Are we reproducible in measurement of NET liver metastasis?

Salma Moalla, Julia Arfi Rouche, Stéphanie Foulon, Caroline Caramella, Nils Ternes, David Planchard, Diane Goere, Michel Ducreux, Jean Yves Scoazec, Frederic Deschamps, Clarisse Dromain, Eric Baudin

    Research output: Contribution to journalArticlepeer-review

    5 Citations (Scopus)

    Abstract

    Accurate measurement of well-differentiated neuroendocrine tumours (NET) liver metastases is critical to determine tumour slope and to assess treatment efficacy. Our objectives were to determine which CT or MRI sequence is the most reproducible to measure NET liver metastases and to assess the percentage of variability of measurements. Intra and inter-observer variability were studied on triphasic abdominal CT or liver MRI in 22 and 32 NET patients respectively. Patients were treatment-naïve or under somatostatin analogues. A maximum of 5 liver target lesions per patient was defined and three radiologists measured them on each sequence. Reproducibility were analysed by calculating the relative variation (RV) as defined by RECIST criteria. We analysed 1656 target measurements for CT and 3384 for MRI. Intra-observers RV were better than inter-observers. T2 for MRI and portal-phase for CT were associated with the lowest measurement variability. The MRI sequence offering the best intra and inter-observer reproducibility is the T2W-sequence. MRI allows more reproducible measurement than CT (inter-observer RV <20% in 96.8% for MRI and 81% for CT). Our study demonstrates intermediate to high imaging reproducibility of liver metastases measurements in NET patients. Non-enhanced MRI should be preferred to triphasic-CT for follow-up, assessment of treatment and trials.

    Original languageEnglish
    Pages (from-to)1121-1127
    Number of pages7
    JournalDigestive and Liver Disease
    Volume49
    Issue number10
    DOIs
    Publication statusPublished - 1 Oct 2017

    Keywords

    • Metastasis
    • Neuroendocrine tumor
    • RECIST
    • Reproducibility

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