Association of metabolic and genetic heterogeneity in head and neck squamous cell carcinoma with prognostic implications: integration of FDG PET and genomic analysis

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Abstract

Purpose

The linkage between the genetic and phenotypic heterogeneity of the tumor has not been thoroughly evaluated. Herein, we investigated how the genetic and metabolic heterogeneity features of the tumor are associated with each other in head and neck squamous cell carcinoma (HNSC). We further assessed the prognostic significance of those features.

Methods

The mutant-allele tumor heterogeneity (MATH) score ( n = 508), a genetic heterogeneity feature, and tumor glycolysis feature (GlycoS) ( n = 503) were obtained from the HNSC dataset in the cancer genome atlas (TCGA). We identified matching patients ( n = 33) who underwent 18F-fluorodeoxyglucose positron emission tomography (FDG PET) from the cancer imaging archive (TCIA) and obtained the following information from the primary tumor: metabolic, metabolic-volumetric, and metabolic heterogeneity features. The association between the genetic and metabolic features and their prognostic values were assessed.

Results

Tumor metabolic heterogeneity and metabolic-volumetric features showed a mild degree of association with MATH ( n = 25, ρ = 0.4~0.5, P < 0.05 for all features). The patients with higher FDG PET features and MATH died sooner. Combination of MATH and tumor metabolic heterogeneity features showed a better stratification of prognosis than MATH. Also, higher MATH and GlycoS were associated with significantly worse overall survival ( n = 499, P = 0.002 and 0.0001 for MATH and GlycoS, respectively). Furthermore, both MATH and GlycoS independently predicted overall survival after adjusting for clinicopathologic features and the other ( P = 0.015 and 0.006, respectively).

Conclusion

Both tumor metabolic heterogeneity and metabolic-volumetric features assessed by FDG PET showed a mild degree of association with genetic heterogeneity in HNSC. Both metabolic and genetic heterogeneity features were predictive of survival and there was an additive prognostic value when the metabolic and genetic heterogeneity features were combined. Also, MATH and GlycoS were independent prognostic factors in HNSC; they can be used for precise prognostication once validated.

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Most cited references 32

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LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity

Christophe Nioche, Fanny Orlhac, Sarah Boughdad … (2018)

Textural and shape analysis is gaining considerable interest in medical imaging, particularly to identify parameters characterizing tumor heterogeneity and to feed radiomic models. Here, we present a free, multiplatform, and easy-to-use freeware called LIFEx, which enables the calculation of conventional, histogram-based, textural, and shape features from PET, SPECT, MR, CT, and US images, or from any combination of imaging modalities. The application does not require any programming skills and was developed for medical imaging professionals. The goal is that independent and multicenter evidence of the usefulness and limitations of radiomic features for characterization of tumor heterogeneity and subsequent patient management can be gathered. Many options are offered for interactive textural index calculation and for increasing the reproducibility among centers. The software already benefits from a large user community (more than 800 registered users), and interactions within that community are part of the development strategy.Significance: This study presents a user-friendly, multi-platform freeware to extract radiomic features from PET, SPECT, MR, CT, and US images, or any combination of imaging modalities. Cancer Res; 78(16); 4786-9. ©2018 AACR.

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18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort.

Mathieu Hatt, Mohamed Majdoub, Martin Vallières … (2015)

Intratumoral uptake heterogeneity in (18)F-FDG PET has been associated with patient treatment outcomes in several cancer types. Textural feature analysis is a promising method for its quantification. An open issue associated with textural features for the quantification of intratumoral heterogeneity concerns its added contribution and dependence on the metabolically active tumor volume (MATV), which has already been shown to be a significant predictive and prognostic parameter. Our objective was to address this question using a larger cohort of patients covering different cancer types.

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Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis.

Fanny Orlhac, Irène Buvat, Michael Soussan … (2014)

