Literature DB >> 31830558

Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges.

Shigao Huang1, Jie Yang2, Simon Fong3, Qi Zhao4.   

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.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Cancer diagnosis; Deep learning; Deep neural network; Machine learning; Prognosis prediction

Year:  2019        PMID: 31830558     DOI: 10.1016/j.canlet.2019.12.007

Source DB:  PubMed          Journal:  Cancer Lett        ISSN: 0304-3835            Impact factor:   8.679


  43 in total

Review 1.  Smart diagnostics devices through artificial intelligence and mechanobiological approaches.

Authors:  Dinesh Yadav; Ramesh Kumar Garg; Deepak Chhabra; Rajkumar Yadav; Ashwani Kumar; Pratyoosh Shukla
Journal:  3 Biotech       Date:  2020-07-22       Impact factor: 2.406

Review 2.  Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications.

Authors:  Yawen Wu; Michael Cheng; Shuo Huang; Zongxiang Pei; Yingli Zuo; Jianxin Liu; Kai Yang; Qi Zhu; Jie Zhang; Honghai Hong; Daoqiang Zhang; Kun Huang; Liang Cheng; Wei Shao
Journal:  Cancers (Basel)       Date:  2022-02-25       Impact factor: 6.639

3.  Clinician perspectives on machine learning prognostic algorithms in the routine care of patients with cancer: a qualitative study.

Authors:  Ravi B Parikh; Christopher R Manz; Maria N Nelson; Chalanda N Evans; Susan H Regli; Nina O'Connor; Lynn M Schuchter; Lawrence N Shulman; Mitesh S Patel; Joanna Paladino; Judy A Shea
Journal:  Support Care Cancer       Date:  2022-01-30       Impact factor: 3.603

Review 4.  A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery.

Authors:  Gayathri Ashok; Sudha Ramaiah
Journal:  Med Oncol       Date:  2022-09-29       Impact factor: 3.738

Review 5.  Artificial intelligence and machine learning in precision and genomic medicine.

Authors:  Sameer Quazi
Journal:  Med Oncol       Date:  2022-06-15       Impact factor: 3.738

6.  Prediction of Incident Cancers in the Lifelines Population-Based Cohort.

Authors:  Francisco O Cortés-Ibañez; Sunil Belur Nagaraj; Ludo Cornelissen; Gerjan J Navis; Bert van der Vegt; Grigory Sidorenkov; Geertruida H de Bock
Journal:  Cancers (Basel)       Date:  2021-04-28       Impact factor: 6.639

Review 7.  Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview.

Authors:  Hanya Mahmood; Muhammad Shaban; Nasir Rajpoot; Syed A Khurram
Journal:  Br J Cancer       Date:  2021-04-19       Impact factor: 9.075

Review 8.  Towards population-based genetic screenings for breast and ovarian cancer: A comprehensive review from economic evaluations to patient perspectives.

Authors:  Filomena Ficarazzi; Manuela Vecchi; Maurizio Ferrari; Marco A Pierotti
Journal:  Breast       Date:  2021-05-12       Impact factor: 4.380

Review 9.  Translational precision medicine: an industry perspective.

Authors:  Dominik Hartl; Valeria de Luca; Anna Kostikova; Jason Laramie; Scott Kennedy; Enrico Ferrero; Richard Siegel; Martin Fink; Sohail Ahmed; John Millholland; Alexander Schuhmacher; Markus Hinder; Luca Piali; Adrian Roth
Journal:  J Transl Med       Date:  2021-06-05       Impact factor: 5.531

10.  The role of AI technology in prediction, diagnosis and treatment of colorectal cancer.

Authors:  Chaoran Yu; Ernest Johann Helwig
Journal:  Artif Intell Rev       Date:  2021-07-04       Impact factor: 8.139

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