We evaluated the effectiveness of dynamic contrast-enhanced magnetic resonance imaging (MRI) in distinguishing vital and non-vital teeth using a time-intensity curve pattern.
Study Design
We retrospectively analyzed patients who underwent dynamic contrast-enhanced MRI examinations, including those with jaw lesions and vital test results. The vital status of the dental pulp was assessed using the electric pulp test. MRI was performed using a 3T scanner and a 16-channel head and neck coil, with intravenous administration of gadoterate meglumine. Two radiologists measured signal intensity using T1-weighted imaging, T2-weighted imaging, and gadolinium-enhanced T1-weighted imaging, while time-intensity curve classification was evaluated using time-resolved angiography with interleaved stochastic trajectory images.
Results
We analyzed 26 vital and 15 non-vital teeth, using the signal- and time-intensity curve classifications. Signal intensity measurements showed no significant differences between vital and non-vital teeth on T1-weighted imaging, gadolinium-enhanced T1-weighted imaging, and T2-weighted imaging. Time-intensity curve classification effectively distinguished between increasing- and non-increasing-type teeth. Only one case showed a discrepancy between the time-intensity curve and electric pulp test, but accuracy remained high at 98%.
Conclusion
Time-intensity curves derived from dynamic contrast-enhanced MRI accurately differentiated vital from non-vital teeth in jawbone lesions, comparable to the electric pulp test.
{"title":"Dynamic contrast-enhanced magnetic resonance imaging for distinguishing vital and non-vital teeth in jaw lesions: a pilot study","authors":"Supasith Yomtako PhD , Sakiko Kume D.D.S. , Hiroshi Watanabe PhD , Ami Kuribayashi PhD , Shin Nakamura PhD , Akiko Imaizumi PhD , Hiroshi Tomisato RT , Yoshikazu Nomura PhD , Miharu Taguchi PhD , Arisa Oki PhD , Masahiko Miura PhD","doi":"10.1016/j.oooo.2025.09.019","DOIUrl":"10.1016/j.oooo.2025.09.019","url":null,"abstract":"<div><h3>Objective</h3><div>We evaluated the effectiveness of dynamic contrast-enhanced magnetic resonance imaging (MRI) in distinguishing vital and non-vital teeth using a time-intensity curve pattern.</div></div><div><h3>Study Design</h3><div>We retrospectively analyzed patients who underwent dynamic contrast-enhanced MRI examinations, including those with jaw lesions and vital test results. The vital status of the dental pulp was assessed using the electric pulp test. MRI was performed using a 3T scanner and a 16-channel head and neck coil, with intravenous administration of gadoterate meglumine. Two radiologists measured signal intensity using T1-weighted imaging, T2-weighted imaging, and gadolinium-enhanced T1-weighted imaging, while time-intensity curve classification was evaluated using time-resolved angiography with interleaved stochastic trajectory images.</div></div><div><h3>Results</h3><div>We analyzed 26 vital and 15 non-vital teeth, using the signal- and time-intensity curve classifications. Signal intensity measurements showed no significant differences between vital and non-vital teeth on T1-weighted imaging, gadolinium-enhanced T1-weighted imaging, and T2-weighted imaging. Time-intensity curve classification effectively distinguished between increasing- and non-increasing-type teeth. Only one case showed a discrepancy between the time-intensity curve and electric pulp test, but accuracy remained high at 98%.</div></div><div><h3>Conclusion</h3><div>Time-intensity curves derived from dynamic contrast-enhanced MRI accurately differentiated vital from non-vital teeth in jawbone lesions, comparable to the electric pulp test.</div></div>","PeriodicalId":49010,"journal":{"name":"Oral Surgery Oral Medicine Oral Pathology Oral Radiology","volume":"141 2","pages":"Pages 239-244"},"PeriodicalIF":1.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145589565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-09DOI: 10.1016/j.oooo.2025.09.018
Sebastião Silvério Sousa-Neto DDS, MSc , Thaís Cerqueira Reis Nakamura BSc , Giovanna Calabrese dos Santos BSc , Daniela Giraldo-Roldan DDS, MSc , Moisés Willian Aparecido Gonçalves DDS, MSc , Cinthia Verónica Bardález López de Cáceres DDS, MSc, PhD , Felipe Paiva Fonseca DDS, MSc, PhD , Victor Hugo Lopes de Oliveira Moreira DDS , Elismauro Francisco Mendonça DDS, MSc, PhD , Manoela Domingues Martins DDS, MSc, PhD , Amanda De Farias Gabriel DDS , Albina Altemani MD, PhD , Fernanda Viviane Mariano DDS, MSc, PhD , Alan Roger Santos-Silva DDS, MSc, PhD , Marcio Ajudarte Lopes DDS, MSc, PhD , Luiz Paulo Kowalski MD, PhD , Anna Luíza Damaceno Araújo DDS, MSc, PhD , Matheus Cardoso Moraes BSc, MSc, PhD , Pablo Agustin Vargas DDS, MSc, PhD, FRCPath
Objective
Although artificial intelligence tools show promise for enhancing the diagnosis of head and neck lesions, few studies have tested these resources for the microscopic diagnosis of salivary gland tumors. Specifically, the microscopic differentiation between acinic cell carcinoma and secretory carcinoma has never been addressed in this context. Therefore, this exploratory study aimed to comparatively evaluate the feasibility of applying convolutional neural networks for the microscopic differentiation between acinic cell carcinomas and secretory carcinomas.
