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Early Diagnosis of oral cancer is very important and can save you from some oral malignancies. However, while this approach aids in the rapid healing of patients and the preservation of their lives, there are several causes for poor and wrong diagnosis of oral cancer. In recent years, the use of computer-aided design diagnosis tools as an auxiliary tool alongside clinicians has greatly benefited in more accurate identification of this malignancy. The current study proposes a new approach for identifying oral cancer patients based on image processing and deep learning. The current study employs a recently integrated model of an improved tunicate swarm algorithm to produce an efficient tool for improving a convolutional neural network and delivering an accurate cancer diagnostic system. The approach is then implemented on the oral cancer pictures dataset. The approach is then validated by comparing it to other published papers using various measurement markers. The proposed model achieved an accuracy of 98.70% and a recall of 93.71% in detecting oral cancerous lesions from photographic images. The model also achieved an F1-score of 90.08% and a precision of 96.42%. The final results demonstrate that the offered approach can produce more exact results and can be used in conjunction with clinicians to help in diagnosing oral cancer., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Objective: To explore and evaluate the feasibility and accuracy of using optical coherence tomography and optical coherence microscope (OCT/OCM) for diagnosis of oral cancer. Methods: In this study, OCT/OCM was utilized to image the oral mucosa specimens. A total of 289 ex vivo oral mucosa specimens were collected from 68 patients with oral cancer who were hospitalized at Department of Oral and Maxillofacial Head and Neck Tumors, Capital Medical University School of Stomatology, between January 2021 and February 2023, resulting in a dataset of 1 445 OCT/OCM images. By observing the characteristic patterns in the OCT/OCM images, including normal oral mucosa, epithelial abnormal proliferation (mild, moderate, severe), and oral cancer, these patterns were matched with corresponding pathological images. A diagnostic study was conducted, employing pathological diagnosis as the gold standard and utilizing a double-blind experimental design involving three diagnostic evaluators who participated in the analysis and diagnosis of OCT/OCM images. Results: The OCT/OCM images demonstrated good correlation with the corresponding pathological images, and diagnostic criteria were established based on the comparative results. In the diagnostic study involving three investigators, the accuracy was 82%, sensitivity was 84% (95% CI : 80%-88%), and specificity was 81% (95% CI : 77%-85%). There was a high level of agreement among the observers (kappa=0.614), indicating substantial concordance in the diagnostic results among the three investigators. Conclusions: This study demonstrates the potential of OCT/OCM for diagnosis of oral cancer. The technology accurately distinguishes between normal oral mucosa, epithelial abnormal proliferation and oral cancer.

Background: To investigate whether radiomics models derived from pretreatment CT could help to predict response to immunotherapy in oral squamous cell carcinoma (OSCC)., Methods: Retrospectively, a total of 40 patients with measurable OSCC were included. The patients were divided into responder group and non-responder group according to the comparison of pre-treatment and post-treatment CT findings. Radiomics features were extracted from pre-treatment CT images, and optimal features were selected by univariate analysis and the least absolute shrinkage and selection operator (LASSO) regression analysis. Neural network, support vector machine, random forest and logistic regression models were used to predict response to immunotherapy in OSCC, and leave-one-out cross validation was employed to assess the performance of the classifiers. The area under the curve (AUC), accuracy, sensitivity and specificity were calculated to quantify the predictive efficacy., Results: A total of 7 features were selected to build models upon machine learning methods. By comparing different machine learning based models, the neural network model achieved the best predictive ability, with an AUC of 0.864, an accuracy of 82.5%, a sensitivity of 82.5%, and a specificity of 82.5%., Conclusions: The pretreatment CT-based radiomics model showed good performance in predicting response to immunotherapy in OSCC. Pretreatment CT-based radiomics model might provide an alternative approach for the selection of patients who benefit from immunotherapy., (© 2024. The Author(s).)

