Long-term health-related quality of life in head and neck cancer survivors: A large multinational study.

Head and neck cancer (HNC) patients suffer from a range of health-related quality of life (HRQoL) issues, but little is known about their long-term HRQoL. This study explored associations between treatment group and HRQoL at least 5 years' post-diagnosis in HNC survivors. In an international cross-sectional study, HNC survivors completed the European Organization for Research and Treatment of Cancer (EORTC) quality of life core questionnaire (EORTC-QLQ-C30) and its HNC module (EORTC-QLQ-H&N35). Meaningful HRQoL differences were examined between five treatment groups: (a) surgery, (b) radiotherapy, (c) chemo-radiotherapy, (d) radiotherapy ± chemotherapy and neck dissection and (e) any other surgery (meaning any tumour surgery that is not a neck dissection) and radiotherapy ± chemotherapy. Twenty-six sites in 11 countries enrolled 1105 survivors. They had a median time since diagnosis of 8 years, a mean age of 66 years and 71% were male. After adjusting for age, sex, tumour site and UICC stage, there was evidence for meaningful differences (10 points or more) in HRQoL between treatment groups in seven domains (Fatigue, Mouth Pain, Swallowing, Senses, Opening Mouth, Dry Mouth and Sticky Saliva). Survivors who had single-modality treatment had better or equal HRQoL in every domain compared to survivors with multimodal treatment, with the largest differences for Dry Mouth and Sticky Saliva. For Global Quality of Life, Physical and Social Functioning, Constipation, Dyspnoea and Financial Difficulties, at least some treatment groups had better outcomes compared to a general population. Our data suggest that multimodal treatment is associated with worse HRQoL in the long-term compared to single modality.

Evaluation of DNA methylation levels of SEPT9 and SHOX2 in plasma of patients with head and neck squamous cell carcinoma using droplet digital PCR.

Head and neck squamous cell carcinoma (HNSCC) is the seventh most commonly diagnosed cancer globally. HNSCC develops from the mucosa of the oral cavity, pharynx and larynx. Methylation levels of septin 9 (SEPT9) and short stature homeobox 2 (SHOX2) genes in circulating cell­free DNA (ccfDNA) are considered epigenetic biomarkers and have shown predictive value in preliminary reports in HNSCC. Liquid biopsy is a non­invasive procedure that collects tumor­derived molecules, including ccfDNA. In the present study, a droplet digital PCR (ddPCR)­based assay was developed to detect DNA methylation levels of circulating SEPT9 and SHOX2 in the plasma of patients with HNSCC. The assay was first set up using commercial methylated and unmethylated DNA. The dynamic changes in the methylation levels of SEPT9 and SHOX2 were then quantified in 20 patients with HNSCC during follow­up. The results highlighted: i) The ability of the ddPCR­based assay to detect very low copies of methylated molecules; ii) the significant decrease in SEPT9 and SHOX2 methylation levels in the plasma of patients with HNSCC at the first time points of follow­up with respect to T0; iii) a different trend of longitudinally DNA methylation variations in small groups of stratified patients. The absolute and precise quantification of SEPT9 and SHOX2 methylation levels in HNSCC may be useful for studies with translational potential.

Reliability of large language models in managing odontogenic sinusitis clinical scenarios: a preliminary multidisciplinary evaluation.

PURPOSE: This study aimed to evaluate the utility of large language model (LLM) artificial intelligence tools, Chat Generative Pre-Trained Transformer (ChatGPT) versions 3.5 and 4, in managing complex otolaryngological clinical scenarios, specifically for the multidisciplinary management of odontogenic sinusitis (ODS). METHODS: A prospective, structured multidisciplinary specialist evaluation was conducted using five ad hoc designed ODS-related clinical scenarios. LLM responses to these scenarios were critically reviewed by a multidisciplinary panel of eight specialist evaluators (2 ODS experts, 2 rhinologists, 2 general otolaryngologists, and 2 maxillofacial surgeons). Based on the level of disagreement from panel members, a Total Disagreement Score (TDS) was calculated for each LLM response, and TDS comparisons were made between ChatGPT3.5 and ChatGPT4, as well as between different evaluators. RESULTS: While disagreement to some degree was demonstrated in 73/80 evaluator reviews of LLMs' responses, TDSs were significantly lower for ChatGPT4 compared to ChatGPT3.5. Highest TDSs were found in the case of complicated ODS with orbital abscess, presumably due to increased case complexity with dental, rhinologic, and orbital factors affecting diagnostic and therapeutic options. There were no statistically significant differences in TDSs between evaluators' specialties, though ODS experts and maxillofacial surgeons tended to assign higher TDSs. CONCLUSIONS: LLMs like ChatGPT, especially newer versions, showed potential for complimenting evidence-based clinical decision-making, but substantial disagreement was still demonstrated between LLMs and clinical specialists across most case examples, suggesting they are not yet optimal in aiding clinical management decisions. Future studies will be important to analyze LLMs' performance as they evolve over time.

