESULT: This question was considered critically important by 63.2% of patients and 37.0% of healthcare professionals who participated in the first round of survey.



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* 7. We still do not know whether in a real life setting, pre-biopsy mpMRI would be successful at predicting biopsy and patient outcomes. Furthermore, the added value of targeted biopsies in positive mpMRI investigations remains unclear as well. The studies that have been performed are very heterogeneous in patient selection and are performed in expert centers which could have an enormous impact on reproducibility.

PICO of the question is copied below:

PICO: Pre-biopsy multiparametric MRI imaging for exclusion of positive prostate biopsies.

How could big data help us? Setting up a prospective dataset by combining data from high- and low- throughput centers would allow us to answer these questions. After risk stratification (based on clinical risk calculators for example), the value of mpMRI (+targeted biopsies) could be investigated in a prospective way.

ROUND 1 RESULT: This question was considered critically important by 89.3% of patients and 52.1% of healthcare professionals who participated in the first round of survey.

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* 8. Although mpMRI in expert hands overall has good NPVs and PPVs, still some tumors will never be captured by imaging. We do not know whether these tumors are pathologically different.

PICO of this question is copied below:

PICO: Comparing clinical tumor features of MRI detected versus MRI-undetected (but biopsy detected) prostate cancer

How could big data help us?  Big data could help us identify a sufficiently large group of patients with “MRI invisible” PCa and let us compare clinical and pathological data.

ROUND 1 RESULT: This question was considered critically important by 82.1% of patients and 45.1% of healthcare professionals who participated in the first round of survey.

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* 9. We do not know which risk calculator is the best risk calculator and whether there are differences between their efficiencies between populations. Furthermore, up to now it is not known either whether the use of a risk calculator would make the use of a pre-biopsy mpMRI obsolete.

PICO of this question is copied below:

PICO: Pre-biopsy multiparametric MRI imaging for exclusion of positive prostate biopsies in patients at risk of PCa preselected by available risk calculators.

How could big data help us? Big data would allow us to re-evaluate the efficiency of different risk calculators that have been developed already, which could indicate which one should be preferably used in clinical practice and whether there are differences between European subpopulations. By combining multicenter databases, we could evaluate the NPV of pre-biopsy mpMRI after a pre-selection of patients has been made based on the most desirable risk calculator.

ROUND 1 RESULT: This question was considered critically important by 66.7% of patients and 47.1% of healthcare professionals who participated in the first round of survey.

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* 10. At this moment, multiple commercially available biomarker tests have shown success in increasing PCa diagnosis efficiency. However, we do not know how this has contributed to PCa diagnosis dynamics in Europe.

PICO of the question is copied below:

PICO: Tumor characteristics comparing patients diagnosed with versus without the use of biomarkers.

How could big data help us? Using big data we could determine whether the introduction of biomarkers has resulted in a higher efficiency of PCa detection and most interestingly whether more aggressive tumors were diagnosed and indolent tumors were not.

ROUND 1 RESULT: This question was considered critically important by 53.6% of patients and 46.6% of healthcare professionals who participated in the first round of survey.

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* 11. Currently, the scientific community generally applies the EAU Guidelines PCa risk stratification, stratifying patients into low-, intermediate- and high-risk PCa. This is based on the risk of recurrent disease of patients after radical treatments. However, this risk stratification still has its limits and patients still have very heterogeneous outcomes especially in the high-risk group. What we still do not know is what differentiates patients with lethal vs non-lethal disease, irrespective of their risk stratification.

PICO of this question is copied below:

PICO: For patients treated with localized disease, comparing patient and tumor features of lethal versus non-lethal prostate cancer cases.

How could big data help us? Using big data, we should have a sufficient number of events (PCa specific deaths) with sufficiently long follow-up to compare them to patients who do not develop lethal disease. This way we could identify new prognostic factors increasing the discriminative power of currently used tools.

ROUND 1 RESULT: This question was considered critically important by 75.4% of patients and 65.3% of healthcare professionals who participated in the first round of survey.

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* 12. At the moment we still do not know whether PSA screening is a viable strategy to detect PCa and if there are any other strategies defining patients who should under PSA screening. For example in patients with a positive family history, BRCA screening has been proposed and its results could be applicable to increase PSA screening efficiency. Up to now, this is based on small but valuable studies. Furthermore, other genetic tests (germline mutations in DNA damage repair genes or SNP studies for example) could be proposed to define this subset of patients that could benefit from PSA screening.

