Revisiting the "Puffed Cheek" Technique: Advantages, Fallacies, and Potential Solutions.

The "puffed cheek" technique is routinely performed during CT neck studies in patients with suspected oral cavity cancers. The insufflation of air within the oral vestibule helps in the detection of small buccal mucosal lesions, with better delineation of lesion origin, depth, and extent of spread. The pitfalls associated with this technique are often underrecognized and poorly understood. They can mimic actual lesions, forfeiting the technique's primary purpose. This review provides an overview of the puffed cheek technique and its associated pitfalls. These pitfalls include pneumoparotid, soft palate elevation that resembles a nasopharyngeal mass, various tongue displacements or distortions that obscure tongue lesions or mimic them, sublingual gland herniation, an apparent exacerbation of the airway edema, vocal cord adduction that hinders glottic evaluation, and false indications of osteochondronecrosis in laryngeal cartilage. Most stem from a common underlying mechanism of unintentional Valsalva maneuver engaged in by the patient while trying to perform a puffed cheek, creating a closed air column under positive pressure with resultant surrounding soft-tissue displacement. These pitfalls can thus be avoided by instructing the patient to maintain continuous nasal breathing while puffing out their cheek during image acquisition, preventing the formation of the closed air column. Keywords: CT, Head/Neck © RSNA, 2024.

The innovation of AI-based software in oral diseases: clinical-histopathological correlation diagnostic accuracy primary study.

BACKGROUND: Machine learning (ML) through artificial intelligence (AI) could provide clinicians and oral pathologists to advance diagnostic problems in the field of potentially malignant lesions, oral cancer, periodontal diseases, salivary gland disease, oral infections, immune-mediated disease, and others. AI can detect micro-features beyond human eyes and provide solution in critical diagnostic cases. OBJECTIVE: The objective of this study was developing a software with all needed feeding data to act as AI-based program to diagnose oral diseases. So our research question was: Can we develop a Computer-Aided Software for accurate diagnosis of oral diseases based on clinical and histopathological data inputs? METHOD: The study sample included clinical images, patient symptoms, radiographic images, histopathological images and texts for the oral diseases of interest in the current study (premalignant lesions, oral cancer, salivary gland neoplasms, immune mediated oral mucosal lesions, oral reactive lesions) total oral diseases enrolled in this study was 28 diseases retrieved from the archives of oral maxillofacial pathology department. Total 11,200 texts and 3000 images (2800 images were used for training data to the program and 100 images were used as test data to the program and 100 cases for calculating accuracy, sensitivity& specificity). RESULTS: The correct diagnosis rates for group 1 (software users), group 2 (microscopic users) and group 3 (hybrid) were 87%, 90.6, 95% respectively. The reliability for inter-observer value was done by calculating Cronbach's alpha and interclass correlation coefficient. The test revealed for group 1, 2 and 3 the following values respectively 0.934, 0.712 & 0.703. All groups showed acceptable reliability especially for Diagnosis Oral Diseases Software (DODS) that revealed higher reliability value than other groups. However, The accuracy, sensitivity & specificity of this software was lower than those of oral pathologists (master's degree). CONCLUSION: The correct diagnosis rate of DODS was comparable to oral pathologists using standard microscopic examination. The DODS program could be utilized as diagnostic guidance tool with high reliability & accuracy.

Diagnostic accuracy of contrast-enhanced computed tomography in assessing bone invasion in patients with oral squamous cell carcinoma.

OBJECTIVES: This study aimed to evaluate the diagnostic accuracy of contrast-enhanced computed tomography (CT) in detecting bone invasion in oral squamous cell carcinoma (OSCC) patients and to explore clinicopathological factors associated with its reliability. MATERIALS AND METHODS: 417 patients underwent preoperative contrast-enhanced CT followed by radical surgery. The presence or absence of bone invasion served as the outcome variable, with histopathologic examination of the resection specimen considered the gold standard. Statistical analyses, comprising correlation analyses and the determination of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were conducted. RESULTS: CT exhibited 76.85% sensitivity, 82.20% specificity, 47.14% PPV, and 89.67% NPV. False-positive and false-negative rates were 11.27% and 5.99%, respectively. Artifacts affected assessment in 44 patients, but not in those with bone invasion. Tumor size, depth of invasion (DOI), tumor localization at the upper jaw, lymphatic invasion, and perineural invasion correlated with incorrect identification of bone invasion (Chi-square, p < 0.05). CONCLUSIONS: Despite utilizing thin-section CT, notable false-positive and false-negative results persisted. Patients with T3 tumors, DOI ≥ 10 mm, or upper jaw tumors are at higher risk for misidentification of bone invasion. Combining multiple methods may enhance diagnostic accuracy, and the integration of artificial intelligence or tracking electrolyte disturbances by tumor depth profiling shows promise for further assessment of bone invasion before histopathology. CLINICAL RELEVANCE: Surgeons should consider these insights when planning tumor resection. Supplementary imaging may be warranted in cases with high risk factors for misidentification. Further methodological advancements are crucial for enhancing diagnostic precision.

