Automatized hepatic tumor volume analysis of neuroendocrine liver metastases by gd‐eob mri—a deep‐learning model to support multidisciplinary cancer conference decision‐making

Abstract

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.