Published July 2019 | Version v1
Journal article

Exploratory radiomic features from integrated 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging are associated with contemporaneous metastases in oesophageal/gastroesophageal cancer

  • 1. Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna (Italy)
  • 2. Advanced Research Center for Electronic Systems (ARCES), University of Bologna (Italy)
  • 3. Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London (United Kingdom)
  • 4. King's College London & Guy's and St Thomas' PET Centre, St Thomas' Hospital, London (United Kingdom)
  • 5. MR Research Collaborations, Siemens Healthcare, Frimley (United Kingdom)
  • 6. Department of Computer Science and Engineering (DISI), University of Bologna (Italy)
  • 7. Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, Lambeth Wing, St Thomas Hospital, London (United Kingdom)

Description

The purpose of this study was to determine if 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MRI) features are associated with contemporaneous metastases in patients with oesophageal/gastroesophageal cancer. Following IRB approval and informed consent, patients underwent a staging PET/MRI following 18F-FDG injection (326 ± 28 MBq) and 156 ± 23 min uptake time. First-order histogram and second-order grey level co-occurrence matrix features were computed for PET standardized uptake value (SUV) and MRI T1-W, T2-W, diffusion weighted (DWI) and apparent diffusion coefficient (ADC) images for the whole tumour volume. K-means clustering assessed the correlation of feature-pairs with metastases. Multivariate analysis of variance (MANOVA) was performed to assess the statistical separability of the groups identified by feature-pairs. Sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and accuracy (ACC) were calculated for these features and compared with SUVmax, ADCmean and maximum diameter alone for predicting contemporaneous metastases. Twenty patients (18 males, 2 female; median 67 years, range 52–86) comprised the final study cohort; ten patients had metastases. Lower second-order SUV entropy combined with higher second-order ADC entropy were the best feature-pair for discriminating metastatic patients, MANOVA p value <0.001 (SN = 80%, SP = 80%, PPV = 80%, NPV = 80%, ACC = 80%). SUVmax (SN = 30%, SP = 80%, PPV = 60%, NPV = 53%, ACC = 55%), ADCmean (SN = 20%, SP = 70%, PPV = 40%, NPV = 47%, ACC = 45%) and tumour maximum diameter (SN = 10%, SP = 90%, PPV = 50%, NPV = 50%, ACC = 50%) had poorer sensitivity and accuracy. High ADC entropy combined with low SUV entropy is associated with a higher prevalence of metastases and a promising initial signature for future study.

Availability note (English)

Available from: http://dx.doi.org/10.1007/s00259-019-04306-7

Additional details

Identifiers

Publishing Information

Journal Title
European Journal of Nuclear Medicine and Molecular Imaging
Journal Volume
46
Journal Issue
7
Journal Page Range
p. 1478-1484
ISSN
1619-7070
CODEN
EJNMA6