Beneath the surface: A systematic review on intraoperative imaging techniques for deep margin assessment in oral squamous cell carcinoma.

Resection margins of oral squamous cell carcinoma (SCC) are often inadequate. A systematic review on clinical intraoperative whole-specimen imaging techniques to obtain adequate deep resection margins in oral SCC is lacking. Such a review may render better alternatives for the current insufficient intraoperative techniques: palpation and frozen section analyses (FSA). This review resulted in ten publications investigating ultrasound (US), four investigating fluorescence, and three investigating MRI. Both US and fluorescence were able to image the tumor intraorally and perform ex-vivo imaging of the resection specimen. Fluorescence was also able to image residual tumor tissue in the wound bed. MRI could only be used on the ex-vivo specimen. The 95 % confidence intervals for sensitivity and specificity were large, due to the small sample sizes for all three techniques. The sensitivity and specificity of US for identifying < 5 mm margins ranged from 0 % to 100 % and 60 % to 100 %, respectively. For fluorescence, this ranged from 0 % to 100 % and 76 % to 100 %, respectively. For MRI, this ranged from 7 % to 100 % and 81 % to 100 %, respectively. US, MRI and fluorescence are the currently available imaging techniques that can potentially be used intraoperatively and which can image the entire tumor-free margin, although they have insufficient sensitivity for identifying < 5 mm margins. Further research on larger cohorts is needed to improve the sensitivity by determining cut-off points on imaging for inadequate margins. This improves the number of adequate resections of oral SCC's and pave the way for routine clinical implementation of these techniques.

N0 neck trial: Does intensification of follow-up (Ultrasound + Physical Examination) influence outcomes in early-stage oral cancer?

AIM OF THE STUDY: We previously reported a survival benefit of elective neck dissection (END) over therapeutic neck dissection (TND) in patients with clinically node-negative early-stage oral cancer. We now report the results of the second question in the same study addressing the impact of adding neck ultrasound to physical examination during follow-up on outcomes. METHODS: Patients with lateralized T1/T2 oral squamous cell carcinoma (SCC) were randomized to END or TND and to follow-up with physical-examination plus neck ultrasound (PE+US) versus physical-examination (PE). The primary endpoint was overall survival (OS). RESULTS: Between January 2004 and June 2014, 596 patients were enrolled. This is an intention to treat analysis of 592 analysable patients, of whom 295 were allocated to PE+US and 297 to PE with a median follow-up of 77.47 months (interquartile range (IQR) 54.51-126.48). There was no significant difference (unadjusted hazard ratio [HR], 0.92, 95% CI, 0.71-1.20, p = 0.54) in 5-year OS between PE+US (70.8%, 95% CI, 65.51-76.09) and PE (67.3%, 95% CI, 61.81-72.79). Among 131 patients with neck node relapse as the first event, the median time to relapse detection was 4.85 (IQR 2.33-9.60) and 7.62 (IQR 3.22-9.86) months in PE+US and PE arms, respectively. The N stage in the PE+US arm was N1 33.8%, N2a 7.4%, N2b/c 44.1% and N3 14.7% while in PE was N1 28.6%, N2a 9.5%, N2b/c 39.7%, N3 20.6% and unknown 1.6%. CONCLUSION: Adding neck ultrasound to physical examination during follow-up detects nodal relapses earlier but does not improve overall survival.

Enhancing oral squamous cell carcinoma detection: a novel approach using improved EfficientNet architecture.