Methods
A cross-sectional study using whole-slide images from 46 patients with acinic cell carcinoma (n = 26) or secretory carcinoma (n = 20) was conducted. Eight CNNs (ResNet-50, InceptionV3, VGG16, Xception, MobileNet, DenseNet121, EfficientNetB0, and EfficientNetV2B0) were trained and evaluated for accuracy, sensitivity, specificity, F1-score, and AUC. Performance was measured in training, validation, and test subsets. Accuracy and loss curves were also presented.
Results
InceptionV3 demonstrated the best overall performance, with the lowest loss (1.39), highest accuracy (0.81), sensitivity (0.90), and F1-score (0.81). VGG16 achieved the highest AUC (0.86) and precision (0.77). DenseNet121 showed the lowest performance in terms of accuracy (0.65) and F1-Score (0.52), but the highest specificity (0.85).
Conclusion
This proof-of-concept study suggests that convolutional neural networks may be feasible tools to support the microscopic differentiation between acinic cell carcinoma and secretory carcinoma. The performance of these models critically depends on the size of the dataset and the quality of annotations. The findings should be interpreted cautiously given the limited dataset and potential sources of bias. Further validation with larger, multicenter datasets is needed before any clinical application can be considered.
{"title":"Comparative analysis of convolutional neural network models for the histopathological differentiation of acinic cell carcinoma and secretory carcinoma","authors":"Sebastião Silvério Sousa-Neto DDS, MSc , Thaís Cerqueira Reis Nakamura BSc , Giovanna Calabrese dos Santos BSc , Daniela Giraldo-Roldan DDS, MSc , Moisés Willian Aparecido Gonçalves DDS, MSc , Cinthia Verónica Bardález López de Cáceres DDS, MSc, PhD , Felipe Paiva Fonseca DDS, MSc, PhD , Victor Hugo Lopes de Oliveira Moreira DDS , Elismauro Francisco Mendonça DDS, MSc, PhD , Manoela Domingues Martins DDS, MSc, PhD , Amanda De Farias Gabriel DDS , Albina Altemani MD, PhD , Fernanda Viviane Mariano DDS, MSc, PhD , Alan Roger Santos-Silva DDS, MSc, PhD , Marcio Ajudarte Lopes DDS, MSc, PhD , Luiz Paulo Kowalski MD, PhD , Anna Luíza Damaceno Araújo DDS, MSc, PhD , Matheus Cardoso Moraes BSc, MSc, PhD , Pablo Agustin Vargas DDS, MSc, PhD, FRCPath","doi":"10.1016/j.oooo.2025.09.018","DOIUrl":"10.1016/j.oooo.2025.09.018","url":null,"abstract":"<div><h3>Objective</h3><div>Although artificial intelligence tools show promise for enhancing the diagnosis of head and neck lesions, few studies have tested these resources for the microscopic diagnosis of salivary gland tumors. Specifically, the microscopic differentiation between acinic cell carcinoma and secretory carcinoma has never been addressed in this context. Therefore, this exploratory study aimed to comparatively evaluate the feasibility of applying convolutional neural networks for the microscopic differentiation between acinic cell carcinomas and secretory carcinomas.</div></div><div><h3>Methods</h3><div>A cross-sectional study using whole-slide images from 46 patients with acinic cell carcinoma (n = 26) or secretory carcinoma (n = 20) was conducted. Eight CNNs (ResNet-50, InceptionV3, VGG16, Xception, MobileNet, DenseNet121, EfficientNetB0, and EfficientNetV2B0) were trained and evaluated for accuracy, sensitivity, specificity, F1-score, and AUC. Performance was measured in training, validation, and test subsets. Accuracy and loss curves were also presented.</div></div><div><h3>Results</h3><div>InceptionV3 demonstrated the best overall performance, with the lowest loss (1.39), highest accuracy (0.81), sensitivity (0.90), and F1-score (0.81). VGG16 achieved the highest AUC (0.86) and precision (0.77). DenseNet121 showed the lowest performance in terms of accuracy (0.65) and F1-Score (0.52), but the highest specificity (0.85).