Preoperative Differentiation of Jaw Cystic Lesions Based on Radiomics From Computed Tomography Images: A Multicenter, Prospective Machine Learning Study
Conditions: Jawbone Cysitc Lesion
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Clinical Study Protocol 
 
Project Title: Preoperative Differentiation of Jaw Cystic Lesions Based on Radiomics 
from Computed Tomography Images: A Multicenter, Prospective Machine Learning 
Study 
 
Sponsor: Sun Yat-sen Memorial Hospital, Sun Yat-sen University 
 
Version Number: V2 
 
Version Date: June 9, 2024 
 
Principal Investigator:  
 
 
Protocol Signature Confirmation: 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

1. Study Protocol Summary 
Objective: In previous studies, we have demonstrated the feasibility of developing an 
effective predictive diagnostic model using machine learning techniques combined with 
computed tomography (CT) radiomics for the preoperative differentiation of various 
jaw cystic lesions. This study aims to utilize the existing diagnostic predictive model to 
further conduct a multicenter, prospective study to construct a preoperative diagnostic 
predictive model, providing more reliable scientific references for clinical diagnosis 
and treatment. 
Methods: In this multicenter, prospective study, we plan to enroll a total of 300 patients 
with jaw cystic lesions. We will collect all patient information and CT images, delineate 
the region of interest (ROI), extract radiomic features, and input them into the predictive 
diagnostic model after screening. We will verify the consistency between preoperative 
predictions and postoperative pathology to refine the model, aiming to provide 
scientific evidence for the precise selection of treatment plans for different lesions. 
2. Study Background (including introduction of the disease, current treatment status 
at home and abroad, significance of the study, etc.) 
Jaw cystic lesions include odontogenic tumors and non-tumorous cystic lesions 
occurring within the jawbone. Among these, ameloblastoma is the most common 
odontogenic tumor, while cystic lesions can be divided into odontogenic and non-
odontogenic cysts. Currently, the treatment focus varies for different types of jaw cystic 
lesions. Ameloblastomas can recur and metastasize, often requiring surgical resection, 
whereas cystic lesions may be more amenable to curettage and marsupialization. 
Therefore, accurate preoperative differential diagnosis of various jaw lesions and the 
subsequent selection of appropriate treatment plans are crucial for achieving optimal 
treatment outcomes for patients. Inappropriate treatment choices may delay the disease 
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 plan for the 
treatment of jaw cystic lesions. Establishing an objective and scientific preoperative 
diagnostic predictive model has important clinical significance. In previous research, 
we successfully developed an effective predictive diagnostic model by combining 
machine learning techniques with CT radiomics, achieving a maximum AUC value 
of >0.8, indicating good performance and clinical reference value in predictive 
diagnosis. In the current study, we aim to conduct a multicenter, prospective machine 
learning study to further enhance the predictive diagnostic performance of the model 
and assist clinical diagnosis and treatment. 
3. Study Objectives 
In previous studies, we have demonstrated the feasibility of developing an effective 
predictive diagnostic model using machine learning techniques combined with CT 
radiomics for the preoperative differentiation of various jaw cystic lesions. This study 
aims to utilize the existing diagnostic predictive model to further conduct a multicenter, 
prospective study to construct a preoperative diagnostic predictive model, providing 
more reliable scientific references for clinical diagnosis and treatment, while further 
optimizing and enhancing the performance of the predictive diagnostic model. 
4. Study Design 