PROBLEM: Oral squamous cell carcinoma (OSCC) is the eighth most prevalent cancer globally, leading to the loss of structural integrity within the oral cavity layers and membranes. Despite its high prevalence, early diagnosis is crucial for effective treatment. AIM: This study aimed to utilize recent advancements in deep learning for medical image classification to automate the early diagnosis of oral histopathology images, thereby facilitating prompt and accurate detection of oral cancer. METHODS: A deep learning convolutional neural network (CNN) model categorizes benign and malignant oral biopsy histopathological images. By leveraging 17 pretrained DL-CNN models, a two-step statistical analysis identified the pretrained EfficientNetB0 model as the most superior. Further enhancement of EfficientNetB0 was achieved by incorporating a dual attention network (DAN) into the model architecture. RESULTS: The improved EfficientNetB0 model demonstrated impressive performance metrics, including an accuracy of 91.1%, sensitivity of 92.2%, specificity of 91.0%, precision of 91.3%, false-positive rate (FPR) of 1.12%, F1 score of 92.3%, Matthews correlation coefficient (MCC) of 90.1%, kappa of 88.8%, and computational time of 66.41%. Notably, this model surpasses the performance of state-of-the-art approaches in the field. CONCLUSION: Integrating deep learning techniques, specifically the enhanced EfficientNetB0 model with DAN, shows promising results for the automated early diagnosis of oral cancer through oral histopathology image analysis. This advancement has significant potential for improving the efficacy of oral cancer treatment strategies.

Rare presentation of solitary fibrous tumour in the floor of the mouth.

A male in his 20s, a tobacco chewer, presented to the outpatient department with a history of painless, slowly progressive swelling in the floor of the mouth. After a thorough history and clinical examination, MRI was done and the tumour was completely excised. Histopathological examination revealed the mass to be a solitary fibrous tumour, confirmed with immunohistochemical markers. On subsequent follow-ups, the patient was found to be asymptomatic with no clinical signs of recurrence.

Oral mucosa - an examination map for confocal laser endomicroscopy within the oral cavity: an experimental clinical study.

OBJECTIVES: Confocal laser endomicroscopy (CLE) is an optical method that enables microscopic visualization of oral mucosa. Previous studies have shown that it is possible to differentiate between physiological and malignant oral mucosa. However, differences in mucosal architecture were not taken into account. The objective was to map the different oral mucosal morphologies and to establish a "CLE map" of physiological mucosa as baseline for further application of this powerful technology. MATERIALS AND METHODS: The CLE database consisted of 27 patients. The following spots were examined: (1) upper lip (intraoral) (2) alveolar ridge (3) lateral tongue (4) floor of the mouth (5) hard palate (6) intercalary line. All sequences were examined by two CLE experts for morphological differences and video quality. RESULTS: Analysis revealed clear differences in image quality and possibility of depicting tissue morphologies between the various localizations of oral mucosa: imaging of the alveolar ridge and hard palate showed visually most discriminative tissue morphology. Labial mucosa was also visualized well using CLE. Here, typical morphological features such as uniform cells with regular intercellular gaps and vessels could be clearly depicted. Image generation and evaluation was particularly difficult in the area of the buccal mucosa, the lateral tongue and the floor of the mouth. CONCLUSION: A physiological "CLE map" for the entire oral cavity could be created for the first time. CLINICAL RELEVANCE: This will make it possible to take into account the existing physiological morphological features when differentiating between normal mucosa and oral squamous cell carcinoma in future work.

Application of gel based on medical image inspection in total closure resection of oral cancer patients.

Oral cancer is a common malignant tumor, and total closed resection is a common treatment. However, it has always been a challenge to determine the exact extent of excision during surgery. The application of medical image examination in surgery can provide important reference information, but the current methods still have some limitations. This study explored the application of gels based on medical image examination in the total closed resection of oral cancer patients to improve the accuracy of resection range and surgical treatment effect. The study collected medical image data of patients with oral cancer for image enhancement and determination of resection boundaries. By comparing the results of the experimental group and the control group, the application effect of gel in operation was evaluated. Through the application of medical image inspection technology, the determination of surgical resection boundary is more accurate, and the positive incisal margin of patients is effectively avoided. Gel technology improves the success rate and efficacy of surgery, and this method helps to improve the accuracy of surgery and the certainty of the scope of resection, which is of great significance for improving the surgical treatment effect and the survival rate of patients.

Desmoid fibromatosis-a diagnostic dilemma.

