Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis

A Radicular Cyst With Extensive Bone Loss Requires Surgical and Endodontic Management: Case Presentation and Literature Review

The radicular cyst is an inflammatory pathology affecting the periapical bone tissue associated with teeth exhibiting pulp necrosis. Without timely endodontic intervention, this pathology can lead to significant bone tissue destruction due to its growth pattern, necessitating both endodontic and surgical approaches to halt its progression and remove the involved tissue. This case study details the management of a 23-year-old patient with a history of prior trauma, presenting with pressure in the anterior maxillary region. Tomographic examination revealed a hypodense area involving Teeth 2.1, 2.2, and 2.3. A surgical approach involving enucleation and endodontic treatment was undertaken. The diagnosis was confirmed histopathologically, and follow-up radiographs demonstrated satisfactory bone filling.

Application of radiomics features in differential diagnosis of odontogenic cysts

OBJECTIVES

Cysts in jaws may have similar radiographic features. However, it is important to clarify the diagnosis prior to surgery. The aim of this study was to compare the radiomic features of radicular cysts (RCs), dentigerous cysts (DCs), and odontogenic keratocysts (OKCs) as a non-invasive diagnostic alternative to biopsy.

METHODS

In total, 161 odontogenic cysts diagnosed histopathologically (55 RCs, 53 DCs, and 53 OKCs) were included in the present study. Each cyst was semi-automatically segmented on CBCT images, and radiomic features were extracted by an observer. A second observer repeated 20% of the evaluations and the radiomic features. Those achieving an inter-observer agreement level above 0.850 were included in the study. Consequently, five shape-based and 22 textural features were investigated in the study. Statistical analysis was performed comparing both three cyst features and making pairwise comparisons.

RESULTS

All features included in the study showed statistical differences between cysts, with the exception of one textural feature (NGTDM coarseness) (P < .05). However, only one shape-based feature (shericity) and one textural feature (GLSZM large area emphasis) were statistically different in pairwise comparisons of all three cysts (P < .05).

CONCLUSION

Radiomics features of the RCs, DCs, and OKCs showed significant differences, and may have the potential to be used as a non-invasive method in the differential diagnosis of cysts.

Radiomics-Driven CBCT Texture Analysis as a Novel Biosensor for Quantifying Periapical Bone Healing: A Comparative Study of Intracanal Medications

This study aimed to evaluate the effectiveness of two intracanal medications in promoting periapical bone healing following endodontic treatment using radiomics-enabled texture analysis of cone-beam computed tomography (CBCT) images as a novel biosensing technique. By quantifying tissue changes through advanced image analysis, this approach seeks to enhance the monitoring and assessment of endodontic treatment outcomes. Thirty-four single-rooted teeth with pulp necrosis and periapical lesions were allocated to two groups (17 each): calcium hydroxide +2% chlorhexidine gel (CHX) and Ultracal XS®. CBCT scans were obtained immediately after treatment and three months later. Texture analysis performed using MaZda software extracted 11 parameters based on the gray level co-occurrence matrix (GLCM) across two inter-pixel distances and four directions. Statistical analysis revealed significant differences between medications for S [0,1] inverse difference moment (p = 0.043), S [0,2] difference of variance (p = 0.014), and S [0,2] difference of entropy (p = 0.004). CHX treatment resulted in a more organized bone tissue structure post-treatment, evidenced by reduced entropy and variance parameters, while Ultracal exhibited less homogeneity, indicative of fibrous or immature tissue formation. These findings demonstrate the superior efficacy of CHX in promoting bone healing and underscore the potential of texture analysis as a powerful tool for assessing CBCT images in endodontic research.

Age estimation by radiomics analysis of mandibular condylar cone beam computed tomography images

OBJECTIVES

The aim of this study was to estimate the legal age using the parameters obtained from radiomic analysis of the mandibular condyle in cone beam computed tomography (CBCT) images.

MATERIAL AND METHODS

The study group consisted of 300 mandibular condyles, which were categorized into six groups based on the age of the patients: 8-11 years, 12-14 years, 15-17 years, 18-20 years, 21-23 years, and over 24 years. Each patient's condyle was segmented individually using the 3D Slicer program. Radiomic features were extracted from the segmented images using the SlicerRadiomics plugin. Subsequently, three distinct models were developed with reference to three specific subgroups of the 12-14 age group, 15-17 age group, 18-20 age group and the efficacy of radiomic features in predicting the age of the patient was evaluated.

RESULTS

The ROC analysis of the three radiomics scores (RS) yielded AUC values of 0.927, 0.860, and 0.769 for RS12-14, RS15-17, and RS18-20, respectively. The RS12-14 model exhibited the highest sensitivity and specificity values among the models, with 88% and 84.4%, respectively.

CONCLUSION

Among the radiomic features extracted from the mandibular condyle in CBCT images, the most significant features, identified based on developed models and their respective coefficients, can be applied to estimate patients' ages. Future studies hold substantial potential for advancing this method, particularly in automating both the segmentation process and the derivation of formulae for age estimation. The use of radiomic features for age prediction presents a promising alternative method for developing fully automated systems in clinical practice.

Unveiling degenerative bone changes in the condyle: a texture analysis approach using cone-beam computed tomography

PURPOSE

The degenerative joint disease is a temporomandibular disorder. By analysing texture parameters, it becomes possible to characterize and differentiate various tissues, based on their textural properties according to cone-beam computed tomography (CBCT). This study evaluated degenerative diseases in the temporomandibular joint through texture analysis.

