Underlying bone change in oral squamous cell carcinoma observed from magnetic resonance imaging and computed tomography: Potential implications for tumor aggressiveness and prognosis.

Background: /purposeIn cases where oral squamous cell carcinoma (OSCC) invades the jawbone, clinicians frequently observe abnormal attenuation on computed tomography (CT) and pathologic signal intensity (SI) on magnetic resonance (MR) imaging of the affected underlying bone marrow. This study introduced a concept of "underlying bone change" to examine its association with clinicopathological features and prognosis of OSCC, as well as its correlation with medullary invasion. Materials and methods: We enrolled 93 consecutive patients diagnosed with OSCC, who underwent mandibulectomy between 2010 and 2016. CT and MR images, along with electronic medical records, were reviewed to evaluate correlations between underlying bone changes, clinicopathological features, five-year overall survival, and medullary invasion. Results: Of the 93 patients, 69 (74.2%) exhibited underlying bone sclerosis on CT, and 74 (79.6%) displayed pathological SI on MR images. These underlying bone changes correlated with the T stage and recurrence, but not with overall survival.Medullary invasion, observed in 61 (65.6%) patients, was strongly associated with T and TNM stages and was linked to poorer overall survival.The underlying bone changes on CT and MR images were positively associated with medullary invasion; however, no significant differences were found in the occurrence of underlying bone changes between the subtypes based on the extent of medullary invasion. Conclusion: Underlying bone changes on CT and MR images can provide valuable insights into the aggressiveness of bone invasion by OSCC. Accurate interpretation of these imaging findings might be crucial for correctly delineating surgical margins and preventing the overestimation of tumor extent.

Developing evidence-based clinical imaging guidelines for the diagnosis of vertically fractured teeth.

OBJECTIVES: This study aimed to develop an evidence-based clinical imaging guideline for teeth suspected with vertical root fractures. METHODS: An adaptation methodology based on the Korean Clinical Imaging Guidelines (K-CIG) was used in the guideline development process. After searching for guidelines using major databases such as Ovid-Medline, Elsevier-Embase, National Guideline Clearinghouse, and Guideline International Network, as well as domestic databases such as KoreaMed, KMbase, and KoMGI, two reviewers analyzed the retrieved articles. The retrieved articles were included in this review using well-established inclusion criteria. RESULTS: Twenty articles were identified through an online search, of which three were selected for guideline development. Based on these three guidelines, this study developed specific recommendations concerning the optimal imaging modality for diagnosing teeth suspected of vertical root fractures. CONCLUSIONS: Periapical radiography is the preferred method for assessing teeth with mastication-related pain and suspected vertical root fractures. However, if intraoral radiographs do not provide sufficient information about root fractures, a small FOV CBCT may be considered. However, the use of CBCT in endodontically treated teeth is significantly constrained by the presence of artificial shading.

Preclinical and Preliminary Evaluation of Perceived Image Quality of AI-Processed Low-Dose CBCT Analysis of a Single Tooth.

This study assessed AI-processed low-dose cone-beam computed tomography (CBCT) images for single-tooth diagnosis. Human-equivalent phantoms were used to evaluate CBCT image quality with a focus on the right mandibular first molar. Two CBCT machines were used for evaluation. The first CBCT machine was used for the experimental group, in which images were acquired using four protocols and enhanced with AI processing to improve quality. The other machine was used for the control group, where images were taken in one protocol without AI processing. The dose-area product (DAP) was measured for each protocol. Subjective clinical image quality was assessed twice by five dentists, with a 2-month interval in between, using 11 parameters and a six-point rating scale. Agreement and statistical significance were assessed with Fleiss' kappa coefficient and intra-class correlation coefficient. The AI-processed protocols exhibited lower DAP/field of view values than non-processed protocols, while demonstrating subjective clinical evaluation results comparable to those of non-processed protocols. The Fleiss' kappa coefficient value revealed statistical significance and substantial agreement. The intra-class correlation coefficient showed statistical significance and almost perfect agreement. These findings highlight the importance of minimizing radiation exposure while maintaining diagnostic quality as the usage of CBCT increases in single-tooth diagnosis.