PURPOSE: Desmoid fibromatosis in head and neck is infrequent and poses a significant challenge to the clinicians due to its non-specific characteristics. METHODS: This case report focuses on a 69-year-old male who presented to a tertiary healthcare center in Karnataka, India with a swelling in the oral cavity. RESULTS: Despite initial suspicions of malignancy based on clinical examination and findings on computed tomography imaging, subsequent histopathology and immunohistochemistry revealed an unexpected finding. CONCLUSION: The case highlights the importance of clinical suspicion and histopathological evaluation as well as the need for greater awareness to facilitate early diagnosis and appropriate management of desmoid fibromatosis. We also present a literature review of varied presentations of desmoid tumors afflicting various subsites of the head and neck.

FD-Net: Feature Distillation Network for Oral Squamous Cell Carcinoma Lymph Node Segmentation in Hyperspectral Imagery.

Oral squamous cell carcinoma (OSCC) has the characteristics of early regional lymph node metastasis. OSCC patients often have poor prognoses and low survival rates due to cervical lymph metastases. Therefore, it is necessary to rely on a reasonable screening method to quickly judge the cervical lymph metastastic condition of OSCC patients and develop appropriate treatment plans. In this study, the widely used pathological sections with hematoxylin-eosin (H&E) staining are taken as the target, and combined with the advantages of hyperspectral imaging technology, a novel diagnostic method for identifying OSCC lymph node metastases is proposed. The method consists of a learning stage and a decision-making stage, focusing on cancer and non-cancer nuclei, gradually completing the lesions' segmentation from coarse to fine, and achieving high accuracy. In the learning stage, the proposed feature distillation-Net (FD-Net) network is developed to segment the cancerous and non-cancerous nuclei. In the decision-making stage, the segmentation results are post-processed, and the lesions are effectively distinguished based on the prior. Experimental results demonstrate that the proposed FD-Net is very competitive in the OSCC hyperspectral medical image segmentation task. The proposed FD-Net method performs best on the seven segmentation evaluation indicators: MIoU, OA, AA, SE, CSI, GDR, and DICE. Among these seven evaluation indicators, the proposed FD-Net method is 1.75%, 1.27%, 0.35%, 1.9%, 0.88%, 4.45%, and 1.98% higher than the DeepLab V3 method, which ranks second in performance, respectively. In addition, the proposed diagnosis method of OSCC lymph node metastasis can effectively assist pathologists in disease screening and reduce the workload of pathologists.

Application of deep learning radiomics in oral squamous cell carcinoma-Extracting more information from medical images using advanced feature analysis.

OBJECTIVE: To conduct a systematic review with meta-analyses to assess the recent scientific literature addressing the application of deep learning radiomics in oral squamous cell carcinoma (OSCC). MATERIALS AND METHODS: Electronic and manual literature retrieval was performed using PubMed, Web of Science, EMbase, Ovid-MEDLINE, and IEEE databases from 2012 to 2023. The ROBINS-I tool was used for quality evaluation; random-effects model was used; and results were reported according to the PRISMA statement. RESULTS: A total of 26 studies involving 64,731 medical images were included in quantitative synthesis. The meta-analysis showed that, the pooled sensitivity and specificity were 0.88 (95 %CI: 0.87∼0.88) and 0.80 (95 %CI: 0.80∼0.81), respectively. Deeks' asymmetry test revealed there existed slight publication bias (P = 0.03). CONCLUSIONS: The advances in the application of radiomics combined with learning algorithm in OSCC were reviewed, including diagnosis and differential diagnosis of OSCC, efficacy assessment and prognosis prediction. The demerits of deep learning radiomics at the current stage and its future development direction aimed at medical imaging diagnosis were also summarized and analyzed at the end of the article.

Performance of cone-beam computed tomography (CBCT) in comparison to conventional computed tomography (CT) and magnetic resonance imaging (MRI) for the detection of bone invasion in oral squamous cell cancer (OSCC): a prospective study.

