Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model

Kimiyo N. Yamamoto, Masatsugu Ishii, Yoshihiro Inoue, Fumitoshi Hirokawa, Ben D. MacArthur, Akira Nakamura, Hiroshi Haeno, Kazuhisa Uchiyama

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Although the capacity of the liver to recover its size after resection has enabled extensive liver resection, post-hepatectomy liver failure remains one of the most lethal complications of liver resection. Therefore, it is clinically important to discover reliable predictive factors after resection. In this study, we established a novel mathematical framework which described post-hepatectomy liver regeneration in each patient by incorporating quantitative clinical data. Using the model fitting to the liver volumes in series of computed tomography of 123 patients, we estimated liver regeneration rates. From the estimation, we found patients were divided into two groups: i) patients restored the liver to its original size (Group 1, n = 99); and ii) patients experienced a significant reduction in size (Group 2, n = 24). From discriminant analysis in 103 patients with full clinical variables, the prognosis of patients in terms of liver recovery was successfully predicted in 85-90% of patients. We further validated the accuracy of our model prediction using a validation cohort (prediction = 84-87%, n = 39). Our interdisciplinary approach provides qualitative and quantitative insights into the dynamics of liver regeneration. A key strength is to provide better prediction in patients who had been judged as acceptable for resection by current pragmatic criteria.

Original languageEnglish
Article number34214
JournalScientific reports
Publication statusPublished - Oct 3 2016

All Science Journal Classification (ASJC) codes

  • General


Dive into the research topics of 'Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model'. Together they form a unique fingerprint.

Cite this