[Cystic lesions of the jaws]

Odontogenic cystic lesion segmentation on cone-beam CT using an auto-adapting multi-scaled UNet

Objectives

Precise segmentation of Odontogenic Cystic Lesions (OCLs) from dental Cone-Beam Computed Tomography (CBCT) is critical for effective dental diagnosis. Although supervised learning methods have shown practical diagnostic results in segmenting various diseases, their ability to segment OCLs covering different sub-class varieties has not been extensively investigated.

Methods

In this study, we propose a new supervised learning method termed OCL-Net that combines a Multi-Scaled U-Net model, along with an Auto-Adapting mechanism trained with a combined supervised loss. Anonymous CBCT images were collected retrospectively from one hospital. To assess the ability of our model to improve the diagnostic efficiency of maxillofacial surgeons, we conducted a diagnostic assessment where 7 clinicians were included to perform the diagnostic process with and without the assistance of auto-segmentation masks.

Results

We collected 300 anonymous CBCT images which were manually annotated for segmentation masks. Extensive experiments demonstrate the effectiveness of our OCL-Net for CBCT OCLs segmentation, achieving an overall Dice score of 88.84%, an IoU score of 81.23%, and an AUC score of 92.37%. Through our diagnostic assessment, we found that when clinicians were assisted with segmentation labels from OCL-Net, their average diagnostic accuracy increased from 53.21% to 55.71%, while the average time spent significantly decreased from 101s to 47s (P<0.05).

Conclusion

The findings demonstrate the potential of our approach as a robust auto-segmentation system on OCLs in CBCT images, while the segmented masks can be used to further improve OCLs dental diagnostic efficiency.

Machine Learning Model Based on Radiomics for Preoperative Differentiation of Jaw Cystic Lesions

OBJECTIVE

This study aims to use machine learning techniques together with radiomics methods to build a preoperative predictive diagnostic model from spiral computed tomography (CT) images. The model is intended for the differential diagnosis of common jaw cystic lesions.

STUDY DESIGN

Retrospective, case-control study.

SETTING

This retrospective study was conducted at Sun Yat-sen Memorial Hospital of Sun Yat-sen University (Guangzhou, Guangdong, China). All the data used to build the predictive diagnostic model were collected from 160 patients, who were treated at the Department of Oral and Maxillofacial Surgery at Sun Yat-sen Memorial Hospital of Sun Yat-sen University between 2019 and 2023.

METHODS

We included a total of 160 patients in this study. We extracted 107 radiomic features from each patient's CT scan images. After a feature selection process, we chose 15 of these radiomic features to construct the predictive diagnostic model.

RESULTS

Among the preoperative predictive diagnostic models built using 3 different machine learning methods (support vector machine, random forest [RF], and multivariate logistic regression), the RF model showed the best predictive performance. It demonstrated a sensitivity of 0.923, a specificity of 0.643, an accuracy of 0.825, and an area under the receiver operating characteristic curve of 0.810.

CONCLUSION

The preoperative predictive model, based on spiral CT radiomics and machine learning algorithms, shows promising differential diagnostic capabilities. For common jaw cystic lesions, this predictive model has potential clinical application value, providing a scientific reference for treatment decisions.

Combining CNN with Pathological Information for the Detection of Transmissive Lesions of Jawbones from CBCT Images

Cone-Beam Computed Tomography (CBCT) imaging modality is used to acquire 3D volumetric image of the human body. CBCT plays a vital role in diagnosing dental diseases, especially cyst or tumour-like lesions. Current computer-aided detection and diagnostic systems have demonstrated diagnostic value in a range of diseases, however, the capability of such a deep learning method on transmissive lesions has not been investigated. In this study, we propose an automatic method for the detection of transmissive lesions of jawbones using CBCT images. We integrated a pre-trained DenseNet with pathological information to reduce the intra-class variation within a patient's images in the 3D volume (stack) that may affect the performance of the model. Our proposed method separates each CBCT stacks into seven intervals based on their disease manifestation. To evaluate the performance of our method, we created a new dataset containing 353 patients' CBCT data. A patient-wise image division strategy was employed to split the training and test sets. The overall lesion detection accuracy of 80.49% was achieved, outperforming the baseline DenseNet result of 77.18%. The result demonstrates the feasibility of our method for detecting transmissive lesions in CBCT images.Clinical relevance - The proposed strategy aims at providing automatic detection of the transmissive lesions of jawbones with the use of CBCT images that can reduce the workload of clinical radiologists, improve their diagnostic efficiency, and meet the preliminary requirement for the diagnosis of this kind of disease when there is a lack of radiologists.