Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges

Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient's survival rate. Developmen...

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Vydáno v:	Cancer letters Ročník 471; s. 61 - 71
Hlavní autoři:	Huang, Shigao, Yang, Jie, Fong, Simon, Zhao, Qi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Ireland Elsevier B.V 28.02.2020 Elsevier Limited
Témata:	Accuracy Algorithms Artificial intelligence Bioinformatics Cancer Cancer diagnosis Cancer therapies Computer applications Deep learning Deep neural network Diagnosis Learning algorithms Machine learning Medical diagnosis Medical prognosis Medical research Medical statistics Metastasis Oncology Prognosis Prognosis prediction Software Statistical analysis Statistics Survival United States > US China Deep learning Deep neural network Prognosis prediction Cancer diagnosis Machine learning
ISSN:	0304-3835, 1872-7980, 1872-7980
On-line přístup:	Získat plný text
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Abstract	Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient's survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future. •Artificial intelligence (AI) has reached new heights in clinical cancer research in recent years.•AI is applied to assist cancer diagnosis and prognosis, given its unprecedented accuracy level, which is even higher than that of general statistical expert.•An overview of how AI applied in clinical cancer could be leveraged in this area and thereby contribute to improved human health.
AbstractList	Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient's survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future. •Artificial intelligence (AI) has reached new heights in clinical cancer research in recent years.•AI is applied to assist cancer diagnosis and prognosis, given its unprecedented accuracy level, which is even higher than that of general statistical expert.•An overview of how AI applied in clinical cancer could be leveraged in this area and thereby contribute to improved human health. Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient's survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future.Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient's survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future. Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient's survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future.
Author	Zhao, Qi Fong, Simon Yang, Jie Huang, Shigao
Author_xml	– sequence: 1 givenname: Shigao surname: Huang fullname: Huang, Shigao organization: Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao, China – sequence: 2 givenname: Jie surname: Yang fullname: Yang, Jie organization: Department of Computer and Information Science, University of Macau, Taipa, Macau, China – sequence: 3 givenname: Simon surname: Fong fullname: Fong, Simon email: ccfong@um.edu.mo organization: Department of Computer and Information Science, University of Macau, Taipa, Macau, China – sequence: 4 givenname: Qi surname: Zhao fullname: Zhao, Qi email: qizhao@um.edu.mo organization: Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao, China
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31830558$$D View this record in MEDLINE/PubMed
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Snippet	Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and...
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SubjectTerms	Accuracy Algorithms Artificial intelligence Bioinformatics Cancer Cancer diagnosis Cancer therapies Computer applications Deep learning Deep neural network Diagnosis Learning algorithms Machine learning Medical diagnosis Medical prognosis Medical research Medical statistics Metastasis Oncology Prognosis Prognosis prediction Software Statistical analysis Statistics Survival
Title	Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges
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