</div></div><div><h3>Conclusion</h3><div>This proof-of-concept study suggests that convolutional neural networks may be feasible tools to support the microscopic differentiation between acinic cell carcinoma and secretory carcinoma. The performance of these models critically depends on the size of the dataset and the quality of annotations. The findings should be interpreted cautiously given the limited dataset and potential sources of bias. Further validation with larger, multicenter datasets is needed before any clinical application can be considered.</div></div>","PeriodicalId":49010,"journal":{"name":"Oral Surgery Oral Medicine Oral Pathology Oral Radiology","volume":"141 3","pages":"Pages 387-399"},"PeriodicalIF":1.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-08DOI: 10.1016/j.oooo.2025.09.022
Philip Roebers MSc , Ralf Schulze MD
Objective
This study investigates the use of neural networks to predict potential osteoporotic metabolic conditions using the Panoramic Mandibular Index (PMI) derived from panoramic radiographs.
Study Design
A total of n = 750 radiographs were collected and 1,500 regions of interest (ROIs) were analysed. Convolutional neural network (CNN), specifically ResNet-50, were used with transfer learning, achieving the highest accuracy. Three classification approaches were tested: binary classification (PMI > 0.25 vs. PMI < 0.25), 3-class classification (PMI > 0.25, 0.2-0.25, PMI < 0.2), and a modified 2-class classification (PMI > 0.25 vs. PMI < 0.2).
Results
The best classification, the modified 2-class classification, achieved an accuracy of 89.5%. Data augmentation techniques including rotation, reflection, and scaling, improved model robustness.
Conclusions
This study demonstrates that a neural network can be trained to effectively classify PMI values in panoramic radiographs, providing an easy applicable and noninvasive method to identify patients at risk of osteoporosis.
{"title":"Use of neural networks to predict a potential osteoporotic metabolic condition based on the panoramic mandibular index","authors":"Philip Roebers MSc , Ralf Schulze MD","doi":"10.1016/j.oooo.2025.09.022","DOIUrl":"10.1016/j.oooo.2025.09.022","url":null,"abstract":"<div><h3>Objective</h3><div>This study investigates the use of neural networks to predict potential osteoporotic metabolic conditions using the Panoramic Mandibular Index (PMI) derived from panoramic radiographs.</div></div><div><h3>Study Design</h3><div>A total of n = 750 radiographs were collected and 1,500 regions of interest (ROIs) were analysed. Convolutional neural network (CNN), specifically ResNet-50, were used with transfer learning, achieving the highest accuracy. Three classification approaches were tested: binary classification (PMI > 0.25 vs. PMI < 0.25), 3-class classification (PMI > 0.25, 0.2-0.25, PMI < 0.2), and a modified 2-class classification (PMI > 0.25 vs. PMI < 0.2).</div></div><div><h3>Results</h3><div>The best classification, the modified 2-class classification, achieved an accuracy of 89.5%. Data augmentation techniques including rotation, reflection, and scaling, improved model robustness.</div></div><div><h3>Conclusions</h3><div>This study demonstrates that a neural network can be trained to effectively classify PMI values in panoramic radiographs, providing an easy applicable and noninvasive method to identify patients at risk of osteoporosis.</div></div>","PeriodicalId":49010,"journal":{"name":"Oral Surgery Oral Medicine Oral Pathology Oral Radiology","volume":"141 2","pages":"Pages 245-254"},"PeriodicalIF":1.9,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145589482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objective: This study aimed to evaluate the influence of trabecular bone microstructure on the accuracy of dental implant placement using dynamic navigation.
Study design: Thirty-seven implants were placed in 35 patients. Bone biopsies were analyzed to measure bone volume fraction (BV/TV), trabecular thickness (Tb.Th), number (Tb.N), and separation (Tb.Sp). Implant deviations were assessed with navigation software.
