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Background <p>To 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.</p> Methods <p>DL-FLRVS, which consists of five 3D U-Net models for the automated segmentation of liver anatomy on contrast-enhanced CT, was developed (<i>n</i> = 307, 170, 170, 170, and 492, respectively) and validated (<i>n</i> = 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.</p> Results <p>The mean FLR and FLR% values were (493.51 ± 284.77) cm<sup>3</sup> and (38.53 ± 19.38) %, respectively, when DL-FLRVS was used, (489.23 ± 286.29) cm<sup>3</sup> 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 (<i>P</i> &gt; 0.99 and <i>P</i> &gt; 0.99, respectively) or between the hepatic VCAR and human doctors (<i>P</i> &gt; 0.99 and <i>P</i> = 0.07, respectively).</p> Conclusion <p>DL-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.</p>

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Deep learning-based volumetry of the future liver remnants and prediction of candidates for major hepatectomy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月9日

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  • E. Tuya,
  • Hao Li,
  • Yongbin Li,
  • Jingyu Zhou,
  • Demin Xu,
  • Ziwei Liu,
  • Zixuan Hua,
  • Tianqi Zhu,
  • Huiming Shan,
  • Yaofeng Zhang,
  • Xiaoying Wang,
  • Kun Ma,
  • Guanxun Cheng,
  • Tingting Xie

摘要

Background

To 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.

Methods

DL-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.

Results

The 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).

Conclusion

DL-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.