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Cited by 138AI in cancer detectionRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 138 of the 138 references it cites were found in a registry or an index.

Application of Artificial Intelligence in Lung Cancer

Open access
Chiu, Hwa-Yen, Chao, Heng-Sheng, Chen, Yuh-Min·2022·Cancers
DOI 10.3390/cancers14061370PMC PMC8946647
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Abstract

Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient’s prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.

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Registry

Crossref · 10.3390/cancers14061370

Cites

138 of 138 references verified0 citation errors · 0 not checkable

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Figures

Figure 1 Venn diagram of artificial intelligence (AI), machine learning (ML), neural network, deep learning, and further algorithms in each category. AI is a general term for a program that predicts an answer to a certain problem, where one of the conventional methods is logistic regression. ML learns the algorithm through input data without explicit programming. ML includes algorithms such as decision trees (DTs), support vector machines (SVMs), and Bayesian networks (BNs). By using each ML algorithm as a neuron with multiple inputs and a single output, a neural network is a structure that mimics the human brain. Deep learning is formed with multiple layers of neural networks, and convolutional neural network (CNN) is one of the elements of the famous architecture.
Figure 1 Venn diagram of artificial intelligence (AI), machine learning (ML), neural network, deep learning, and further algorithms in each category. AI is a general term for a program that predicts an answer to a certain problem, where one of the conventional methods is logistic regression. ML learns the algorithm through input data without explicit programming. ML includes algorithms such as decision trees (DTs), support vector machines (SVMs), and Bayesian networks (BNs). By using each ML algorithm as a neuron with multiple inputs and a single output, a neural network is a structure that mimics the human brain. Deep learning is formed with multiple layers of neural networks, and convolutional neural network (CNN) is one of the elements of the famous architecture.CC BY · PubMed Central
Figure 2 The concept map of supervised learning, unsupervised learning and reinforcement learning.
Figure 2 The concept map of supervised learning, unsupervised learning and reinforcement learning.CC BY · PubMed Central
Figure 3 The comparison of traditional AI server architecture and federated learning server architecture. (a) In traditional server architecture, the main server processes all the raw data at the same site, leading to concerns about privacy; (b) In federated learning, the datasets are processed at each individual site and only the trained models are shared with the main server. Privacy of each dataset is protected.
Figure 3 The comparison of traditional AI server architecture and federated learning server architecture. (a) In traditional server architecture, the main server processes all the raw data at the same site, leading to concerns about privacy; (b) In federated learning, the datasets are processed at each individual site and only the trained models are shared with the main server. Privacy of each dataset is protected.CC BY · PubMed Central

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Citations

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Research works printed but not linked: 3

  1. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., and Shpanskaya, K. (2017). Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv.
  2. Nguyen, H.C., Le, T.T., Pham, H., and Nguyen, H.Q. (2021). VinDr-RibCXR: A Benchmark Dataset for Automatic Segmentation and Labeling of Individual Ribs on Chest X-rays. arXiv.
  3. Jain, S., Agrawal, A., Saporta, A., Truong, S.Q., Bui, T., Chambon, P., Zhang, Y., Lungren, M.P., Ng, A.Y., and Langlotz, C. (2021). RadGraph: Extracting Clinical Entities and Relations from Radiology Reports. arXiv.

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Metadata

Authors
Chiu, H.-Y., Chao, H.-S., & Chen, Y.-M.
Affiliations
National Yang Ming Chiao Tung University (TW)
Corresponding author
Chao, Heng-Sheng
ORCID
Venue
Cancers
Publisher
MDPI AG
Published
2022-03-08
Volume
14
Issue
6
Pages
1370
ISSN
2072-6694 (ISSN-L)
DOI
10.3390/cancers14061370
PMC
PMC8946647
OpenAlex
W4220889814
Cited by
138
References
138
Citations by year
2026: 21 · 2025: 47 · 2024: 37 · 2023: 29 · 2022: 4
FWCI
2.04
Type
journal article
Language
en
Open access
Licence
Locations
  • Cancers (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Ministry of Science and Technology (MOST 110-2321-B-075-001)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
lung cancer, artificial intelligence, lung cancer screening, chest CT, chest radiography, whole slide imaging, EGFR mutation, PD-L1 expression, outcome prediction, drug response prediction, clinical decision support

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