Radiomics for Preoperative Jaw Cyst Differentiation

Overview

This study focuses on jawbone cystic lesions, including odontogenic tumors like ameloblastoma and various cysts. Treatment approaches differ; ameloblastomas often require surgical excision due to potential recurrence and metastasis, while cystic lesions may be treated with curettage and marsupialization. Accurate preoperative diagnosis is crucial for optimal treatment outcomes, as inappropriate choices can lead to delayed treatment or overtreatment, affecting patient quality of life. Currently, there is no standard protocol for differential diagnosis, highlighting the need for a predictive diagnostic model. The study will be a multicenter, prospective machine learning research involving 300 patients across 12 centers. It aims to enhance a previously developed predictive model that integrates machine learning with CT radiomics. Patients will be grouped based on imaging modalities, with data processed uniformly to improve diagnostic predictions. Inclusion criteria ensure comprehensive preoperative data, while exclusion criteria eliminate incomplete or previously treated cases. The study seeks to optimize the model's performance and provide valuable clinical insights.

Jawbone cystic lesions include odontogenic tumors and non-tumorous cystic lesions occurring within the jawbone, with ameloblastoma being the most common among the former, and odontogenic and non-odontogenic cysts among the latter. Currently, the treatment focus varies for different types of jawbone cystic lesions. Ameloblastomas, which may recur and metastasize, are primarily treated with surgical excision, while cystic lesions are more broadly treated with procedures like curettage and marsupialization. Therefore, accurate preoperative differential diagnosis of various jawbone lesions and the subsequent selection of appropriate treatment plans are crucial for achieving optimal patient outcomes. Inappropriate treatment choices may delay the condition or lead to overtreatment, affecting the patient's quality of life. At present, there is still a lack of an objective and accurate standard and differential diagnosis protocol for the treatment of jawbone cystic lesions, making the establishment of an objective and scientific preoperative diagnostic prediction model of significant clinical importance. In previous research, investigators successfully developed an effective predictive diagnostic model by integrating machine learning techniques with computed tomography (CT) radiomics, achieving a maximum AUC ( area under curve ) value \>0.8, indicating good predictive performance and clinical reference value. In the current study, investigators aim to conduct a multicenter, prospective machine learning study to further enhance the model's predictive diagnostic performance and assist clinical diagnosis and treatment. This study is designed as a multicenter, prospective machine learning study, involving 300 patients with jawbone cystic lesions across 12 centers, as detailed in the list of collaborating institutions. Based on research group's previous investigation of the actual diagnostic and treatment conditions at each research center, investigators plan to utilize different types of imaging data for grouping according to the imaging examinations conducted, and to standardize the processing of imaging data from different units and types for subsequent work. Sun Yat-sen Memorial Hospital of Sun Yat-sen University will serve as the main center, with other institutions as sub-centers. The specific grouping is as follows: the spiral CT group includes six general hospitals; the cone beam CT (CBCT) group includes one general hospital and five specialized dental hospitals. During the study, after enrolling participants who meet the inclusion criteria, investigators will collect maxillofacial CT imaging data, import them into the software (LIFEx version 6.30), and delineate the region of interest (ROI). Radiomic features within the ROI will be extracted using Pyradiomics software, selected, and used for preoperative diagnostic predictions with the existing model. After surgical treatment, the pathological results of the lesions will be tracked and recorded. If conditions permit, the model's predictive performance can be further optimized in phases during the study, or methodological adjustments and reconstructions of the predictive model can be attempted using all available data to achieve a more ideal preoperative diagnostic prediction.