Measurement variability of liver metastases from neuroendocrine tumors on different magnetic resonance imaging sequences

Lestra, T.; Kanagaratnam, Lukshe; Mulé, Sebastien; Janvier, A; Brixi, Hédia; Cadiot, Guillaume; Dohan, A.; Hoeffel, Christine

doi:10.1016/j.diii.2017.12.009

Cited by 17 publications

(20 citation statements)

References 18 publications

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“…Hepatic metastases were analyzed with respect to tumor long axis (i.e., largest axial diameter), number of metastases present in the liver (with a maximum of 10 metastases per patient). Metastasis dimensions were measured using calipers in the axial plane on magnified DW images [18].…”

Section: Methodsmentioning

confidence: 99%

Magnetic Resonance Imaging May Be Able to Identify the Origin of Neuroendocrine Tumor Liver Metastases

et al. 2020

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Objectives: To discriminate hepatic metastases from pancreatic neuroendocrine tumors (pNET) and hepatic metastases from midgut neuroendocrine tumors (mNET) with magnetic resonance imaging (MRI). Methods: MRI examinations of 24 patients with hepatic metastases from pNET were quantitatively and qualitatively assessed by two blinded readers and compared to those obtained in 23 patients with hepatic metastases from mNET. Inter-reader agreement was calculated with kappa and intraclass correlation coefficient (ICC). Sensitivity, specificity and accuracy of each variable for the diagnosis of hepatic metastasis from pNET were calculated. Associations between variables and primary tumor (i.e., pNET vs. mNET) were assessed at univariate and multivariate analysis. A nomogram was developed and validated using an external cohort of 20 patients with pNET and 20 patients with mNET. Results: Interobserver agreement was strong to perfect (k=0.893-1) for qualitative criteria and excellent for quantitative variables (ICC: 0.9817-0.9996). At univariate analysis, homogeneity on T1-weighted images was the most discriminating variable for the diagnosis of pNET (OR, 6.417; P=0.013) with greatest sensitivity (88%; 21/24; 95% CI: 68-97%). At multivariate analysis, tumor homogeneity on T1-weighted images (P=0.007; OR, 17.607; 95%CI: 2.179–142.295) and target sign on DW images (P=0.007; OR, 19.869; 95%CI: 2.305–171.276) were independently associated with pNET. Nomogram yielded a corrected AUC of 0.894 (95%CI: 0.796–0.992) for the diagnosis of pNET in the training cohort and 0.805 (95%CI: 0.662–0.948) in the validation cohort. Conclusions: MRI provides qualitative features that can help discriminate between hepatic metastases from pNET and those from mNET.

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Section: Methodsmentioning

confidence: 99%

Magnetic Resonance Imaging May Be Able to Identify the Origin of Neuroendocrine Tumor Liver Metastases

et al. 2020

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“…Compared to T2-weighted, DWI is superior in the diagnostic work-up of focal liver lesions, showing better performance in terms of detection rate and sensitivity even for small lesions (1–3 cm), which may be otherwise missed or misinterpreted [ 30 ]. DWI is considered reliable for detection and monitoring of liver metastases, especially those from neuroendocrine tumors, which are highly-cellulated, thereby being easily recognized in DWI [ 31 ].…”

Section: Hepatobiliarymentioning

confidence: 99%

Diffusion-Weighted Imaging in Oncology: An Update

Messina

Bignone

Bruno

et al. 2020

Cancers

125

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To date, diffusion weighted imaging (DWI) is included in routine magnetic resonance imaging (MRI) protocols for several cancers. The real additive role of DWI lies in the “functional” information obtained by probing the free diffusivity of water molecules into intra and inter-cellular spaces that in tumors mainly depend on cellularity. Although DWI has not gained much space in some oncologic scenarios, this non-invasive tool is routinely used in clinical practice and still remains a hot research topic: it has been tested in almost all cancers to differentiate malignant from benign lesions, to distinguish different malignant histotypes or tumor grades, to predict and/or assess treatment responses, and to identify residual or recurrent tumors in follow-up examinations. In this review, we provide an up-to-date overview on the application of DWI in oncology.

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“…These include the comparatively high costs due to the contrast agent itself and the resulting examination time, the general side effects and possible deposition of gadolinium [ 48 ]. As a non-contrast alternative with high sensitivity, DWI sequences can also be effectively used to measure NELM without the disadvantages of Gd-EOB MRI [ 49 , 50 ]. Currently, however, DWI sequences are used for detection rather than measurements of liver lesions.…”

Section: Discussionmentioning

confidence: 99%

Automatized Hepatic Tumor Volume Analysis of Neuroendocrine Liver Metastases by Gd-EOB MRI—A Deep-Learning Model to Support Multidisciplinary Cancer Conference Decision-Making

Fehrenbach

Xin

Hartenstein

et al. 2021

Cancers

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Background: Rapid quantification of liver metastasis for diagnosis and follow-up is an unmet medical need in patients with secondary liver malignancies. We present a 3D-quantification model of neuroendocrine liver metastases (NELM) using gadoxetic-acid (Gd-EOB)-enhanced MRI as a useful tool for multidisciplinary cancer conferences (MCC). Methods: Manual 3D-segmentations of NELM and livers (149 patients in 278 Gd-EOB MRI scans) were used to train a neural network (U-Net architecture). Clinical usefulness was evaluated in another 33 patients who were discussed in our MCC and received a Gd-EOB MRI both at baseline and follow-up examination (n = 66) over 12 months. Model measurements (NELM volume; hepatic tumor load (HTL)) with corresponding absolute (ΔabsNELM; ΔabsHTL) and relative changes (ΔrelNELM; ΔrelHTL) between baseline and follow-up were compared to MCC decisions (therapy success/failure). Results: Internal validation of the model’s accuracy showed a high overlap for NELM and livers (Matthew’s correlation coefficient (φ): 0.76/0.95, respectively) with higher φ in larger NELM volume (φ = 0.80 vs. 0.71; p = 0.003). External validation confirmed the high accuracy for NELM (φ = 0.86) and livers (φ = 0.96). MCC decisions were significantly differentiated by all response variables (ΔabsNELM; ΔabsHTL; ΔrelNELM; ΔrelHTL) (p < 0.001). ΔrelNELM and ΔrelHTL showed optimal discrimination between therapy success or failure (AUC: 1.000; p < 0.001). Conclusion: The model shows high accuracy in 3D-quantification of NELM and HTL in Gd-EOB-MRI. The model’s measurements correlated well with MCC’s evaluation of therapeutic response.

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Measurement variability of liver metastases from neuroendocrine tumors on different magnetic resonance imaging sequences

Abstract: An increase<9% in measurement and a decrease of -10% on DWI should not be considered as true changes, with 95% confidence, versus 24% and -22% on arterial and 17%, -16% on portal phases, respectively. DWI might thus be the most reliable MR sequence for monitoring size variations of LMNETs.

Cited by 17 publications

References 18 publications

Magnetic Resonance Imaging May Be Able to Identify the Origin of Neuroendocrine Tumor Liver Metastases

Magnetic Resonance Imaging May Be Able to Identify the Origin of Neuroendocrine Tumor Liver Metastases

Diffusion-Weighted Imaging in Oncology: An Update

Automatized Hepatic Tumor Volume Analysis of Neuroendocrine Liver Metastases by Gd-EOB MRI—A Deep-Learning Model to Support Multidisciplinary Cancer Conference Decision-Making

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