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Correlations between metabolic texture features, genetic heterogeneity, and mutation burden in patients with lung cancer

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

This study investigated the correlations between parameters of 18F-fluorodeoxyglucose (FDG) uptake on positron emission tomography (PET) scan and indices of genetic properties, heterogeneity index (HI), and tumor mutation burden (TMB), in patients with lung cancer.

Methods

We produced 106 PET indices for each tumor site that underwent genomic analysis in a total of 176 study subjects (age, 62.0 ± 10.0 y; males, 68.2%), comprising 101 adenocarcinoma (ADC), 29 squamous cell carcinoma (SQCC), and 46 small cell lung cancer (SCLC) patients. We then examined the correlations of the PET parameters with genetic properties of HI and TMB, according to pathology and tumor site.

Results

Comparisons between PET parameters and the genetic properties with false discovery rate (FDR) correction revealed that the surface standard uptake value (SUV) entropy of SUV statistics had a significant correlation with HI only in patients with SCLC who underwent a genetic test in lymph nodes (r = 0.592, p = 0.028), whereas PET parameters did not show a significant correlation with HI or TMB in patients with SCLC who underwent a genetic test in lung tissue. In patients with ADC and SQCC, there was no significant correlation between PET parameters and the genetic properties. Although SUVmax showed raw p values less than 0.05 in correlation with HI (r = 0.315, raw p = 0.048) and TMB (r = 0.206, raw p = 0.043) in ADC, and SUVpeak had a raw p value less than 0.05 in correlation with HI (r = 0.394, raw p = 0.046) in SQCC, these parameters were not significant when corrected by FDR.

Conclusions

In this study, surface SUV entropy had a significant correlation with HI in SCLC. Regarding other PET parameters and tumors, no significant correlation with genetic parameters existed.

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Acknowledgements

The authors thank Kyunga Kim and Min-Ji Kim from the Statistics and Data Center, Research Institute for Future Medicine, Samsung Medical Center for their important contributions to our statistical analysis. They also thank Yu-Hua Fang from Department of Biomedical Engineering, National Cheng Kung University and Hongyoon Choi from Department of Nuclear Medicine, Seoul National University Hospital for their important contributions to our imaging analysis.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No.NRF-2016R1C1B2013411).

Author information

Author notes

  1. Seung Hwan Moon and Jinho Kim contributed equally to this work.

  2. Seung Hwan Moon and Se-Hoon Lee contributed equally to this work.

Authors and Affiliations

  1. Department of Nuclear Medicine and Molecular Imaging, Samsung Medical Center, Seoul, Republic of Korea

    Seung Hwan Moon, Joon Young Choi, Kyung-Han Lee & Byung-Tae Kim

  2. Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea

    Jinho Kim, Je-Gun Joung & Hongui Cha

  3. Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea

    Hongui Cha & Se-Hoon Lee

  4. Samsung Genome Institute, Samsung Medical Center, Samsung Advanced Institute of Health Science and Technology, Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Woong-Yang Park

  5. Division of Hematology/Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Jin Seok Ahn, Myung-Ju Ahn, Keunchil Park & Se-Hoon Lee

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  1. Seung Hwan Moon
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Corresponding authors

Correspondence to Seung Hwan Moon or Se-Hoon Lee.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

The institutional review board approved that the requirement for written informed consents were waived in this study.

Electronic supplementary material

Supplemental Figure 1

The variance of FDG PET features. CV of non-texture features (a) and texture features (b) which were obtained from nine different volumetric tumor segmentations on PET/CT are plotted. CV, Coefficient of variance (PNG 989 kb)

High resolution image a (TIF 74 kb)

b (PNG 2228 kb)

High resolution image b (TIF 126 kb)

Supplemental Figure 2

Visualization of the correlation between genetic parameters and image features of PET/CT. Heatmaps of correlation values of 30 PET features with HI in 113 subjects (a) and TMB in 176 subjects (b) according to tumor pathology and site are illustrated. HI, heterogenetic index; TMB, tumor mutation burden (JPEG 178 kb)

b (JPEG 187 kb)

Supplemental Table 1

(DOCX 32 kb)

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Moon, S.H., Kim, J., Joung, JG. et al. Correlations between metabolic texture features, genetic heterogeneity, and mutation burden in patients with lung cancer. Eur J Nucl Med Mol Imaging 46, 446–454 (2019). https://doi.org/10.1007/s00259-018-4138-5

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  • DOI: https://doi.org/10.1007/s00259-018-4138-5

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