Unknown

Dataset Information

0

A volumetric prediction model for postoperative cyst shrinkage.


ABSTRACT:

Objectives

With only limited information available on dimensional changes after jaw cyst surgery, postoperative cyst shrinkage remains largely unpredictable. We aimed to propose a model for volumetric shrinkage based on time elapsed since cyst surgery.

Material and methods

We used data from patients that underwent cyst enucleation or decompression between 2007 and 2017 and had at least three computed tomography (CT) scans per patient. We fitted one simple exponential decay model [V(t) = V0 · e-ɑt] and one model with a patient-specific decay rate [Vk(t) = V0 · e-βt + γkt].

Results

Based on 108 CT scans from 36 patients (median age at surgery: 45.5 years, IQR: 32.3-55.3, 44% female), our simple exponential decay model is V(t) = V0 · e-0.0035t where V(t) is the residual cyst volume after time t elapsed since surgery, V0 is the initial cyst volume, and e is the base of the natural logarithm. Considering a patient-specific decay rate, the model is Vk(t) = V0 · e-0.0049t + γkt where γk is normally distributed, with expectation 0 and standard deviation 0.0041.

Conclusions

Using an exponential regression model, we were able to reliably estimate volumetric shrinkage after jaw cyst surgery. The patient-specific decay rate substantially improved the fit of the model, whereas adding specific covariates as interaction effects to model the decay rate did not provide any significant improvement.

Clinical relevance

Estimating postoperative cyst shrinkage is relevant for both treatment planning of jaw cyst surgery as well as evaluating the clinical success of the surgical approach.

SUBMITTER: Feher B 

PROVIDER: S-EPMC8531058 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC6423357 | biostudies-literature
| S-EPMC9390440 | biostudies-literature
| S-EPMC4334678 | biostudies-literature
| S-EPMC3750442 | biostudies-literature
| S-EPMC6467998 | biostudies-literature
| S-EPMC8107540 | biostudies-literature
| S-EPMC7589383 | biostudies-literature
| S-EPMC10161528 | biostudies-literature
| S-EPMC9338704 | biostudies-literature