DOI: 10.1007/s00259-018-4139-4Pages: 1-12

Discovery of pre-therapy 2-deoxy-2-18F-fluoro-D-glucose positron emission tomography-based radiomics classifiers of survival outcome in non-small-cell lung cancer patients

1. Hammersmith Hospital, Imperial College London Cancer Imaging Centre, Department of Surgery & Cancer

2. Hammersmith Hospital, Imperial College Healthcare NHS Trust, Departments of Clinical Oncology, Radiology and Nuclear Medicine

3. Charing Cross Hospital

4. St James’s University Hospital, Department of Nuclear Medicine, Level 1, Bexley Wing

5. University of Leeds, Leeds Institute of Cancer and Pathology, School of Medicine

6. Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital

7. The Royal Marsden Hospital, Department of Nuclear Medicine

8. Nottingham University Hospital, Department of Nuclear Medicine, Queen’s Medical Centre

9. Mount Vernon Hospital, Department of Clinical Oncology

Correspondence to:
Eric O. Aboagye
Email: eric.aboagye@imperial.ac.uk

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Abstract

Purpose

The aim of this multi-center study was to discover and validate radiomics classifiers as image-derived biomarkers for risk stratification of non-small-cell lung cancer (NSCLC).

Patients and methods

Pre-therapy PET scans from a total of 358 Stage I–III NSCLC patients scheduled for radiotherapy/chemo-radiotherapy acquired between October 2008 and December 2013 were included in this seven-institution study. A semi-automatic threshold method was used to segment the primary tumors. Radiomics predictive classifiers were derived from a training set of 133 scans using TexLAB v2. Least absolute shrinkage and selection operator (LASSO) regression analysis was used for data dimension reduction and radiomics feature vector (FV) discovery. Multivariable analysis was performed to establish the relationship between FV, stage and overall survival (OS). Performance of the optimal FV was tested in an independent validation set of 204 patients, and a further independent set of 21 (TESTI) patients.

Results

Of 358 patients, 249 died within the follow-up period [median 22 (range 0–85) months]. From each primary tumor, 665 three-dimensional radiomics features from each of seven gray levels were extracted. The most predictive feature vector discovered (FVX) was independent of known prognostic factors, such as stage and tumor volume, and of interest to multi-center studies, invariant to the type of PET/CT manufacturer. Using the median cut-off, FVX predicted a 14-month survival difference in the validation cohort (N = 204, p = 0.00465; HR = 1.61, 95% CI 1.16–2.24). In the TESTI cohort, a smaller cohort that presented with unusually poor survival of stage I cancers, FVX correctly indicated a lack of survival difference (N = 21, p = 0.501). In contrast to the radiomics classifier, clinically routine PET variables including SUVmax, SUVmean and SUVpeak lacked any prognostic information.

Conclusion

PET-based radiomics classifiers derived from routine pre-treatment imaging possess intrinsic prognostic information for risk stratification of NSCLC patients to radiotherapy/chemo-radiotherapy.

This article is freely available, click here to access the full text/PDF

  • Accepted: Aug 16, 2018
  • Online: Sep 1, 2018

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