Results: BV/TV showed a significant negative correlation with apical (r = -0.396, P < .05) and angular deviation (r = -0.402, P < .05), but not with coronal deviation. Critically, no other parameters (Tb.Th, Tb.N, Tb.Sp) demonstrated any significant correlation with implant positioning accuracy (all P > .05). Multivariate analysis confirmed BV/TV as the sole significant predictor of angular deviation (β = -0.079, P < .05).
Conclusion: While higher bone volume fraction improves angular accuracy, other trabecular microstrctural parameters do not significantly influence placement precision under dynamic navigation.
目的：探讨骨小梁微结构对动态导航种植体定位精度的影响。研究设计：在35例患者中放置37个植入物。骨活检测定骨体积分数（BV/TV）、骨小梁厚度(Tb。Th)，数（Tb）N)和分离（Tb.Sp）。使用导航软件评估种植体偏差。结果：BV/TV与冠状偏移呈显著负相关（r = -0.396, P < 0.05），与冠状偏移呈显著负相关（r = -0.402, P < 0.05）。关键是，没有其他参数(Tb。Th,结核病。N,结核病。Sp)显示与种植体定位精度有显著相关性（均P < 0.05）。多因素分析证实BV/TV是角偏差的唯一显著预测因子（β = -0.079, P < 0.05）。结论：在动态导航下，较高的骨体积分数可以提高骨小梁的角度定位精度，而其他骨小梁微观结构参数对骨小梁的定位精度影响不显著。
{"title":"Cross-sectional study on the correlation between trabecular bone microstructures and the accuracy of implant placement.","authors":"Yingying Zhang, Jia Chen, Qian Han, Jing Lv, Zhihong Zhang, Honghong Liu","doi":"10.1016/j.oooo.2025.09.020","DOIUrl":"https://doi.org/10.1016/j.oooo.2025.09.020","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to evaluate the influence of trabecular bone microstructure on the accuracy of dental implant placement using dynamic navigation.</p><p><strong>Study design: </strong>Thirty-seven implants were placed in 35 patients. Bone biopsies were analyzed to measure bone volume fraction (BV/TV), trabecular thickness (Tb.Th), number (Tb.N), and separation (Tb.Sp). Implant deviations were assessed with navigation software.</p><p><strong>Results: </strong>BV/TV showed a significant negative correlation with apical (r = -0.396, P < .05) and angular deviation (r = -0.402, P < .05), but not with coronal deviation. Critically, no other parameters (Tb.Th, Tb.N, Tb.Sp) demonstrated any significant correlation with implant positioning accuracy (all P > .05). Multivariate analysis confirmed BV/TV as the sole significant predictor of angular deviation (β = -0.079, P < .05).</p><p><strong>Conclusion: </strong>While higher bone volume fraction improves angular accuracy, other trabecular microstrctural parameters do not significantly influence placement precision under dynamic navigation.</p>","PeriodicalId":49010,"journal":{"name":"Oral Surgery Oral Medicine Oral Pathology Oral Radiology","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145472242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-06DOI: 10.1016/j.oooo.2025.09.021
Lísia Daltro Borges Alves MSc , Ana Carolina dos Santos Menezes MSc , Alfredo Viamonte Marin PhD , Claudio Castelo Branco Viegas MSc , Andreia Cristina de Melo MD, PhD , Juliana Chaves Carneiro Carvalho MD , Marco Túlio Cunha Santos BSc , Bruna Lamis Alvarenga MSc , Caio Weber Mendanha Ribeiro BSc , Yara dos Santos Dabdab BSc , Thiago Bernardino da Silveira MSc , Isabele Ávila Small PhD , Daniel Cohen-Goldemberg PhD , Héliton Spíndola Antunes PhD
Objectives
To evaluate the radiation dose in the oral cavity structures (through in vivo dosimetry and treatment planning system) of head and neck cancer patients; and secondarily, evaluate sialometry and oral mucositis.
Study Design
This is a prospective cohort study with patients over 18 years old, diagnosed with oral cavity and major salivary glands cancer, treated with radiotherapy. Group A patients used an intraoral stent (n = 14), and those in Group B (n = 14) did not. In Group A, in vivo dosimetry was performed by positioning 2 thermoluminescent dosimeters in the intraoral stent; unstimulated and stimulated salivary flow were measured; and oral mucositis was evaluated.
