Advances in and the Applicability of Machine Learning-Based Screening and Early Detection Approaches for Cancer: A Primer

Despite the efforts of the past decades, cancer is still among the key drivers of global mortality. To increase the detection rates, screening programs and other efforts to improve early detection were initiated to cover the populations at a particular risk for developing a specific malignant condit...

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Published inCancers Vol. 14; no. 3; p. 623
Main Authors Benning, Leo, Peintner, Andreas, Peintner, Lukas
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 26.01.2022
MDPI
Subjects
Artificial intelligence
Biochemical markers
Breast cancer
Cancer
Cardiovascular diseases
Cellular biology
Computer applications
Deep learning
Disease
Learning algorithms
Machine learning
Medical screening
Mortality
Neural networks
Prostate cancer
Review
Skin cancer
Trends
deep learning
CNN
DNN
cancer diagnostics
machine learning
high throughput
artificial intelligence
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ISSN2072-6694
2072-6694
DOI10.3390/cancers14030623

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Summary:Despite the efforts of the past decades, cancer is still among the key drivers of global mortality. To increase the detection rates, screening programs and other efforts to improve early detection were initiated to cover the populations at a particular risk for developing a specific malignant condition. These diagnostic approaches have, so far, mostly relied on conventional diagnostic methods and have made little use of the vast amounts of clinical and diagnostic data that are routinely being collected along the diagnostic pathway. Practitioners have lacked the tools to handle this ever-increasing flood of data. Only recently, the clinical field has opened up more for the opportunities that come with the systematic utilisation of high-dimensional computational data analysis. We aim to introduce the reader to the theoretical background of machine learning (ML) and elaborate on the established and potential use cases of ML algorithms in screening and early detection. Furthermore, we assess and comment on the relevant challenges and misconceptions of the applicability of ML-based diagnostic approaches. Lastly, we emphasise the need for a clear regulatory framework to responsibly introduce ML-based diagnostics in clinical practice and routine care.
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These authors contributed equally to this work.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers14030623