Real-Time Laryngeal Cancer Boundaries Delineation on White Light and Narrow-Band Imaging Laryngoscopy with Deep Learning.

OBJECTIVE: To investigate the potential of deep learning for automatically delineating (segmenting) laryngeal cancer superficial extent on endoscopic images and videos. METHODS: A retrospective study was conducted extracting and annotating white light (WL) and Narrow-Band Imaging (NBI) frames to train a segmentation model (SegMENT-Plus). Two external datasets were used for validation. The model's performances were compared with those of two otolaryngology residents. In addition, the model was tested on real intraoperative laryngoscopy videos. RESULTS: A total of 3933 images of laryngeal cancer from 557 patients were used. The model achieved the following median values (interquartile range): Dice Similarity Coefficient (DSC) = 0.83 (0.70-0.90), Intersection over Union (IoU) = 0.83 (0.73-0.90), Accuracy = 0.97 (0.95-0.99), Inference Speed = 25.6 (25.1-26.1) frames per second. The external testing cohorts comprised 156 and 200 images. SegMENT-Plus performed similarly on all three datasets for DSC (p = 0.05) and IoU (p = 0.07). No significant differences were noticed when separately analyzing WL and NBI test images on DSC (p = 0.06) and IoU (p = 0.78) and when analyzing the model versus the two residents on DSC (p = 0.06) and IoU (Senior vs. SegMENT-Plus, p = 0.13; Junior vs. SegMENT-Plus, p = 1.00). The model was then tested on real intraoperative laryngoscopy videos. CONCLUSION: SegMENT-Plus can accurately delineate laryngeal cancer boundaries in endoscopic images, with performances equal to those of two otolaryngology residents. The results on the two external datasets demonstrate excellent generalization capabilities. The computation speed of the model allowed its application on videolaryngoscopies simulating real-time use. Clinical trials are needed to evaluate the role of this technology in surgical practice and resection margin improvement. LEVEL OF EVIDENCE: III Laryngoscope, 2024.

Classifying Vocal Folds Fixation from Endoscopic Videos with Machine Learning.

Vocal folds motility evaluation is paramount in both the assessment of functional deficits and in the accurate staging of neoplastic disease of the glottis. Diagnostic endoscopy, and in particular videoendoscopy, is nowadays the method through which the motility is estimated. The clinical diagnosis, however, relies on the examination of the videoendoscopic frames, which is a subjective and professional-dependent task. Hence, a more rigorous, objective, reliable, and repeatable method is needed. To support clinicians, this paper proposes a machine learning (ML) approach for vocal cords motility classification. From the endoscopic videos of 186 patients with both vocal cords preserved motility and fixation, a dataset of 558 images relative to the two classes was extracted. Successively, a number of features was retrieved from the images and used to train and test four well-grounded ML classifiers. From test results, the best performance was achieved using XGBoost, with precision = 0.82, recall = 0.82, F1 score = 0.82, and accuracy = 0.82. After comparing the most relevant ML models, we believe that this approach could provide precise and reliable support to clinical evaluation.Clinical Relevance- This research represents an important advancement in the state-of-the-art of computer-assisted otolaryngology, to develop an effective tool for motility assessment in the clinical practice.

Revisiting the concept of neoadjuvant and induction therapy in head and neck cancer with the advent of immunotherapy.