METHODS

Eighty images of the jaw condyle with three types of degenerative diseases, flattening, osteophytes, erosion and control group were analysed, obtained through CBCT. The analyses were carried out through texture analysis with three regions of interest (ROIs) corresponding to specific bone sites. The scans were exported to MaZda software, in which the ROIs were delimited following previously marked contours, and the co-occurrence matrix values were calculated for selected texture analysis parameters.

RESULTS

The erosion group showed a significantly different behaviour from the other groups for all analysed parameters.

CONCLUSION

This study highlights the potential of texture analysis in characterizing medullary bone changes in condyles affected by erosion. Texture analysis allows for a more comprehensive assessment of bone condition on CBCT images. These results have implications for early detection and monitoring of degenerative changes in the temporomandibular joint, thus allowing preventive intervention and personalized treatment planning, improving the prognosis of the disease.

Periapical cysts in dogs: 10 cases (2000-2020)

Objective

To characterize the clinical, diagnostic imaging, and histologic features with description of treatment outcome of periapical cysts in dogs.

Animals

Ten client-owned dogs diagnosed with periapical cysts biopsied between July 1, 2000 and June 30, 2020.

Procedures

Medical records of the Matthew J. Ryan Veterinary Hospital of the University of Pennsylvania were retrospectively searched to identify dogs that had surgical biopsy specimens of cavitary lesions diagnosed as odontogenic cysts and that met additional inclusion criteria. Complete medical records were reviewed.

Results

Range age and body weight of affected dogs were 2.5-12.1 years and 4.3-38.4 kg (9.5-84.7 lb), respectively. All periapical cysts were affecting the incisive bone and/or the maxilla. Nine dogs presented with a fluctuant swelling of the oral mucosa and gingiva; one dog without clinical swelling presented with a history of difficulty breathing. All cysts originated from a non-vital tooth with a structural defect (wear or fracture without pulp exposure) and/or intrinsic staining. Extraction of the associated non-vital teeth, enucleation of the cysts, and curettage of the surgical sites resulted in resolution of the clinical signs.

Conclusions and clinical relevance

The findings indicate that periapical cysts are associated with a non-vital tooth without pulp exposure. Complete evaluation of the clinical, diagnostic imaging, and histologic features of the lesion in affected dogs is necessary to differentiate periapical cysts from other odontogenic cysts and tumors.

Image-based multi-omics analysis for oral science: Recent progress and perspectives

OBJECTIVES

The diagnosis and treatment of oral and dental diseases rely heavily on various types of medical imaging. Deep learning-mediated multi-omics analysis can extract more representative features than those identified through traditional diagnostic methods. This review aims to discuss the applications and recent advances in image-based multi-omics analysis in oral science and to highlight its potential to enhance traditional diagnostic approaches for oral diseases.

STUDY SELECTION, DATA, AND SOURCES

A systematic search was conducted in the PubMed, Web of Science, and Google Scholar databases, covering all available records. This search thoroughly examined and summarized advances in image-based multi-omics analysis in oral and maxillofacial medicine.

CONCLUSIONS

This review comprehensively summarizes recent advancements in image-based multi-omics analysis for oral science, including radiomics, pathomics, and photographic-based omics analysis. It also discusses the ongoing challenges and future perspectives that could provide new insights into exploiting the potential of image-based omics analysis in the field of oral science.

CLINICAL SIGNIFICANCE

This review article presents the state of image-based multi-omics analysis in stomatology, aiming to help oral clinicians recognize the utility of combining omics analyses with imaging during diagnosis and treatment, which can improve diagnostic accuracy, shorten times to diagnosis, save medical resources, and reduce disparity in professional knowledge among clinicians.

Radiomics-Based Diagnosis in Dentomaxillofacial Radiology: A Systematic Review

Radiomics is a quantitative tool for digital image analysis. This systematic review aims to investigate the scientific articles to evaluate the potential implications of Radiomics analysis in Dentomaxillofacial Radiology (DMFR). Studies regarding Radiomics applications in DMFR and human samples, in vivo study, a case reports/series if ≧5 samples were included, while case reports/series if < 5 samples, articles other than in English, abstracts without full text, and studies published before 2015 were excluded. Fifty-one articles were selected from 3789 literatures. The QUADAS-2 tool was used for risk of bias assessment. The accuracy of predicting dentomaxillofacial pathologies was considered as the primary outcome, and the modeling type of Radiomics was considered as the secondary outcome. A meta-analysis could not be performed due to the lack of information and standardization among the reported accuracies. The reported accuracies were found between 0.66 and 99.65%. Logistic regression (n = 6) was found to be the most common Radiomics modeling type, followed by Support Vector Machine and Decision Tree (n = 5). Second-order statistics (n = 38) was the most common type of Radiomics application, followed by first-order (n = 26), higher-order (n = 20), and shape-based (n = 15) statistics. Further work is needed to increase standardization in the Radiomics workflow. Quantitative image analysis is an alternative tool for conventional visual radiographic evaluation. Radiomics systems depend on elements such as imaging modality, feature type, data mining, or statistical method. Radiomics applications do not justify digital transformation on their own, but the potential of its integration into the digital workflow is considerable.

The efficacy of CBCT-based radiomics techniques in differentiating between conventional and unicystic ameloblastoma

OBJECTIVE

The aim of this study was to develop a cone beam computed tomography (CBCT) radiomics-based model that differentiates between conventional and unicystic ameloblastoma (AB).

