Artificial intelligence (AI) and big data in cancer and precision oncology

Dlamini, Zodwa; Francies, Flavia Zita; Hull, Rodney; Marima, Rahaba

doi:10.1016/j.csbj.2020.08.019

Cited by 196 publications

(116 citation statements)

References 105 publications

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“…When all models were constructed using a given set of clinical inputs, the ANN model was clearly superior to other forecasting models. Furthermore, unlike previous works in which the analyses were performed using a dataset for a single medical center, our study used prospective and longitudinal data from multiple medical centers, which provides a more accurate depiction of current treatment for breast cancer patients after surgery [ 7 , 8 , 9 ]. Additionally, in contrast with previous series studies that have used data for a single institution, this study used registry data to obtain a more accurate depiction of breast cancer surgery treatment in large populations.…”

Section: Discussionmentioning

confidence: 99%

“…Although many forecasting models for predicting outcomes after breast cancer surgery have been proposed in recent years, models for predicting recurrence within 10 years after breast cancer surgery have had major shortcomings: (1) recently proposed forecasting models have lower prediction accuracy compared to conventional models [ 6 , 7 ], (2) proposed forecasting models require use of health insurance claims data, which may be unavailable for real-time use in clinical settings [ 8 , 9 ], and (3) predictions of postoperative recurrence after breast surgery do not consider demographic characteristics, clinical characteristics, quality of care and preoperative health-related quality of life [ 10 , 11 ]. Successful applications of statistical data mining and machine learning methods have been demonstrated in the medical field [ 7 , 8 , 9 , 10 , 11 ]. Clinical and genetic information can be used to improve precision in estimating prognosis and to obtain a comprehensive overview of a disease.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study

Lou

Hou

Chang

et al. 2020

Cancers

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No studies have discussed machine learning algorithms to predict recurrence within 10 years after breast cancer surgery. This study purposed to compare the accuracy of forecasting models to predict recurrence within 10 years after breast cancer surgery and to identify significant predictors of recurrence. Registry data for breast cancer surgery patients were allocated to a training dataset (n = 798) for model development, a testing dataset (n = 171) for internal validation, and a validating dataset (n = 171) for external validation. Global sensitivity analysis was then performed to evaluate the significance of the selected predictors. Demographic characteristics, clinical characteristics, quality of care, and preoperative quality of life were significantly associated with recurrence within 10 years after breast cancer surgery (p < 0.05). Artificial neural networks had the highest prediction performance indices. Additionally, the surgeon volume was the best predictor of recurrence within 10 years after breast cancer surgery, followed by hospital volume and tumor stage. Accurate recurrence within 10 years prediction by machine learning algorithms may improve precision in managing patients after breast cancer surgery and improve understanding of risk factors for recurrence within 10 years after breast cancer surgery.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study

Lou

Hou

Chang

et al. 2020

Cancers

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“…AmCad BioMed Corporation has also developed and received FDA approval for AmCAD-UT, primarily for the detection of thyroid cancer [48]. In July 2017, the Second Reader type CADx (QuantX) by Quantitative Insights received its first FDA approval [49]. This device is designed to target breast magnetic resonance imaging (MRI) and assist physicians in the differential diagnosis of breast cancer.…”

Section: Radiologymentioning

confidence: 99%

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Hamamoto

Suvarna

Yamada³

et al. 2020

Cancers

136

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In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, “precision medicine,” a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

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“…AI presents a new opportunity to enhance patient safety at the clinical level by identifying adverse drug reactions, preventing prescription errors and promoting the practice of patient specific treatments [10,23,24].…”

Section: Stage 4: Approval Post Release Monitoring and Marketingmentioning

confidence: 99%

The Integration of Artificial Intelligence in Drug Discovery and Development

Archer

Germain

2021

International Journal of Digital Health

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The application of Artificial Intelligence (AI) has risen astronomically within modern society, but its influence within the pharmaceutical and healthcare sectors is set to be hugely significant. Artificial intelligence can be defined as the idea and development of computers that are able to perform tasks traditionally thought of as requiring human intelligence and subsequent learning. In this article, we will bring to light some of the diverse roles and select limitations of AI, from drug discovery and development, to marketing and purposing drugs for clinical use. The pathway of drug discovery is well documented to be time and financially intensive. With the digitsation of data and advancements in technology combined with the cumbersome process of drug discovery and subsequent development, it creates a perfect environment for integration of AI. AI can be implemented in all stages of drug discovery from helping to identify potential targets and molecules through to aiding the trial design and finally it has uses with ongoing monitoring of post market products. We consider this to be pivotal time in using AI to revolutionise the discovery process, allowing us to move away from traditional drug discovery with high value drugs and towards an enhanced number of discoveries with shorter lead time. It should be appreciated that there will be challenges still posed by this integration; data acquisition and specifically high-quality data, interpretation of the data and whether this can translate into real world results.

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Artificial intelligence (AI) and big data in cancer and precision oncology

Abstract: Graphical abstract

Cited by 196 publications

References 105 publications

Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study

Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

The Integration of Artificial Intelligence in Drug Discovery and Development

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