Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I–III

  • Marie-Charlotte Desseroit
  • Dimitris Visvikis
  • Florent Tixier
  • Mohamed Majdoub
  • Rémy Perdrisot
  • Rémy Guillevin
  • Catherine Cheze Le Rest
  • Mathieu Hatt
Original Article

DOI: 10.1007/s00259-016-3325-5

Cite this article as:
Desseroit, MC., Visvikis, D., Tixier, F. et al. Eur J Nucl Med Mol Imaging (2016) 43: 1477. doi:10.1007/s00259-016-3325-5

Abstract

Purpose

Our goal was to develop a nomogram by exploiting intratumour heterogeneity on CT and PET images from routine 18F-FDG PET/CT acquisitions to identify patients with the poorest prognosis.

Methods

This retrospective study included 116 patients with NSCLC stage I, II or III and with staging 18F-FDG PET/CT imaging. Primary tumour volumes were delineated using the FLAB algorithm and 3D Slicer™ on PET and CT images, respectively. PET and CT heterogeneities were quantified using texture analysis. The reproducibility of the CT features was assessed on a separate test–retest dataset. The stratification power of the PET/CT features was evaluated using the Kaplan-Meier method and the log-rank test. The best standard metric (functional volume) was combined with the least redundant and most prognostic PET/CT heterogeneity features to build the nomogram.

Results

PET entropy and CT zone percentage had the highest complementary values with clinical stage and functional volume. The nomogram improved stratification amongst patients with stage II and III disease, allowing identification of patients with the poorest prognosis (clinical stage III, large tumour volume, high PET heterogeneity and low CT heterogeneity).

Conclusion

Intratumour heterogeneity quantified using textural features on both CT and PET images from routine staging 18F-FDG PET/CT acquisitions can be used to create a nomogram with higher stratification power than staging alone.

Keywords

PET/CT Textural features Heterogeneity Prognosis NSCLC 

Supplementary material

259_2016_3325_MOESM1_ESM.docx (507 kb)
Figure 1The workflow of the nomogram construction. (DOCX 506 kb)
259_2016_3325_MOESM2_ESM.docx (317 kb)
Figure 2The Kaplan-Meier curves obtained for stratification with (a) stage and MATV (b) stage, MATV and PET heterogeneity and (c) stage, MATV and CT heterogeneity. (DOCX 317 kb)
259_2016_3325_MOESM3_ESM.docx (317 kb)
ESM 1(DOCX 317 kb)
259_2016_3325_MOESM4_ESM.docx (314 kb)
ESM 2(DOCX 313 kb)
259_2016_3325_MOESM5_ESM.docx (305 kb)
Figure 3Distribution of co-occurrence entropy from FDG PET and zone percentage from attenuation CT for stage II and III patients. (DOCX 305 kb)
259_2016_3325_MOESM6_ESM.docx (353 kb)
Figure 4Nomogram result excluding the 14 patients treated with palliative chemotherapy only (N=102). (DOCX 353 kb)
259_2016_3325_MOESM7_ESM.docx (56 kb)
Figure 5Distributions of (a) FDG PET entropy and (b) CT zone percentage according to N staging in stage II and III patients. (DOCX 55 kb)
259_2016_3325_MOESM8_ESM.docx (56 kb)
ESM 3(DOCX 56 kb)
259_2016_3325_MOESM9_ESM.docx (37 kb)
Supplemental table 1(DOCX 36 kb)

Funding information

Funder NameGrant NumberFunding Note
CominLabs
  • ANR-10-LABX-07-01
Varian
    City of Brest

      Copyright information

      © Springer-Verlag Berlin Heidelberg 2016

      Authors and Affiliations

      • Marie-Charlotte Desseroit
        • 1
        • 2
      • Dimitris Visvikis
        • 2
      • Florent Tixier
        • 1
        • 3
      • Mohamed Majdoub
        • 2
      • Rémy Perdrisot
        • 1
        • 3
      • Rémy Guillevin
        • 3
        • 4
      • Catherine Cheze Le Rest
        • 1
        • 3
      • Mathieu Hatt
        • 2
      1. 1.Nuclear MedicineUniversity HospitalPoitiersFrance
      2. 2.INSERM, UMR 1101, LaTIM, CHRU Morvan, University of BrestBrestFrance
      3. 3.Medical school, EE DACTIMUniversity of PoitiersPoitiersFrance
      4. 4.RadiologyUniversity hospitalPoitiersFrance

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