METHODS

In this retrospective study, CBCT images were collected from 100 patients who had ABs that were diagnosed histopathologically as conventional or unicystic AB after surgical treatment. The patients were randomly divided into training (70) and validation (30) cohorts. Radiomics features were extracted from the images, and the optimal features were incorporated into 5 models: Logistic Regression, Support Vector Machine, Linear Discriminant Analysis, Random Forest, and XGBoost for prediction of tumor type. Model performance was evaluated using the area under the curve (AUC) from receiver operating characteristic analysis, sensitivity, specificity, accuracy, calibration curves, and decision curve analysis (DCA).

RESULTS

The 20 optimal radiomics features were incorporated into the Logistic Regression (LR) model, which exhibited the best overall performance with AUC = 0.936 (95% confidence interval [CI] = 0.877-0.996) for the training cohort and AUC = 0.929 (95% CI = 0.832-1.000) for the validation cohort. The nomogram combined the clinical features and the radiomics signature and resulted in the best predictive performance.

CONCLUSIONS

The LR model demonstrated the ability of radiomics and the nomogram to distinguish between the 2 types of AB and may have the potential to replace biopsies under noninvasive conditions.

Texture Analysis in Volumetric Imaging for Dentomaxillofacial Radiology: Transforming Diagnostic Approaches and Future Directions

This narrative review explores texture analysis as a valuable technique in dentomaxillofacial diagnosis, providing an advanced method for quantification and characterization of different image modalities. The traditional imaging techniques rely primarily on visual assessment, which may overlook subtle variations in tissue structure. In contrast, texture analysis uses sophisticated algorithms to extract quantitative information from imaging data, thus offering deeper insights into the spatial distribution and relationships of pixel intensities. This process identifies unique "texture signatures", serving as markers for accurately characterizing tissue changes or pathological processes. The synergy between texture analysis and radiomics allows radiologists to transcend traditional size-based or semantic descriptors, offering a comprehensive understanding of imaging data. This method enhances diagnostic accuracy, particularly for the assessment of oral and maxillofacial pathologies. The integration of texture analysis with radiomics expands the potential for precise tissue characterization by moving beyond the limitations of human eye evaluations. This article reviews the current trends and methodologies in texture analysis within the field of dentomaxillofacial imaging, highlights its practical applications, and discusses future directions for research and dental clinical practice.

Differentiation of radicular cysts and radicular granulomas via texture analysis of multi-slice computed tomography images

OBJECTIVES

This study aimed to establish a method for differentiating radicular cysts from granulomas via texture analysis (TA) of multi-slice computed tomography (CT) images.

METHODS

A total of 222 lesions with multi-slice computed tomography images acquired at our hospital between 2013 and 2022 that were pathologically diagnosed were included in this study. Cases of contrast-enhanced images, severe metallic artefacts, and lesions that were not sufficiently large to be analysed were excluded. The images were chronologically divided into a training group and a validation group. The radiological characteristics were determined. Subsequently, a TA was performed. Pyradiomics software was used for the TA of three-dimensionally segmented volumes extracted from 2 mm slice thickness images with a soft-tissue algorithm. Features that differed significantly between the two lesions in the training group were extracted and used to create machine-learning models. The discriminative ability of these models was evaluated in the validation group using receiver operating characteristic curve analysis.

RESULTS

A total of 131 lesions, comprising 28 radicular cysts and 103 granulomas, were analysed. Forty-three texture features that exhibited significant variations were extracted. A support vector machine and decision tree model, with areas under the curves of 0.829 and 0.803, respectively, were created. These models showed high discriminative abilities, even for the validation group, with areas under the curve of 0.727 and 0.701, respectively. Both models showed superior performance compared with that of the models based on radiographic findings.

CONCLUSION

Discriminatory models were established for the TA of radicular cysts and granulomas using CT images.

Radiographic texture analysis of the hard tissue changes following socket preservation with allograft and xenograft materials for dental implantation: a randomized clinical trial

OBJECTIVES

This study aimed to assess the hard tissue changes following socket preservation with allograft and xenograft materials for dental implantation by texture analysis (TA) using cone-beam computed tomography (CBCT).

MATERIALS AND METHODS

This prospective clinical trial was conducted on 25 patients who required the extraction of carious mandibular posterior teeth and their subsequent replacement with dental implants. The patients were categorized into three groups: (I) no socket preservation, (II) socket preservation with xenograft material, and (III) socket preservation with allograft material. Four months after tooth extraction, the patients were recalled for preoperative assessment before dental implantation, and CBCT scans were obtained (Kvp:110, mA:1.94, S:3.6). MaZda software was used to compare homogeneity, contrast, and texture complexity on axial CBCT sections among the three groups.

RESULTS

Significant differences existed among the three groups in all parameters (P < 0.05) except for the mean correlation parameter (P > 0.05). The results showed no significant difference between the no graft and xenograft groups regarding contrast and differential (dif.) entropy (P > 0.05). Also, no significant difference was found between the xenograft and allograft groups regarding the dif. variance and also between the no graft and allograft groups regarding the inverse difference moment(InvDfMom) and dif. variance parameters (P > 0.05). All other pairwise comparisons revealed significant differences (P < 0.05).

CONCLUSION

TA can be used for the quantification of radiographic changes of bone following socket preservation and potentially accelerate the process of decision-making for dental implant treatment.