Results
In vivo dosimetry demonstrated higher doses in the tumoral area in comparison with the opposite tumor area (p < .001). The planning system predicted doses in nontumor area were lower in Group A. Unstimulated sialometry was higher in Group A in the intermediate and final sessions (p < .05), and hyposalivation was more frequent in Group B (p = .015). No statistically significant differences were observed between the groups regarding the maximum grade and duration of oral mucositis (p = .499; and p = .466, respectively).
Conclusion
The intraoral stent was associated with opposite tumor area lower doses, measured using in vivo dosimetry, the first time this method was applied in studies on this device. Individuals treated with the intraoral stent presented higher salivary flows during and after radiotherapy.
{"title":"In vivo dosimetry in head and neck cancer radiotherapy patients using an intraoral stent: prospective cohort","authors":"Lísia Daltro Borges Alves MSc , Ana Carolina dos Santos Menezes MSc , Alfredo Viamonte Marin PhD , Claudio Castelo Branco Viegas MSc , Andreia Cristina de Melo MD, PhD , Juliana Chaves Carneiro Carvalho MD , Marco Túlio Cunha Santos BSc , Bruna Lamis Alvarenga MSc , Caio Weber Mendanha Ribeiro BSc , Yara dos Santos Dabdab BSc , Thiago Bernardino da Silveira MSc , Isabele Ávila Small PhD , Daniel Cohen-Goldemberg PhD , Héliton Spíndola Antunes PhD","doi":"10.1016/j.oooo.2025.09.021","DOIUrl":"10.1016/j.oooo.2025.09.021","url":null,"abstract":"<div><h3>Objectives</h3><div>To evaluate the radiation dose in the oral cavity structures (through <em>in vivo</em> dosimetry and treatment planning system) of head and neck cancer patients; and secondarily, evaluate sialometry and oral mucositis.</div></div><div><h3>Study Design</h3><div>This is a prospective cohort study with patients over 18 years old, diagnosed with oral cavity and major salivary glands cancer, treated with radiotherapy. Group A patients used an intraoral stent (n = 14), and those in Group B (n = 14) did not. In Group A<em>, in vivo</em> dosimetry was performed by positioning 2 thermoluminescent dosimeters in the intraoral stent; unstimulated and stimulated salivary flow were measured; and oral mucositis was evaluated.</div></div><div><h3>Results</h3><div><em>In vivo</em> dosimetry demonstrated higher doses in the tumoral area in comparison with the opposite tumor area (<em>p</em> < .001). The planning system predicted doses in nontumor area were lower in Group A. Unstimulated sialometry was higher in Group A in the intermediate and final sessions (<em>p</em> < .05), and hyposalivation was more frequent in Group B (<em>p</em> = .015). No statistically significant differences were observed between the groups regarding the maximum grade and duration of oral mucositis (<em>p</em> = .499; and <em>p</em> = .466, respectively).</div></div><div><h3>Conclusion</h3><div>The intraoral stent was associated with opposite tumor area lower doses, measured <em>using in vivo</em> dosimetry, the first time this method was applied in studies on this device. Individuals treated with the intraoral stent presented higher salivary flows during and after radiotherapy.</div></div>","PeriodicalId":49010,"journal":{"name":"Oral Surgery Oral Medicine Oral Pathology Oral Radiology","volume":"141 3","pages":"Pages 323-333"},"PeriodicalIF":1.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145477192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-06DOI: 10.1016/j.oooo.2025.09.017
Melike Beyza Kaplanoglu, Beste Cilli, Idil Ozden, Hesna Sazak Ovecoglu
Objectives: This study evaluates the implications of clinical and radiographic diagnostic errors on the treatment of non-endodontic and truly endodontic periapical lesions, as determined by histopathologic findings.
Study design: A retrospective review was conducted on 236 histopathological reports of periapical lesions collected via apical surgery, extraction, or cystectomy (2020-2024). Clinical diagnoses based on 2-dimensional radiographs and CBCT were compared with histopathological results. Cases with soft tissue lesions, lesions with no detectable radiographic findings, or non-periapical pathoses were excluded. Data were categorized and statistically analyzed to determine diagnostic accuracy.
Results: Of 236 cases, 136 (57.62%) were of endodontic origin (apical granulomas and radicular cysts), while 100 (42.37%) were nonendodontic, including 56 (23.73%) odontogenic cysts, 18 (7.63%) tumors, 16 (6.78%) non-odontogenic cysts, and 10 (4.24%) fibro-osseous lesions. Misdiagnosis led to inappropriate treatments such as unnecessary extractions or ineffective root canal therapy. CBCT with histopathological confirmation significantly improved diagnostic precision. Notably, 63 (26.79%) of teeth linked to odontogenic cysts were vital, indicating that root canal therapy was unwarranted.