The treatment of locally advanced (LA) Head and Neck Squamous Cell Carcinoma (HNSCC) is based on surgery followed by (chemo)radiation or on curative (chemo)radiation, depending on site and stage. Despite optimal locoregional treatment, about 50% of patients recur, with a huge impact on prognosis and substantial morbidity. The advent of immunotherapy (IT) with immune checkpoint inhibitors (ICIs) changed the paradigm of systemic treatment for recurrent/metastatic (RM) disease, showing activity, efficacy, and safety in both platinum-resistant and platinum-naïve patients. Such data led clinicians to design clinical trials to investigate early administration of IT even in the neoadjuvant or window of opportunity setting. In this review, we examine the published and ongoing trials investigating IT in the neoadjuvant setting for LA HNSCC. We address the current challenges of this treatment modality: optimal patient selection for neoadjuvant IT; choosing the appropriate systemic approach to enhance response without compromising tolerability; determining the ideal study endpoint, with a focus on major pathological response as a potential surrogate for overall survival; evaluating treatment response through imaging, considering the discordance between radiological and pathological assessments; and the influence of neoadjuvant IT response on locoregional treatment de-escalation strategies.

Treatment and outcomes of minor salivary gland cancers of the larynx and trachea: a systematic review.

Objectives: Malignant minor salivary glands carcinomas (MiSGC) of the larynx and trachea are rare tumours and published evidence is sparse. We conducted a systematic review to describe shareable treatment strategies and oncological outcomes of these neoplastic entities. Methods: Full text English manuscripts published from January 1st 2000 to December 14th 2022 were included. Data on demographics, treatments and outcomes were collected. A pooled analysis of 5-year overall survival (OS) was performed. Results: Seventeen articles and 365 patients met the inclusion criteria. The most common subsites involved were subglottic and distal trachea. Adenoid cystic carcinoma was, by far, the most frequent histotype. The first-choice treatment strategy was surgery (86.8%), while adjuvant treatments were delivered in 57.4% of patients. Only 12.9% were treated with definitive radiotherapy with/without chemotherapy. The mean follow-up was 68.3 months. One hundred nine (34.9%) deaths were recorded and 62.4% were cancer-related. Five-year OS ranged from 20% to 100% and, at pooled analysis, it was 83% (range, 78-87%). Conclusions: In case of MiSGC of the larynx and trachea, surgery remains the mainstay of treatment. Adjuvant treatments are frequently delivered. Survival estimates are good overall, but highly heterogeneous.

Impact of lymph node ratio and number of lymph node metastases on survival and recurrence in laryngeal squamous cell carcinoma.

INTRODUCTION: The aim of this study is to assess the impact of lymph node ratio (LNR) and number of positive lymph nodes (NPLN) on mortality and recurrence rates in patients with laryngeal squamous cell carcinoma. MATERIALS AND METHODS: We conducted a retrospective multicenter international study involving 24 Otorhinolaryngology-Head and Neck Surgery divisions. Disease-specific survival (DSS) and disease-free survival (DFS) were evaluated as the main outcomes. The curves for DSS and DFS according to NPLN and LNR were analyzed to identify significant variations and establish specific cut-off values. RESULTS: 2507 patients met the inclusion criteria. DSS and DFS were significantly different in the groups of patients stratified according to LNR and NPLN. The 5-year DSS and DFS based on LNR and NPLN demonstrated an improved ability to stratify patients when compared to pN staging. CONCLUSION: Our data demonstrate the potential prognostic value of NPLN and LNR in laryngeal squamous cell carcinoma.

Adenosquamous cell carcinoma of the head and neck: a retrospective single institution series.

Objectives: Head and neck adenosquamous cell carcinoma (HN-ASCC) is a rare, aggressive neoplasm, with limited data reported in the literature. The aim of this study was to assess tumour behaviour and prognostic factors impacting overall survival (OS) in a retrospective, single institution series. Methods: A retrospective study on patients affected by HN-ASCC who were treated surgically between 2002 and 2019 at the Department of Otorhinolaryngology - Head and Neck Surgery of the University of Brescia was conducted. Demographics, clinical data, OS, and relative prognostic factors were analysed. Results: The study included 32 patients, with a median age of 66 years, mostly males (84.4%) and untreated (68.8%). Adjuvant treatments followed surgery in 28.1% of patients. Compared to conventional SCC, ASCC showed a higher proportion of cases arising in the larynx (40.6%); no difference was found in other features. Advanced (pT3-4) local stage at presentation (p = 0.023), perineural invasion (PNI, p = 0.01), and positive margins (p = 0.007) were independent negative prognostic factors for OS. Conclusions: HN-ASCC is a rare, aggressive cancer, most frequently arising in the larynx of elderly males, usually diagnosed in an advanced local stage. OS is generally poor, affected by local advanced stage, PNI, and positive resection margins.

Instance segmentation of upper aerodigestive tract cancer: site-specific outcomes.

