The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Early lung cancer diagnostic biomarker discovery by machine learning methods
Open accessAbstract
Early diagnosis has been proved to improve survival rate of lung cancer patients. The availability of blood-based screening could increase early lung cancer patient uptake. Our present study attempted to discover Chinese patients' plasma metabolites as diagnostic biomarkers for lung cancer. In this work, we use a pioneering interdisciplinary mechanism, which is firstly applied to lung cancer, to detect early lung cancer diagnostic biomarkers by combining metabolomics and machine learning methods. We collected total 110 lung cancer patients and 43 healthy individuals in our study. Levels of 61 plasma metabolites were from targeted metabolomic study using LC-MS/MS. A specific combination of six metabolic biomarkers note-worthily enabling the discrimination between stage I lung cancer patients and healthy individuals (AUC = 0.989, Sensitivity = 98.1%, Specificity = 100.0%). And the top 5 relative importance metabolic biomarkers developed by FCBF algorithm also could be potential screening biomarkers for early detection of lung cancer. Naïve Bayes is recommended as an exploitable tool for early lung tumor prediction. This research will provide strong support for the feasibility of blood-based screening, and bring a more accurate, quick and integrated application tool for early lung cancer diagnostic. The proposed interdisciplinary method could be adapted to other cancer beyond lung cancer.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Applications of machine learning in drug discovery and development
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228
Non-small-cell lung cancers: a heterogeneous set of diseases
Chen, Z., Fillmore, C. M., Hammerman, P. S., Kim, C. F., & Wong, K.-K. (2014) · Nature Reviews Cancer · doi:10.1038/nrc3775 · Cited by 1,942
Travis, W. D., Brambilla, E., Burke, A. P., Marx, A., & Nicholson, A. G. (2015) · Journal of Thoracic Oncology · doi:10.1097/jto.0000000000000663 · Cited by 1,905
Orange: data mining toolbox in python
Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hočevar, T., Milutinovič, M., Možina, M., Polajnar, M., Toplak, M., Starič, A., et al. (2013) · Journal of Machine Learning Research · Cited by 1,655
The Ensembl genome database project
Hubbard, T. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.38 · Cited by 1,637
Applications of Support Vector Machine (SVM) Learning in Cancer Genomics
(2018) · Cancer Genomics & Proteomics · doi:10.21873/cgp.20063 · Cited by 1,528
Non–Small Cell Lung Cancer, Version 5.2017, NCCN Clinical Practice Guidelines in Oncology
Ettinger, D. S., Wood, D. E., Aisner, D. L., Akerley, W., Bauman, J., Chirieac, L. R., D'Amico, T. A., DeCamp, M. M., Dilling, T. J., Dobelbower, M., et al. (2017) · Journal of the National Comprehensive Cancer Network · doi:10.6004/jnccn.2017.0050 · Cited by 1,303
Integrating genomic features for non-invasive early lung cancer detection
Chabon, J. J., Hamilton, E. G., Kurtz, D. M., Esfahani, M. S., Moding, E. J., Stehr, H., Schroers-Martin, J., Nabet, B. Y., Chen, B., Chaudhuri, A. A., et al. (2020) · Nature · doi:10.1038/s41586-020-2140-0 · Cited by 685
Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges
Huang, S., Yang, J., Fong, S., & Zhao, Q. (2020) · Cancer Letters · doi:10.1016/j.canlet.2019.12.007 · Cited by 672
Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis
Ambale-Venkatesh, B., Yang, X., Wu, C. O., Liu, K., Hundley, W. G., McClelland, R., Gomes, A. S., Folsom, A. R., Shea, S., Guallar, E., et al. (2017) · Circulation Research · doi:10.1161/circresaha.117.311312 · Cited by 639
Cited by
Evaluation of artificial intelligence techniques in disease diagnosis and prediction
Ghaffar Nia, N., Kaplanoglu, E., & Nasab, A. (2023) · Discover Artificial Intelligence · doi:10.1007/s44163-023-00049-5 · Cited by 426
Metadata
- Authors
- Xie, Y., Meng, W.-Y., Li, R.-Z., Wang, Y.-W., Qian, X., Chan, C., Yu, Z.-F., Fan, X.-X., Pan, H.-D., Xie, C., Wu, Q.-B., Yan, P.-Y., Liu, L., Tang, Y.-J., Yao, X.-J., Wang, M.-F., & Leung, E. L.-H.
- Venue
- Translational Oncology
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 14
- Issue
- 1
- Pages
- 100907
- ISSN
- 1936-5233 (ISSN-L)
- OpenAlex
- W3105613063
- Cited by
- 269
- References
- 64
- FWCI
- 7.54
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Early lung cancer diagnostic biomarker discovery by machine learning methods. https://app.cochat.ai/papers/10.1016/j.tranon.2020.100907
- The paper
- Xie, Y., Meng, W.-Y., Li, R.-Z., Wang, Y.-W., Qian, X., Chan, C., Yu, Z.-F., Fan, X.-X., Pan, H.-D., Xie, C., Wu, Q.-B., Yan, P.-Y., Liu, L., Tang, Y.-J., Yao, X.-J., Wang, M.-F., & Leung, E. L.-H. (2021). Early lung cancer diagnostic biomarker discovery by machine learning methods. Translational Oncology. https://doi.org/10.1016/j.tranon.2020.100907
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