This study is designed as a multicenter, prospective machine learning study, planned to 
be conducted across 12 centers, enrolling a total of 300 patients with jaw cystic lesions. 
Participating centers include: Sun Yat-sen Memorial Hospital of Sun Yat-sen University 
(25 cases), Xiangya Hospital Central South University (25 cases), Hospital of 
Stomatology, Sun Yat-sen University (25 cases), Stomatological Hospital, Southern 
Medical University (25 cases), Shanghai Ninth People's Hospital, Shanghai JiaoTong 
University School of Medicine (25 cases), Hospital of Stomatology Wuhan University 
(25 cases), The Second Affiliated Hospital Zhejiang University School of Medicine (25 
cases), The Fourth Military Medical University (25 cases), The People's Hospital Of 
QianNan (25 cases), The Second Xiangya Hospital of Central South University (25 
cases), The First Affiliated Hospital of Xinjiang Medical University (25 cases), and 
Guangxi Medical University College of Stomatology (25 cases). Based on preliminary 
investigations of the actual diagnosis and treatment conditions at each research center, 
we plan to utilize different imaging data for grouping according to different imaging 
examinations conducted, and uniformly process the imaging data from different units 
and examination types for subsequent work. Sun Yat-sen Memorial Hospital of Sun 
Yat-sen University will serve as the primary center, with other units as sub-centers. 
Specific groupings are as follows: the spiral CT group includes Sun Yat-sen Memorial 
Hospital of Sun Yat-sen University, Second Affiliated Hospital of Zhejiang University 
School of Medicine, Xiangya Hospital of Central South University, Qiannan People's 
Hospital, Ninth People's Hospital Affiliated to Shanghai Jiao Tong University, and First 
Affiliated Hospital of Xinjiang Medical University; the cone beam CT (CBCT) group 
includes Wuhan University Stomatological Hospital, Stomatological Hospital of 
Southern Medical University, Third Affiliated Hospital of Air Force Medical University, 
Stomatological Hospital of Sun Yat-sen University, Xiangya Second Hospital of Central 
South University, and Stomatological Hospital Affiliated to Guangxi Medical 
University. 
During the study, once patients meeting the inclusion criteria are enrolled, their 
maxillofacial CT examination images will be collected and imported into computer 
software (LIFEx version 6.30) for ROI delineation. Radiomic features within the ROI 
will be extracted using Pyradiomics software, screened, and used for preoperative 
diagnostic prediction 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 may be further optimized in phases during the study, or 
methodological adjustments and reconstructions of the predictive model may be 
attempted for all existing data, ultimately achieving a more ideal preoperative 
diagnostic prediction. 
5. Study Protocol and Technical Route (including sample size calculation, inclusion, 
exclusion, and withdrawal criteria, evaluation indicators and methods of study results, 
statistical analysis plan, informed consent, ethics, etc.) 
This project plans to include a total of 300 patients with jaw cystic lesions from Sun 
Yat-sen Memorial Hospital of Sun Yat-sen University and other sub-centers for 
validation, prediction, and optimization of the existing model. Inclusion criteria: 1) 
Patients who are visiting for the first time and have not received other treatment 

interventions; 2) Patients with complete preoperative medical records, imaging 
examinations, and imaging data; 3) Patients who underwent maxillofacial CT 
examination preoperatively, with complete CT data, no artifact interference in the lesion 
area, and a lesion size with the longest diameter not less than 2 cm; 4) Patients who can 
tolerate surgical treatment, with specimens sent for routine pathological examination 
after surgery. Exclusion criteria: 1) Incomplete medical records, such as missing 
information on specialist examinations and therapeutic operations; 2) Patients who 
received therapeutic operations at an external hospital during the first visit, not 
completely cured or relapsed; 3) Patients who did not undergo CT examination 
preoperatively, with incomplete CT data, severe artifact interference in the lesion area, 
or lesion size not meeting requirements; 4) Patients whose lesions were not sent for 
pathological examination during surgery, with no routine pathological examination; 5) 
Patients with unclear postoperative pathological reports, or pathological diagnoses 
other than odontogenic cysts and non-solid ameloblastomas. 
For different groups, the corresponding scan parameters for imaging examinations in 
this study are as follows: 
① Spiral CT group: When conducting maxillofacial spiral CT examinations, units 
should use 64-slice or higher multi-slice spiral CT, with scan reconstruction layer 
thickness set between 0.75-1.25 mm, and the scan range must include from the skull 
base down to the submandibular area, fully covering the maxillary and mandibular 
regions; ② CBCT group: Axial scan reconstruction thickness set to 0.25-0.3 mm, with 
the scan area reaching the maximum field of vision (FOV) as much as possible and 
fully covering the lesion area. 
The evaluation indicators of the study results mainly refer to statistical analysis 
indicators (such as model prediction AUC value and ACC value, etc.). Statistical 
analysis will be conducted using SPSS software and R language, with results analyzed 
using chi-square tests, lasso regression, and other statistical methods. Regarding 
informed consent, all enrolled patients will sign informed consent forms. In terms of 
ethics, the relevant examinations and treatment plans conform to the treatment process 
for maxillofacial jaw cystic lesions, will not increase the financial burden or life safety 
risk for patients, and pose no major ethical risks. It should be noted that to ensure the 
smooth progress of the project, adaptive adjustments to the grouping and actual number 
of cases collected by each unit may be made if necessary during the actual progress. 
6. Safety Evaluation (including definitions and assessments of adverse events and 
serious adverse events, etc.) 
This study is a low-risk medical data machine learning project. Adverse events mainly 
include data leakage, with no additional interventions or potential harm to patients. All 
image processing for patients in this study will strictly adhere to confidentiality clauses 
and relevant regulations, with no major adverse events expected. 
7. Data Collection and Management 
Patient case data, imaging data, and pathology reports will be collected by 
corresponding groups from different research centers, filled in according to a 
predetermined form template, and uploaded to a unified EDC system, then collected 
and summarized by our unit. Our unit has a dedicated computer for collecting and 