Primary malignant melanoma of the parotid gland, a rare case arising from a duct: a case report and literature review.

Malignant melanoma of the parotid gland is an unusual tumor in the head and neck region, and most parotid melanoma is reported as a metastatic lesion of cutaneous malignant melanoma. We report a case of primary malignant melanoma arising in the parotid gland duct with diagnostic challenge. The patient was a 68-year-old man who complained of repeated right facial swelling that presented 3 months prior. Swelling was detected along the Stensen's duct of the cheek, and brown-colored saliva-like fluid was aspirated. On MR and CT images, a fluid-filled duct with small nodule and heterogeneously enhancing mass in the parotid parenchyma was detected. The nodular mass on the ductal wall grew rapidly, and the hyperintense T1 signal became significant on follow-up images. The final diagnosis via histopathologic examination using biopsy and parotidectomy specimen revealed the lesion as malignant melanoma of the duct and pleomorphic adenoma of the parenchyma. Even if the incidence of primary malignant melanoma is very low among tumors occurring in the parotid gland, efforts supporting an early diagnosis using imaging characteristics are important.

CACSNet for automatic robust classification and segmentation of carotid artery calcification on panoramic radiographs using a cascaded deep learning network.

Stroke is one of the major causes of death worldwide, and is closely associated with atherosclerosis of the carotid artery. Panoramic radiographs (PRs) are routinely used in dental practice, and can be used to visualize carotid artery calcification (CAC). The purpose of this study was to automatically and robustly classify and segment CACs with large variations in size, shape, and location, and those overlapping with anatomical structures based on deep learning analysis of PRs. We developed a cascaded deep learning network (CACSNet) consisting of classification and segmentation networks for CACs on PRs. This network was trained on ground truth data accurately determined with reference to CT images using the Tversky loss function with optimized weights by balancing between precision and recall. CACSNet with EfficientNet-B4 achieved an AUC of 0.996, accuracy of 0.985, sensitivity of 0.980, and specificity of 0.988 in classification for normal or abnormal PRs. Segmentation performances for CAC lesions were 0.595 for the Jaccard index, 0.722 for the Dice similarity coefficient, 0.749 for precision, and 0.756 for recall. Our network demonstrated superior classification performance to previous methods based on PRs, and had comparable segmentation performance to studies based on other imaging modalities. Therefore, CACSNet can be used for robust classification and segmentation of CAC lesions that are morphologically variable and overlap with surrounding structures over the entire posterior inferior region of the mandibular angle on PRs.

Automatic segmentation and classification of frontal sinuses for sex determination from CBCT scans using a two-stage anatomy-guided attention network.

Sex determination is essential for identifying unidentified individuals, particularly in forensic contexts. Traditional methods for sex determination involve manual measurements of skeletal features on CBCT scans. However, these manual measurements are labor-intensive, time-consuming, and error-prone. The purpose of this study was to automatically and accurately determine sex on a CBCT scan using a two-stage anatomy-guided attention network (SDetNet). SDetNet consisted of a 2D frontal sinus segmentation network (FSNet) and a 3D anatomy-guided attention network (SDNet). FSNet segmented frontal sinus regions in the CBCT images and extracted regions of interest (ROIs) near them. Then, the ROIs were fed into SDNet to predict sex accurately. To improve sex determination performance, we proposed multi-channel inputs (MSIs) and an anatomy-guided attention module (AGAM), which encouraged SDetNet to learn differences in the anatomical context of the frontal sinus between males and females. SDetNet showed superior sex determination performance in the area under the receiver operating characteristic curve, accuracy, Brier score, and specificity compared with the other 3D CNNs. Moreover, the results of ablation studies showed a notable improvement in sex determination with the embedding of both MSI and AGAM. Consequently, SDetNet demonstrated automatic and accurate sex determination by learning the anatomical context information of the frontal sinus on CBCT scans.

Feasibility of intraoperative pathologic examination for sentinel lymph nodes during sentinel node navigation surgery in early gastric cancer: results of pathologic protocol for SENORITA trial.