Oral squamous cell carcinoma recognition presents a challenge due to late diagnosis and costly data acquisition. A cost-efficient, computerized screening system is crucial for early disease detection, minimizing the need for expert intervention and expensive analysis. Besides, transparency is essential to align these systems with critical sector applications. Explainable Artificial Intelligence (XAI) provides techniques for understanding models. However, current XAI is mostly data-driven and focused on addressing developers' requirements of improving models rather than clinical users' demands for expressing relevant insights. Among different XAI strategies, we propose a solution composed of Case-Based Reasoning paradigm to provide visual output explanations and Informed Deep Learning (IDL) to integrate medical knowledge within the system. A key aspect of our solution lies in its capability to handle data imperfections, including labeling inaccuracies and artifacts, thanks to an ensemble architecture on top of the deep learning (DL) workflow. We conducted several experimental benchmarks on a dataset collected in collaboration with medical centers. Our findings reveal that employing the IDL approach yields an accuracy of 85%, surpassing the 77% accuracy achieved by DL alone. Furthermore, we measured the human-centered explainability of the two approaches and IDL generates explanations more congruent with the clinical user demands., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Artificial intelligence (AI)-based tools have shown promise in histopathology image analysis in improving the accuracy of oral squamous cell carcinoma (OSCC) detection with intent to reduce human error., Objectives: This systematic review and meta-analysis evaluated deep learning (DL) models for OSCC detection on histopathology images by assessing common diagnostic performance evaluation metrics for AI-based medical image analysis studies., Methods: Diagnostic accuracy studies that used DL models for the analysis of histopathological images of OSCC compared to the reference standard were analyzed. Six databases (PubMed, Google Scholar, Scopus, Embase, ArXiv, and IEEE) were screened for publications without any time limitation. The QUADAS-2 tool was utilized to assess quality. The meta-analyses included only studies that reported true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) in their test sets., Results: Of 1267 screened studies, 17 studies met the final inclusion criteria. DL methods such as image classification (n = 11) and segmentation (n = 3) were used, and some studies used combined methods (n = 3). On QUADAS-2 assessment, only three studies had a low risk of bias across all applicability domains. For segmentation studies, 0.97 was reported for accuracy, 0.97 for sensitivity, 0.98 for specificity, and 0.92 for Dice. For classification studies, accuracy was reported as 0.99, sensitivity 0.99, specificity 1.0, Dice 0.95, F1 score 0.98, and AUC 0.99. Meta-analysis showed pooled estimates of 0.98 sensitivity and 0.93 specificity., Conclusion: Application of AI-based classification and segmentation methods on image analysis represents a fundamental shift in digital pathology. DL approaches demonstrated significantly high accuracy for OSCC detection on histopathology, comparable to that of human experts in some studies. Although AI-based models cannot replace a well-trained pathologist, they can assist through improving the objectivity and repeatability of the diagnosis while reducing variability and human error as a consequence of pathologist burnout., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Purpose: Local recurrence remains the main cause of death in stage III-IV nonmetastatic head and neck cancer (HNC), with relapse-prone regions within high 18 F-fluorodeoxyglucose positron emission tomography ( 18 F-FDG-PET)-signal gross tumor volume. We investigated if dose escalation within this subvolume combined with a 3-phase treatment adaptation could increase local (LC) and regional (RC) control at equal or minimized radiation-induced toxicity, by comparing adaptive 18 F-FDG-PET voxel intensity-based dose painting by numbers (A-DPBN) with nonadaptive standard intensity modulated radiation therapy (S-IMRT)., Methods and Materials: This 2-center randomized controlled phase 2 trial assigned (1:1) patients to receive A-DPBN or S-IMRT (+/-chemotherapy). Eligibility: nonmetastatic HNC of oral cavity, oro-/hypopharynx, or larynx, needing radio(chemo)therapy; T1-4N0-3 (exception: T1-2N0 glottic); KPS ≥ 70; ≥18 years; and informed consent., Primary Outcomes: 1-year LC and RC. The dose prescription for A-DPBN was intercurrently adapted in 2 steps to an absolute dose-volume limit (≤1.75 cm 3 can receive >84 Gy and normalized isoeffective dose >96 Gy) as a safety measure during the study course after 4/7 A-DPBN patients developed ≥G3 mucosal ulcers., Results: Ninety-five patients were randomized (A-DPBN, 47; S-IMRT, 48). Median follow-up was 31 months (IQR, 14-48 months); 29 patients died (17 of cancer progression). A-DPBN resulted in superior LC compared with S-IMRT, with 1- and 2-year LC of 91% and 88% versus 78% and 75%, respectively (hazard ratio, 3.13; 95% CI, 1.13-8.71; P = .021). RC and overall survival were comparable between arms, as was overall grade (G) ≥3 late toxicity (36% vs 20%; P = .1). More ≥G3 late mucosal ulcers were observed in active smokers (29% vs 3%; P = .005) and alcohol users (33% vs 13%; P = .02), independent of treatment arm. Similarly, in the A-DPBN arm, significantly more patients who smoked at diagnosis developed ≥G3 (46% vs 12%; P = .005) and ≥G4 (29% vs 8%; P = .048) mucosal ulcers. One arterial blowout occurred after a G5 mucosal toxicity., Conclusions: A-DPBN resulted in superior 1- and 2-year LC for HNC compared with S-IMRT. This supports further exploration in multicenter phase 3 trials. It will, however, be challenging to recruit a substantial patient sample for such trials, as concerns have arisen regarding the association of late mucosal ulcers when escalating the dose in continuing smokers., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Oral cancer is a global health challenge with a difficult histopathological diagnosis. The accurate histopathological interpretation of oral cancer tissue samples remains difficult. However, early diagnosis is very challenging due to a lack of experienced pathologists and inter- observer variability in diagnosis. The application of artificial intelligence (deep learning algorithms) for oral cancer histology images is very promising for rapid diagnosis. However, it requires a quality annotated dataset to build AI models. We present ORCHID (ORal Cancer Histology Image Database), a specialized database generated to advance research in AI-based histology image analytics of oral cancer and precancer. The ORCHID database is an extensive multicenter collection of high-resolution images captured at 1000X effective magnification (100X objective lens), encapsulating various oral cancer and precancer categories, such as oral submucous fibrosis (OSMF) and oral squamous cell carcinoma (OSCC). Additionally, it also contains grade-level sub-classifications for OSCC, such as well- differentiated (WD), moderately-differentiated (MD), and poorly-differentiated (PD). The database seeks to aid in developing innovative artificial intelligence-based rapid diagnostics for OSMF and OSCC, along with subtypes., (© 2024. The Author(s).)

Background: This study aims to evaluate the integration of optical coherence tomography (OCT) and peripheral blood immune indicators for predicting oral cancer prognosis by artificial intelligence., Methods: In this study, we examined patients undergoing radical oral cancer resection and explored inherent relationships among clinical data, OCT images, and peripheral immune indicators for oral cancer prognosis. We firstly built a peripheral blood immune indicator-guided deep learning feature representation method for OCT images, and further integrated a multi-view prognostic radiomics model incorporating feature selection and logistic modeling. Thus, we can assess the prognostic impact of each indicator on oral cancer by quantifying OCT features., Results: We collected 289 oral mucosal samples from 68 patients, yielding 1,445 OCT images. Using our deep radiomics-based prognosis model, it achieved excellent discrimination for oral cancer prognosis with the area under the receiver operating characteristic curve (AUC) of 0.886, identifying systemic immune-inflammation index (SII) as the most informative feature for prognosis prediction. Additionally, the deep learning model also performed excellent results with 85.26% accuracy and 0.86 AUC in classifying the SII risk., Conclusions: Our study effectively merged OCT imaging with peripheral blood immune indicators to create a deep learning-based model for inflammatory risk prediction in oral cancer. Additionally, we constructed a comprehensive multi-view radiomics model that utilizes deep learning features for accurate prognosis prediction. The study highlighted the significance of the SII as a crucial indicator for evaluating patient outcomes, corroborating our clinical statistical analyses. This integration underscores the potential of combining imaging and blood indicators in clinical decision-making., Trial Registration: The clinical trial associated with this study was prospectively registered in the Chinese Clinical Trial Registry with the trial registration number (TRN) ChiCTR2200064861. The registration was completed on 2021., (© 2024. The Author(s).)