The Effect of Lasers on the Healing of Periapical Lesion: A Systematic Review

Introduction: Endodontic treatment of teeth with periapical lesions presents more clinical difficulty. Various lasers in several methods are used in endodontics, and most of them are utilized as an adjunctive protocol in order to reduce the bacterial load of the root canal system. Improved disinfection plays a crucial role in enhanced and accelerated healing of periapical lesions. This review aims to summarize studies assessing the effect of lasers on periapical lesion healing. Methods: PubMed/MEDLINE, Scopus, Embase, and Web of Science (ISI) online databases were searched, with no publication year or status restriction, for relevant articles on April 2023. Clinical studies evaluating the effect of laser application on the periapical lesion of patients using radiographic assessment were considered eligible for inclusion. Results: Eight studies were included after carefully screening the obtained articles, first by their title and abstract and then by their full texts. Diode (4), Er, Cr: YSGG (3), and Nd: YAG (1) lasers were used with output powers that varied from 0.75 to 2 watts. Photodynamic therapy was employed in two studies, and in other studies, the root canal system was directly irradiated. Irradiation of the root canal system was adjunctive to standard preparation in all studies. The healing of the teeth treated with lasers was not inferior to those conventionally treated. In all of the included studies, laser application outperformed the standard cleaning and shaping protocol; however, this improved or faster healing was not statistically significant in most studies. Conclusion: Lasers might expedite and improve the healing process of periapical lesions. Since lasers enhance the quality of cleaning of the root canal system, it is hard to point out the exact mechanism of it. Further investigations are needed to realize the effectiveness of this treatment modality and to discover the underlying biological concepts.

Differential diagnosis of cemento-osseous dysplasia and periapical cyst using texture analysis of CBCT

BACKGROUND

Radiolucencies found at the root apex in patients with cemento-osseous dysplasia (COD) may be mistaken for periapical cysts (PC) of endodontic origin. The purpose of this study was to examine the utility of quantitative texture analysis using cone-beam computed tomography (CBCT) to differentiate between COD and PC.

METHODS

Patients who underwent CBCT at Wonkwang University Daejeon Dental Hospital between January 2019 and December 2022 and were diagnosed with COD and PC by clinical, radiologic, and, if necessary, histopathologic examination were included. Twenty-five patients each were retrospectively enrolled in the COD and PC group. All lesions observed on axial CBCT images were manually segmented using the open-access software MaZda version 4.6 to establish the regions of interest, which were then subjected to texture analysis. Among the 279 texture features obtained, 10 texture features with the highest Fisher coefficients were selected. Statistical analysis was performed using the Mann-Whitney U-test, Welch's t-test, or Student's t-test. Texture features that showed significant differences were subjected to receiver operating characteristics (ROC) curve analysis to evaluate the differential diagnostic ability of COD and PC.

RESULTS

The COD group consisted of 22 men and 3 women, while the PC group consisted of 14 men and 11 women, showing a significant difference between the two groups in terms of sex (p=0.003). The 10 selected texture features belonged to the gray level co-occurrence matrix and included the sum of average, sum of entropy, entropy, and difference of entropy. All 10 selected texture features showed statistically significant differences (p<0.05) when comparing patients with COD (n=25) versus those with PC (n=25), osteolytic-stage COD (n=11) versus PC (n=25), and osteolytic-stage COD (n=11) versus cementoblastic-stage COD (n=14). ROC curve analysis to determine the ability to differentiate between COD and PC showed a high area under the curve ranging from 0.96 to 0.98.

CONCLUSION

Texture analysis of CBCT images has shown good diagnostic value in the differential diagnosis of COD and PC, which can help prevent unnecessary endodontic treatment, invasive biopsy, or surgical intervention associated with increased risk of infection.

Texture Analysis of Temporomandibular Joint Disc Changes Associated with Effusion Using Magnetic Resonance Images

The purpose of this study was to identify changes in the temporomandibular joint disc affected by effusion by using texture analysis of magnetic resonance images (MRIs).

METHODS

A total of 223 images of the TMJ, 42 with joint effusion and 181 without, were analyzed. Three consecutive slices were then exported to MaZda software, in which two oval ROIs (one in the anterior band and another in the intermediate zone of the joint disc) were determined in each slice and eleven texture parameters were calculated by using a gray-level co-occurrence matrix. Spearman's correlation coefficient test was used to assess the correlation between texture variables and to select variables for analysis. The Mann-Whitney test was used to compare the groups.

RESULTS

The significance level was set at 5%, with the results demonstrating that there was no high correlation between the parameter directions. It was possible to observe a trend between the average parameters, in which the group with effusion always had smaller values than the group without effusion, except for the parameter measuring the difference in entropy.

CONCLUSION

The trend towards lower overall values for the texture parameters suggested a different behavior between TMJ discs affected by effusion and those not affected, indicating that there may be intrinsic changes.

Reproducibility and location-stability of radiomic features derived from cone-beam computed tomography: a phantom study

OBJECTIVES

This study aims to determine the reproducibility and location-stability of cone-beam computed tomography (CBCT) radiomic features.

METHODS

Centrifugal tubes with six concentrations of K2HPO4 solutions (50, 100, 200, 400, 600, and 800 mg ml-1) were imaged within a customized phantom. For each concentration, images were captured twice as test and retest sets. Totally, 69 radiomic features were extracted by LIFEx. The reproducibility was assessed between the test and retest sets. We used the concordance correlation coefficient (CCC) to screen qualified features and then compared the differences in the numbers of them under 24 series (four locations groups * six concentrations). The location-stability was assessed using the Kruskal-Wallis test under different concentration sets; likewise, the numbers of qualified features under six test sets were analyzed.

