The University of Southampton
University of Southampton Institutional Repository

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

Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model
Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model
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.
1-9
Yamamoto, Kimiyo
17427059-f5f0-48be-a27f-cdf389266748
Ishii, Masatsugu
cb6d610b-f3ad-4379-a257-b749fd78c2fe
Hirokawa, Fumitoshi
49e783d6-fbeb-4d6e-9841-d02f5a5d8a99
Macarthur, Benjamin
2c0476e7-5d3e-4064-81bb-104e8e88bb6b
Nakamura, Akira
3a4b804b-279e-4933-a59a-97c134cda882
Haeno, Hiroshi
ccb7bdfe-ac12-49a8-a6a0-1025508de7c5
Uchiyama, Kazuhisa
230a3068-dcd6-4f6b-bd2e-dc59bf799f4a
Yamamoto, Kimiyo
17427059-f5f0-48be-a27f-cdf389266748
Ishii, Masatsugu
cb6d610b-f3ad-4379-a257-b749fd78c2fe
Hirokawa, Fumitoshi
49e783d6-fbeb-4d6e-9841-d02f5a5d8a99
Macarthur, Benjamin
2c0476e7-5d3e-4064-81bb-104e8e88bb6b
Nakamura, Akira
3a4b804b-279e-4933-a59a-97c134cda882
Haeno, Hiroshi
ccb7bdfe-ac12-49a8-a6a0-1025508de7c5
Uchiyama, Kazuhisa
230a3068-dcd6-4f6b-bd2e-dc59bf799f4a

Yamamoto, Kimiyo, Ishii, Masatsugu, Hirokawa, Fumitoshi, Macarthur, Benjamin, Nakamura, Akira, Haeno, Hiroshi and Uchiyama, Kazuhisa (2016) Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model. Scientific Reports, 6 (34214), 1-9. (doi:10.1038/srep34214).

Record type: Article

Abstract

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.

Text
srep34214(1).pdf - Version of Record
Available under License Creative Commons Attribution.
Download (757kB)

More information

Accepted/In Press date: 9 September 2016
e-pub ahead of print date: 3 October 2016
Organisations: Human Development & Health

Identifiers

Local EPrints ID: 404461
URI: http://eprints.soton.ac.uk/id/eprint/404461
PURE UUID: 5fbcf03c-c52b-4482-bc23-d8168947c754

Catalogue record

Date deposited: 10 Jan 2017 14:01
Last modified: 06 Oct 2020 17:20

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×