Purpose: A dose calculation algorithm Computed Tomography (CT)-based analytical dose calculation method (CT anly ), which can correct for subject inhomogeneity and size-dependent scatter doses, was applied to the 198 Au seed. In this study, we evaluated the effectiveness of the CT anly method by comparing the gold standard Monte Carlo (MC) method and the conventional TG43 method on two virtual phantoms and patient CT images simulating oral cancer., Methods: As virtual phantoms, a water phantom and a heterogeneous phantom with soft tissue inserted cubic fat, lung, and bone were used. A 2-mm-thick lead plate was also inserted into the heterogeneous phantom as a dose attenuator. Virtual 198 Au seeds and a 2-mm-thick lead plate were placed on the patient CT images. Dose distributions obtained via the TG43 and CT anly methods were compared with those of the MC by gamma analysis with 2%/2-mm thresholds. The computation durations were also compared., Results: In the water phantom, dose distributions comparable to those obtained via the MC method were obtained regardless of the algorithm. For the inhomogeneity phantom and patient case, the CT anly method showed an improvement in the gamma passing rate and dose distributions similar to those of the MC method were obtained. The computation time, which was days with the MC method, was reduced to minutes with the CT anly method., Conclusions: The CT anly method is effective for 198 Au seed dose calculations and takes a shorter time to obtain the dose distributions than the MC method., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

Objective: This review aims to summarize the latest application of optical coherence tomography (OCT) in oral mucosal diseases, promoting an accurate and earlier diagnosis of such disorders, which are difficult to be differentiated., Subjective and Methods: References on the application of OCT in oral mucosal diseases were mainly obtained from PubMed, Embase, Web of Science and Scopus databases, using the keywords: "optical coherence tomography and 'oral mucosa/oral cancers/oral potentially malignant diseases/oral lichen planus/oral leukoplakia/oral erythroplakia/discoid lupus erythematosus/oral autoimmune bullous diseases/oral ulcers/erythema multiforme/oral mucositis'"., Results: It is found that OCT is showing a promising application potential in the early detection, diagnosis, differential diagnosis, monitoring of oral cancer and oral dysplastic lesions, as well as the delineation of tumor margins. OCT is also playing an increasingly important role in the diagnosis of oral potentially malignant disorders, oral mucosal bullous diseases, oral ulcerative diseases, erythema multiforme, and the early detection of oral mucositis., Conclusion: Optical coherence tomography, as a novel optical technique featured by real-time, noninvasive, dynamic and high-resolution imaging, is of great use to serve as an adjunct tool for the diagnosis, differential diagnosis, monitoring and therapy evaluation of oral mucosal diseases., (© 2024 Wiley Periodicals LLC.)

Background: We aimed to construct an artificial intelligence-based model for detecting oral cancer and dysplastic leukoplakia using oral cavity images captured with a single-lens reflex camera., Subjects and Methods: We used 1043 images of lesions from 424 patients with oral squamous cell carcinoma (OSCC), leukoplakia, and other oral mucosal diseases. An object detection model was constructed using a Single Shot Multibox Detector to detect oral diseases and their locations using images. The model was trained using 523 images of oral cancer, and its performance was evaluated using images of oral cancer (n = 66), leukoplakia (n = 49), and other oral diseases (n = 405)., Results: For the detection of only OSCC versus OSCC and leukoplakia, the model demonstrated a sensitivity of 93.9% versus 83.7%, a negative predictive value of 98.8% versus 94.5%, and a specificity of 81.2% versus 81.2%., Conclusions: Our proposed model is a potential diagnostic tool for oral diseases., (© 2024 The Author(s). Head & Neck published by Wiley Periodicals LLC.)

Background and Objective: Papanicolaou staining has been successfully used to assist early detection of cervix cancer for several decades. We postulate that this staining technique can also be used for assisting early detection of oral cancer, which is responsible for about 300,000 deaths every year. The rational for such claim includes two key observations: (i) nuclear atypia, i.e., changes in volume, shape, and staining properties of the cell nuclei can be linked to rapid cell proliferation and genetic instability; and (ii) Papanicolaou staining allows one to reliably segment cells' nuclei and cytoplasms. While Papanicolaou staining is an attractive tool due to its low cost, its interpretation requires a trained pathologist. Our goal is to automate the segmentation and classification of morphological features needed to evaluate the use of Papanicolaou staining for early detection of mouth cancer., Methods: We built a convolutional neural network (CNN) for automatic segmentation and classification of cells in Papanicolaou-stained images. Our CNN was trained and evaluated on a new image dataset of cells from oral mucosa consisting of 1,563 Full HD images from 52 patients, annotated by specialists. The effectiveness of our model was evaluated against a group of experts. Its robustness was also demonstrated on five public datasets of cervical images captured with different microscopes and cameras, and having different resolutions, colors, background intensities, and noise levels., Results: Our CNN model achieved expert-level performance in a comparison with a group of three human experts on a set of 400 Papanicolaou-stained images of the oral mucosa from 20 patients. The results of this experiment exhibited high Interclass Correlation Coefficient (ICC) values. Despite being trained on images from the oral mucosa, it produced high-quality segmentation and plausible classification for five public datasets of cervical cells. Our Papanicolaou-stained image dataset is the most diverse publicly available image dataset for the oral mucosa in terms of number of patients., Conclusion: Our solution provides the means for exploring the potential of Papanicolaou-staining as a powerful and inexpensive tool for early detection of oral cancer. We are currently using our system to detect suspicious cells and cell clusters in oral mucosa slide images. Our trained model, code, and dataset are available and can help practitioners and stimulate research in early oral cancer detection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objectives: This study aims to address the critical gap of unavailability of publicly accessible oral cavity image datasets for developing machine learning (ML) and artificial intelligence (AI) technologies for the diagnosis and prognosis of oral cancer (OCA) and oral potentially malignant disorders (OPMD), with a particular focus on the high prevalence and delayed diagnosis in Asia., Materials and Methods: Following ethical approval and informed written consent, images of the oral cavity were obtained from mobile phone cameras and clinical data was extracted from hospital records from patients attending to the Dental Teaching Hospital, Peradeniya, Sri Lanka. After data management and hosting, image categorization and annotations were done by clinicians using a custom-made software tool developed by the research team., Results: A dataset comprising 3000 high-quality, anonymized images obtained from 714 patients were classified into four distinct categories: healthy, benign, OPMD, and OCA. Images were annotated with polygonal shaped oral cavity and lesion boundaries. Each image is accompanied by patient metadata, including age, sex, diagnosis, and risk factor profiles such as smoking, alcohol, and betel chewing habits., Conclusion: Researchers can utilize the annotated images in the COCO format, along with the patients' metadata, to enhance ML and AI algorithm development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Purpose: Quantitative muscle ultrasound (QMUS) is a patient friendly tool for examining orofacial muscles. Resection of tissue can have an effect on the architecture and function of these muscles. The aim of this study is to investigate the feasibility of visualizing and quantifying muscle changes in postoperative oral cancer patients and to relate the findings to tumor and patient characteristics., Methods: Adult patients with a resected first primary pT1 or T2 oral squamous cell carcinoma, at least one year post operatively, where included. Ultrasound data were collected of the geniohyoid muscle, digastric muscles, masseter muscle, transverse muscle and genioglossus muscle. Ultrasound images were labeled as clearly visible, questionable or unclear. Of the clear muscles, echogenicity and muscle thickness were measured., Results: 37 patients were included. The masseter muscle was clearly visible in all ultrasound images, both intrinsic tongue muscles had the lowest visibility (45.9%). There was a significant correlation between visibility and tumor localization for the genioglossus (p = 0.029). Age correlated with the visibility of the genioglossus muscle, BMI with the genioglossus and transverse muscles. Echogenicity and muscle thickness of the clearly identified muscles did not differ from normative values., Conclusion: QMUS of orofacial muscles is feasible in postoperative oral cancer patients with relatively small tumor sizes. Tongue resections negatively affected the visibility of the two intrinsic tongue muscles. These preliminary results for particular muscles indicate that the use of ultrasound might be promising in oral cancer patients to help determine targeted goals in post-operative rehabilitation., (© 2024. The Author(s).)