How could big data help us? The use of big data could allow us to answer these questions. By combining databases across Europe, better insight could be given in which patients (based on clinical or genetic data, whichever is available) could benefit most of PSA screening. As an example, we could identify patients with positive family history who have undergone BRCA screening and determine the incidence of PCa on biopsy and compare to patients with positive family history without BRCA mutations.

ROUND 1 RESULT: This question was considered critically important by 73.2% of patients and 55.6% of healthcare professionals who participated in the first round of survey.

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* 13. What is the natural history of untreated PC? Which patients can safely be managed expectantly? What is the clinical relevance of PSA relapse after surgery or radiation?

ROUND 1 RESULT: This question was considered critically important by 84.2% of patients and 67.1% of healthcare professionals who participated in the first round of survey.

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* 14. It is vital to better assess the balance point which specific PC patients could be managed with expectant management only and which men need radical therapy. The currently available parameters used as selection criteria may not accurately classify the true stage and grade of disease. This risk assessment (including tumor and patient factors, also in a disease recurrence setting) needs to be improved.

ROUND 1 RESULT: This question was considered critically important by 84.2% of patients and 60.3% of healthcare professionals who participated in the first round of survey.

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* 15. What is the rate of long-term side effects specified per treatment type (surgery versus radiation)? How does surgeon training and experience impact outcomes?

ROUND 1 RESULT: This question was considered critically important by 82.5% of patients and 46.6% of healthcare professionals who participated in the first round of survey.

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* 16. How does the rate of local problems compare between surgery and radiation in these cases? How often is lymph node dissection performed and What is the effect on secondary treatments and outcomes? Which patient would be candidate for a primary surgical treatment (multimodality when needed)? Surgery as primary treatment could avoid the need for hormonal therapy. Oncological and quality of life outcomes are essential in Comparison between treatments.

How could big data help us? Combining the data of available patient series using multimodality treatment of prostate cancer (PC) provides insight in the value of primary surgery (followed by radiation therapy when necessary) in advanced PC cases. On a national level, hospital treatment codes (e.g. prostatectomy + radiation versus radiation) may be linked to outcomes in cancer outcome registries. This data would ideally both cover oncological outcomes as well as quality of life data. Lymph node dissection treatment codes (followed by radiation or in combination with surgery codes) may be linked to cancer outcome registries on a national level and provide insight in the value of lymph node dissection. Combining the data of different available studies on upfront chemotherapy provides insight in the value of this strategy for newly diagnosed advanced (but M0) PC cases. On a Bigger Data level, all pharmacy codes for chemotherapy (combined with hormones) may be linked to tumour details and comorbidity codes and finally to cancer outcomes registries to show outcomes in specific patient groups for usage of chemotherapy.


ROUND 1 RESULT: This question was considered critically important by 89.5% of patients and 58.9% of healthcare professionals who participated in the first round of survey.

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* 17. Which specific patient groups benefit most of upfront chemotherapy? What are the side effects and What is impact on quality of life in real-life practice of chemotherapy in this setting? the benefit of potentially toxic upfront chemotherapy appears to be highly individual. Other factors to predict who would benefit most are needed. the benefit of chemotherapy in the subgroup patients who have recurrence after primary treatment is not known.

How could big data help us? Combining data of the limited number of studies on the role of upfront chemo available will help to improve our knowledge of which patient will benefit of chemotherapy. Correlation with genomic information (such as ‘mammaprint’ in breast cancer) will result in a personalized approach of treating patients with metastasized PC. On a Bigger Data level, all pharmacy codes for chemotherapy (combined with hormones) may be linked to tumour details and comorbidity codes and finally to cancer outcomes registries.

ROUND 1 RESULT: This question was considered critically important by 76.8% of patients and 72.6% of healthcare professionals who participated in the first round of survey.

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* 18. What is the impact of targeting the local tumour on the development of metastases and outcomes?

How could big data help us? Combining data sets on outcomes of patients with metastases who underwent local treatment (surgery or radiation) will answer the question whether men will benefit of this approach. on a Bigger data level, national diagnosis and treatment codes, combined with comorbidity codes, may be linked to cancer outcomes registries.

ROUND 1 RESULT: This question was considered critically important by 84.2% of patients and 60.3% of healthcare professionals who participated in the first round of survey.

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* 19. Which specific patient benefits from different available treatment options for CRPC? Although the predictive value for response and survival outcomes of different patient-specific and tumour-specific parameters has been studied, the individual studies don’t allow for conclusions in subgroup analyses due to power limitations.

