ArticleBMC medical imaging2025
Deep learning-based volumetry of the future liver remnants and prediction of candidates for major hepatectomy.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundTo develop and validate a deep learning-based future liver remnant (FLR) volumetry system (DL-FLRVS) by secondary utilization of existing preoperative CT images, and to compare the differences between the currently used method and DL-FLRVS in volumetry of FLR and the application in candidate categorization before major hepatectomy.
methodsDL-FLRVS, which consists of five 3D U-Net models for the automated segmentation of liver anatomy on contrast-enhanced CT, was developed (n = 307, 170, 170, 170, and 492, respectively) and validated (n = 178) in external validation cohorts. The FLR and FLR% of patients were measured using DL-FLRVS and the hepatic VCAR (i.e., a semi-automated segmentation program on a dedicated workstation) for different types of major hepatectomy. Manual measurements were used as a reference. The differences in FLR assessment and candidate categorization between the two methods were compared using Spearman analysis and McNemar’s test, respectively.
resultsThe mean FLR and FLR% values were (493.51 ± 284.77) cm3 and (38.53 ± 19.38) %, respectively, when DL-FLRVS was used, (489.23 ± 286.29) cm3 and (37.77 ± 19.12) %, when the hepatic VCAR was used. No significant differences in the categorization of candidates for major hepatectomy were found between the DL-FLRVS and human doctors (P > 0.99 and P > 0.99, respectively) or between the hepatic VCAR and human doctors (P > 0.99 and P = 0.07, respectively).
conclusionDL-FLRVS represents a potential alternative to the currently used method in volumetry of FLR and FLR-based candidate categorization in major hepatectomy in clinical practice.
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