RESULTS

There were 20 and 23 qualified features in the reproducibility and location-stability experiments, respectively. In the reproducibility experiment, the performance of the peripheral groups and high-concentration sets was significantly better than the center groups and low-concentration sets. The effect of concentration on the location-stability of features was not monotonic, and the number of qualified features in the low-concentration sets was greater than that in the high-concentration sets. No features were qualified in both experiments.

CONCLUSIONS

The density and location of the target object can affect the number of reproducible radiomic features, and its density can also affect the number of location-stable radiomic features. The problem of feature reliability should be treated cautiously in radiomic research on CBCT.

Periapical foreign body findings - histological and radiological comparison

OBJECTIVE

This study aimed to clarify the perceptibility of periapical foreign materials in imaging compared with histopathology. We hypothesized that dentoalveolar imaging is sufficient to detect periapical foreign bodies.

MATERIAL AND METHODS

Radiological and histopathological records of patients diagnosed with periapical granuloma or radicular cyst from 2000 to 2013 were evaluated retrospectively. Patients with histologically verified foreign bodies were included in the study and their pathological samples and radiological images were reviewed. The outcome variable was radiologically detectable foreign material. The predictor variables were histopathological diagnosis, type of inflammation, type and number of foreign bodies, imaging modality, and site of foreign material.

RESULTS

Compared to the histopathological diagnosis of foreign bodies as the gold standard, the level of radiologic detectability was mild. Histologically verified foreign material could be detected by imaging in 32/59 (53.5%) patients. Histological diagnosis, type of inflammation, type or number of foreign bodies, imaging modality or site of foreign material had no association with radiological detectability (p > 0.05).

CONCLUSIONS

According to our results, histopathology is a more accurate diagnostic tool than radiology in periapical foreign bodies or foreign body reactions. Clinicians should keep in mind the limitations of imaging when setting the diagnosis and planning treatment.

Conservative endodontic microsurgery to protect critical anatomical structures-selective curettage: a case series

BACKGROUND

Endodontic microsurgery has yielded highly successful outcomes in preserving teeth with persistent or recurrent cases of periapical periodontitis that could not be successfully treated by nonsurgical endodontic approaches. To avoid complications in conditions in which periapical lesions invade anatomical structures such as the nasopalatine nerve tube and mandibular canal, selective curettage has been proposed as an alternative choice of complete curettage in surgery.

CASE PRESENTATION

The 8 cases reported herein had undergone root canal treatment and/or retreatment but still presented with symptoms, such as recurring sinus tracts and persistent dull pain. The radiographic examination indicated a large area of radiolucency that was associated with the tooth and had invaded adjacent critical anatomical structures. The patients opted for selective curettage via endodontic microsurgery, and the lesions were histologically confirmed as periapical cysts or granulomas. The follow-up results for one year or more indicated that the affected teeth were clinically asymptomatic and exhibited complete or incomplete healing radiographically.

CONCLUSION

This case series provides clinical evidence for the feasibility of selective curettage in endodontic microsurgery, which can avoid complications caused by damage to the adjacent critical anatomical structures.

A deep learning approach for radiological detection and classification of radicular cysts and periapical granulomas

OBJECTIVES

Dentists and oral surgeons often face difficulties distinguishing between radicular cysts and periapical granulomas on panoramic imaging. Radicular cysts require surgical removal while root canal treatment is the first-line treatment for periapical granulomas. Therefore, an automated tool to aid clinical decision making is needed.

METHODS

A deep learning framework was developed using panoramic images of 80 radicular cysts and 72 periapical granulomas located in the mandible. Additionally, 197 normal images and 58 images with other radiolucent lesions were selected to improve model robustness. The images were cropped into global (affected half of the mandible) and local images (only the lesion) and then the dataset was split into 90% training and 10% testing sets. Data augmentation was performed on the training dataset. A two-route convolutional neural network using the global and local images was constructed for lesion classification. These outputs were concatenated into the object detection network for lesion localization.

RESULTS

The classification network achieved a sensitivity of 1.00 (95% C.I. 0.63 - 1.00), specificity of 0.95 (0.86 - 0.99), and AUC (area under the receiver-operating characteristic curve) of 0.97 for radicular cysts and a sensitivity of 0.77 (0.46 - 0.95), specificity of 1.00 (0.93 - 1.00), and AUC of 0.88 for periapical granulomas. Average precision for the localization network was 0.83 for radicular cysts and 0.74 for periapical granulomas.

CONCLUSIONS

The proposed model demonstrated reliable diagnostic performance for the detection and differentiation of radicular cysts and periapical granulomas. Using deep learning, diagnostic efficacy can be enhanced leading to a more efficient referral strategy and subsequent treatment efficacy.

CLINICAL SIGNIFICANCE

A two-route deep learning approach using global and local images can reliably differentiate between radicular cysts and periapical granulomas on panoramic imaging. Concatenating its output to a localizing network creates a clinically usable workflow for classifying and localizing these lesions, enhancing treatment and referral practices.

Automatic Segmentation of Teeth, Crown-Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls

BACKGROUND

The aim of our study is to provide successful automatic segmentation of various objects on orthopantomographs (OPGs).

