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. 2026 Apr 30;6:1841924. doi: 10.3389/fbinf.2026.1841924

Correction: An integrated automated deep learning framework for annotating tumor-infiltrating lymphocytes in lung adenocarcinoma pathology

Xia Li 1, Kang-Lai Wei 2, Zhao-Quan Huang 1,*, Zi-Yan Huang 3,*
PMCID: PMC13173451  PMID: 42147404

In the published article, there was an error in Table 5 as published. The table should have presented the agreement analysis for TILs semi-quantitative grading based on Kappa. The corrected Table 5 and its caption appear below.

TABLE 5.

Agreement analysis for TILs semi-quantitative grading.

Comparison group Kappa value (κ) 95% confidence interval Agreement strength
Pathologist 1 vs. pathologist 2 1.000 1.000–1.000 Almost perfect
AI pipeline vs. pathologist 1 0.833 0.500–1.000 Almost perfect
AI pipeline vs. pathologist 2 0.833 0.500–1.000 Almost perfect
Overall (Fleiss’Kappa) 0.889 0.667–1.000 Almost perfect

Agreement strength was interpreted per Landis and Koch (1977): <0.00 poor; 0.00–0.20 slight; 0.21–0.40 fair; 0.41–0.60 moderate; 0.61–0.80 substantial; 0.81–1.00 almost perfect. Grading was based on three categories: low (<100 cells/HPF), moderate (100–300 cells/HPF), high (>300 cells/HPF).

The original article has been updated.

Footnotes

Edited and reviewed by: Rahul Singh Jasrotia, The University of Texas Health Science Center at San Antonio, United States

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