Abstract
Purpose
It was reported that individual heterogeneity among malignancies (IHAM) might correlate well to the prognosis of lung cancer; however, seldom radiomic study is on this field. Standard deviation (SD) in statistics could scale average amount of variability of a variable; therefore, we used SD of CT feature (FeatureSD) among primary tumor and malignant lymph nodes (LNs) in an individual to represent IHAM, and its prognostic ability was explored.
Methods
The enrolled patients who had accepted PET/CT scans were selected from our previous study (ClinicalTrials.gov, NCT03648151). The patients had primary tumor and at least one LN, and standardized uptake value of LN higher than 2.0 and 2.5 were enrolled as the cohort 1 (n = 94) and 2 (n = 88), respectively. FeatureSD from the combined or thin-section CT were calculated among primary tumor and malignant LNs in each patient, and were separately selected by the survival XGBoost method. Finally, their prognostic ability was compared to the significant patient characteristics identified by the Cox regression.
Results
In the univariate and multi-variate Cox analysis, surgery, target therapy, and TNM stage were significantly against OS in the both cohorts. In the survival XGBoost analysis of the thin-section CT dataset, none FeatureSD could be repeatably ranked on the top list of the both cohorts. For the combined CT dataset, only one FeatureSD ranked in the top three of both cohorts, but the three significant factors in the Cox regression were not even on the list. Both in the cohort 1 and 2, C-index of the model composed of the three factors could be improved by integrating the continuous FeatureSD; furthermore, that of each factor was obviously lower than FeatureSD.
Conclusion
Standard deviation of CT features among malignant foci within an individual was a powerful prognostic factor in vivo for lung cancer patients.
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Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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Funding
The work was funded by grants from Science and Technology project of Health Commission of Anhui Province (No. AHWJ2021b148), Collaborative Innovation Center for Molecular Imaging and Precise D&T Center (Grant No. MP201604) and Shenzhen Science and Technology Innovation Commission (JCYJ20200109120205924).
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SH, LS, XS and CJ contributed to the study conception and design. Material preparation, data collection and analysis were performed by XS, WR, LL and. The first draft of the manuscript was written by HX and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Hongwei, S., Xinzhong, H., Huiqin, X. et al. Standard deviation of CT radiomic features among malignancies in each individual: prognostic ability in lung cancer patients. J Cancer Res Clin Oncol 149, 7165–7173 (2023). https://doi.org/10.1007/s00432-023-04649-7
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DOI: https://doi.org/10.1007/s00432-023-04649-7