BACKGROUND: During sentinel node navigation surgery in patients with gastric cancer, intraoperative pathologic examination of sentinel nodes is crucial in determining the extent of surgery. In this study, we evaluated the feasibility and accuracy of intraoperative pathologic protocols using data from a prospective, multicenter, randomized trial. METHODS: A retrospective analysis was conducted using data from the SEntinel Node ORIented Tailored Approach trials from 2013 to 2016. All sentinel lymph nodes were evaluated during surgery with hematoxylin-eosin (HE) staining using a representative section at the largest plane for lymph nodes. For permanent histologic evaluation, sentinel basin nodes were stained with HE and cytokeratin immunohistochemistry in formalin-fixed, paraffin-embedded (FFPE) sections and examined with HE for three deeper-step sections at 200-µm intervals. The failure rate of identification by frozen section and the metastasis rate in non-sentinel basins were investigated. RESULTS: Of the 237 patients who underwent sentinel node basin dissection, 30 had lymph node metastases on permanent pathology. Thirteen patients had macrometastasis confirmed in frozen sections as well as FFPE sections (failure rate: 0%). Patients with negative sentinel nodes in frozen sections but micrometastasis in FFPE sections had no lymph node recurrence during the follow-up period (0%, 0/6). However, in cases with tumor-positive nodes in frozen sections, metastases in non-sentinel basins were detected in the paraffin blocks (8.3%, 2/24). CONCLUSIONS: The single-section HE staining method is sufficient for detecting macrometastasis via intraoperative pathological examination. If a negative frozen-section result is confirmed, sentinel basin dissection can be performed safely. Otherwise, standard surgery is required.

Deep learning-based automatic segmentation of the mandibular canal on panoramic radiographs: A multi-device study.

Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion: This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.

Multislice computed tomography demonstrating mental nerve paresthesia caused by periapical infection: A case report.

Components derived from an infected lesion within the bone can spread through various passages in the mandible, particularly via the mental foramen. Radiologically, the spread of infection is typically nonspecific and challenging to characterize; however, multislice computed tomography (MSCT) can effectively detect pathological changes in soft tissues and the bone marrow space. This report describes the case of a 55-year-old woman who experienced mental nerve paresthesia due to a periapical infection of the right mandibular second premolar. MSCT imaging revealed increased attenuation around the periapical lesion extending into the mandibular canal and loss of the juxtamental foraminal fat pad. Following endodontic treatment of the tooth suspected to be the source of the infection, the patient's symptoms resolved, and the previous MSCT imaging findings were no longer present. Increased bone marrow attenuation and obliteration of the fat plane in the buccal aspect of the mental foramen may serve as radiologic indicators of inflammation spreading from the bone marrow space.

Radiological manifestations and clinical findings of patients with oncologic and osteoporotic medication-related osteonecrosis of the jaw.

Medication-related osteonecrosis of the jaw (MRONJ) poses a challenging form of osteomyelitis in patients undergoing antiresorptive therapies in contrast to conventional osteomyelitis. This study aimed to compare the clinical and radiological features of MRONJ between patients receiving low-dose medications for osteoporosis and those receiving high-dose medications for oncologic purposes. The clinical, panoramic radiographic, and computed tomography data of 159 patients with MRONJ (osteoporotic group, n = 120; oncologic group, n = 39) who developed the condition after using antiresorptive medications for the management of osteoporosis or bone malignancy were analyzed. The osteoporotic group was older (75.8 vs. 60.4 years, p < 0.01) and had a longer duration of medication usage than the oncologic group (58.1 vs. 28.0 months, p < 0.01). Pus discharge and swelling were more common in the osteoporotic group (p < 0.05), whereas bone exposure was more frequent in the oncologic group (p < 0.01). The mandibular cortical index (MCI) in panoramic radiographs was higher in the osteoporotic group (p < 0.01). The mean sequestra size was larger in the oncologic group than in the osteoporotic group (15.3 vs. 10.6 mm, p < 0.05). The cured rate was significantly higher in the osteoporotic group (66.3% vs. 33.3%, p < 0.01). Oncologic MRONJ exhibited distinct clinical findings including rapid disease onset, fewer purulent signs, and lower cure rates than osteoporotic MRONJ. Radiological features such as sequestrum size on CT scan, and MCI values on panoramic radiographs, may aid in differentiating MRONJ in osteoporotic and oncologic patients.