METHODS

8138 OPGs obtained from the archives of the Department of Dentomaxillofacial Radiology were included. OPGs were converted into PNGs and transferred to the segmentation tool's database. All teeth, crown-bridge restorations, dental implants, composite-amalgam fillings, dental caries, residual roots, and root canal fillings were manually segmented by two experts with the manual drawing semantic segmentation technique.

RESULTS

The intra-class correlation coefficient (ICC) for both inter- and intra-observers for manual segmentation was excellent (ICC > 0.75). The intra-observer ICC was found to be 0.994, while the inter-observer reliability was 0.989. No significant difference was detected amongst observers (p = 0.947). The calculated DSC and accuracy values across all OPGs were 0.85 and 0.95 for the tooth segmentation, 0.88 and 0.99 for dental caries, 0.87 and 0.99 for dental restorations, 0.93 and 0.99 for crown-bridge restorations, 0.94 and 0.99 for dental implants, 0.78 and 0.99 for root canal fillings, and 0.78 and 0.99 for residual roots, respectively.

CONCLUSIONS

Thanks to faster and automated diagnoses on 2D as well as 3D dental images, dentists will have higher diagnosis rates in a shorter time even without excluding cases.

Magnetic resonance image texture analysis of the lateral pterygoid muscle in patients with rheumatoid arthritis: a preliminary report

OBJECTIVES

Magnetic resonance imaging (MRI) is useful for assessing temporomandibular disorders (TMDs). However, few studies have attempted texture analysis of the lateral pterygoid muscle in patients with rheumatoid arthritis (RA). This study aims to investigate the usefulness of MRI texture analysis of the lateral pterygoid muscle of patients with RA of the temporomandibular joint (TMJ).

METHODS

We analyzed the data from 36 patients (18 non-RA patients and 18 RA patients) who complained of pain and underwent MRI between April 2008 and August 2021. From the MRI scans of these patients, 279 radiomics features were extracted using STIR image data of the ROIs on the lateral pterygoid muscle of patients with RA and analyzed using MaZda ver. 3.3. Seven gray-level co-occurrence matrix features (Sum entropy, Sum variance) were picked up using the Fisher coefficient, for comparison between the RA and non-RA groups. Data analysis was performed using the Mann-Whitney U test A P value of < 0.05 was considered as statistically significant.

RESULTS

All seven lateral pterygoid muscle radiomic features indicated significant differences between the non-RA and RA groups (P < 0.05).

CONCLUSION

MRI texture analysis shows potential for application in radiomics diagnosis of RA in TMJ.

Magnetic resonance imaging texture analysis for quantitative evaluation of the mandibular condyle in juvenile idiopathic arthritis

OBJECTIVES

Juvenile idiopathic arthritis (JIA) is a chronic inflammatory disease that affects the joints and other organs, including the development of the former in a growing child. This study aimed to evaluate the feasibility of texture analysis (TA) based on magnetic resonance imaging (MRI) to provide biomarkers that serve to identify patients likely to progress to temporomandibular joint damage by associating JIA with age, gender and disease onset age.

METHODS

The radiological database was retrospectively reviewed. A total of 45 patients were first divided into control group (23) and JIA group (22). TA was performed using grey-level co-occurrence matrix (GLCM) parameters, in which 11 textural parameters were calculated using MaZda software. These 11 parameters were ranked based on the p value obtained with ANOVA and then correlated with age, gender and disease onset age.

RESULTS

Significant differences in texture parameters of condyle were demonstrated between JIA group and control group (p < 0.05). There was a progressive loss of uniformity in the grayscale pixels of MRI with an increasing age in JIA group.

CONCLUSIONS

MRI TA of the condyle can make it possible to detect the alterations in bone marrow of patients with JIA and promising tool which may help the image analysis.

Efficacy of magnetic resonance imaging texture features of the lateral pterygoid muscle in distinguishing rheumatoid arthritis and osteoarthritis of the temporomandibular joint

OBJECTIVES

The aim of this study was to assess whether magnetic resonance imaging (MRI) texture features of the lateral pterygoid muscle can distinguish between rheumatoid arthritis (RA) and osteoarthritis (OA) of the temporomandibular joint (TMJ).

METHODS

The authors extracted 279 texture features from 36 patients with RA and OA from the region of interest set for the lateral pterygoid muscle on short tau inversion recovery (STIR) images using MaZda Ver.3.3. A total of 10 texture features were selected using Fisher's coefficients, as well as probability of error and average correlation coefficients. Data observed to have a non-normal distribution using the Kolmogorov-Smirnov test were compared using the Mann-Whitney U-test. Receiver operating characteristic (ROC) curves were used to assess the ability of the 10 texture features to distinguish RA and OA of the TMJ.

RESULTS

A total of 10 features (5 Correlation, 3 Run Length Nonuniformity, 1 Sigma, and 1 Teta) were selected from 279 texture features. These texture features revealed significant differences between the RA and OA groups (p < 0.01). The sensitivity, specificity, accuracy, and area under the ROC curve of the texture features for distinguishing RA from OA were 0.78-0.94, 0.89-1.0, 0.86-0.92, and 0.89-0.95, respectively.

CONCLUSION

MRI texture analysis of the lateral pterygoid muscle may be useful for distinguishing between RA and OA of the TMJ.

Cone-beam computed tomography texture analysis can help differentiate odontogenic and non-odontogenic maxillary sinusitis

Purpose

This study aimed to assess texture analysis (TA) of cone-beam computed tomography (CBCT) images as a quantitative tool for the differential diagnosis of odontogenic and non-odontogenic maxillary sinusitis (OS and NOS, respectively).

