Introducing PIONEER: a project to harness big data in prostate cancer research

Omar, Muhammad Imran; Roobol, Monique J.; Ribal, María J.; Abbott, Thomas; Agapow, Paul; Araujo, Sonia; Asiimwe, Alex; Auffray, Charles; Balaur, Irina; Beyer, Katharina; Bernini, Chiara; Bjartell, Anders; Briganti, Alberto; Butler-Ransohoff, John-Edward; Campi, Riccardo; Cavelaars, Marinel; Meulder, Bertrand De; Devecseri, Zsuzsanna; Voß, Marc D.; Dimitropoulos, Konstantinos; Evans-Axelsson, Susan; Franks, Billy; Fullwood, Louise; Horgan, Denis; Smith, Emma Jane; Kiran, Amit; Kivinummi, Kati; Lambrecht, Mark; Lancet, Doron; Lindgren, Peter; MacLennan, Sara; MacLennan, Steven; Nogueira, Maria Manuela; Moen, Fredrik; Moinat, Maxim; Papineni, Kishore; Reich, Christian; Reiche, Kristin; Rogiers, Stijn; Sartini, Claudio; Bochove, Kees van; Diggelen, Femke van; Hemelrijck, Mieke Van; Poppel, Hendrik Van; Zong, Jihong; N’Dow, James; Andersson, Emelie; Arala, Heidi; Auvinen, Anssi; Bangma, Chris H.; Burke, Danny; Cardone, Antonella; Casariego, J.; Cuperus, Guido; Dabestani, Saeed; Esperto, Francesco; Fossati, Nicola; Fridhammar, Adam; Gandaglia, Giorgio; Tandefelt, Delila Gasi; Horn, Friedemann; Huber, Johannes; Hugosson, Jonas; Huisman, Henkjan; Josefsson, Andreas; Kilkku, Olavi; Kreuz, Markus; Lardas, Michael; Lawson, Joe; Lefresne, Florence; Lejeune, S.; Longden-Chapman, Elaine; McVie, Gordon; Moris, Lisa; Mottet, Nicolas; Murtola, T.; Nicholls, Charlie; Pang, Karl H.; Pascoe, Katie; Picozzi, M.; Plass, Karin; Pohjanjousi, Pasi; Reaney, Matthew; Remmers, Sebastiaan; Robinson, Paul; Schalken, Jack A.; Schravendeel, Max; Seisen, Thomas; Servan, Angela; Shiranov, Kirill; Snijder, Robert; Steinbeisser, Carl; Taibi, Nesrine; Talala, Kirsi; Tilki, Derya; Broeck, Thomas Van den; Vassilev, Zdravko P.; Voima, Olli; Vradi, Eleni; Waldeck, Reg; Weistra, Ward; Willemse, Peter‐Paul M.; Wirth, Manfred P.; Wolfinger, Russ; Kermani, Nazanin Zounemat

doi:10.1038/s41585-020-0324-x

Cited by 29 publications

(20 citation statements)

References 36 publications

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“…Initiatives where data from all over the world (from both genders, many ethnicities, and many age groups) are combined into a central database produce results that are less biased. For example, in the field of prostate cancer such projects are Movember GAP3 ( [69], combining 25 different cohorts) and PIONEER ( [70], with 32 public and private partners). Next to privacy regulations, machine-readability and bias, scientists should also work on building trust in AI.…”

Section: Discussionmentioning

confidence: 99%

Literature Analysis of Artificial Intelligence in Biomedicine

Hulsen¹

2021

Preprint

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Artificial Intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning, deep learning and neural networks. AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of ‘big data’ and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-5 of diseases studied using AI; the United States, China, United Kingdom, South Korea and Canada are publishing the most articles in AI research; MIT is the world’s leading university in AI research; and convolutional neural networks are by far the most popular deep learning algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.