Objective: To determine the precision and accuracy of intraoral sonography in assessing the depth of invasion in oral cancer., Material and Methods: A prospective study was conducted in 30 cases of oral cancer. Subjects were subjected to computerised tomography and intraoral sonography before surgery. The depth of invasion assessed through clinical palpation and radiological tools was compared with surgical histopathology., Results: The depth of invasion assessed on clinical palpation and computerized tomography had statistically significant difference with histopathology whereas intraoral sonography didn't show any difference. The intraoral sonography and computerised tomography had comparable precision and accuracy, with a slight dominance of the computerised tomography in assessing the tumor's depth of invasion greater than 4 mm. However, intraoral sonography was more precise and accurate than computerised tomography in assessing the depth of invasion beyond 10 mm., Conclusion: Intraoral sonography was found to be a reliable tool in the assessment of the depth of invasion in oral cancer. It can prove beneficial during surgery in achieving tumour-free surgical margins., (Copyright © 2024 Sociedad Española de Otorrinolaringología y Cirugía de Cabeza y Cuello. Published by Elsevier España, S.L.U. All rights reserved.)

Significance: Oral cancer surgery requires accurate margin delineation to balance complete resection with post-operative functionality. Current in vivo fluorescence imaging systems provide two-dimensional margin assessment yet fail to quantify tumor depth prior to resection. Harnessing structured light in combination with deep learning (DL) may provide near real-time three-dimensional margin detection., Aim: A DL-enabled fluorescence spatial frequency domain imaging (SFDI) system trained with in silico tumor models was developed to quantify the depth of oral tumors., Approach: A convolutional neural network was designed to produce tumor depth and concentration maps from SFDI images. Three in silico representations of oral cancer lesions were developed to train the DL architecture: cylinders, spherical harmonics, and composite spherical harmonics (CSHs). Each model was validated with in silico SFDI images of patient-derived tongue tumors, and the CSH model was further validated with optical phantoms., Results: The performance of the CSH model was superior when presented with patient-derived tumors ( P -value < 0.05 ). The CSH model could predict depth and concentration within 0.4 mm and 0.4    μ g / mL , respectively, for in silico tumors with depths less than 10 mm., Conclusions: A DL-enabled SFDI system trained with in silico CSH demonstrates promise in defining the deep margins of oral tumors., (© 2024 The Authors.)

Background and Purpose: The aims of our study are to evaluate the diagnostic performance and prognostic value of radiological lymph node (LN) characteristics in pN+ oral cavity squamous carcinoma (OSCC)., Materials and Methods: pN+ OSCC treated between 2012 and 2020 were included. Preoperative imaging was reviewed by a single radiologist blinded to pathologic findings for the following nodal features: imaging-positive LN (iN+), laterality and total number, and image-identified extranodal extension (iENE). The sensitivity of iN+ for pN+ was calculated. The diagnostic performance of other nodal features was evaluated in the iN+ subgroup. The association of radiologic nodal features with overall survival (OS) was evaluated. Inter-rater kappa for radiologic nodal features was assessed in 100 randomly selected cases., Results: Of 406 pN+ OSCC, 288 were iN+. The sensitivity of iN+ for pN+ was 71% overall, and improved to 89% for pN+ LN >1.5 cm. Within iN+, sensitivity/specificity for LN size (>3 cm), total LN number (>4), and ENE were 0.44/0.95, 0.57/0.84, and 0.27/0.96, respectively. Sensitivity of iENE was higher in the subset, with major (>2 mm) versus minor (≤2 mm) pENE (43% vs. 13%, p = 0.001). Reduced OS was observed in iN+ versus iN- (p = 0.006), iENE+ versus iENE- (p = 0.004), LN size >3 versus ≤3 cm (p < 0.001), and higher LN number (p < 0.001). Inter-rater kappa for iN+, laterality, total LN number, and presence of iENE were 0.71, 0.57, 0.78, and 0.69, respectively., Conclusion: Our study shows that despite modest sensitivity of most radiological nodal features, the specificity of image-identified nodal features is high and their prognostic values are retained in pN+ OSCC., Level of Evidence: 3 (retrospective review comparing cases and controls) Laryngoscope, 134:4947-4955, 2024., (© 2024 The Author(s). The Laryngoscope published by Wiley Periodicals LLC on behalf of The American Laryngological, Rhinological and Otological Society, Inc.)

Objectives: Radiomics, a recently developed image-processing technology, holds potential in medical diagnostics. This study aimed to propose a machine-learning (ML) model and evaluate its effectiveness in detecting oral squamous cell carcinoma (OSCC) and predicting bone metastasis using magnetic resonance imaging (MRI)., Materials-Methods: MRI radiomic features were extracted and analyzed to identify malignant lesions. A total of 86 patients (44 with benign lesions without bone invasion and 42 with malignant lesions with bone invasion) were included. Data and clinical information were managed using the RadCloud Platform (Huiying Medical Technology Co., Ltd., Beijing, China). The study employed a hand-crafted radiomics model, with the dataset randomly split into training and validation sets in an 8:2 ratio using 815 random seeds., Results: The results revealed that the ML method support vector machine (SVM) performed best for detecting bone invasion (AUC = 0.999) in the test set. Radiomics tumor features derived from MRI are useful to predicting bone invasion from oral squamous cell carcinoma with high accuracy., Conclusions: This study introduces an ML model utilizing SVM and radiomics to predict bone invasion in OSCC. Despite the promising results, the small sample size necessitates larger multicenter studies to validate and expand these findings., Competing Interests: Declarations Conflict of interest The authors declare that they have no conflict of interest. Ethical approval Human and animal rights statement All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1964 and later versions. Ethical approval was obtained from Lokman Hekim University Non-interventional Clinical Research Ethics Committee (Protocol No: 2024051)., (© 2024. The Author(s).)