Materials and Methods

CBCT images of 40 patients diagnosed with OS (N=20) and NOS (N=20) were evaluated. The gray level co-occurrence (GLCM) matrix parameters, and gray level run length matrix texture (GLRLM) parameters were extracted using manually placed regions of interest on lesion images. Seven texture parameters were calculated using GLCM and 4 parameters using GLRLM. The Mann-Whitney test was used for comparisons between the groups, and the Levene test was performed to confirm the homogeneity of variance (α=5%).

Results

The results showed statistically significant differences (P<0.05) between the OS and NOS patients regarding 3 TA parameters. NOS patients presented higher values for contrast, while OS patients presented higher values for correlation and inverse difference moment. Greater textural homogeneity was observed in the OS patients than in the NOS patients, with statistically significant differences in standard deviations between the groups for correlation, sum of squares, sum of entropy, and entropy.

Conclusion

TA enabled quantitative differentiation between OS and NOS on CBCT images by using the parameters of contrast, correlation, and inverse difference moment.

In vivo study of cone beam computed tomography texture analysis of mandibular condyle and its correlation with gender and age

OBJECTIVE

Texture analysis is an image processing method that aims to assess the distribution of gray-level intensity and spatial organization of the pixels in the image. The purpose of this study was to investigate whether the texture analysis applied to cone beam computed tomography (CBCT) images could detect variation in the condyle trabecular bone of individuals from different age groups and genders.

METHODS

The sample consisted of imaging exams from 63 individuals divided into three groups according to age groups of 03-13, 14-24 and 25-34. For texture analysis, the MaZda^® software was used to extract the following parameters: second angular momentum, contrast, correlation, sum of squares, inverse difference moment, sum entropy and entropy. Statistical analysis was performed using Mann-Whitney test for gender and Kruskal-Wallis test for age (P = 5%).

RESULTS

No statistically significant differences were found between age groups for any of the parameters. Males had lower values for the parameter correlation than those of females (P < 0.05).

CONCLUSION

Texture analysis proved to be useful to discriminate mandibular condyle trabecular bone between genders.

Risk assessment of external apical root resorption associated with orthodontic treatment using computed tomography texture analysis

OBJECTIVES

This study aimed to quantitatively assess maxillary central incisor roots using pre-orthodontics computed tomography (CT) texture analysis as part of a radiomics quantitative analysis.

METHODS

This retrospective case-control study included 16 patients with external apical root resorption (EARR) and 16 age- and sex-matched patients without EARR, after orthodontic treatment who underwent pre-orthodontics CT for jaw deformities. All patients were treated with a fixed orthodontic appliance before and after surgical orthodontic treatment. EARR was defined as root resorption ≥ 2 mm of the left and right maxillary central incisors on CT images more than 2 years after the start of orthodontic treatment. Texture features of the maxillary central incisor with and without EARR after orthodontic treatment were analyzed using the open-access software, MaZda Ver. 3.3. Ten texture features were selected using the Fisher method in MaZda from 279 original parameters, which were calculated for each of the maxillary central incisors with and without EARR. The results were tested using the Student's t test, Welch's t test, or Mann-Whitney U test.

RESULTS

Four gray-level run length matrix features and six gray-level co-occurrence matrix features displayed significant differences between both the groups (p < 0.01).

CONCLUSIONS

CT texture analysis was able to quantitatively assess maxillary central incisor roots and distinguish between maxillary central incisor roots with and without EARR. CT texture analysis may be a useful method for predicting EARR after orthodontic treatment.

Radiomics in bone pathology of the jaws

OBJECTIVE

To define which are and how the radiomics features of jawbone pathologies are extracted for diagnosis, predicting prognosis and therapeutic response.

METHODS

A comprehensive literature search was conducted using eight databases and gray literature. Two independent observers rated these articles according to exclusion and inclusion criteria. 23 papers were included to assess the radiomics features related to jawbone pathologies. Included studies were evaluated by using JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies.

RESULTS

Agnostic features were mined from periapical, dental panoramic radiographs, cone beam CT, CT and MRI images of six different jawbone alterations. The most frequent features mined were texture-, shape- and intensity-based features. Only 13 studies described the machine learning step, and the best results were obtained with Support Vector Machine and random forest classifier. For osteoporosis diagnosis and classification, filtering, shape-based and Tamura texture features showed the best performance. For temporomandibular joint pathology, gray-level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), first-order statistics analysis and shape-based analysis showed the best results. Considering odontogenic and non-odontogenic cysts and tumors, contourlet and SPHARM features, first-order statistical features, GLRLM, GLCM had better indexes. For odontogenic cysts and granulomas, first-order statistical analysis showed better classification results.

CONCLUSIONS

GLCM was the most frequent feature, followed by first-order statistics, and GLRLM features. No study reported predicting response, prognosis or therapeutic response, but instead diseases diagnosis or classification. Although the lack of standardization in the radiomics workflow of the included studies, texture analysis showed potential to contribute to radiologists' reports, decreasing the subjectivity and leading to personalized healthcare.