BACKGROUND: Oral squamous carcinoma (OSCC) is often diagnosed at late stages and bone erosion or invasion of the jawbone is frequently present. Computed tomography (CT) and magnetic resonance imaging (MRI) are known to have high diagnostic sensitivities, specificities, and accuracies in detecting these bone affections in patients suffering from OSCC. To date, the existing data regarding the impact of cone-beam computed tomography (CBCT) have been weak. Therefore, this study aimed to investigate whether CBCT is a suitable tool to detect bone erosion or invasion in patients with OSCC. METHODS: We investigated in a prospective trial the impact of CBCT in the diagnosis of bone erosion or invasion in patients with OSCC who underwent surgery. Every participant received a CBCT, CT, and MRI scan during staging. Imaging modalities were evaluated by two specialists in oral and maxillofacial surgery (CBCT) and two specialists in radiology (CT and MRI) in a blinded way, to determine whether a bone affection was present or not. Reporting used the following 3-point system: no bony destruction ("0"), cortical bone erosion ("1"), or medullary bone invasion ("2"). Histological examination or a follow-up served to calculate the sensitivities, specificities, and accuracies of the imaging modalities. RESULTS: Our results revealed high diagnostic sensitivities (95.6%, 84.4%, and 88.9%), specificities (87.0%, 91.7%, and 91.7%), and accuracies (89.5%, 89.5%, and 90.8%) for CBCT, CT, and MRI. A pairwise comparison found no statistical difference between CBCT, CT, and MRI. CONCLUSION: Our data support the routine use of CBCT in the diagnosis of bone erosion and invasion in patients with OSCC as diagnostic accuracy is equal to CT and MRI, the procedure is cost-effective, and it can be performed during initial contact with the patient.

Prenatally Diagnosed Congenital Oral Teratoma Successfully Treated in a Neonate.

Teratomas are congenital malformations that rarely occur in the oral cavity. In the case reported here, fetal magnetic resonance imaging performed at 30 weeks of gestation informed the decision-making of the multidisciplinary management team, who closely followed the pregnancy until the scheduled cesarean delivery at 38 weeks of gestation. After delivery, tracheal intubation was performed to ensure airway patency, and tumor resection was scheduled immediately after ruling out contraindications to surgery based on preoperative examinations, allowing for safe excising of the tumor. Postoperative follow-up at 3 months showed no abnormalities.

Malignant Lesions of the Oral Region.

This article focuses on the radiographic presentations of various malignant conditions that affect the oral region and delineates the role of CT, MR imaging, and PET in oral cancer imaging.

Automated evaluation of masseter muscle volume: deep learning prognostic approach in oral cancer.

BACKGROUND: Sarcopenia has been identified as a potential negative prognostic factor in cancer patients. In this study, our objective was to investigate the relationship between the assessment method for sarcopenia using the masseter muscle volume measured on computed tomography (CT) images and the life expectancy of patients with oral cancer. We also developed a learning model using deep learning to automatically extract the masseter muscle volume and investigated its association with the life expectancy of oral cancer patients. METHODS: To develop the learning model for masseter muscle volume, we used manually extracted data from CT images of 277 patients. We established the association between manually extracted masseter muscle volume and the life expectancy of oral cancer patients. Additionally, we compared the correlation between the groups of manual and automatic extraction in the masseter muscle volume learning model. RESULTS: Our findings revealed a significant association between manually extracted masseter muscle volume on CT images and the life expectancy of patients with oral cancer. Notably, the manual and automatic extraction groups in the masseter muscle volume learning model showed a high correlation. Furthermore, the masseter muscle volume automatically extracted using the developed learning model exhibited a strong association with life expectancy. CONCLUSIONS: The sarcopenia assessment method is useful for predicting the life expectancy of patients with oral cancer. In the future, it is crucial to validate and analyze various factors within the oral surgery field, extending beyond cancer patients.

Demographic and imaging features of oral squamous cell cancer in Serbia: a retrospective cross-sectional study.

BACKGROUND: The mortality of oral squamous cell cancer (OSCC) in Serbia increased in the last decade. Recent studies on the Serbian population focused mainly on the epidemiological aspect of OSCC. This study aimed to investigate the demographic and imaging features of OSCC in the Serbian population at the time of diagnosis. METHODS: We retrospectively analyzed computed tomography (CT) images of 276 patients with OSCC diagnosed between 2017 and 2022. Age, gender, tumor site, tumor volume (CT-TV, in cm3), depth of invasion (CT-DOI, in mm), and bone invasion (CT-BI, in %) were evaluated. TNM status and tumor stage were also analyzed. All parameters were analyzed with appropriate statistical tests. RESULTS: The mean age was 62.32 ± 11.39 and 63.25 ± 11.71 for males and females, respectively. Male to female ratio was 1.63:1. The tongue (36.2%), mouth floor (21.0%), and alveolar ridge (19.9%) were the most frequent sites of OSCC. There was a significant gender-related difference in OSCC distribution between oral cavity subsites (Z=-4.225; p < 0.001). Mean values of CT-TV in males (13.8 ± 21.5) and females (5.4 ± 6.8) were significantly different (t = 4.620; p < 0.001). CT-DOI also differed significantly (t = 4.621; p < 0.001) between males (14.4 ± 7.4) and females (10.7 ± 4.4). CT-BI was detected in 30.1%, the most common in the alveolar ridge OSCC. T2 tumor status (31.4%) and stage IVA (28.3%) were the most dominant at the time of diagnosis. Metastatic lymph nodes were detected in 41.1%. CONCLUSION: Our findings revealed significant gender-related differences in OSCC imaging features. The predominance of moderate and advanced tumor stages indicates a long time interval to the OSCC diagnosis.