How could big data help us? Combining data of the available studies including CRPC patients receiving different forms of treatment would provide insight into predictive factors for the success of a specific treatment strategy. Adding data from genetic studies may lead to additional options for individualized approach of CRPC patients.

ROUND 1 RESULT: This question was considered critically important by 78.6% of patients and 74.0% of healthcare professionals who participated in the first round of survey.

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* 20. How should the available therapeutical options be sequenced in order to achieve response and best outcomes in individual patients and in specific settings? effects ideally need to be maximized while limiting side effects.

How could big data help us? Meta-analysis of available studies is one option. Also, information from pharmacy coding with diagnosis and comorbidity registration may be performed. This will provide unique knowledge on both the efficacy of different treatments as well as sequencing.

ROUND 1 RESULT: This question was considered critically important by 78.9% of patients and 67.6% of healthcare professionals who participated in the first round of survey.

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* 21. Which is the best prognostic marker for prostate cancer patients treated with active surveillance?

How could big data help us? Development of novel predictive models including novel molecular markers

ROUND 1 RESULT: This question was considered critically important by 73.2% of patients and 61.6% of healthcare professionals who participated in the first round of survey.

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* 22. Are available markers able to predict stronger endpoints such as metastases-free survival in prostate cancer patients?

How could big data help us? Evaluation of prognostic markers in terms of hard clinical end-points (namely, distant metastasis and survival).

ROUND 1 RESULT: This question was considered critically important by 65.5% of patients and 48.6% of healthcare professionals who participated in the first round of survey.

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* 23. Are currently available predictive models for prostate cancer outcomes generalizable to a population level?

How could big data help us? Validation of existing predictive models based on both clinical and molecular markers

ROUND 1 RESULT: This question was considered critically important by 28.6% of patients and 42.3% of healthcare professionals who participated in the first round of survey.

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* 24. Which are the most clinically relevant functional and oncologic outcomes that should be collected during follow-up in prostate cancer patients?

How could big data help us?

• Identification of prostate cancer patients who should receive local and/or systemic salvage therapies in the case of recurrence during follow-up after primary treatment
• Assessment of the best timing for molecular imaging (i.e., PET/CT scan or bone scan) in prostate cancer patients with biochemical recurrence after primary treatment
• Evaluation of the role of biomarkers in identifying patients who should receive a more stringent follow-up and/or early administration of systemic therapies

ROUND 1 RESULT: This question was considered critically important by 75.4% of patients and 56.2% of healthcare professionals who participated in the first round of survey.

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* 25. Should we individualize follow-up according to treatment modality and disease characteristics in patients with prostate cancer?

How could big data help us?

• Identification of prostate cancer patients who should receive local and/or systemic salvage therapies in the case of recurrence during follow-up after primary treatment
• Assessment of the best timing for molecular imaging (i.e., PET/CT scan or bone scan) in prostate cancer patients with biochemical recurrence after primary treatment
• Evaluation of the role of biomarkers in identifying patients who should receive a more stringent follow-up and/or early administration of systemic therapies

ROUND 1 RESULT: This question was considered critically important by 71.9% of patients and 52.1% of healthcare professionals who participated in the first round of survey.

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* 26. Which is the best test to be used during follow-up in prostate cancer patients?

How could big data help us?

• Identification of prostate cancer patients who should receive local and/or systemic salvage therapies in the case of recurrence during follow-up after primary treatment
• Assessment of the best timing for molecular imaging (i.e., PET/CT scan or bone scan) in prostate cancer patients with biochemical recurrence after primary treatment
• Evaluation of the role of biomarkers in identifying patients who should receive a more stringent follow-up and/or early administration of systemic therapies

ROUND 1 RESULT: This question was considered critically important by 77.2% of patients and 52.8% of healthcare professionals who participated in the first round of survey.

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* 27. Which are the most effective strategies to improve functional outcomes recovery and mitigate side effects associated with systemic therapies in prostate cancer patients?

How could big data help us?

• Identification of prostate cancer patients who should receive local and/or systemic salvage therapies in the case of recurrence during follow-up after primary treatment
• Assessment of the best timing for molecular imaging (i.e., PET/CT scan or bone scan) in prostate cancer patients with biochemical recurrence after primary treatment
• Evaluation of the role of biomarkers in identifying patients who should receive a more stringent follow-up and/or early administration of systemic therapies

ROUND 1 RESULT: This question was considered critically important by 75.0% of patients and 38.0% of healthcare professionals who participated in the first round of survey.