storing data for this study (registered with the Equipment Department and Research 
Department), and related work will be handled by this equipment to ensure the 
confidentiality of patient data. 
8. Quality Management Plan (please introduce measures to ensure project quality and 
progress) 
(1) After ethical approval and project establishment, before officially starting, the 
research team will draft a cooperation agreement for signing with sub-center 
cooperation units when conducting the study (if necessary, if sub-center units do not 
require it, it will not be submitted for review). After formal project establishment, online 
discussion meetings or offline surveys will be conducted with the heads of each sub-
center to understand the actual diagnosis and treatment conditions at different centers, 
determine whether different centers can admit enough eligible cases within the study 
period, and make adaptive adjustments to the planned enrollment numbers based on the 
results. Subsequently, personnel responsible for actual project advancement at different 
centers will be trained to ensure that enrolled patients meet the inclusion and exclusion 
criteria and that clinical and imaging data quality is controllable. 
(2) Determine the inclusion and exclusion criteria for jaw cystic lesions, screen patients 
with jaw cystic lesions visiting for consultation according to the inclusion criteria, 
collect case data for those meeting the criteria, and exclude those not meeting the 
criteria. 
(3) Collect clinical and imaging data for patients, fill in clinical data according to the 
predetermined template form, upload it to the electronic data capture system (EDC), 
transfer imaging data DICOM files to our center, where researchers will extract imaging 
features using software, input them into the predictive diagnostic model, and obtain 
preoperative diagnostic predictions. 
(4) During the mid-term phase, statistical analysis of data will be conducted at different 
time points, observing the model's predictive results. If necessary, phase optimization 
of the model may be conducted through discussion to further enhance clinical 
application value. 
(5) Submit the independent validation set for final model prediction, then complete the 
writing of the article and publish academic results. 
9. Preliminary Assessment of Project Risks and Benefits and Risk Control 
Plan (please describe the potential risks and benefits for researchers, subjects, and the 
medical institution when conducting this project; if there are risks, please introduce risk 
control measures and feasibility) 
The main research outcome of this project is the establishment of a preoperative 
predictive model for jaw cystic lesions. The existing model has been validated, and 
statistical indicators prove its predictive efficacy is practically feasible and applicable 
in clinical settings. It can be considered for assisting differential diagnosis in difficult 
cases. Benefits mainly include patients achieving ideal survival outcomes, avoiding 
overtreatment, misdiagnosis, and missed diagnosis. In terms of risks, this project is a 
multicenter, prospective study, with the main risk being data leakage. The project team 
will strictly maintain confidentiality for all data used, ensuring that all data and related 
materials are not used for any other projects or purposes, and all data will not be 

disseminated externally in any form before desensitization. Researchers commit to 
taking effective confidentiality measures in accordance with national laws, regulations, 
and relevant policies, and will not disclose data. If the contents of the commitment are 
violated, they will bear full responsibility for all consequences arising from it. 
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