Integrating Heterogeneous Biomedical Data for Cancer Research: the CARPEM infrastructure

Rance, Bastien; Canuel, Vincent; Countouris, Hector; Laurent‐Puig, Pierre; Burgun, Anita

doi:10.4338/aci-2015-09-ra-0125

Cited by 27 publications

(15 citation statements)

References 22 publications

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“…All features were extracted from our clinical data warehouse (CDW), that relies on the Informatics for Integrating Biology and the Bedside (i2b2) model - an open source infrastructure developed by Harvard Medical School and adopted by more than 130 academic hospitals around the world 34 , 35 . The i2b2 warehouse uses an Entity-Attribute-Value (EAV) data model for its adaptability and dynamic nature.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer

Bibault¹,

Giraud²,

Housset³

et al. 2018

Sci Rep

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169

129

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Treatment of locally advanced rectal cancer involves chemoradiation, followed by total mesorectum excision. Complete response after chemoradiation is an accurate surrogate for long-term local control. Predicting complete response from pre-treatment features could represent a major step towards conservative treatment. Patients with a T2-4 N0-1 rectal adenocarcinoma treated between June 2010 and October 2016 with neo-adjuvant chemoradiation from three academic institutions were included. All clinical and treatment data was integrated in our clinical data warehouse, from which we extracted the features. Radiomics features were extracted from the tumor volume from the treatment planning CT Scan. A Deep Neural Network (DNN) was created to predict complete response, as a methodological proof-of-principle. The results were compared to a baseline Linear Regression model using only the TNM stage as a predictor and a second model created with Support Vector Machine on the same features used in the DNN. Ninety-five patients were included in the final analysis. There were 49 males (52%) and 46 females (48%). Median tumour size was 48 mm (15–130). Twenty-two patients (23%) had pathologic complete response after chemoradiation. One thousand six hundred eighty-three radiomics features were extracted. The DNN predicted complete response with an 80% accuracy, which was better than the Linear Regression model (69.5%) and the SVM model (71.58%). Our model correctly predicted complete response after neo-adjuvant rectal chemoradiotherapy in 80% of the patients of this multicenter cohort. Our results may help to identify patients who would benefit from a conservative treatment, rather than a radical resection.

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Section: Methodsmentioning

confidence: 99%

Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer

Bibault¹,

Giraud²,

Housset³

et al. 2018

Sci Rep

Self Cite

169

129

View full text Add to dashboard Cite

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“…When the two committees first met, CARPEM was creating its own TR platform by transforming existing clinical biorepositories into a unique research-oriented one integrating large collections of patient samples together with clinical, pathologic and outcome data stored in a unique data warehouse [15, 21, 22]. The previous patient information process was inappropriate because many human samples were collected during the care pathway without gathering any informed consent for research purposes or were accompanied by an obsolete care one.…”

Section: Resultsmentioning

confidence: 99%

“…A growing body of literature highlights the need to rethink the current bioethical and regulatory frameworks [7, 10–14] and as Nicol et al [13] postulate, to reformulate in this context the classical tensions between community welfare and individual liberty, risk and benefit, and autonomy and paternalism. Indeed, on one hand, data and samples may be re-used in future unknown research and on the other, genetic or genomic analysis may result in a diagnostic or therapeutic application and in proposals to participate in a clinical trial of precision medicine [15–17]. …”

Section: Introductionmentioning

confidence: 99%

Partnering with patients in translational oncology research: ethical approach

et al. 2017

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BackgroundThe research program CARPEM (cancer research and personalized medicine) brings together the expertise of researchers and hospital-based oncologists to develop translational research in the context of personalized or “precision” medicine for cancer. There is recognition that patient involvement can help to take into account their needs and priorities in the development of this emerging practice but there is currently no consensus about how this can be achieved. In this study, we developed an empirical ethical research action aiming to improve patient representatives’ involvement in the development of the translational research program together with health professionals. The aim is to promote common understanding and sharing of knowledge between all parties and to establish a long-term partnership integrating patient’s expectations.MethodsTwo distinct committees were settled in CARPEM: an “Expert Committee”, gathering healthcare and research professionals, and a “Patient Committee”, gathering patients and patient representatives. A multidisciplinary team trained in medical ethics research ensured communication between the two committees as well as analysis of discussions, minutes and outputs from all stakeholders.ResultsThe results highlight the efficiency of the transfer of knowledge between interested parties. Patient representatives and professionals were able to identify new ethical challenges and co-elaborate new procedures to gather information and consent forms for adapting to practices and recommendations developed during the process. Moreover, included patient representatives became full partners and participated in the transfer of knowledge to the public via conferences and publications.ConclusionsEmpirical ethical research based on a patient-centered approach could help in establishing a fair model for coordination and support actions during cancer research, striking a balance between the regulatory framework, researcher needs and patient expectations. Our approach addresses the concept of translational ethics as a way to handle the main remaining gap between combining care and research activities in the medical pathway and the existing framework.

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“…In the case of the Erlanger University Hospital IDR [ 25 ], terminology is mapped using vocabularies that are manually curated and mapped through an automatic workflow that processes the raw data to the final data warehouse format. Other IDRs that make use of multiple terminologies are health care enterprise repository for ontological narration [ 16 ], Research for PErsonalized Medicine (CARPEM) [ 28 ], and STRIDE [ 18 ], but further details of their mapping processes were not available.…”

Section: Resultsmentioning

confidence: 99%

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

Gagalova¹,

Elizalde²,

Portales-Casamar³

et al. 2020

JMIR Form Res

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Background Integrated data repositories (IDRs), also referred to as clinical data warehouses, are platforms used for the integration of several data sources through specialized analytical tools that facilitate data processing and analysis. IDRs offer several opportunities for clinical data reuse, and the number of institutions implementing an IDR has grown steadily in the past decade. Objective The architectural choices of major IDRs are highly diverse and determining their differences can be overwhelming. This review aims to explore the underlying models and common features of IDRs, provide a high-level overview for those entering the field, and propose a set of guiding principles for small- to medium-sized health institutions embarking on IDR implementation. Methods We reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2020, and selected those that specifically describe IDR architectures. Of 255 shortlisted articles, we found 34 articles describing 29 different architectures. The different IDRs were analyzed for common features and classified according to their data processing and integration solution choices. Results Despite common trends in the selection of standard terminologies and data models, the IDRs examined showed heterogeneity in the underlying architecture design. We identified 4 common architecture models that use different approaches for data processing and integration. These different approaches were driven by a variety of features such as data sources, whether the IDR was for a single institution or a collaborative project, the intended primary data user, and purpose (research-only or including clinical or operational decision making). Conclusions IDR implementations are diverse and complex undertakings, which benefit from being preceded by an evaluation of requirements and definition of scope in the early planning stage. Factors such as data source diversity and intended users of the IDR influence data flow and synchronization, both of which are crucial factors in IDR architecture planning.

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Integrating Heterogeneous Biomedical Data for Cancer Research: the CARPEM infrastructure

Cited by 27 publications

References 22 publications

Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer

Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer

Partnering with patients in translational oncology research: ethical approach

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

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