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* 28. When should we stop follow-up in patients with localized prostate cancer?

How could big data help us?

• Identification of prostate cancer patients who should receive local and/or systemic salvage therapies in the case of recurrence during follow-up after primary treatment
• Assessment of the best timing for molecular imaging (i.e., PET/CT scan or bone scan) in prostate cancer patients with biochemical recurrence after primary treatment
• Evaluation of the role of biomarkers in identifying patients who should receive a more stringent follow-up and/or early administration of systemic therapies

ROUND 1 RESULT: This question was considered critically important by 51.8% of patients and 49.3% of healthcare professionals who participated in the first round of survey.

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* 29. Should we offer imaging during follow-up in men treated with androgen deprivation therapy for prostate cancer?

How could big data help us?

• Identification of prostate cancer patients who should receive local and/or systemic salvage therapies in the case of recurrence during follow-up after primary treatment
• Assessment of the best timing for molecular imaging (i.e., PET/CT scan or bone scan) in prostate cancer patients with biochemical recurrence after primary treatment
• Evaluation of the role of biomarkers in identifying patients who should receive a more stringent follow-up and/or early administration of systemic therapies

ROUND 1 RESULT: This question was considered critically important by 66.1% of patients and 41.4% of healthcare professionals who participated in the first round of survey.

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* 30. When should we treat patients who experience prostate cancer recurrence after primary treatment and which are the most effective therapeutic approaches?

How could big data help us?

• Identification of prostate cancer patients who should receive local and/or systemic salvage therapies in the case of recurrence during follow-up after primary treatment
• Assessment of the best timing for molecular imaging (i.e., PET/CT scan or bone scan) in prostate cancer patients with biochemical recurrence after primary treatment
• Evaluation of the role of biomarkers in identifying patients who should receive a more stringent follow-up and/or early administration of systemic therapies

ROUND 1 RESULT: This question was considered critically important by 87.5% of patients and 56.2% of healthcare professionals who participated in the first round of survey.

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* 31. Are results obtained using currently available data sources generalizable to all PCa patients?

How could big data help us?

• Identification and prospective collection of high-quality clinically relevant data and validated outcomes in PCa patients
• Data sharing with participants to provide clinicians with comparative performance feedback (i.e., means for quality improvement)
• Collection of tissue- or serum-based biomarker data that allow for the identification of novel models to predict outcomes

ROUND 1 RESULT: This question was considered critically important by 69.6% of patients and 43.8% of healthcare professionals who participated in the first round of survey.

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* 32. Which are the most clinically relevant outcomes in PCa patients that should be collected by all cancer registries?

How could big data help us?

• Identification and prospective collection of high-quality clinically relevant data and validated outcomes in PCa patients
• Data sharing with participants to provide clinicians with comparative performance feedback (i.e., means for quality improvement)
• Collection of tissue- or serum-based biomarker data that allow for the identification of novel models to predict outcomes

ROUND 1 RESULT: This question was considered critically important by 60.0% of patients and 54.8% of healthcare professionals who participated in the first round of survey.

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* 33. How can we reduce heterogeneity in the outcomes reported by different data sources?

How could big data help us?

• Identification and prospective collection of high-quality clinically relevant data and validated outcomes in PCa patients
• Data sharing with participants to provide clinicians with comparative performance feedback (i.e., means for quality improvement)
• Collection of tissue- or serum-based biomarker data that allow for the identification of novel models to predict outcomes

ROUND 1 RESULT: This question was considered critically important by 64.3% of patients and 46.5% of healthcare professionals who participated in the first round of survey.

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* 34. Should we routinely implement quality control initiatives to improve the quality of data collected?

How could big data help us?

• Identification and prospective collection of high-quality clinically relevant data and validated outcomes in PCa patients
• Data sharing with participants to provide clinicians with comparative performance feedback (i.e., means for quality improvement)
• Collection of tissue- or serum-based biomarker data that allow for the identification of novel models to predict outcomes

ROUND 1 RESULT: This question was considered critically important by 66.1% of patients and 47.9% of healthcare professionals who participated in the first round of survey.

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* 35. Can we integrate data coming from randomized trials into population-based and prospective cancer registries?

How could big data help us?

• Identification and prospective collection of high-quality clinically relevant data and validated outcomes in PCa patients
• Data sharing with participants to provide clinicians with comparative performance feedback (i.e., means for quality improvement)
• Collection of tissue- or serum-based biomarker data that allow for the identification of novel models to predict outcomes

ROUND 1 RESULT: This question was considered critically important by 49.1% of patients and 53.5% of healthcare professionals who participated in the first round of survey. 

