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Biomarkers in Lung Cancer: From Early Detection to Novel Therapeutics and Decision Making

Scott, April, Salgia, Ravi·2008·Biomarkers in Medicine
DOI 10.2217/17520363.2.6.577PMC PMC2742385
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  • A predictive model incorporating clinical parameters, radiologic characteristics of pulmonary nodules, LDCT-derived AI outputs, and liquid biopsy data demonstrated sensitivity of 89.53%, specificity of 81.31%, and an AUC of 0.880 in the training cohort.

    “In another prospective investigation, a predictive model incorporating clinical parameters (age and smoking status), radiologic characteristics of pulmonary nodules (including nodule size, number, upper-lobe location, malignant edge features, and subsolid appearance), LDCT-derived AI outputs, and liquid biopsy data demonstrated optimal performance in the training cohort, with a sensitivity of 89.5”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [34]

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Cited by

Sorted by citations

Authors
Scott, A., & Salgia, R.
Affiliations
University of Chicago, Department of Medicine, Section of Hematology/Oncology, and University of Chicago Cancer Research Center, 5841 S Maryland Avenue, Chicago, IL60637, USA.
Venue
Biomarkers in Medicine
Publisher
Informa UK Limited
Published
2008-12
Volume
2
Issue
6
Pages
577-586
ISSN
  • 1752-0363
  • 1752-0371
DOI
10.2217/17520363.2.6.577
PMC
PMC2742385
Cited by
53
References
3
Language
en

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