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Review

The Role of Artificial Intelligence in Early Cancer Diagnosis

by 1,2, 1,3 and 1,4,5,*,†
1
Early Diagnosis and Detection Centre, The Royal Marsden NHS Foundation Trust, Chelsea, London SW3 6JJ, UK
2
Department of Surgery and Cancer, Imperial College London, London SW7 2BX, UK
3
Artificial Intelligence for Healthcare Centre for Doctoral Training, Imperial College London, London SW7 2BX, UK
4
National Heart and Lung Institute, Imperial College London, London SW7 2BX, UK
5
Early Diagnosis and Detection, Genetics and Epidemiology, The Institute for Cancer Research, London SW7 3RP, UK
*
Author to whom correspondence should be addressed.
Current address: Consultant Respiratory Physician, The Royal Marsden Hospital, Lung Unit, 203 Fulham Road, Chelsea, London SW3 6JJ, UK.
Academic Editors: Hayley C. Whitaker and Kelly Coffey
Cancers 2022, 14(6), 1524; https://doi.org/10.3390/cancers14061524
Received: 11 February 2022 / Revised: 8 March 2022 / Accepted: 10 March 2022 / Published: 16 March 2022
(This article belongs to the Special Issue Early Diagnosis of Cancer)
Diagnosing cancer at an early stage increases the chance of performing effective treatment in many tumour groups. Key approaches include screening patients who are at risk but have no symptoms, and rapidly and appropriately investigating those who do. Machine learning, whereby computers learn complex data patterns to make predictions, has the potential to revolutionise early cancer diagnosis. Here, we provide an overview of how such algorithms can assist doctors through analyses of routine health records, medical images, biopsy samples and blood tests to improve risk stratification and early diagnosis. Such tools will be increasingly utilised in the coming years.
Improving the proportion of patients diagnosed with early-stage cancer is a key priority of the World Health Organisation. In many tumour groups, screening programmes have led to improvements in survival, but patient selection and risk stratification are key challenges. In addition, there are concerns about limited diagnostic workforces, particularly in light of the COVID-19 pandemic, placing a strain on pathology and radiology services. In this review, we discuss how artificial intelligence algorithms could assist clinicians in (1) screening asymptomatic patients at risk of cancer, (2) investigating and triaging symptomatic patients, and (3) more effectively diagnosing cancer recurrence. We provide an overview of the main artificial intelligence approaches, including historical models such as logistic regression, as well as deep learning and neural networks, and highlight their early diagnosis applications. Many data types are suitable for computational analysis, including electronic healthcare records, diagnostic images, pathology slides and peripheral blood, and we provide examples of how these data can be utilised to diagnose cancer. We also discuss the potential clinical implications for artificial intelligence algorithms, including an overview of models currently used in clinical practice. Finally, we discuss the potential limitations and pitfalls, including ethical concerns, resource demands, data security and reporting standards. View Full-Text
Keywords: early diagnosis; artificial intelligence; machine learning; deep learning; screening early diagnosis; artificial intelligence; machine learning; deep learning; screening
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MDPI and ACS Style

Hunter, B.; Hindocha, S.; Lee, R.W. The Role of Artificial Intelligence in Early Cancer Diagnosis. Cancers 2022, 14, 1524. https://doi.org/10.3390/cancers14061524

AMA Style

Hunter B, Hindocha S, Lee RW. The Role of Artificial Intelligence in Early Cancer Diagnosis. Cancers. 2022; 14(6):1524. https://doi.org/10.3390/cancers14061524

Chicago/Turabian Style

Hunter, Benjamin, Sumeet Hindocha, and Richard W. Lee. 2022. "The Role of Artificial Intelligence in Early Cancer Diagnosis" Cancers 14, no. 6: 1524. https://doi.org/10.3390/cancers14061524

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