Literature DB >> 33256107

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

Ryuji Hamamoto1,2,3, Kruthi Suvarna4, Masayoshi Yamada1,5, Kazuma Kobayashi1,2,3, Norio Shinkai1,2,3, Mototaka Miyake6, Masamichi Takahashi1,7, Shunichi Jinnai8, Ryo Shimoyama1, Akira Sakai1,3, Ken Takasawa1,2, Amina Bolatkan1,2, Kanto Shozu1, Ai Dozen1, Hidenori Machino1,2, Satoshi Takahashi1,2, Ken Asada1,2, Masaaki Komatsu1,2, Jun Sese1,9, Syuzo Kaneko1,2.   

Abstract

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.

Entities:  

Keywords:  artificial intelligence; deep learning; machine learning; omics; pathology; precision medicine; radiology

Year:  2020        PMID: 33256107      PMCID: PMC7760590          DOI: 10.3390/cancers12123532

Source DB:  PubMed          Journal:  Cancers (Basel)        ISSN: 2072-6694            Impact factor:   6.639


  154 in total

1.  Lifecycle Regulation of Artificial Intelligence- and Machine Learning-Based Software Devices in Medicine.

Authors:  Thomas J Hwang; Aaron S Kesselheim; Kerstin N Vokinger
Journal:  JAMA       Date:  2019-12-17       Impact factor: 56.272

2.  Adversarial Domain Adaptation and Pseudo-Labeling for Cross-Modality Microscopy Image Quantification.

Authors:  Fuyong Xing; Tell Bennett; Debashis Ghosh
Journal:  Med Image Comput Comput Assist Interv       Date:  2019-10-10

3.  RB1 methylation by SMYD2 enhances cell cycle progression through an increase of RB1 phosphorylation.

Authors:  Hyun-Soo Cho; Shinya Hayami; Gouji Toyokawa; Kazuhiro Maejima; Yuka Yamane; Takehiro Suzuki; Naoshi Dohmae; Masaharu Kogure; Daechun Kang; David E Neal; Bruce A J Ponder; Hiroki Yamaue; Yusuke Nakamura; Ryuji Hamamoto
Journal:  Neoplasia       Date:  2012-06       Impact factor: 5.715

4.  Progressive Transfer Learning and Adversarial Domain Adaptation for Cross-Domain Skin Disease Classification.

Authors:  Yanyang Gu; Zongyuan Ge; C Paul Bonnington; Jun Zhou
Journal:  IEEE J Biomed Health Inform       Date:  2019-09-23       Impact factor: 5.772

Review 5.  [A trial of integrated telepathology (iTP) in Nagano prefecture].

Authors:  Akihiko Yoshizawa
Journal:  Rinsho Byori       Date:  2013-01

6.  Oncogenic histone methyltransferase EZH2: A novel prognostic marker with therapeutic potential in endometrial cancer.

Authors:  Shinya Oki; Kenbun Sone; Katsutoshi Oda; Ryuji Hamamoto; Masako Ikemura; Daichi Maeda; Makoto Takeuchi; Michihiro Tanikawa; Mayuyo Mori-Uchino; Kazunori Nagasaka; Aki Miyasaka; Tomoko Kashiyama; Yuji Ikeda; Takahide Arimoto; Hiroyuki Kuramoto; Osamu Wada-Hiraike; Kei Kawana; Masashi Fukayama; Yutaka Osuga; Tomoyuki Fujii
Journal:  Oncotarget       Date:  2017-06-20

7.  Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings.

Authors:  Charlotte A Nelson; Atul J Butte; Sergio E Baranzini
Journal:  Nat Commun       Date:  2019-07-10       Impact factor: 14.919

8.  Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning.

Authors:  Nicolas Coudray; Paolo Santiago Ocampo; Theodore Sakellaropoulos; Navneet Narula; Matija Snuderl; David Fenyö; Andre L Moreira; Narges Razavian; Aristotelis Tsirigos
Journal:  Nat Med       Date:  2018-09-17       Impact factor: 53.440

9.  SMYD3-mediated lysine methylation in the PH domain is critical for activation of AKT1.

Authors:  Yuichiro Yoshioka; Takehiro Suzuki; Yo Matsuo; Makoto Nakakido; Giichiro Tsurita; Cristiano Simone; Toshiaki Watanabe; Naoshi Dohmae; Yusuke Nakamura; Ryuji Hamamoto
Journal:  Oncotarget       Date:  2016-11-15

10.  Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data.