Conclusions: Based on the findings of this retrospective study, a substantial proportion of periapical lesions are non-endodontic, emphasizing the importance of clinical evaluation, including vitality testing, alongside imaging in the differential diagnosis as opposed to reliance on imaging alone. Reliance solely on imaging can cause treatment errors. Improved protocols and further research are essential to optimize outcomes.
{"title":"The importance of histopathologic diagnosis in the treatment of periapical lesions: a retrospective study.","authors":"Melike Beyza Kaplanoglu, Beste Cilli, Idil Ozden, Hesna Sazak Ovecoglu","doi":"10.1016/j.oooo.2025.09.017","DOIUrl":"https://doi.org/10.1016/j.oooo.2025.09.017","url":null,"abstract":"<p><strong>Objectives: </strong>This study evaluates the implications of clinical and radiographic diagnostic errors on the treatment of non-endodontic and truly endodontic periapical lesions, as determined by histopathologic findings.</p><p><strong>Study design: </strong>A retrospective review was conducted on 236 histopathological reports of periapical lesions collected via apical surgery, extraction, or cystectomy (2020-2024). Clinical diagnoses based on 2-dimensional radiographs and CBCT were compared with histopathological results. Cases with soft tissue lesions, lesions with no detectable radiographic findings, or non-periapical pathoses were excluded. Data were categorized and statistically analyzed to determine diagnostic accuracy.</p><p><strong>Results: </strong>Of 236 cases, 136 (57.62%) were of endodontic origin (apical granulomas and radicular cysts), while 100 (42.37%) were nonendodontic, including 56 (23.73%) odontogenic cysts, 18 (7.63%) tumors, 16 (6.78%) non-odontogenic cysts, and 10 (4.24%) fibro-osseous lesions. Misdiagnosis led to inappropriate treatments such as unnecessary extractions or ineffective root canal therapy. CBCT with histopathological confirmation significantly improved diagnostic precision. Notably, 63 (26.79%) of teeth linked to odontogenic cysts were vital, indicating that root canal therapy was unwarranted.</p><p><strong>Conclusions: </strong>Based on the findings of this retrospective study, a substantial proportion of periapical lesions are non-endodontic, emphasizing the importance of clinical evaluation, including vitality testing, alongside imaging in the differential diagnosis as opposed to reliance on imaging alone. Reliance solely on imaging can cause treatment errors. Improved protocols and further research are essential to optimize outcomes.</p>","PeriodicalId":49010,"journal":{"name":"Oral Surgery Oral Medicine Oral Pathology Oral Radiology","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145410575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-28DOI: 10.1016/j.oooo.2025.09.001
Serhat Can DDS , Sinem Büşra Kıraç Can DDS , Sultan Seren Karakuş MD , Gülcan Berkel MD
{"title":"Corrigendum to “Does the use of the levonorgestrel intrauterine system increase the prevalence of masticatory muscles tenderness?” [Oral Surg Oral Med Oral Pathol Oral Radiol. 140/4 (2025) Pages 384-390]","authors":"Serhat Can DDS , Sinem Büşra Kıraç Can DDS , Sultan Seren Karakuş MD , Gülcan Berkel MD","doi":"10.1016/j.oooo.2025.09.001","DOIUrl":"10.1016/j.oooo.2025.09.001","url":null,"abstract":"","PeriodicalId":49010,"journal":{"name":"Oral Surgery Oral Medicine Oral Pathology Oral Radiology","volume":"140 6","pages":"Page 814"},"PeriodicalIF":1.9,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-27DOI: 10.1016/j.oooo.2025.09.015
Pushkar Mehra DMD, MS, FACS , David B. Powers DMD, MD, FRCS, FACS , Deepak Kademani DMD, MD, FACS , Paul S. Tiwana DDS, MD, FACS
Oral and Maxillofacial Surgery (OMS) residency programs in the United States have recently experienced a notable decline in match rates. While it may be challenging to identify the root cause of this change, similar experiences in other specialties have identified contributing factors such as the increase in the number of residency positions, changes in specialty perception and workforce needs as contributing factors. This article examines the current state of OMS residency training and provides recommendations aimed to attract more people to the field, take a deeper dive into understanding of the factors contributing to changes in the specialty, and the development of evidence-based interventions.
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Pub Date : 2025-09-25DOI: 10.1016/S2212-4403(25)01180-0
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