A fully automated and explainable algorithm for predicting malignant transformation in oral epithelial dysplasia.

Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. Its grading suffers from significant inter-/intra-observer variability, and does not reliably predict malignancy progression, potentially leading to suboptimal treatment decisions. To address this, we developed an artificial intelligence (AI) algorithm, that assigns an Oral Malignant Transformation (OMT) risk score based on the Haematoxylin and Eosin (H&E) stained whole slide images (WSIs). Our AI pipeline leverages an in-house segmentation model to detect and segment both nuclei and epithelium. Subsequently, a shallow neural network utilises interpretable morphological and spatial features, emulating histological markers, to predict progression. We conducted internal cross-validation on our development cohort (Sheffield; n = 193 cases) and independent validation on two external cohorts (Birmingham and Belfast; n = 89 cases). On external validation, the proposed OMTscore achieved an AUROC = 0.75 (Recall = 0.92) in predicting OED progression, outperforming other grading systems (Binary: AUROC = 0.72, Recall = 0.85). Survival analyses showed the prognostic value of our OMTscore (C-index = 0.60, p = 0.02), compared to WHO (C-index = 0.64, p = 0.003) and binary grades (C-index = 0.65, p < 0.001). Nuclear analyses elucidated the presence of peri-epithelial and intra-epithelial lymphocytes in highly predictive patches of transforming cases (p < 0.001). This is the first study to propose a completely automated, explainable, and externally validated algorithm for predicting OED transformation. Our algorithm shows comparable-to-human-level performance, offering a promising solution to the challenges of grading OED in routine clinical practice.

Prognostic importance of mitosis quantification and PHH3 expression in oral epithelial dysplasia.

Oral epithelial dysplasia (OED) is diagnosed and graded using a range of histological features, making grading subjective and challenging. Mitotic counting and phosphohistone-H3 (PHH3) staining have been used for the prognostication of various malignancies; however, their importance in OED remains unexplored. This study conducts a quantitative analysis of mitotic activity in OED using both haematoxylin and eosin (H&E)-stained slides and immunohistochemical (IHC) staining for PHH3. Specifically, the diagnostic and prognostic importance of mitotic number, mitotic type and intra-epithelial location is evaluated. Whole slide images (WSI) of OED (n = 60) and non-dysplastic tissue (n = 8) were prepared for analysis. Five-year follow-up data was collected. The total number of mitosis (TNOM), mitosis type and intra-epithelial location was manually evaluated on H&E images and a digital mitotic count performed on PHH3-stained WSI. Statistical associations between these features and OED grade, malignant transformation and OED recurrence were determined. Mitosis count increased with grade severity (H&E: p < 0.005; IHC: p < 0.05), and grade-based differences were seen for mitosis type and location (p < 0.05). The ratio of normal-to-abnormal mitoses was higher in OED (1.61) than control (1.25) and reduced with grade severity. TNOM, type and location were better predictors when combined with histological grading, with the most prognostic models demonstrating an AUROC of 0.81 for transformation and 0.78 for recurrence, exceeding conventional grading. Mitosis quantification and PHH3 staining can be an adjunct to conventional H&E assessment and grading for the prediction of OED prognosis. Validation on larger multicentre cohorts is needed to establish these findings.

Demystifying oral epithelial dysplasia: a histological guide.

Oral epithelial dysplasia is a histologically diagnosed potentially premalignant disorder of the oral mucosa, which carries a risk of malignant transformation to squamous cell carcinoma. The diagnosis and grading of oral epithelial dysplasia is challenging, with cases often referred to specialist oral and maxillofacial pathology centres for second opinion. Even still there is poor inter-examiner and intra-examiner agreement in a diagnosis. There are a total of 28 features of oral epithelial dysplasia listed in the 5th edition of World Health Organization classification of tumours of the head and neck. Each of these features is poorly defined and subjective in its interpretation. Moreover, how these features contribute to dysplasia grading and risk stratification is even less well defined. This article discusses each of the features of oral epithelial dysplasia with examples and provides an overview of the common mimics, including the normal histological features of the oral mucosa which may mimic atypia. This article also highlights the paucity of evidence defining these features while offering suggested definitions. Ideally, these definitions will be refined, and the most important features identified to simplify the diagnosis of oral epithelial dysplasia. Digital whole slide images of the figures in this paper can be found at: https://www.pathogenesis.co.uk/r/demystifying-dysplasia-histology-dataset.