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* 36. How can we integrate clinical and biomarker data in prostate cancer data sources to develop novel predictive tools?

How could big data help us?

• Identification and prospective collection of high-quality clinically relevant data and validated outcomes in PCa patients
• Data sharing with participants to provide clinicians with comparative performance feedback (i.e., means for quality improvement)
• Collection of tissue- or serum-based biomarker data that allow for the identification of novel models to predict outcomes

ROUND 1 RESULT: This question was considered critically important by 64.3% of patients and 45.2% of healthcare professionals who participated in the first round of survey. 

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* 37. For each part of the prostate cancer care pathway, what important baseline or pre-intervention characteristics are important? What is the best way of measuring them?

How could big data help us? Hypothesis-generating findings: Assessment of big data across countries and institutions will enable the assessment of data based on outcomes which are novel or previously unidentified, or clinical variables which until now were thought to be unimportant. These may include biomarkers, genetic or molecular markers, novel imaging techniques or classification systems, or clinical parameters which may predict outcomes better than currently available surrogate outcomes such as biochemical recurrence based on PSA. These hypothesis-generating findings can then be further assessed through prospective comparative studies.

ROUND 1 RESULT: This question was considered critically important by 62.5% of patients and 37.0% of healthcare professionals who participated in the first round of survey. 



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* 38. What are the most important outcomes across different parts of the prostate cancer care pathway? The outcome domains can be subdivided into the following groups:
a. Oncological
b. Functional
c. Process and recovery
d. Complications and/or adverse events
e. Quality of life
f. Health economic and cost effectiveness

How could big data help us? Validation of outcome measures: Big data will enable the meaningful appraisal and assessment of various outcome measures, including Patient-Reported Outcome Measures (PROMS) for functional or quality of life outcomes, as part of their validation, which would otherwise be difficult to achieve through routine clinical practice or through conventional validation studies. Whilst such an assessment normally requires a large prospective study, the availability of big data already prospectively collected across different countries, cultures and healthcare systems which is carefully matched and adjusted for homogeneity may obviate the need for a de novo large, multinational study which is expensive, time consuming and labour-intensive to set up, run and maintain, and yet still have limited external validity compared with what can be achieved through big data.

ROUND 1 RESULT: This question was considered critically important by 64.3% of patients and 54.8% of healthcare professionals who participated in the first round of survey. 

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* 39. What is the best way of measuring those outcomes identified above (question 37)? The outcome measures can be sub-stratified further into the following domains: a. Definitions (e.g. biochemical recurrence following radical prostatectomy or radical radiotherapy)
b. Thresholds
c. Outcome measuring instrument (including PROMS for functional or quality of life outcomes)
d. Metrics of measurement (change from baseline or discrete endpoints)
e. Reporting statistic
f. Time point of measurement

How could big data help us? Understanding how important outcomes relate to each other: By combining prospectively collected data across different countries over long periods of time (often more than 20 years), we may be able to better understand how different important outcomes across the patient care pathway spectrum influence each other, and if they are predictive of each other. For instance, is there a better way of risk-stratifying patients with localised prostate cancer than presently available systems based on clinical stage, grade and PSA? Can we develop an individualised predictive nomogram based on the integration of pre-treatment, intra-treatment, and early post-treatment variables to predict oncological and functional outcomes in the short and long-term? The availability of large datasets already prospectively collected across different countries over a long period of time is desirable and advantageous, because they will provide essential background data, and guide and inform future confirmatory prospective studies.

ROUND 1 RESULT: This question was considered critically important by 58.2% of patients and 56.9% of healthcare professionals who participated in the first round of survey. 

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* 40. How best to co-ordinate care between multiple health professionals during and following completion of treatment for prostate cancer?

How could big data help us?

Need to map data back against cancer care pathway [see Figure 1] to improve understanding and inform the design and delivery of better care. 


More complete and joined up understanding of what the person experiences (physical and psychosocial) as a result of diagnosis and treatment for prostate cancer and how / when we can intervene to improve this.

ROUND 1 RESULT: This question was considered critically important by 83.9% of patients and 41.7% of healthcare professionals who participated in the first round of survey. 

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* 41. Which patients [demographics] experience side effects and late effects of different treatment modalities for prostate cancer? What are these side effects and late effects? When do they occur in the cancer care and aftercare pathway?

