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

SIMPLE SUMMARY: Quantification of liver metastases on imaging is of utmost importance in therapy response assessment, wherein gadoxetic acid (Gd-EOB)-enhanced magnetic resonance imaging (MRI) shows the highest accuracy. Common criteria for assessing therapy response simplify measuring liver metastasis, as full 3D quantification is very time-consuming. Therefore, we trained a deep-learning model using manual 3D segmentation of liver metastases and hepatic parenchyma in 278 Gd-EOB MRI scans of 149 patients with neuroendocrine neoplasms (NEN). The clinical relevance of the model was evaluated in 33 additional consecutive patients with NEN and liver metastases, comparing the model’s segmentation of baseline and follow-up examinations with the therapy response evaluation of an expert multidisciplinary cancer conference (MCC). The model showed high accuracy in quantifying liver metastases and hepatic tumor load, and its measurements matched the response evaluation of an MCC so that its use to support treatment decision-making would be possible. 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 (Δ(abs)NELM; Δ(abs)HTL) and relative changes (Δ(rel)NELM; Δ(rel)HTL) 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 (Δ(abs)NELM; Δ(abs)HTL; Δ(rel)NELM; Δ(rel)HTL) (p < 0.001). Δ(rel)NELM and Δ(rel)HTL 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.