Development and validation of a multivariable model for prediction of malignant transformation and recurrence of oral epithelial dysplasia.

BACKGROUND: Oral epithelial dysplasia (OED) is the precursor to oral squamous cell carcinoma which is amongst the top ten cancers worldwide. Prognostic significance of conventional histological features in OED is not well established. Many additional histological abnormalities are seen in OED, but are insufficiently investigated, and have not been correlated to clinical outcomes. METHODS: A digital quantitative analysis of epithelial cellularity, nuclear geometry, cytoplasm staining intensity and epithelial architecture/thickness is conducted on 75 OED whole-slide images (252 regions of interest) with feature-specific comparisons between grades and against non-dysplastic/control cases. Multivariable models were developed to evaluate prediction of OED recurrence and malignant transformation. The best performing models were externally validated on unseen cases pooled from four different centres (n = 121), of which 32% progressed to cancer, with an average transformation time of 45 months. RESULTS: Grade-based differences were seen for cytoplasmic eosin, nuclear eccentricity, and circularity in basal epithelial cells of OED (p < 0.05). Nucleus circularity was associated with OED recurrence (p = 0.018) and epithelial perimeter associated with malignant transformation (p = 0.03). The developed model demonstrated superior predictive potential for malignant transformation (AUROC 0.77) and OED recurrence (AUROC 0.74) as compared with conventional WHO grading (AUROC 0.68 and 0.71, respectively). External validation supported the prognostic strength of this model. CONCLUSIONS: This study supports a novel prognostic model which outperforms existing grading systems. Further studies are warranted to evaluate its significance for OED prognostication.

A digital score of peri-epithelial lymphocytic activity predicts malignant transformation in oral epithelial dysplasia.

Oral squamous cell carcinoma (OSCC) is amongst the most common cancers, with more than 377,000 new cases worldwide each year. OSCC prognosis remains poor, related to cancer presentation at a late stage, indicating the need for early detection to improve patient prognosis. OSCC is often preceded by a premalignant state known as oral epithelial dysplasia (OED), which is diagnosed and graded using subjective histological criteria leading to variability and prognostic unreliability. In this work, we propose a deep learning approach for the development of prognostic models for malignant transformation and their association with clinical outcomes in histology whole slide images (WSIs) of OED tissue sections. We train a weakly supervised method on OED cases (n = 137) with malignant transformation (n = 50) and mean malignant transformation time of 6.51 years (±5.35 SD). Stratified five-fold cross-validation achieved an average area under the receiver-operator characteristic curve (AUROC) of 0.78 for predicting malignant transformation in OED. Hotspot analysis revealed various features of nuclei in the epithelium and peri-epithelial tissue to be significant prognostic factors for malignant transformation, including the count of peri-epithelial lymphocytes (PELs) (p < 0.05), epithelial layer nuclei count (NC) (p < 0.05), and basal layer NC (p < 0.05). Progression-free survival (PFS) using the epithelial layer NC (p < 0.05, C-index = 0.73), basal layer NC (p < 0.05, C-index = 0.70), and PELs count (p < 0.05, C-index = 0.73) all showed association of these features with a high risk of malignant transformation in our univariate analysis. Our work shows the application of deep learning for the prognostication and prediction of PFS of OED for the first time and offers potential to aid patient management. Further evaluation and testing on multi-centre data is required for validation and translation to clinical practice. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Prediction of malignant transformation and recurrence of oral epithelial dysplasia using architectural and cytological feature specific prognostic models.

