Radiomics and deep learning in lung cancer
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2020/05/04
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Journal
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Indian UGC (journal)
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Refrences86
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Citations109
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Michele Avanzo
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Joseph Stancanello
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Giovanni Pirrone
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Giovanna Sartor
Cite
Avanzo, Michele, et al. “Radiomics and Deep Learning in Lung Cancer”. Strahlentherapie Und Onkologie, vol. 196, no. 10, 2020, pp. 879-87, https://doi.org/10.1007/s00066-020-01625-9.
Avanzo, M., Stancanello, J., Pirrone, G., & Sartor, G. (2020). Radiomics and deep learning in lung cancer. Strahlentherapie Und Onkologie, 196(10), 879-887. https://doi.org/10.1007/s00066-020-01625-9
Avanzo M, Stancanello J, Pirrone G, Sartor G. Radiomics and deep learning in lung cancer. Strahlentherapie und Onkologie. 2020;196(10):879-87.
Journal Categories
Medicine
Internal medicine
Neoplasms
Tumors
Oncology
Including cancer and carcinogens
Medicine
Medicine (General)
Medicine
Medicine (General)
Medical physics
Medical radiology
Nuclear medicine
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Radiomics for the prediction of EGFR mutation subtypes in non‐small cell lung cancer | Medical Physics |
| 46 | 2019 |
| AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics | European Journal of Nuclear Medicine and Molecular Imaging |
| 24 | 2019 |
| Assessing PD-L1 Expression Level by Radiomic Features From PET/CT in Nonsmall Cell Lung Cancer Patients: An Initial Result | Academic Radiology |
| 68 | 2019 |
| CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer | Radiotherapy and Oncology |
| 135 | 2016 |
| Radiomic phenotype features predict pathological response in non-small cell lung cancer | Radiotherapy and Oncology |
| 214 | 2016 |
Refrences Analysis
The category
Medicine: Medicine (General) 41
is the most frequently represented among the references in this article. It primarily includes studies from Medical Physics The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Enhancing the prediction of symptomatic radiation pneumonitis for locally advanced non-small-cell lung cancer by combining 3D deep learning-derived imaging features with dose–volume metrics: a two-center study | Strahlentherapie und Onkologie |
| 2024 | |
| The Classification of Lumbar Spondylolisthesis X-Ray Images Using Convolutional Neural Networks | Journal of Imaging Informatics in Medicine |
| 2024 | |
| Indicators for Hospitalization in Acute Pulmonary Embolism: Uncover the Association Between D-dimer Levels, Thrombus Volume and Radiomics | Academic Radiology |
| 2024 | |
| Differentiating the Invasiveness of Lung Adenocarcinoma Manifesting as Ground Glass Nodules: Combination of Dual-energy CT Parameters and Quantitative-semantic Features | Academic Radiology |
| 2024 | |
| Diagnostic Performance of Radiomics and Deep Learning to Identify Benign and Malignant Soft Tissue Tumors: A Systematic Review and Meta-analysis | Academic Radiology |
| 2024 |
Citations Analysis
The category
Medicine: Medicine (General) 76
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Radiomics applied to pulmonary infection: A review and was published in 2021. The most recent citation comes from a 2024 study titled The Classification of Lumbar Spondylolisthesis X-Ray Images Using Convolutional Neural Networks. This article reached its peak citation in 2023, with 49 citations. It has been cited in 69 different journals, 33% of which are open access. Among related journals, the Frontiers in Oncology cited this research the most, with 17 citations. The chart below illustrates the annual citation trends for this article.