Implementation of Big Imaging Data Pipeline Adhering to FAIR Principles for Federated Machine Learning in Oncology

Abstract

Cancer is a fatal disease and one of the leading causes of death worldwide. The cure rate in cancer treatment remains low; hence, cancer treatment is gradually shifting toward personalized treatment. Artificial intelligence (AI) and radiomics have been recognized as one of the potential areas of research in personalized medicine in oncology. Several researchers have identified the capabilities of AI and radiomics to characterize phenotype and there by predict the outcome of treatment in oncology. Although AI and radiomics have shown promising initial results in diagnosis and treatment in oncology, these technologies are also facing challenges of standardization and scalability. In the last few years, researchers have been trying to develop a research infrastructure for federated machine learning that increases the usability of Big Data for clinical research. These research infrastructures are based on the findable, accessible, interoperable, and reusable (i.e., FAIR) data principles. The India-Dutch "big imaging data approach for oncology in a Netherlands India collaboration" (BIONIC) is a jointly funded initiative by the Dutch Research Council (NWO) and the Indian Ministry of Electronics and Information Technology (MeitY), aiming to introduce radiomic-based research into clinical environments using federated machine learning on geographically dispersed collections of FAIR data. This article described a prototype end-to-end research infrastructure implemented through the BIONIC partnership into a leading cancer care public hospital in India.

Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology

“…Pipelines can also accommodate specific needs, such as those related to the analysis of "big data", with their corresponding challenges of standardization and scalability. As described in [28], in a clinical oncology setting, this may require a research infrastructure for federated ML based on the findable, accessible, interoperable, and reusable (FAIR) principles. Alternatively, we can aspire to automate the ML pipeline definition using Automated ML (AutoML) principles, as in [29], where Su and co-workers used a Tree-based Pipeline Optimization Tool (TPOT) in the process of selecting radiomics features predictive of mutations associated with midline gliomas.…”

Transfer learning with BERT and ClinicalBERT models for multiclass classification of radiology imaging reports

“…The data selection process was the same as in our previous study (27). Reports were anonymized and cleaned using a Python script (27)(28)(29).…”

Big Data Architectures for FAIR-compliant Repositories: A Systematic Review

“…The authors propose a pipeline that consists of five modules to collect, annotate, analyze, interpret, and publish brain and physical data and its associated metadata. In Jha et al (2022), the authors propose a pipeline to extract data and metadata from several healthcare systems. A series of Python scripts is employed to extract image features and perform data cleaning and integration, storing the result as data triples.…”

“…For instance, applying the FAIR Principles to health-related data is a common occurrence. Between the studies of Group 2 (Section 3.2), the following adhere to this context: (i) Pestryakova et al (2022), with data from COVID-19 research publications; (ii) Brůha et al (2022), using health and brain data; (iii) Jha et al (2022), employing oncology related data; (iv) Borges et al (2022), using data from COVID-19 patients; and (v) Deng et al (2022), leveraging immunology data. Additionally, both BigFAIR [Castro et al 2022a] and CloudFAIR [Castro et al 2022b] use COVID-19 patients data in their experiments, whereas GADDS [Vazquez et al 2022] employs fiber cell tissue research data during its instantiation.…”