Oral epithelial dysplasia (OED) is a precursor state usually preceding oral squamous cell carcinoma (OSCC). Histological grading is the current gold standard for OED prognostication but is subjective and variable with unreliable outcome prediction. We explore if individual OED histological features can be used to develop and evaluate prognostic models for malignant transformation and recurrence prediction. Digitised tissue slides for a cohort of 109 OED cases were reviewed by three expert pathologists, where the prevalence and agreement of architectural and cytological histological features was assessed and association with clinical outcomes analysed using Cox proportional hazards regression and Kaplan-Meier curves. Within the cohort, the most prevalent features were basal cell hyperplasia (72%) and irregular surface keratin (60%), and least common were verrucous surface (26%), loss of epithelial cohesion (30%), lymphocytic band and dyskeratosis (34%). Several features were significant for transformation (p < 0.036) and recurrence (p < 0.015) including bulbous rete pegs, hyperchromatism, loss of epithelial cohesion, loss of stratification, suprabasal mitoses and nuclear pleomorphism. This led us to propose two prognostic scoring systems including a '6-point model' using the six features showing a greater statistical association with transformation and recurrence (bulbous rete pegs, hyperchromatism, loss of epithelial cohesion, loss of stratification, suprabasal mitoses, nuclear pleomorphism) and a 'two-point model' using the two features with highest inter-pathologist agreement (loss of epithelial cohesion and bulbous rete pegs). Both the 'six point' and 'two point' models showed good predictive ability (AUROC ≥ 0.774 for transformation and 0.726 for recurrence) with further improvement when age, gender and histological grade were added. These results demonstrate a correlation between individual OED histological features and prognosis for the first time. The proposed models have the potential to simplify OED grading and aid patient management. Validation on larger multicentre cohorts with prospective analysis is needed to establish their usefulness in clinical practice.

Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview.

BACKGROUND: This paper reviews recent literature employing Artificial Intelligence/Machine Learning (AI/ML) methods for diagnostic evaluation of head and neck cancers (HNC) using automated image analysis. METHODS: Electronic database searches using MEDLINE via OVID, EMBASE and Google Scholar were conducted to retrieve articles using AI/ML for diagnostic evaluation of HNC (2009-2020). No restrictions were placed on the AI/ML method or imaging modality used. RESULTS: In total, 32 articles were identified. HNC sites included oral cavity (n = 16), nasopharynx (n = 3), oropharynx (n = 3), larynx (n = 2), salivary glands (n = 2), sinonasal (n = 1) and in five studies multiple sites were studied. Imaging modalities included histological (n = 9), radiological (n = 8), hyperspectral (n = 6), endoscopic/clinical (n = 5), infrared thermal (n = 1) and optical (n = 1). Clinicopathologic/genomic data were used in two studies. Traditional ML methods were employed in 22 studies (69%), deep learning (DL) in eight studies (25%) and a combination of these methods in two studies (6%). CONCLUSIONS: There is an increasing volume of studies exploring the role of AI/ML to aid HNC detection using a range of imaging modalities. These methods can achieve high degrees of accuracy that can exceed the abilities of human judgement in making data predictions. Large-scale multi-centric prospective studies are required to aid deployment into clinical practice.

Convolutional Neural Network-Based Clinical Predictors of Oral Dysplasia: Class Activation Map Analysis of Deep Learning Results.