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Section: Discussionmentioning

confidence: 99%

Literature Analysis of Artificial Intelligence in Biomedicine

Hulsen¹

2021

Preprint

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“…In the face of such a paucity of data, the PIONEER Consortium, which is a novel project of the Innovative Medicine Initiative's (IMI's) "Big Data for Better Outcomes" program with the mission to transform PCa care with particular focus on improving cancer-related outcomes, health system e ciency and the quality of health and social care across Europe, aims to assess the question "Which are the longterm outcomes of prostate cancer patients undergoing non-interventional management (i.e., watchful waiting) and what is the impact of comorbidities and life expectancy?". This question emerged as the one with the second highest agreement score among different stakeholders after an international consensus to identify and prioritize the most important questions in the eld of PCa performed within the IMI PIONEER project [9]. The EAU Prostate Cancer Guideline panel and other prostate cancer Key Opinion Leaders were consulted to propose the most critical questions in the eld of PCa to be answered using big data.…”

Section: Introductionmentioning

confidence: 99%

“…The question "Which are the long-term outcomes of prostate cancer patients undergoing non-interventional management (i.e., watchful waiting) and what is the impact of comorbidities and life expectancy?" was ranked second among the 56 questions identi ed as "critically important" by the PIONEER project [9]. 5.…”

Section: Introductionmentioning

confidence: 99%

Research Protocol for an Observational Health Data Analysis to Assess the Long-term Outcomes of Prostate Cancer Patients Undergoing Non-Interventional Management (i.e., Watchful Waiting) and the Impact of Comorbidities and Life Expectancy – PIONEER IMI’s “Big Data for Better Outcomes” program

Gandaglia

Bochove²,

Bjartell³

et al. 2021

Preprint

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This is a study protocol for an observational health data analysis, submitted as a preprint to facilitate transparency and open science. Watchful waiting (WW) represents a deferred treatment option for prostate cancer (PCa) patients when curative treatment seems overtreatment right from the outset. Patients are ‘watched’ for the development of local or systemic progression with disease-related symptoms, at which stage they are then treated palliatively according to their symptoms, in order to maintain quality of life. When choosing WW, it is important to adequately assess life expectancy of patients. Although previous studies reported the outcomes of PCa patients managed with WW, which is the impact of individual patient characteristics and comorbidities on long-term outcomes is still largely unknown. The PIONEER, which is a novel project of the Innovative Medicine Initiative’s (IMI’s) “Big Data for Better Outcomes” program with the mission to transform PCa care with particular focus on improving cancer-related outcomes, health system efficiency and the quality of health and social care across Europe, aims at assessing which are the long-term outcomes of PCa patients undergoing WW overall and after stratification according to disease characteristics, comorbidities and life expectancy. Of note, this topic emerged as the second one with the highest agreement score among different stakeholders after an international consensus to identify and prioritize the most important questions in the field of PCa. This study aims to describe demographics, clinical characteristics and estimate outcomes of PCa patients under delayed treatment (WW) across a network of databases in the overall population and subgroups of patients identified by individual disease characteristics, demographics and comorbidities. The study will rely on large observational data, namely population-based registries, electronic health records and insurance claims data. The study will be an observational cohort study based on routinely collected health care data which has been mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).

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“…Outcomes" program, aims to improve PCa care across Europe through the application of big data analytics (7). Within the context of PIONEER, the objective of the current study is to apply data driven strategies to identify predictors for symptomatic progression, hospitalization, ER visit, treatment initiation and death in order to support clinical decisions making for the management of WW.…”

Section: Introduction Introductionmentioning

confidence: 99%

Development and validation of patient-level prediction models for symptoms, hospitalization and treatment initiation amongst prostate cancer patients on watchful waiting

Remmers

Lly

Schimmelpfennig

et al. 2021

Preprint

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The objective of this study is to develop and validate patient-level prediction models for patients on watchful waiting (WW) estimating the risk of developing symptomatic progression, hospitalization, ER visit, initiation of curative or palliative treatment, and survival. Estimation for all clinical models will be done based on 1) age and clinical measurements (e.g., PSA) 6 months before diagnosis, 2) age, clinical measurements 6 months before diagnosis, and clinical conditions one year before diagnosis. Finally, a clinically usable model will be developed based on expert clinical input. All prediction models will be implemented using Lasso logistic regression for the time at risk analyses.

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Introducing PIONEER: a project to harness big data in prostate cancer research

Cited by 29 publications

References 36 publications

Literature Analysis of Artificial Intelligence in Biomedicine

Literature Analysis of Artificial Intelligence in Biomedicine

Development and validation of patient-level prediction models for symptoms, hospitalization and treatment initiation amongst prostate cancer patients on watchful waiting

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