Improved intraoperative identification of close margins in oral squamous cell carcinoma resections using a dual aperture fluorescence ratio approach: first in-human results.

Significance: Surgical excision is the main treatment for solid tumors in oral squamous cell carcinomas, where wide local excision (achieving a healthy tissue margin of >5 mm around the excised tumor) is the goal as it results in reduced local recurrence rates and improved overall survival. Aim: No clinical methods are available to assess the complete surgical margin intraoperatively while the patient is still on the operating table; and while recent intraoperative back-bench fluorescence-guided surgery approaches have shown promise for detecting "positive" inadequate margins (<1 mm), they have had limited success in the detection of "close" inadequate margins (1 to 5 mm). Here, a dual aperture fluorescence ratio (dAFR) approach was evaluated as a means of improving detection of close margins. Approach: The approach was evaluated on surgical specimens from patients who were administered a tumor-specific fluorescent imaging agent (cetuximab-800CW) prior to surgery. The dAFR approach was compared directly against standard wide-field fluorescence imaging and pathology measurements of margin thickness in specimens from three patients and a total of 12 margin locations (1 positive, 5 close, and 6 clear margins). Results: The area under the receiver operating characteristic curve, representing the ability to detect close compared to clear margins (>5 mm) was found to be 1.0 and 0.57 for dAFR and sAF, respectively. Improvements in dAFR were found to be statistically significant (p<0.02). Conclusions: These results provide evidence that the dAFR approach potentially improves detection of close surgical margins.

Fluorescent Guided Sentinel Lymph Mapping of the Oral Cavity with Fluorescent-Labeled Tilmanocept.

OBJECTIVE: With the shift toward utilization of sentinel lymph node biopsy (SLNB) in oral cavity cancer, improved techniques for intraoperative sentinel node identification are needed. This study investigates the feasibility of fluorescently labeled tilmanoscept in SLNB in an oral cancer rabbit model. METHODS: An animal study was designed using 21 healthy male New Zealand rabbits. Gallium-68-labeled tilmanocept labeled with IRDye800CW was injected submucosally into the buccal mucosa (n = 6) or lateral tongue (n = 7) followed by PET imaging. One hour after injection, SLNB was performed using fluorescence imaging followed by a bilateral neck dissection and sampling of non-nodal surrounding tissue. All tissues were measured for radioactivity and fluorescence. In addition, eight rabbits were injected with delayed SLNB performed 48 h after injection. RESULTS: Buccal injections all had ipsilateral SLN drainage and tongue injections exhibited 18.2% contralateral drainage. An average of 1.9 ± 1.0 SLN (range 1-5) were identified. In addition, an average of 16.9 ± 3.3 non-sentinel lymph nodes were removed per animal. SLNs had an average of 0.69 ± 0.60 percent-of-injected dose (%ID) compared with non-sentinel nodes with 0.012 ± 0.025 %ID and surrounding tissue with 0.0067 ± 0.015 %ID. There was 98.0% agreement between sentinel lymph nodes identified using fluorescence compared to radioactivity with Cohen's kappa coefficient of 0.879. In 48-h delayed SLNB, results were consistent with 97.8% agreement with radioactivity and Cohen's Kappa coefficient of 0.884. Fluorescence identified additional lymph nodes that were not identified by radioactivity, and with one false negative. CONCLUSION: Fluorescent-labeled Tc-99 m-tilmanocept represents a highly accurate adjunct to enhance SLNB for oral cavity cancer. LEVEL OF EVIDENCE: N/A Laryngoscope, 134:1299-1307, 2024.