References
Shimada Y, Kudo Y, Maehara S, et al. Radiomics with artificial intelligence for the prediction of early recurrence in patients with clinical stage ia lung cancer. Ann Surg Oncol. 2022. https://doi.org/10.1245/S10434-022-12516-X.
Hattori A, Matsunaga T, Takamochi K, et al. Importance of ground glass opacity component in clinical stage IA radiologic invasive lung cancer. Ann Thorac Surg. 2017. https://doi.org/10.1016/j.athoracsur.2017.01.076.
Suzuki K, Kusumoto M, Watanabe SI, Tsuchiya R, et al. Radiologic classification of small adenocarcinoma of the lung: radiologic-pathologic correlation and its prognostic impact. Ann Thorac Surg. 2006. https://doi.org/10.1016/j.athoracsur.2005.07.058.
Janiszewska M. The microcosmos of intratumor heterogeneity: the space-time of cancer evolution. Oncogene. 2019. https://doi.org/10.1038/s41388-019-1127-5.
Mattonen SA, Davidzon GA, Bakr S. [18F] FDG Positron emission tomography (PET) tumor and penumbra imaging features predict recurrence in non–small cell lung cancer. Tomography. 2019. https://doi.org/10.18383/J.TOM.2018.00026.
Usuzaki T, Takahashi K, Umemiya K. A new radiomics feature: image frequency analysis. arXiv. 2021. https://doi.org/10.1177/ToBeAssigned.
Prasanna P, Tiwari P, Madabhushi A. Co-occurrence of local anisotropic gradient orientations (CoLlAGe): a new radiomics descriptor. Sci Reports. 2016. https://doi.org/10.1038/srep37241.
Vuong D, Tanadini-Lang S, Wu Z, et al. Radiomics feature activation maps as a new tool for signature interpretability. Front Oncol. 2020. https://doi.org/10.3389/FONC.2020.578895/BIBTEX.
Author information
Authors and Affiliations
Contributions
T.U.: conceptualization, investigation, writing; original draft, project administration. K.T.: conceptualization, investigation, writing; original draft, project administration. M.I.: writing; review and editing. T.O.: writing; review and editing. T.Y.: writing; review and editing. M.K.: writing; review and editing. K.M.: writing; review and editing. T.U. and K.T. contributed equally to this work as first authors.
Corresponding author
Ethics declarations
Disclosure
The authors have no conflicts of interest to declare.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Usuzaki, T., Takahashi, K., Ishikuro, M. et al. Letter to the Editor: Comment on ‘‘Radiomics with Artificial Intelligence for the Prediction of Early Recurrence in Patients with Clinical Stage IA Lung Cancer’’. Ann Surg Oncol 30, 912–913 (2023). https://doi.org/10.1245/s10434-022-12809-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1245/s10434-022-12809-1