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. 2019 Jun 19;9(1):8730.
doi: 10.1038/s41598-019-45117-y.

Prognostic Impact of Longitudinal Monitoring of Radiomic Features in Patients with Advanced Non-Small Cell Lung Cancer

So Hyeon Bak  1   2 , Hyunjin Park  3   4 , Insuk Sohn  5 , Seung Hak Lee  6 , Myung-Ju Ahn  7 , Ho Yun Lee  8   9
Affiliations

Prognostic Impact of Longitudinal Monitoring of Radiomic Features in Patients with Advanced Non-Small Cell Lung Cancer

So Hyeon Bak et al. Sci Rep. .

Abstract

Tumor growth dynamics vary substantially in non-small cell lung cancer (NSCLC). We aimed to develop biomarkers reflecting longitudinal change of radiomic features in NSCLC and evaluate their prognostic power. Fifty-three patients with advanced NSCLC were included. Three primary variables reflecting patterns of longitudinal change were extracted: area under the curve of longitudinal change (AUC1), beta value reflecting slope over time, and AUC2, a value obtained by considering the slope and area over the longitudinal change of features. We constructed models for predicting survival with multivariate cox regression, and identified the performance of these models. AUC2 exhibited an excellent correlation between patterns of longitudinal volume change and a significant difference in overall survival time. Multivariate regression analysis based on cut-off values of radiomic features extracted from baseline CT and AUC2 showed that kurtosis of positive pixel values and surface area from baseline CT, AUC2 of density, skewness of positive pixel values, and entropy at inner portion were associated with overall survival. For the prediction model, the areas under the receiver operating characteristic curve (AUROC) were 0.948 and 0.862 at 1 and 3 years of follow-up, respectively. Longitudinal change of radiomic tumor features may serve as prognostic biomarkers in patients with advanced NSCLC.

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Conflict of interest statement

All authors contributed to the research, writing, and preparation of this manuscript. None of the authors has a potential conflict of interest or financial relationship to disclose.

Figures

Figure 1
Figure 1
Schematic illustration of the values AUC1, beta and AUC2. AUC1 is defined as the area under the longitudinal change of values. The beta value is the slope calculated by linear regression over time, and represents the slope of the overall longitudinal change. AUC2 is value obtained by considering the slope and area of the longitudinal change. More specifically, subtraction is performed when the slope is negative, and addition is performed when the slope is positive.
Figure 2
Figure 2
Relationship between AUC2 and curve pattern of volume change (A) or beta value (B). (A) The six patterns of volume changes were follows: (1) reduction only but progressive disease due to nontarget lesions, (2) slow progression after rapid response, (3) rapid progression after rapid response, (4) slow progression after slight reduction, (5) rapid progression after slight reduction, and (6) sequential progression.
Figure 3
Figure 3
Comparison of Kaplan-Meier curve for overall survival of patients stratified by AUC1 (A), beta value (B) and AUC2 (C).
Figure 4
Figure 4
Time-dependent receiver operating characteristic (ROC) curve for prediction model with six features predicting overall survival. The area under the ROC curve (AUROC) was 0.948 at 1 year (A), and 0.862 at 3 years (B).
See this image and copyright information in PMC

References

    1. Hayes SA, et al. Comparison of CT volumetric measurement with RECIST response in patients with lung cancer. European journal of radiology. 2016;85:524–533. doi: 10.1016/j.ejrad.2015.12.019. - DOI - PMC - PubMed
    1. Nishino M, Hatabu H, Johnson BE, McLoud TC. State of the art: Response assessment in lung cancer in the era of genomic medicine. Radiology. 2014;271:6–27. doi: 10.1148/radiol.14122524. - DOI - PMC - PubMed
    1. Hochmair MJ, Schwab S, Burghuber OC, Krenbek D, Prosch H. Symptomatic pseudo-progression followed by significant treatment response in two lung cancer patients treated with immunotherapy. Lung cancer (Amsterdam, Netherlands) 2017;113:4–6. doi: 10.1016/j.lungcan.2017.08.020. - DOI - PubMed
    1. Carter BW, Halpenny DF, Ginsberg MS, Papadimitrakopoulou VA, de Groot PM. Immunotherapy in Non-Small Cell Lung Cancer Treatment: Current Status and the Role of Imaging. Journal of thoracic imaging. 2017;32:300–312. doi: 10.1097/rti.0000000000000291. - DOI - PubMed
    1. McCoach CE, et al. Exploratory analysis of the association of depth of response and survival in patients with metastatic non-small-cell lung cancer treated with a targeted therapy or immunotherapy. Annals of oncology: official journal of the European Society for Medical Oncology. 2017;28:2707–2714. doi: 10.1093/annonc/mdx414. - DOI - PMC - PubMed

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