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.

Factors affecting the reduction rate of odontogenic cysts after decompression based on 3-dimensional volumetric analysis

“…Additionally, DICOM measurements have limitations in assessing certain areas, such as the surface areas of tooth roots involved in the cyst and the irregular bone regeneration on the inner walls of the cyst volume after decompression. 29 Therefore, this study used STL files, which tend to provide more information about the surface of the volume, in conjunction with the determination of cystic lesion boundaries using HU. This method accurately predicts both the surface and volume of the cyst and is less time-consuming than making measurements with DICOM files.…”

Emulating Clinical Diagnostic Reasoning for Jaw Cysts with Machine Learning

“…At such a late stage, the extent of the defect can present a risk for neighboring anatomical structures, including teeth, alveolar bone, and nerves [ 4 ]. Furthermore, the time required for complete postoperative osseous regeneration is exponentially proportional to the preoperative volume of the defect [ 5 ]. Consequently, timely diagnosis ensures a smaller osseous defect and a shorter regeneration time, thus an overall better prognosis.…”