Texture indices are of growing interest for tumor characterization in (18)F-FDG PET. Yet, on the basis of results published in the literature so far, it is unclear which indices should be used, what they represent, and how they relate to conventional indices such as standardized uptake values (SUVs), metabolic volume (MV), and total lesion glycolysis (TLG). We investigated in detail 31 texture indices, 5 first-order statistics (histogram indices) derived from the gray-level histogram of the tumor region, and their relationship with SUV, MV, and TLG in 3 different tumor types. Three patient groups corresponding to 3 cancer types at baseline were studied independently: patients with metastatic colorectal cancer (72 lesions), non-small cell lung cancer (24 lesions), and breast cancer (54 lesions). Thirty-one texture indices were studied in addition to SUVs, MV, and TLG, and 5 indices extracted from histogram analysis were also investigated. The relationships between indices were studied as well as the robustness of the various texture indices with respect to the parameters involved in the calculation method (sampling schemes and tumor volume of interest). Regardless of the patient group, many indices were highly correlated (Pearson correlation coefficient |r| ≥ 0.80), making it desirable to focus on only a few uncorrelated indices. Three histogram indices were highly correlated with SUVs (|r| ≥ 0.84). Four texture indices were highly correlated with MV, and none was highly correlated with SUVs (|r| ≤ 0.55). The resampling formula used to calculate texture indices had a substantial impact, and resampling using at least 32 discrete values should be used for texture indices calculation. The texture indices change as a function of the segmentation method was higher than that of peak and maximum SUVs but less than mean SUV for 5 texture indices and was larger than that of MV for 14 texture indices and for the 5 histogram indices. All these results were extremely consistent across the 3 tumor types and explained many of the observations reported in the literature so far. None of the histogram indices and only 17 of 31 texture indices were robust with respect to the tumor-segmentation method. An appropriate resampling formula with at least 32 gray levels should be used to avoid introducing a misleading relationship between texture indices and SUV. Some texture indices are highly correlated or strongly correlate with MV whatever the tumor type. Such correlation should be accounted for when interpreting the usefulness of texture indices for tumor characterization, which might call for systematic multivariate analyses.

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Author and article information

Contributors

Hongyoon Choi: 82-2-2072-3347 , chy1000@gmail.com

Hyung-Jun Im:

ORCID: http://orcid.org/0000-0002-4368-6685

031-888-9187 , iiihjjj@gmail.com , http://tmtl.snu.ac.kr

Journal

Journal ID (nlm-ta): EJNMMI Res

Journal ID (iso-abbrev): EJNMMI Res

Title: EJNMMI Research

Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )

ISSN (Electronic): 2191-219X

Publication date (Electronic): 21 November 2019

Publication date PMC-release: 21 November 2019

Publication date Collection: 2019

Volume: 9

Electronic Location Identifier: 97

Affiliations

[1 ]ISNI 0000 0004 0470 5905, GRID grid.31501.36, Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, , Seoul National University, ; Seoul, Republic of Korea

[2 ]ISNI 0000 0004 0470 5905, GRID grid.31501.36, Radiation Medicine Research Institute, , Seoul National University College of Medicine, ; Seoul, Republic of Korea

[3 ]ISNI 0000 0004 0647 5752, GRID grid.414966.8, Department of Nuclear Medicine, , Seoul ST. Mary’s Hospital, ; Seoul, Republic of Korea

[4 ]ISNI 0000 0004 0470 5905, GRID grid.31501.36, Department of Clinical Pharmacology and Therapeutics, , Seoul National University College of Medicine and Hospital, ; Seoul, Republic of Korea

[5 ]ISNI 0000 0001 0302 820X, GRID grid.412484.f, Department of Nuclear Medicine, , Seoul National University Hospital, ; Seoul, Republic of Korea

Author information

Hyung-Jun Im http://orcid.org/0000-0002-4368-6685

Article

Publisher ID: 563

DOI: 10.1186/s13550-019-0563-0

PMC ID: 6872695

PubMed ID: 31754877

SO-VID: 1914ecfa-aa89-4782-8747-b455f07a9518

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

History

Date received : 24 July 2019

Date accepted : 24 September 2019

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100002701, Ministry of Education;

Award ID: NRF-2017R1D1A1B03035556

Award Recipient : Hyung-Jun Im

Funded by: Ministry of Science and ICT (KR)

Award ID: NRF-2019M2D2A1A01058210

Award Recipient : Hyung-Jun Im

Funded by: FundRef http://dx.doi.org/10.13039/501100003653, Korea National Institute of Health;

Award ID: HI18C0886

Award ID: HI19C0339

Award Recipient : Hyung-Jun Im

Custom metadata

ScienceOpen disciplines: Radiology & Imaging

Keywords: 18f-fluorodeoxyglucose,positron emission tomography,heterogeneity,radiogenomics,math

Data availability:

ScienceOpen disciplines: Radiology & Imaging

Keywords: 18f-fluorodeoxyglucose, positron emission tomography, heterogeneity, radiogenomics, math

Association of metabolic and genetic heterogeneity in head and neck squamous cell carcinoma with prognostic implications: integration of FDG PET and genomic analysis

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Most cited references 32

LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity

18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort.

Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis.

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