Magnetic resonance imaging texture analysis to differentiate ameloblastoma from odontogenic keratocyst

The differentiation between ameloblastoma (AB) and odontogenic keratocyst (OKC) is essential for the formulation of the surgical plan, especially considering the biological behavior of these two pathological entities. Therefore, developing means to increase the accuracy of the diagnostic process is extremely important for a safe treatment. The aim of this study was to use magnetic resonance imaging (MRI) based on texture analysis (TA) as an aid in differentiating AB from OKC. This study comprised 18 patients; eight patients with AB and ten with OKC. All diagnoses were determined through incisional biopsy and later through histological examination of the surgical specimen. MRI was performed using a 3 T scanner with a neurovascular coil according to a specific protocol. All images were exported to segmentation software in which the volume of interest (VOI) was determined by a radiologist, who was blind to the histopathological results. Next, the textural parameters were computed by using the MATLAB software. Spearman's correlation coefficient was used to assess the correlation between texture parameters and the selected variables. Differences in TA parameters were compared between AB and OKC by using the Mann-Whitney test. Mann-Whitney test showed a statistically significant difference between AB and OKC for the parameters entropy (P = 0.033) and sum average (P = 0.033). MRI texture analysis has the potential to discriminate between AB and OKC as a noninvasive method. MRI texture analysis can be an additional tool to differentiate ameloblastoma from odontogenic keratocyst.

Magnetic resonance imaging texture analysis in the quantitative evaluation of acute osteomyelitis of the mandibular bone

OBJECTIVES

Accurate assessment of radiological images can help in early diagnosis and therapy of suppurative osteomyelitis (OM). The purpose of this study was to apply texture analysis to MRI as a means of quantitatively evaluating acute OM of the mandible.

METHODS

We analyzed the data from 38 patients who complained of pain and underwent MRI between April 2017 and March 2019. From the MRIs of these patients, with (n = 19) and without OM (n = 19), 279 radiomics features were extracted using short tau inversion recovery, data of the regions of interest and analyzed with MaZda v. 3.3. 10 features, including one histogram feature (90th percentile), eight gray-level co-occurrence matrix features (Sum Averg), and one gray-level run-length matrix feature (Horzl_RLNonUni), were selected using Fisher coefficient and compared between the acute OM and non-OM groups. The two groups were compared using Mann-Whitney U test with p value set at 0.05.

RESULTS

All 10 radiomics features showed significant differences between the acute OM and non-OM groups (p < 0.05).

CONCLUSIONS

MRI texture analysis has potential application in radiomics diagnosis of acute OM of the mandible.

Differential Diagnosis of Cysts and Granulomas Supported by Texture Analysis of Intraoral Radiographs

The aim of this study was to evaluate whether textural analysis could differentiate between the two common types of lytic lesions imaged with use of radiography. Sixty-two patients were enrolled in the study with intraoral radiograph images and a histological reference study. Full textural analysis was performed using MaZda software. For over 10,000 features, logistic regression models were applied. Fragments containing lesion edges were characterized by significant correlation of structural information. Although the input images were stored using lossy compression and their scale was not preserved, the obtained results confirmed the possibility of distinguishing between cysts and granulomas with use of textural analysis of intraoral radiographs. It was shown that the important information distinguishing the aforementioned types of lesions is located at the edges and not within the lesion.

Quantitative assessment of the maxillary sinusitis using computed tomography texture analysis: odontogenic vs non-odontogenic etiology

OBJECTIVES

The purpose of this study was to investigate computed tomography (CT) texture features of mucosal thickening of maxillary sinus mucosa to differentiate odontogenic maxillary sinusitis (OMS) from non-odontogenic maxillary sinusitis (NOMS).

METHODS

Eighteen OMS patients and age- and gender-matched 18 NOMS patients who underwent sinus CT were retrospectively reviewed. OMS patients were identified by histopathological examination of tissues excised at surgery combined with CT imaging findings. Patients with mucosal thickening in the maxillary sinus without apical periodontitis or advanced periodontal bone loss near the maxillary sinus on CT were defined as NOMS. Patients with thin mucosal thickening (< 10 mm), cyst, tumor, post-operative deformity, severe metal artifact precluding visualization of the maxillary sinus, and age younger than 20 years were excluded. CT texture features of the mucosal thickening were analyzed using an in-house developed Matlab-based texture analysis program. Forty-five texture features were extracted from each segmented volume. The results were tested with the Mann-Whitney U test.

RESULTS

Six histogram features (mean, median, standard deviation, entropy, geometric mean, harmonic mean) and two gray-level co-occurrence matrix features (entropy, correlation) showed significant differences between OMS and NOMS patients.

CONCLUSIONS

CT texture analysis revealed the quantitative differences between OMS and NOMS. The texture features can serve as a quantitative indicator of maxillary sinusitis to differentiate between OMS and NOMS and help prevent incorrect treatment choices.

Texture analysis of cone beam computed tomography images reveals dental implant stability

The aim of this study was to characterize the alveolar bone of edentulous maxillary sites using texture analysis (TA) of cone beam computed tomography (CBCT) images and to correlate the results to the insertion torque, thus verifying whether TA is a predictive tool of final implant treatment. This study was conducted on patients who had received single implants in the maxilla (46 implants) 1year earlier and whose torque values were properly recorded. Three cross-sections of the sites were selected on CBCT scans. Two regions of interest (ROIs) corresponding to the implant bone site and peri-implant bone were also outlined, according to virtual planning. The CBCT scans were exported to MaZda software, where the two ROIs were delimited following the previously demarcated contours. Values for the co-occurrence matrix were calculated for TA. With regard to the insertion torque value, there was a direct correlation with the contrast of the peri-implant bone (P<0.001) and an inverse correlation with the entropy of the implant bone site (P=0.006). A greater contrast indicates a greater torque value for insertion of the implants, and there is a possible association with a lower entropy value of the implant-bone interface.