From multisource data to clinical decision aids in radiation oncology: The need for a clinical data science community

Kaźmierska, Joanna; Hope, Andrew; Spezi, Emiliano; Beddar, Sam; Nailon, William H.; Osong, Biche; Ankolekar, Anshu; Choudhury, Ananya; Dekker, André; Redalen, Kathrine Røe; Traverso, Alberto

doi:10.1016/j.radonc.2020.09.054

Cited by 26 publications

(21 citation statements)

References 74 publications

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“…Incorporating powerful AI algorithms has been instrumental in changing the presentation and common sense of high-intensity radiation oncology. However, this is possible by establishing a clinical scientific community with radiation oncology [46]. 4 Computational and Mathematical Methods in Medicine 3.2.…”

Section: Applications Of Big Data and Aimentioning

confidence: 99%

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Yang

Islam

Noor

et al. 2021

Computational and Mathematical Methods in Medicine

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Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people’s lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.

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Section: Applications Of Big Data and Aimentioning

confidence: 99%

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Yang

Islam

Noor

et al. 2021

Computational and Mathematical Methods in Medicine

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“…CT images in particular might not accurately reflect the biological tumour characteristics due to insu cient resolution, sensitivity to acquisition parameters and noise 48,49 , as well as the source of image contrast, which is essentially electron density of the tissue which demonstrates little texture at current image scales. This highlights the broader need of greater collaboration between ML researchers, clinicians and physicists, also in data selection and experiment design -with reciprocal feedback 50,51 .…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data

Haibe‐Kains¹,

Kazmierski²,

Welch³

et al. 2021

Preprint

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Accurate prognosis for an individual patient is a key component of precision oncology. Recent advances in machine learning have enabled the development of models using a wider range of data, including imaging. Radiomics aims to extract quantitative predictive and prognostic biomarkers from routine medical imaging, but evidence for computed tomography radiomics for prognosis remains inconclusive. We have conducted an institutional machine learning challenge to develop an accurate model for overall survival prediction in head and neck cancer using clinical data etxracted from electronic medical records and pre-treatment radiological images, as well as to evaluate the true added benefit of radiomics for head and neck cancer prognosis. Using a large, retrospective dataset of 2,552 patients and a rigorous evaluation framework, we compared 12 different submissions using imaging and clinical data, separately or in combination. The winning approach used non-linear, multitask learning on clinical data and tumour volume, achieving high prognostic accuracy for 2-year and lifetime survival prediction and outperforming models relying on clinical data only, engineered radiomics and deep learning. Combining all submissions in an ensemble model resulted in improved accuracy, with the highest gain from a image-based deep learning model. Our results show the potential of machine learning and simple, informative prognostic factors in combination with large datasets as a tool to guide personalized cancer care.

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“…Despite all this activity and despite that some ML tools (such as linear regression or atlas‐based segmentation) have been available and used for some time, AI is still in its early days of clinical application in MI and RO, and there are a range of issues and challenges still to be considered and solved 3,4,6,9,12,14,17 . These include potential hazards and risks in clinical use, which require: clear and safe development of AI tools and applications and a responsibility on developers and researchers to provide sufficient detail in documentation and in research papers; rigorous validation and testing of both accuracy and limitations; risk assessment and management of clinical implementation; on‐going quality assurance; and appropriate evolving education and training.…”

Section: Challenges and Requirementsmentioning

confidence: 99%