Objective: This study aimed to identify a quantitative index for optical coherence tomography (OCT) images to discriminate tumours from surrounding tissues., Subjects and Methods: Based on OCT measurements, mean grey values were determined from 432 locations on fifty-four human tissue specimens (eighteen cancerous, para-cancerous, and normal tissues each). These results were histologically evaluated by hematoxylin and eosin staining (H&E)., Results: The mean grey values of oral squamous cell carcinoma (OSCC) measurements were significantly different from those of the surrounding healthy tissue (p value < 0.0001), with the former being higher. The sensitivity and specificity of detecting tumourous tissue using this approach were 93 and 94%, respectively., Conclusions: OCT as a non-invasive, real-time imaging method, correlates well with H&E pathological images. It can effectively distinguish squamous cell carcinoma from normal tissues with high sensitivity and specificity and is thus expected to assist and guide tumour margin evaluation., Clinical Relevance: This discovery highlights the potential of OCT in the objective evaluation of tumour margin during surgery., (© 2024. The Author(s).)

Objective: To compare the diagnostic accuracy of Contrast Enhanced Computed Tomography (CECT) compared to conventional imaging modalities and histopathological investigation in cervical lymph node metastasis in adults through a novel meta- analysis., Method: The review protocol is registered under PROSPERO(CRD42021225704) and performed in accordance to Preferred Reporting Items for Systematic Reviews and Meta-Analysis - Diagnostic Test Accuracy (PRISMA- DTA) checklist. Databases like PubMed, Google Scholar, EBSCOhost were searched from 2000 to 2023 to identify the diagnostic potential of CECT in cervical lymph node metastasis of oral carcinoma. True-positive, false-positive, true-negative, false-negative, sensitivity, specificity values were extracted or calculated if not present for each study. Quality of selected studies was evaluated based on Quality assessment of diagnostic accuracy studies (QUADAS)- 2 tool. Meta-analysis was performed in Meta-Disc 1.4 software and Review Manager 5.3 using a bivariate model parameter for the pooled sensitivity and pooled specificity. Additional analysis was performed in terms of positive likelihood ratio (+LR), negative likelihood ratio (-LR), diagnostic odds ratio (DOR) and summary receiver operating characteristics (SROC) with Area Under Curve (AUC) and p<0.05 as statistically significant., Results: Six studies were included for qualitative synthesis and as well as for meta-analysis. Two studies had high risk of bias while four studies had low risk of bias. 651 patients underwent CECT and were taken for meta-analysis. The meta-analysis revealed that CECT for diagnosing cervical lymph node metastasis had a pooled sensitivity of 71%, pooled specificity of 14% with 60% Area Under Curve (AUC). +LR of 0.84, -LR of 1.36 and DOR of 0.59., Conclusion: CECT has an overall fair diagnostic ability and is a valid and reliable tool in diagnosing the target condition overcoming high reliance on master specialized capacity for their execution and understanding like other conventional imaging techniques. CECT can be concluded for secondary level of prevention for cervical node metastasis of oral carcinoma under early diagnosis and prompt treatment. However, further standardized accuracy studies are indicated to improve the overall diagnostic accuracy of CECT.

