scholarly journals Diagnosis and Prediction of Neuroendocrine Liver Metastases: A Protocol of Six Systematic Reviews

2013 ◽  
Vol 2 (2) ◽  
pp. e60
Author(s):  
Stephan Arigoni ◽  
Stefan Ignjatovic ◽  
Patrizia Sager ◽  
Jonas Betschart ◽  
Tobias Buerge ◽  
...  
2014 ◽  
Vol 3 (2) ◽  
pp. e25 ◽  
Author(s):  
Stephan Arigoni ◽  
Stefan Ignjatovic ◽  
Patrizia Sager ◽  
Jonas Betschart ◽  
Tobias Buerge ◽  
...  

2013 ◽  
Vol 2 (2) ◽  
pp. e58 ◽  
Author(s):  
Reto Stump ◽  
Silvia Haueis ◽  
Nicola Kalt ◽  
Christoph Tschuor ◽  
Përparim Limani ◽  
...  

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2726
Author(s):  
Uli Fehrenbach ◽  
Siyi Xin ◽  
Alexander Hartenstein ◽  
Timo Alexander Auer ◽  
Franziska Dräger ◽  
...  

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.


2017 ◽  
Vol 40 (3) ◽  
pp. 480-480 ◽  
Author(s):  
Jean-Pierre Pelage ◽  
Audrey Fohlen ◽  
Emmanuel Mitry ◽  
Christine Lagrange ◽  
Alain Beauchet ◽  
...  

HPB ◽  
2021 ◽  
Vol 23 ◽  
pp. S113
Author(s):  
S. Scoville ◽  
D. Xourafas ◽  
A. Ejaz ◽  
M. Dillhoff ◽  
A. Tsung ◽  
...  

Author(s):  
Joachim K. Seifert ◽  
Paul J. Cozzi ◽  
David L. Morris

2021 ◽  
pp. 267-281
Author(s):  
Ashley Kieran Clift ◽  
Andrea Frilling

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