Authors:  Satoshi Takahashi; Ken Asada; Ken Takasawa; Ryo Shimoyama; Akira Sakai; Amina Bolatkan; Norio Shinkai; Kazuma Kobayashi; Masaaki Komatsu; Syuzo Kaneko; Jun Sese; Ryuji Hamamoto
Journal:  Biomolecules       Date:  2020-10-19
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  19 in total

Review 1.  Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine.

Authors:  Ryuji Hamamoto; Ken Takasawa; Hidenori Machino; Kazuma Kobayashi; Satoshi Takahashi; Amina Bolatkan; Norio Shinkai; Akira Sakai; Rina Aoyama; Masayoshi Yamada; Ken Asada; Masaaki Komatsu; Koji Okamoto; Hirokazu Kameoka; Syuzo Kaneko
Journal:  Brief Bioinform       Date:  2022-07-18       Impact factor: 13.994

2.  BM-Net: CNN-Based MobileNet-V3 and Bilinear Structure for Breast Cancer Detection in Whole Slide Images.

Authors:  Jin Huang; Liye Mei; Mengping Long; Yiqiang Liu; Wei Sun; Xiaoxiao Li; Hui Shen; Fuling Zhou; Xiaolan Ruan; Du Wang; Shu Wang; Taobo Hu; Cheng Lei
Journal:  Bioengineering (Basel)       Date:  2022-06-20

Review 3.  Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology.

Authors:  Ken Asada; Syuzo Kaneko; Ken Takasawa; Hidenori Machino; Satoshi Takahashi; Norio Shinkai; Ryo Shimoyama; Masaaki Komatsu; Ryuji Hamamoto
Journal:  Front Oncol       Date:  2021-05-12       Impact factor: 6.244

4.  Medical Professional Enhancement Using Explainable Artificial Intelligence in Fetal Cardiac Ultrasound Screening.

Authors:  Akira Sakai; Masaaki Komatsu; Reina Komatsu; Ryu Matsuoka; Suguru Yasutomi; Ai Dozen; Kanto Shozu; Tatsuya Arakaki; Hidenori Machino; Ken Asada; Syuzo Kaneko; Akihiko Sekizawa; Ryuji Hamamoto
Journal:  Biomedicines       Date:  2022-02-25

Review 5.  Artificial intelligence for clinical oncology.

Authors:  Benjamin H Kann; Ahmed Hosny; Hugo J W L Aerts
Journal:  Cancer Cell       Date:  2021-04-29       Impact factor: 38.585

6.  Considerations for artificial intelligence clinical impact in oncologic imaging: an AI4HI position paper.

Authors:  Luis Marti-Bonmati; Dow-Mu Koh; Katrine Riklund; Maciej Bobowicz; Yiannis Roussakis; Joan C Vilanova; Jurgen J Fütterer; Jordi Rimola; Pedro Mallol; Gloria Ribas; Ana Miguel; Manolis Tsiknakis; Karim Lekadir; Gianna Tsakou
Journal:  Insights Imaging       Date:  2022-05-10

7.  Application of Artificial Intelligence for Medical Research.

Authors:  Ryuji Hamamoto
Journal:  Biomolecules       Date:  2021-01-12

Review 8.  A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning.

Authors:  Satoshi Takahashi; Masamichi Takahashi; Shota Tanaka; Shunsaku Takayanagi; Hirokazu Takami; Erika Yamazawa; Shohei Nambu; Mototaka Miyake; Kaishi Satomi; Koichi Ichimura; Yoshitaka Narita; Ryuji Hamamoto
Journal:  Biomolecules       Date:  2021-04-12

9.  Automated system for diagnosing endometrial cancer by adopting deep-learning technology in hysteroscopy.

Authors:  Yu Takahashi; Kenbun Sone; Katsuhiko Noda; Kaname Yoshida; Yusuke Toyohara; Kosuke Kato; Futaba Inoue; Asako Kukita; Ayumi Taguchi; Haruka Nishida; Yuichiro Miyamoto; Michihiro Tanikawa; Tetsushi Tsuruga; Takayuki Iriyama; Kazunori Nagasaka; Yoko Matsumoto; Yasushi Hirota; Osamu Hiraike-Wada; Katsutoshi Oda; Masanori Maruyama; Yutaka Osuga; Tomoyuki Fujii
Journal:  PLoS One       Date:  2021-03-31       Impact factor: 3.240

10.  Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data.

Authors:  Laith Alzubaidi; Muthana Al-Amidie; Ahmed Al-Asadi; Amjad J Humaidi; Omran Al-Shamma; Mohammed A Fadhel; Jinglan Zhang; J Santamaría; Ye Duan
Journal:  Cancers (Basel)       Date:  2021-03-30       Impact factor: 6.639

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