Early diagnosis of potentially malignant disorders, such as oral epithelial dysplasia, is the most reliable way to prevent oral cancer. Computational algorithms have been used as an auxiliary tool to aid specialists in this process. Usually, experiments are performed on private data, making it difficult to reproduce the results. There are several public datasets of histological images, but studies focused on oral dysplasia images use inaccessible datasets. This prevents the improvement of algorithms aimed at this lesion. This study introduces an annotated public dataset of oral epithelial dysplasia tissue images. The dataset includes 456 images acquired from 30 mouse tongues. The images were categorized among the lesion grades, with nuclear structures manually marked by a trained specialist and validated by a pathologist. Also, experiments were carried out in order to illustrate the potential of the proposed dataset in classification and segmentation processes commonly explored in the literature. Convolutional neural network (CNN) models for semantic and instance segmentation were employed on the images, which were pre-processed with stain normalization methods. Then, the segmented and non-segmented images were classified with CNN architectures and machine learning algorithms. The data obtained through these processes is available in the dataset. The segmentation stage showed the F1-score value of 0.83, obtained with the U-Net model using the ResNet-50 as a backbone. At the classification stage, the most expressive result was achieved with the Random Forest method, with an accuracy value of 94.22%. The results show that the segmentation contributed to the classification results, but studies are needed for the improvement of these stages of automated diagnosis. The original, gold standard, normalized, and segmented images are publicly available and may be used for the improvement of clinical applications of CAD methods on oral epithelial dysplasia tissue images., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Purpose: The choice of surgical approach for floor of the mouth (FOM) cancer, particularly for intermediate-stage tumors (cT2-cT3), remains controversial. This study aims to evaluate a method considering mylohyoid muscle (MM) invasion as a determinant for surgical approach selection, utilizing magnetic resonance imaging (MRI) preoperatively and frozen section (FS) analysis intraoperatively., Methods: This observational retrospective cohort study analyzed patients undergoing surgical resection of cT2 and cT3 FOM squamous cell carcinoma (SCC) between January 2013 and June 2023. MM infiltration assessed by preoperative MRI determined the surgical approach: clear infiltration led to compartmental surgery (CS), while doubtful or absent infiltration led to transoral surgery (TOS). Conversion from TOS to CS occurred intraoperatively based on macroscopic evidence or positive FS. Data collected included demographic, clinical, surgical, and pathological variables. Survival analysis was conducted using Kaplan-Meier method., Results: Among 44 patients included, majority had cT2 tumors (59.1%). MM resection was necessary in 22.7% of cases. Overall survival (OS) and progression-free survival (PFS) did not significantly differ between TOS and CS groups. Radiological depth of invasion (rDOI) < 10 mm is correlated with MM preservation in 89% of cases, while rDOI > 10 mm is correlated with MM resection only in 23.8% of cases. Pathological depth of invasion (pDOI) discrepancies were observed in the two groups: in CS group is shown a higher pDOI (> 10 mm) confirmation (90%). Surgical complications and functional outcomes differed between TOS and CS groups., Conclusion: Considering MM invasion for surgical approach selection in cT2-cT3 FOM tumors appears oncologically safe, with better functional outcomes in muscle preservation. Preoperative MRI for MM assessment combined with intraoperative FS analysis provides reliable guidance for surgical decision-making., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Introduction: The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20-30% in early-stage oral cancer and oropharyngeal cancer. There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to help surgeons make precise treatment decisions., Aim: To construct and evaluate a preoperative diagnostic method to predict OCLNM in early-stage oral and oropharyngeal squamous cell carcinoma (OC and OP SCC) based on deep learning features (DLFs) and radiomics features., Methods: A total of 319 patients diagnosed with early-stage OC or OP SCC were retrospectively enrolled and divided into training, test and external validation sets. Traditional radiomics features and DLFs were extracted from their MRI images. The least absolute shrinkage and selection operator (LASSO) analysis was employed to identify the most valuable features. Prediction models for OCLNM were developed using radiomics features and DLFs. The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC), decision curve analysis (DCA), and survival analysis., Results: Seventeen prediction models were constructed. The Resnet50 deep learning (DL) model based on the combination of radiomics and DL features achieves the optimal performance, with AUC values of 0.928 (95% CI: 0.881-0.975), 0.878 (95% CI: 0.766-0.990), 0.796 (95% CI: 0.666-0.927), and 0.834 (95% CI: 0.721-0.947) in the training, test, external validation set1, and external validation set2, respectively. Moreover, the Resnet50 model has great prediction value of prognosis in patients with early-stage OC and OP SCC., Conclusion: The proposed MRI-based Resnet50 DL model demonstrated high capability in diagnosis of OCLNM and prognosis prediction in the early-stage OC and OP SCC. The Resnet50 model could help refine the clinical diagnosis and treatment of the early-stage OC and OP SCC., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background: The objective of this study is to examine the application of artificial intelligence (AI) algorithms in detecting oral potentially malignant disorders (OPMD) and oral cancerous lesions, and to evaluate the accuracy variations among different imaging tools employed in these diagnostic processes., Materials and Methods: A systematic search was conducted in four databases: Embase, Web of Science, PubMed, and Scopus. The inclusion criteria included studies using machine learning algorithms to provide diagnostic information on specific oral lesions, prospective or retrospective design, and inclusion of OPMD. Sensitivity and specificity analyses were also required. Forest plots were generated to display overall diagnostic odds ratio (DOR), sensitivity, specificity, negative predictive values, and summary receiver operating characteristic (SROC) curves. Meta-regression analysis was conducted to examine potential differences among different imaging tools., Results: The overall DOR for AI-based screening of OPMD and oral mucosal cancerous lesions from normal mucosa was 68.438 (95% CI= [39.484-118.623], I2 =86%). The area under the SROC curve was 0.938, indicating excellent diagnostic performance. AI-assisted screening showed a sensitivity of 89.9% (95% CI= [0.866-0.925]; I2 =81%), specificity of 89.2% (95% CI= [0.851-0.922], I2 =79%), and a high negative predictive value of 89.5% (95% CI= [0.851-0.927], I2 =96%). Meta-regression analysis revealed no significant difference among the three image tools. After generating a GOSH plot, the DOR was calculated to be 49.30, and the area under the SROC curve was 0.877. Additionally, sensitivity, specificity, and negative predictive value were 90.5% (95% CI [0.873-0.929], I2 =4%), 87.0% (95% CI [0.813-0.912], I2 =49%) and 90.1% (95% CI [0.860-0.931], I2 =57%), respectively. Subgroup analysis showed that clinical photography had the highest diagnostic accuracy., Conclusions: AI-based detection using clinical photography shows a high DOR and is easily accessible in the current era with billions of phone subscribers globally. This indicates that there is significant potential for AI to enhance the diagnostic capabilities of general practitioners to the level of specialists by utilizing clinical photographs, without the need for expensive specialized imaging equipment., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Addition of neoadjuvant immune checkpoint inhibition to standard-of-care interventions for locally advanced oral cancer could improve clinical outcome., Methods: In this study, 16 evaluable patients with stage III/IV oral cancer were treated with one dose of 480 mg nivolumab 3 weeks prior to surgery. Primary objectives were safety, feasibility, and suitability of programmed death receptor ligand-1 positron emission tomography (PD-L1 PET) as a biomarker for response. Imaging included 18 F-BMS-986192 (PD-L1) PET and 18 F-fluorodeoxyglucose (FDG) PET before and after nivolumab treatment. Secondary objectives included clinical and pathological response, and immune profiling of peripheral blood mononuclear cells (PBMCs) for response prediction. Baseline tumor biopsies and postnivolumab resection specimens were evaluated by histopathology., Results: Grade III or higher adverse events were not observed and treatment was not delayed in relation to nivolumab administration and other study procedures. Six patients (38%) had a pathological response, of whom three (19%) had a major (≥90%) pathological response (MPR). Tumor PD-L1 PET uptake (quantified using standard uptake value) was not statistically different in patients with or without MPR (median 5.3 vs 3.4). All major responders showed a significantly postnivolumab decreased signal on FDG PET. PBMC immune phenotyping showed higher levels of CD8 + T cell activation in MPR patients, evidenced by higher baseline expression levels of PD-1, TIGIT, IFNγ and lower levels of PD-L1., Conclusion: Together these data support that neoadjuvant treatment of advanced-stage oral cancers with nivolumab was safe and induced an MPR in a promising 19% of patients. Response was associated with decreased FDG PET uptake as well as activation status of peripheral T cell populations., Competing Interests: Competing interests: RvdV has received research funding from Genmab BV. TDdG is scientific advisor to Immunicum, GE Health, and Lava Therapeutics, holds stock from LAVA Therapeutics and received research funding from Idera Pharmaceuticals (now Aceragen). RHB received research grants from KWF Kankerbestrijding/Dutch Cancer Society, Cancer Center Amsterdam Foundation, ZonMW and NWO, Genmab BV and the Hanarth Foundation and is on the advisory board of Nanobiotix. He has a scientific collaboration with Orfenix BV and Qialix DoT. CRL received research grants from KWF Kankerbestrijding/Dutch Cancer Society, Cancer Center Amsterdam Foudation, Genmab BV, BMS and the Hanarth Foundation and is on the advisory board of Merck & Co. CWM-vdHvO received research grants from BMS, Boeringher Ingelheim, GSK, Pfizer and AstraZeneca and consulted for GE Health Care, Novartis and EliLilly. All other authors report no competing interests., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Oral Squamous Cell Carcinoma (OSCC) presents significant diagnostic challenges in its early and late stages. This study aims to utilize preoperative MRI and biochemical indicators of OSCC patients to predict the stage of tumors., Methods: This study involved 198 patients from two medical centers. A detailed analysis of contrast-enhanced T1-weighted (ceT1W) and T2-weighted (T2W) MRI were conducted, integrating these with biochemical indicators for a comprehensive evaluation. Initially, 42 clinical biochemical indicators were selected for consideration. Through univariate analysis and multivariate analysis, only those indicators with p-values less than 0.05 were retained for model development. To extract imaging features, machine learning algorithms in conjunction with Vision Transformer (ViT) techniques were utilized. These features were integrated with biochemical indicators for predictive modeling. The performance of model was evaluated using the Receiver Operating Characteristic (ROC) curve., Results: After rigorously screening biochemical indicators, four key markers were selected for the model: cholesterol, triglyceride, very low-density lipoprotein cholesterol and chloride. The model, developed using radiomics and deep learning for feature extraction from ceT1W and T2W images, showed a lower Area Under the Curve (AUC) of 0.85 in the validation cohort when using these imaging modalities alone. However, integrating these biochemical indicators improved the model's performance, increasing the validation cohort AUC to 0.87., Conclusion: In this study, the performance of the model significantly improved following multimodal fusion, outperforming the single-modality approach., Clinical Relevance Statement: This integration of radiomics, ViT models, and lipid metabolite analysis, presents a promising non-invasive technique for predicting the staging of OSCC., (© 2024. The Author(s).)