Oral cancer/oral squamous cell carcinoma is among the top ten most common cancers globally, with over 500,000 new cases and 350,000 associated deaths every year worldwide. There is a critical need for objective, novel technologies that facilitate early, accurate diagnosis. For this purpose, we have developed a method to classify images as "suspicious" and "normal" by performing transfer learning on Inception-ResNet-V2 and generated automated heat maps to highlight the region of the images most likely to be involved in decision making. We have tested the developed method's feasibility on two independent datasets of clinical photographic images of 30 and 24 patients from the UK and Brazil, respectively. Both 10-fold cross-validation and leave-one-patient-out validation methods were performed to test the system, achieving accuracies of 73.6% (±19%) and 90.9% (±12%), F1-scores of 97.9% and 87.2%, and precision values of 95.4% and 99.3% at recall values of 100.0% and 81.1% on these two respective cohorts. This study presents several novel findings and approaches, namely the development and validation of our methods on two datasets collected in different countries showing that using patches instead of the whole lesion image leads to better performance and analyzing which regions of the images are predictive of the classes using class activation map analysis.

Nodular Eruptions as a Rare Complication of Botulinum Neurotoxin Type-A: Case Series and Review of Literature.

Nodular eruption after botulinum neurotoxin type-A (BoNT-A) treatment is exceedingly rare, and the pathogenesis is poorly understood. This case series reports three patients that developed nodular eruptions following administration of Botox® (onabotulinum neurotoxin type A (ONA) injections). These patients had undergone multiple treatments before and after development of the eruptions which were uneventful. In addition to this, we have reviewed the published literature regarding this condition and have compared and contrasted the similarities and differences with regards to the clinical presentation and treatment with our patient cohort. This case series aims to raise awareness of this rare condition, its importance in relation to patient consent and provides a simplified management approach based on our experience. Further evaluation is needed to determine treatment consensus but conducting such research may prove to be challenging due to this condition being an infrequent encounter.

Clinical presentations on a facial pain clinic.

Introduction The complex nature of facial pain conditions creates a diagnostic challenge which may necessitate specialist referral.Aim To identify the case mix presenting to a specialist tertiary care facial pain clinic.Methods A retrospective review of 112 patient records was undertaken. Trends in provisional diagnoses from referrers and the correlation to diagnoses made following specialist consultation were reviewed.Results The most common provisional diagnoses recorded in referral letters were painful temporomandibular disorders, trigeminal neuralgia and persistent idiopathic facial pain (PIFP). Over a quarter of referrals did not include a provisional diagnosis. Following assessment, only one case was not given a definitive diagnosis and no patients were diagnosed with PIFP. A causative factor was identified in all the initially queried PIFP cases, and painful post-traumatic trigeminal neuropathic pain was found in multiple patients.Conclusions Painful post-traumatic trigeminal neuropathic pain should be considered if pain onset coincides with dental treatment or other traumatic events. PIFP is a rare facial pain diagnosis and may be over-diagnosed by dental and medical practitioners. It is important to systematically exclude other causes before reaching this diagnosis. This will facilitate effective treatment, manage patient expectations and potentially reduce unnecessary referrals.

Inferior alveolar nerve anaesthesia: A rare complication of orthodontic tooth movement.

Inferior alveolar nerve (IAN) damage is a rare but recognised complication of dental procedures including third molar surgery, implant surgery, endodontic treatment and local anaesthetic injections. However, it is rarely caused by orthodontic tooth movement. This report highlights a case of temporary IAN anaesthesia to the right mental region, which was likely to have occurred secondary to the orthodontic uprighting of a lingually tilted molar using a high strength arch wire. Immediate deactivation of the appliance and an acute reducing dose of systemic steroids resulted in complete resolution of symptoms. To the best of the author's knowledge, there have been seven previously described cases of IAN paraesthesia but no cases reporting IAN anaesthesia secondary to orthodontic fixed-appliance treatment. This case highlights the importance of dentists practising orthodontics to have an awareness of the clinical and radiographic signs that may indicate a high-risk case requiring appropriate referral for cone beam imaging and careful orthodontic planning. Furthermore, this case emphasises the need to warn high-risk patients of the symptoms of this rare complication and how it may be managed. This will ultimately help to minimise the risk of litigation and optimise patient experience and care.