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Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

Chen, Bihong T., Chen, Zikuan, Ye, Ningrong, Mambetsariev, Isa, Fricke, Jeremy, Daniel, Ebenezer, Wang, George, Wong, Chi Wah +7 more·2020·Frontiers in Oncology
DOI 10.3389/fonc.2020.00593PMC PMC7188953
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  • A Chinese retrospective study of 100 patients with solitary sub-solid nodules confirmed pathologically as minimally invasive or invasive adenocarcinoma found that an integrated model combining CT-based features and radiomic signatures showed strong discrimination in the training set (AUC 0.943) and validation set (AUC 0.912).

    “An integrated model combining CT-based features such as nodule size, shape, margins, and radiomic signatures was constructed, showing strong discrimination in both the training set (AUC 0.943) and validation set (AUC 0.912) [30].”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [30]

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Cited by

Sorted by citations

Authors
Chen, B. T., Chen, Z., Ye, N., Mambetsariev, I., Fricke, J., Daniel, E., Wang, G., Wong, C. W., Rockne, R. C., Colen, R. R., Nasser, M. W., Batra, S. K., Holodny, A. I., Sampath, S., & Salgia, R.
Venue
Frontiers in Oncology
Publisher
Frontiers Media SA
Published
2020-04-22
Volume
10
ISSN
2234-943X
DOI
10.3389/fonc.2020.00593
PMC
PMC7188953
Cited by
52
References
3
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • National Institutes of Health
  • Comprehensive Cancer Center, City of Hope

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