Purpose: Oral cavity cancer, primarily squamous cell carcinoma (SCC), is a prevalent malignancy globally, necessitating accurate clinical assessment and staging to enable effective treatment planning. Diagnosis requires biopsy and is followed by surgical resection and reconstruction as the primary therapeutic modality. Imaging plays a pivotal role during this process, aiding in the evaluation of tumour extent, nodal involvement and distant metastases. However, despite its value, both radiologists and clinicians must recognise its inherent limitations., Methods: This pictorial review article aims to illustrate the application of various imaging modalities in the pre-treatment evaluation of oral cavity SCC and highlights potential pitfalls. It underscores the importance of understanding the anatomical subsites of the oral cavity, the diverse patterns of spread tumours exhibit at each site, alongside the role of imaging in facilitating informed management strategies, while also acknowledging its limitations., Results: The review delves into fundamentals of current staging including nodal involvement, while, emphasising imaging strategies and potential limitations. Finally, it touches on the potential of novel radiomic techniques in characterising tumours and predicting treatment response., Conclusions: Pre-treatment oral cavity cancer staging reflects an ongoing quest for enhanced diagnostic accuracy and prognostic prediction. Recognising the value of imaging alongside its limitations fosters a multidisciplinary approach to treatment planning, ultimately improving patient outcomes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Crown Copyright © 2024. Published by Elsevier B.V. All rights reserved.)

Background: Oral cancer is one of the most common malignancies in the head and neck region. Approximately 90% of oral cancers are oral squamous cell carcinomas (OSCC). 18 F-FDG PET/CT has been used in OSCC patients for its high value in detecting metastatic lymph nodes and distant metastases. PET radiomics and sarcopenia can be measured on the PET and CT components of 18 F-FDG PET/CT., Purpose: This study aimed to investigate the prognostic value of radiomics and sarcopenia measured on the PET and CT components of pre-operation 18 F-FDG PET/CT in OSCC., Methods: A total of 116 patients eventually enrolled in our study were randomly divided into two cohorts: training cohort (n = 58) and validation cohort (n = 58). The Cox model combined with the least absolute shrinkage and selection operator (LASSO) algorithm was applied to construct the radiomics score (Rad&#95;score). The third lumber skeletal muscle index (L3 SMI) was calculated to identify sarcopenia. Univariate and multivariate Cox regression analyses were performed to identify the independent prognostic factors. Based on the clinical factors, the clinical model was constructed, and the combined model was developed through the combination of the clinical model and Rad&#95;score. C index, time-dependent C-index curves, receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis were used to evaluate the performance of prediction models., Results: Three radiomics features constitute the Rad&#95;score for overall survival (OS) and progression-free survival (PFS), respectively. Multivariate Cox regression analysis revealed that Rad&#95;score was an independent prognostic factor, whereas sarcopenia was not. The combined models showed satisfactory performance in both the training cohort (C-index: OS:0.836, PFS:0.776) and the validation cohort (C-index: OS:0.744, PFS:0.712). The combined models were visualized as nomograms. Nomogram scores can realize the risk stratification of OSCC patients. Lower nomogram score is significantly related to the poorer OS (training cohort: p < 0.0001, validation cohort: p < 0.0001, overall cohort: p < 0.0001) and PFS (training cohort: p < 0.0001, validation cohort: p = 0.00017, overall cohort: p < 0.0001)., Conclusions: Rad&#95;score, but not sarcopenia, was an independent prognostic factor for patients with OSCC. The nomograms had a satisfactory performance, which might be helpful for OSCC patients and clinicians in personalized prognostic prediction and treatment decision-making., (© 2024 American Association of Physicists in Medicine.)

Background: Alveolar soft part sarcoma (ASPS) occurs most often in the deep muscles or fascia of the extremities in adults, with only 3.4% of these tumours originating from the head, face and neck. To date, only 17 cases of buccal ASPS have been reported, including the case presented here. Only one case of ASPS recurrence at the primary site, similar to our case, has been reported thus far. Immune checkpoint inhibitors (ICPis)-associated diabetes, with an estimated incidence of 0.43%, is usually seen in older cancer patients and has not been reported in younger people or in patients with ASPS., Case Presentation: A 24-year-old male patient presented with a slowly progressing right cheek mass with a clinical history of approximately 28 months. Sonographic imaging revealed a hypoechoic mass, which was considered a benign tumour. However, a pathological diagnosis of ASPS was made after excision of the mass. Five days later, functional right cervical lymph node dissection was performed. No other adjuvant therapy was administered after surgery. In a periodic follow-up of the patient six months later, blood-rich tumour growth was noted at the primary site, and Positron emission tomography-computedtomography (PET-CT) ruled out distant metastasis in other areas. The patient was referred to the Ninth People's Hospital of Shanghai Jiaotong University. Due to the large extent of the mass, the patient received a combination of a Programmed Cell Death Ligand 1(PD-L1) inhibitor and a targeted drug. Unfortunately, the patient developed three episodes of severe diabetic ketoacidosis after the administration of the drugs. A confirmed diagnosis of ICPis-associated diabetes was confirmed. After the second operation, the postoperative pathological diagnosis was ASPS, and the margins were all negative. Therefore, we made a final clinical diagnosis of ASPS recurrence at the primary site. Currently in the follow-up, the patient is alive, has no distant metastases, and undergoes multiple imaging examinations every 3 months for the monitoring of their condition., Conclusions: In analysing the characteristics of all previously reported cases of buccal ASPS, it was found that the clinical history ranged from 1 to 24 months, with a mean of approximately 3 to 9 months. Tumour recurrence at the primary site has been reported in only one patient with buccal ASPS, and the short-term recurrence in our patient may be related to the extraordinarily long 28-month history. ICPis-associated diabetes may be noted in young patients with rare tumours, and regular insulin level monitoring after use is necessary., (© 2024. The Author(s).)