How could big data help us?

Need to map data back against cancer care pathway [see Figure 1] to improve understanding and inform the design and delivery of better care.

More complete and joined up understanding of what the person experiences (physical and psychosocial) as a result of diagnosis and treatment for prostate cancer and how / when we can intervene to improve this.

ROUND 1 RESULT: This question was considered critically important by 66.7% of patients and 48.6% of healthcare professionals who participated in the first round of survey. 

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* 42. How do we routinely collect cancer survivorship data including current disease status, functional ability, current medications, co-morbidities, quality of life, psychological wellbeing, social outcomes, cancer treatment history and modalities used?

How could big data help us?

Need to map data back against cancer care pathway [see Figure 1] to improve understanding and inform the design and delivery of better care.

More complete and joined up understanding of what the person experiences (physical and psychosocial) as a result of diagnosis and treatment for prostate cancer and how / when we can intervene to improve this.

ROUND 1 RESULT: This question was considered critically important by 58.9% of patients and 42.3% of healthcare professionals who participated in the first round of survey. 

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* 43. Which individuals are most likely to drop out of employment during and following completion of treatment for prostate cancer? When does this occur in the cancer care and aftercare pathway?

How could big data help us?

Need to map data back against cancer care pathway [see Figure 1] to improve understanding and inform the design and delivery of better care.

More complete and joined up understanding of what the person experiences (physical and psychosocial) as a result of diagnosis and treatment for prostate cancer and how / when we can intervene to improve this.

ROUND 1 RESULT: This question was considered critically important by 26.8% of patients and 30.1% of healthcare professionals who participated in the first round of survey. 

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* 44. What support is needed for physical late effects (include musculoskeletal issues, fatigue, last of stamina, urinary / bowel problems, lymphedema, premature menopause, cognitive deficits and sexual dysfunction) following detection? When is this needed in the cancer care and aftercare pathway? What triggers the delivery of this support?

How could big data help us?

Need to map data back against cancer care pathway [see Figure 1] to improve understanding and inform the design and delivery of better care.

More complete and joined up understanding of what the person experiences (physical and psychosocial) as a result of diagnosis and treatment for prostate cancer and how / when we can intervene to improve this.

ROUND 1 RESULT: This question was considered critically important by 66.1% of patients and 39.7% of healthcare professionals who participated in the first round of survey. 

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* 45. What support is needed for psychosocial late effects (fear, anxiety, distress, ptsd, employment) (+ = strengthened relationships, empowerment, appreciation of life) following detection? When is this needed in the cancer care and aftercare pathway? What triggers the delivery of this support?

How could big data help us?

Need to map data back against cancer care pathway [see Figure 1] to improve understanding and inform the design and delivery of better care.

More complete and joined up understanding of what the person experiences (physical and psychosocial) as a result of diagnosis and treatment for prostate cancer and how / when we can intervene to improve this.

ROUND 1 RESULT: This question was considered critically important by 60.7% of patients and 34.2% of healthcare professionals who participated in the first round of survey. 

Additional questions proposed by the patients and healthcare professionals during the first round of survey.

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* 46. What are the oncologic and functional outcomes of patients with clinically localized prostate cancer undergoing experimental therapies that are not currently recommended by international guidelines (e.g., high-intensity focused ultrasound) as compared to the standard of care?

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* 47. Are PSA screening policies for men aged 50 years and early diagnosis improving survival as compared to opportunistic screening?

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* 48. How can we improve patient-physician communication in patients diagnosed with prostate cancer and what is its impact on quality of life patient-reported outcomes?

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* 49. Are men aged 50-75 years old who underwent vasectomy at increased incidence of prostate cancer as compared to individuals who did not receive a vasectomy?

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* 50. Should there be specialized Prostate Cancer Centers certified and re-certified according to the same criteria throughout Europe with public reporting of identical outcomes?

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* 51. What is the risk of prostate cancer death for men on five alpha reductase inhibitors?

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* 52. How do various PC data-sources/databases compare in terms of quality, size, geography, and overlap?

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* 53. How does state-of-the-art risk assessment and treatment regimes for PC compare across major cancer centers, and how has this changed over the past decade?

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* 54. What is the impact of satellite low-volume lesions next to the index lesion in patients suitable for focal therapy?

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* 55. How does focal therapy compare to standard of care in terms of oncological and functional outcomes in patients affected by localized prostate cancer?

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* 56. What are the rates of incidence, prevalence, and mortality of prostate cancer across Europe?