A predictive model for personalized therapeutic interventions in non-small cell lung cancer
Abstract
Non-small cell lung cancer (NSCLC) constitutes the most common type of lung cancer and is frequently diagnosed at advanced stages. Clinical studies have shown that molecular targeted therapies increase survival and improve quality of life in patients. Nevertheless, the realization of personalized therapies for NSCLC faces a number of challenges including the integration of clinical and genetic data and a lack of clinical decision support tools to assist physicians with patient selection. To address this problem, we used frequent pattern mining to establish the relationships of patient characteristics and tumor response in advanced NSCLC. Univariate analysis determined that smoking status, histology, epidermal growth factor receptor (EGFR) mutation, and targeted drug were significantly associated with response to targeted therapy. We applied four classifiers to predict treatment outcome from EGFR tyrosine kinase inhibitors. Overall, the highest classification accuracy was 76.56% and the area under the curve was 0.76. The decision tree used a combination of EGFR mutations, histology, and smoking status to predict tumor response and the output was both easily understandable and in keeping with current knowledge. Our findings suggest that support vector machines and decision trees are a promising approach for clinical decision support in the patient selection for targeted therapy in advanced NSCLC.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
ML and radiomic analyses offer non-invasive means to better evaluate the tumor and its microenvironment, thereby aiding in anticipating treatment responses.
“ML and radiomic analyses, however, offer non-invasive means to better evaluate the tumor and its microenvironment, thereby aiding in anticipating treatment responses.”
From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [42]
Kureshi et al. proposed a data-driven predictive model for assessing tumor response to EGFR-TKI treatment in advanced NSCLC; incorporating clinical profiles, environmental exposures, and EGFR mutation status, the model reached an accuracy of 76%.
“Kureshi et al. proposed a data-driven predictive model for assessing tumor response to EGFR-TKI treatment in advanced NSCLC; by incorporating clinical profiles, environmental exposures, and EGFR mutation status, the model reached an accuracy of 76% [42].”
From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [42]
Machine learning systems can integrate data produced during radiotherapy, including CT imaging, dose distributions, and treatment records, to refine treatment planning.
“Machine learning systems can integrate the substantial amount of high-quality data produced during radiotherapy—CT imaging, dose distributions, and treatment records—to refine treatment planning.”
From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [42]
ML methods have been used to optimize beam configurations, estimate dose-volume histograms, evaluate radiation dose and toxicity, and support clinical decision-making.
“ML methods have been used to optimize beam configurations, estimate dose–volume histograms, evaluate radiation dose and toxicity, and support clinical decision-making [42].”
From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [42]
Cites
EGFR-mutated lung cancer: a paradigm of molecular oncology
Zhang, Z., Stiegler, A. L., Boggon, T. J., Kobayashi, S., & Halmos, B. (2010) · Oncotarget · doi:10.18632/oncotarget.186 · Cited by 197 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Chen, S., & Wu, S. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01695-4 · Cited by 96 · see citing works
Vijayakumar, S., Lee, V. V., Leong, Q. Y., Hong, S. J., Blasiak, A., & Ho, D. (2023) · JMIR Human Factors · doi:10.2196/48476 · Cited by 34 · see citing works
Petrella, F., & Rizzo, S. (2026) · Cancers · doi:10.3390/cancers18020246 · Cited by 10 · see citing works
- Authors
- Kureshi, N., Abidi, S. S. R., & Blouin, C.
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2016-01
- Volume
- 20
- Issue
- 1
- Pages
- 424-431
- ISSN
- 2168-2194
- 2168-2208
- Cited by
- 61
- References
- 1
- Funders
- NSERC Discovery
- CIHR Catalyst
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