Purpose: We investigated reflectance confocal microscopy (RCM) as a possible noninvasive approach for the diagnosis of cancer and real-time assessment of surgical margins., Experimental Design: In a phase I study on 20 patients, we established the RCM imaging morphologic features that distinguish oral squamous cell carcinoma (OSCC) from normal tissue with a newly developed intraoral RCM probe. Our subsequent phase II prospective double-blinded study in 60 patients tested the diagnostic accuracy of RCM against histopathology. Five RCM videos from the tumor and five from normal surrounding mucosa were collected on each patient, followed by a 3-mm punch biopsy of the imaged area. An experienced RCM reader, who was blinded to biopsy location and histologic diagnosis, examined the videos from both regions and classified each as "tumor" or "not tumor" based on RCM features established in phase I. Hematoxylin and eosin slides from the biopsies were read by a pathologist who was blinded to RCM results. Using histology as the gold standard, we calculated the sensitivity and specificity of RCM., Results: We report a high agreement between the blinded readers (95% for normal tissue and 81.7% for tumors), high specificity (98.3%) and negative predictive values (96.6%) for normal tissue identification, and high sensitivity (90%) and positive predictive values (88.2%) for tumor detection., Conclusions: RCM imaging is a promising technology for noninvasive in vivo diagnosis of OSCC and for real-time intraoperative evaluation of mucosal surgical margins. Its inherent constraint, however, stems from the diminished capability to evaluate structures located at more substantial depths within the tissue., (©2024 The Authors; Published by the American Association for Cancer Research.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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., (© 2024. The Author(s).)

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., (© 2024. The Author(s).)

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., (© 2024. The Author(s).)

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., (Copyright © 2024. Published by Elsevier Inc.)

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.

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., (© 2024. The Author(s).)

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., Competing Interests: Competing interests: None declared., (© BMJ Publishing Group Limited 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: Oral cancer is a leading cause of mortality globally, particularly affecting developing regions where oral hygiene is often overlooked. The optical properties of tissues are vital for diagnostics, with polarization imaging emerging as a label-free, contrast-enhancing technique widely employed in medical and scientific research over past few decades., Materials and Methods: We present a novel polarization sensitive quantitative phase imaging of biological tissues by incorporating the conventional polarization microscope and transport of intensity equation-based phase retrieval algorithm. This integration provides access to the birefringence mapping of biological tissues. The inherent optical anisotropy in biological tissues induces the polarization dependent refractive index variations which can provide the detailed insights into the birefringence characteristics of their extracellular constituents. Experimental investigations were conducted on both normal and cancerous oral tissue samples by recording a set of three polarization intensity images for each case with a step size of 2 μm., Results: A noteworthy increment in birefringence quantification was observed in cancerous as compared to the normal tissues, attributed to the proliferation of abnormal cells during cancer progression. The mean birefringence values were calculated for both normal and cancerous tissues, revealing a significant increase in birefringence of cancerous tissues (2.1 ± 0.2) × 10 -2 compared to normal tissues (0.8 ± 0.2) × 10 -2 . Data were collected from 8 patients in each group under identical experimental conditions., Conclusion: This polarization sensitive non-interferometric optical approach demonstrated effective discrimination between cancerous and normal tissues, with various parameters indicating elevated values in cancerous tissues., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Background: For patients with oral cavity squamous cell carcinoma (OCSCC) without evidence of nodal metastasis (cN0) on pre-operative evaluation, there are no clear guidelines who should undergo elective neck dissection (END) versus clinical surveillance., Objective: To identify CT imaging characteristics of sub-centimeter lymph nodes that would help predict the likelihood of nodal metastases on pathology., Methods: Retrospective review of cN0 OCSCC patients at a tertiary academic medical center was performed. Inclusion criteria included elective neck dissection, pre-operative CT imaging and presence of metastatic disease within lymph nodes. Control group consisted of patients without nodal metastases on pathology. CT features that were evaluated included asymmetric size, disrupted fatty hilum, asymmetric number, presence of cortical nodule, cortical nodule size, and round/oval shape. We evaluated the associations between CT LN features and the presence of metastases using multi-level mixed-effects logistic regression models. Model evaluation was performed using 5-fold cross-validation. The positive predictive value (PPV) and negative predictive value (NPV) were calculated., Results: 26 patients in each study and control groups were included. Three-level mixed-effects logistic regression models indicated round/oval shape (OR = 1.39, p = .01), asymmetric number (OR = 7.20, p = .005), and disrupted fatty hilum (OR = 3.31, p = .04) to be independently predictive in a 3-variable model with sensitivity = 38.0%, specificity = 92.0%, and PPV = 93.8%., Conclusions: In cN0 OCSCC patients undergoing END, round/oval shape, asymmetric number, and disrupted fatty hilum of lymph nodes on pre-operative CT imaging are novel and highly predictive of occult nodal disease., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Abstract: The current scoping review's objective was to outline existing applications, recent breakthroughs, and quantum dots' applicability in imaging of oral squamous cell cancer. Quantum dots are nanometric semiconductor crystals with customizable optical characteristics and intense, stable fluorescence suited for bioimaging and labeling. We used the Preferred reporting items for systematic reviews and meta-analyses (PRISMA) recommendations for conducting our systematic search. An analysis of the properties and applications of quantum dots in noninvasive detection of oral squamous cell cancer is presented in this study, which comprehensively explores the available evidence. Following searches in the databases PubMed, Ovid SP, and Cochrane using the search terms quantum dots AND oral squamous cell cancer, 55 published publications were chosen for this review. The review identified a total of eight papers that met the criteria. In noninvasive detection of oral squamous cell carcinoma, quantum dots have the potential to offer an array of therapeutic and diagnostic applications. Furthermore, quantum dots emit near-infrared and visible light, which is advantageous in biological imaging since it reduces light dispersion and absorption of tissue. The future may see quantum dots become a popular noninvasive imaging technique for oral squamous cell cancer. The number of studies accessible is quite limited, and further research is required., (Copyright © 2023 Copyright: © 2023 Journal of Cancer Research and Therapeutics.)

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., Competing Interests: Disclosure The authors declare that they have no conflict of interest., (Copyright © 2023 Elsevier Inc. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 IPEM. Published by Elsevier Ltd. All rights reserved.)

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., (© 2024. The Author(s).)