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European Radiology

, Volume 29, Issue 1, pp 468–475 | Cite as

Clustering approach to identify intratumour heterogeneity combining FDG PET and diffusion-weighted MRI in lung adenocarcinoma

  • Jonghoon Kim
  • Seong-Yoon Ryu
  • Seung-Hak Lee
  • Ho Yun LeeEmail author
  • Hyunjin ParkEmail author
Oncology
  • 249 Downloads

Abstract

Objectives

Malignant tumours consist of biologically heterogeneous components; identifying and stratifying those various subregions is an important research topic. We aimed to show the effectiveness of an intratumour partitioning method using clustering to identify highly aggressive tumour subregions, determining prognosis based on pre-treatment PET and DWI in stage IV lung adenocarcinoma.

Methods

Eighteen patients who underwent both baseline PET and DWI were recruited. Pre-treatment imaging of SUV and ADC values were used to form intensity vectors within manually specified ROIs. We applied k-means clustering to intensity vectors to yield distinct subregions, then chose the subregion that best matched the criteria for high SUV and low ADC to identify tumour subregions with high aggressiveness. We stratified patients into high- and low-risk groups based on subregion volume with high aggressiveness and conducted survival analyses. This approach is referred to as the partitioning approach. For comparison, we computed tumour subregions with high aggressiveness without clustering and repeated the described procedure; this is referred to as the voxel-wise approach.

Results

The partitioning approach led to high-risk (median SUVmax = 14.25 and median ADC = 1.26x10-3 mm2/s) and low-risk (median SUVmax = 14.64 and median ADC = 1.09x10-3 mm2/s) subgroups. Our partitioning approach identified significant differences in survival between high- and low-risk subgroups (hazard ratio, 4.062, 95% confidence interval, 1.21 – 13.58, p-value: 0.035). The voxel-wise approach did not identify significant differences in survival between high- and low-risk subgroups (p-value: 0.325).

Conclusion

Our partitioning approach identified intratumour subregions that were predictors of survival.

Key Points

• Multimodal imaging of PET and DWI is useful for assessing intratumour heterogeneity.

• Data-driven clustering identified subregions which might be highly aggressive for lung adenocarcinoma.

• The data-driven partitioning results might be predictors of survival.

Keywords

Clustering analysis Survival analysis Adenocarcinoma of lung Intratumour heterogeneity Multimodal imaging 

Abbreviations

DCR

Disease control rate

HR

Hazard ratio

ORR

Overall response rate

OS

Overall survival

PFS

Progression free survival

Notes

Funding

This study was supported by the Institute for Basic Science (grant number IBS-R015-D1), the National Research Foundation of Korea (grant numbers NRF-2016R1A2B4008545, NRF-2016R1A2B4013046, and NRF-2017M2A2A7A02018568), the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (grant number HI17C0086), and Guerbet Korea Ltd.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Ho Yun Lee.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• prospective

• diagnostic or prognostic study

• performed at one institution

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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of Electronic Electrical and Computer EngineeringSungkyunkwan UniversitySuwonKorea
  2. 2.Department of Radiology and Center for Imaging Science, Samsung Medical CenterSungkyunkwan University School of MedicineSeoulKorea
  3. 3.School of Electronic and Electrical EngineeringSungkyunkwan UniversitySuwonKorea
  4. 4.Center for Neuroscience Imaging ResearchInstitute for Basic ScienceSuwonKorea

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