Machine Learning in Oncology: What Should Clinicians Know?
Matthew R. Nagy,Nathan Radakovich,Aziz Nazha +2 more
- Vol. 4, Iss: 4, pp 799-810
Reads0
Chats0
TLDR
An overview of the basics of machine learning is provided and current progress and challenges in applying this technology to cancer diagnosis, prognosis, and treatment recommendations are highlighted, including a discussion of current takeaways for clinicians.Abstract:
The volume and complexity of scientific and clinical data in oncology have grown markedly over recent years, including but not limited to the realms of electronic health data, radiographic and histologic data, and genomics. This growth holds promise for a deeper understanding of malignancy and, accordingly, more personalized and effective oncologic care. Such goals require, however, the development of new methods to fully make use of the wealth of available data. Improvements in computer processing power and algorithm development have positioned machine learning, a branch of artificial intelligence, to play a prominent role in oncology research and practice. This review provides an overview of the basics of machine learning and highlights current progress and challenges in applying this technology to cancer diagnosis, prognosis, and treatment recommendations, including a discussion of current takeaways for clinicians.read more
Citations
More filters
Journal ArticleDOI
Requirements and reliability of AI in the medical context.
TL;DR: In this paper, the authors assess the problem of reliable use of the ML methods in oncology, as a representative study case, with the goal to safeguard utility and improve patient care in medicine in general.
Journal ArticleDOI
Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer
Babak Arjmand,Shayesteh Kokabi Hamidpour,Akram Tayanloo-Beik,Parisa Goodarzi,Hamid Reza Aghayan,Hossein Adibi,Bagher Larijani +6 more
TL;DR: It can be concluded that the use of artificial intelligence technologies such as machine learning can have revolutionary roles in the fight against cancer.
Journal ArticleDOI
Experimental Models of Hepatocellular Carcinoma-A Preclinical Perspective.
Alexandru Blidisel,Iasmina Marcovici,Dorina Coricovac,Florin Hut,Cristina Dehelean,Octavian Cretu +5 more
TL;DR: In this article, a review of the currently available preclinical models frequently applied for the study of hepatocellular carcinoma in terms of initiation, development, and progression, as well as for the discovery of efficient treatments, highlighting the advantages and the limitations of each model.
Journal ArticleDOI
Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images
TL;DR: The results of the experiment indicate that the proposed framework (which is evaluated on the benchmark dataset) outperformed advanced methods in terms of accurateness, and could be used to improve classification accuracy.
Journal ArticleDOI
Machine learning-based prediction of survival prognosis in cervical cancer.
Dongyan Ding,Tingyuan Lang,Dongling Zou,Jiawei Tan,Jia Chen,Lei Zhou,Dong Wang,Rong Li,Yunzhe Li,Jingshu Liu,Cui Ma,Qi Zhou +11 more
TL;DR: A survival prediction model for cervical cancer patients with big data and machine learning algorithms was developed and showed that the cervical cancer samples can be separated into two and three subgroups with top 20 identified survival-related microRNAs for best stratification.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Book
Computing Machinery and Intelligence
TL;DR: If the meaning of the words “machine” and “think” are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, “Can machines think?” is to be sought in a statistical survey such as a Gallup poll.
Journal ArticleDOI
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.
Babak Ehteshami Bejnordi,Mitko Veta,Paul J. van Diest,Bram van Ginneken,Nico Karssemeijer,Geert Litjens,Jeroen van der Laak,Meyke Hermsen,Quirine F. Manson,Maschenka Balkenhol,Oscar Geessink,N. Stathonikos,Marcory C. R. F. van Dijk,Peter Bult,Francisco Beca,Andrew H. Beck,Dayong Wang,Aditya Khosla,Rishab Gargeya,Humayun Irshad,Aoxiao Zhong,Qi Dou,Qi Dou,Quanzheng Li,Hao Chen,Huangjing Lin,Pheng-Ann Heng,Christian Haß,Elia Bruni,Quincy Wong,Ugur Halici,Mustafa Umit Oner,Rengul Cetin-Atalay,Matt Berseth,Vitali Khvatkov,Alexei Vylegzhanin,Oren Kraus,Muhammad Shaban,Nasir M. Rajpoot,Nasir M. Rajpoot,Ruqayya Awan,Korsuk Sirinukunwattana,Talha Qaiser,Yee-Wah Tsang,David Tellez,Jonas Annuscheit,Peter Hufnagl,Mira Valkonen,Kimmo Kartasalo,Kimmo Kartasalo,Leena Latonen,Pekka Ruusuvuori,Pekka Ruusuvuori,Kaisa Liimatainen,Shadi Albarqouni,Bharti Mungal,Ami George,Stefanie Demirci,Nassir Navab,Seiryo Watanabe,Shigeto Seno,Yoichi Takenaka,Hideo Matsuda,Hady Ahmady Phoulady,Vassili Kovalev,A. Kalinovsky,Vitali Liauchuk,Gloria Bueno,M. Milagro Fernández-Carrobles,Ismael Serrano,Oscar Deniz,Daniel Racoceanu,Daniel Racoceanu,Rui Venâncio +73 more
TL;DR: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints.
Journal ArticleDOI
Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning.
Nicolas Coudray,Paolo S. Ocampo,Theodore Sakellaropoulos,Navneet Narula,Matija Snuderl,David Fenyö,Andre L. Moreira,Narges Razavian,Aristotelis Tsirigos +8 more
TL;DR: A deep convolutional neural network model is trained on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue and predicts the ten most commonly mutated genes in LUAD.
Related Papers (5)
The NCCN clinical practice guidelines in oncology: a primer for users.
Rodger J. Winn,Joan S. McClure +1 more