Magnetic Resonance Imaging of Neuroendocrine Tumor Hepatic Metastases

Morse, Brian; Jeong, Daniel; Thomas, Kerry; Diallo, Dalanda; Strosberg, Jonathan R.

doi:10.1097/mpa.0000000000000920

Cited by 31 publications

(3 citation statements)

References 15 publications

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“…The high value of Gd-EOB HBP sequences in the determination of NELM size has already been shown and corroborates our approach to using this sequence for 3D segmentation [ 45 ]. The high lesion to liver contrast also provides optimum conditions for automated segmentation [ 46 , 47 ].…”

Section: Discussionsupporting

confidence: 83%

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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Section: Discussionsupporting

confidence: 83%

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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“…The median time to recurrence overall was 10.9 months, and while this is lower than what has been previously reported (10,24), we de ned time to recurrence according to RECIST 1.1 criteria and had comparable results with a study who also applied this model (9). For surveillance, we used MRI abdomen with EOVIST, the most sensitive method for detection of new liver lesions or progression (25) which could impact our time to recurrence. Moreover, we only initiated SSA therapy immediately postoperatively in 20% of patients.…”

Section: Discussionmentioning

confidence: 99%

Single center outcomes from parenchymal-sparing resections and microwave ablations for neuroendocrine tumor liver metastases

Lee,

Williams,

Nordgren

et al. 2023

The American Journal of Surgery

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“…Computed tomography (CT), magnetic resonance imaging (MRI), and somatostatin receptor (SSTR)-based integrated positron emission tomography (PET)/CT are the main diagnostic and staging imaging modalities [21]. Hepatobiliary phase liver MRI was indicated to be more sensitive in detecting liver metastases than CT or SSTR-PET/CT [22][23][24]. Whole-body SSTR imaging with 68Ga/64Cu-PET/CT provides high sensitivity and is recommended to be part of the tumor staging [21].…”

Section: Gastroenteropancreatic Nets (Gep-nets)mentioning

confidence: 99%

Clinicopathological Characteristics of Low to Intermediate Grade Neuroendocrine Tumors with Oncocytic Features

Sun¹,

Cui²,

Paulsen³

2023

Series Endo Diab Met.

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Neuroendocrine tumors (NETs) with oncocytic features are rare. To date, few studies have described the metastatic characteristics of these tumors, most of which were limited by the low number of cases. One article characterized intermediate to high grade (G2 and G3) oncocytic NETs of the pancreas as having an ominous outcome; however, metastatic features of low-grade oncocytic NETs have, to our knowledge, been hitherto unexplored. Our study characterized the clinicopathological and metastatic features of 32 low to intermediate-grade oncocytic NETs of various organs. The most frequent metastatic site was the liver (63%). The metastatic rate of our cases was compared with Riihimaki’s study (metastatic rate of G1/G2 NETs was reported), as well as Heetfeld’s study and Lithgow’s study (metastatic rate of G3 NETs was reported). The overall metastatic rate of our cases is 84.4%, which is significantly higher than that of the G1/2 NETs from Riihimaki’s study (84.4% vs. 25%, p < 0.0001), while similar to that of the G3 NETs from Heetfeld’s study (84.4% vs. 86.5%, p > 0.05) and Lithgow’s study (84.4% vs. 80.8%, p > 0.05). These data indicate that oncocytic features could potentially be characterized as high-risk features in addition to high Ki-67 index, mitotic count, and necrosis. Thus, imaging studies such as liver magnetic resonance imaging (MRI) may be warranted to detect liver metastases in NETs with distinctive oncocytic cytologic features even in the absence of other high-risk features.

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Magnetic Resonance Imaging of Neuroendocrine Tumor Hepatic Metastases

Abstract: Our results support the use of contrast agents with hepatobiliary excretion when imaging neuroendocrine tumors metastatic to liver.

Cited by 31 publications

References 15 publications

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

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

Single center outcomes from parenchymal-sparing resections and microwave ablations for neuroendocrine tumor liver metastases

Clinicopathological Characteristics of Low to Intermediate Grade Neuroendocrine Tumors with Oncocytic Features

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