Objective: To achieve instance segmentation of upper aerodigestive tract (UADT) neoplasms using a deep learning (DL) algorithm, and to identify differences in its diagnostic performance in three different sites: larynx/hypopharynx, oral cavity and oropharynx. Methods: A total of 1034 endoscopic images from 323 patients were examined under narrow band imaging (NBI). The Mask R-CNN algorithm was used for the analysis. The dataset split was: 935 training, 48 validation and 51 testing images. Dice Similarity Coefficient (Dsc) was the main outcome measure. Results: Instance segmentation was effective in 76.5% of images. The mean Dsc was 0.90 ± 0.05. The algorithm correctly predicted 77.8%, 86.7% and 55.5% of lesions in the larynx/hypopharynx, oral cavity, and oropharynx, respectively. The mean Dsc was 0.90 ± 0.05 for the larynx/hypopharynx, 0.60 ± 0.26 for the oral cavity, and 0.81 ± 0.30 for the oropharynx. The analysis showed inferior diagnostic results in the oral cavity compared with the larynx/hypopharynx (p < 0.001). Conclusions: The study confirms the feasibility of instance segmentation of UADT using DL algorithms and shows inferior diagnostic results in the oral cavity compared with other anatomic areas.

Cervical exenteration and its variants for locally advanced thyroid cancer: when, why, and how?

PURPOSE OF REVIEW: To describe the modern surgical approach for management of advanced thyroid cancers infiltrating the cervicovisceral axis with special attention to well differentiated tumors not amenable to organ-sparing techniques. In particular, cervical exenteration, herein defined as the sum of total thyroidectomy, central compartment and lateral neck dissections, variously associated with total laryngectomy and possible partial or total pharyngoesophagectomy, represents an extreme surgical procedure that, in properly selected cases, allows for reasonable palliation of central compartment life-threatening signs/symptoms if not cure for an advanced oncologic condition. RECENT FINDINGS: Cervical exenteration is not contraindicated by the presence of limited distant metastases at presentation. Even though it requires that the patient is in general good health as it can be associated with a number of complications and long in-hospital stay, when appropriately planned and performed according to the most recent reconstructive nuances, it allows good oncologic outcomes that are not inferior to those described for similarly advanced primaries of the upper aerodigestive tract. In addition, quality of life and functional results are not significantly different from those described after total laryngectomy for primary laryngeal squamous cell carcinomas. SUMMARY: Cervical exenteration requires a tertiary, expert, multidisciplinary effort in terms of diagnosis, surgical performance, and postoperative care. A patient-centered decision process is strongly warranted taking into consideration alternative therapeutic and symptom-based palliative strategies.

Serious Long-Term Effects of Head and Neck Cancer from the Survivors' Point of View.

The long-term problems of head and neck cancer survivors (HNCS) are not well known. In a cross-sectional international study aimed at exploring the long-term quality of life in this population, 1114 HNCS were asked to state their two most serious long-term effects. A clinician recorded the responses during face-to-face appointments. A list of 15 example problems was provided, but a free text field was also available. A total of 1033 survivors responded to the question. The most frequent problems were 'dry mouth' (DM) (n = 476; 46%), 'difficulty swallowing/eating' (DSE) (n = 408; 40%), 'hoarseness/difficulty speaking' (HDS) (n = 169; 16%), and 'pain in the head and neck' (PHN) (n = 142; 14%). A total of 5% reported no problems. Logistic regression adjusted for age, gender, treatment, and tumor stage and site showed increased odds of reporting DM and DSE for chemo-radiotherapy (CRT) alone compared to surgery alone (odds ratio (OR): 4.7, 95% confidence interval (CI): 2.5-9.0; OR: 2.1, CI: 1.1-3.9), but decreased odds for HDS and PHN (OR: 0.3, CI: 0.1-0.6; OR: 0.2, CI: 0.1-0.5). Survivors with UICC stage IV at diagnosis compared to stage I had increased odds of reporting HDS (OR: 1.9, CI: 1.2-3.0). Laryngeal cancer survivors had reduced odds compared to oropharynx cancer survivors of reporting DM (OR: 0.4, CI: 0.3-0.6) but increased odds of HDS (OR: 7.2, CI: 4.3-12.3). This study provides evidence of the serious long-term problems among HNCS.

Magnetic resonance diagnosis of laryngeal chondritis after transoral laser microsurgery for laryngeal cancer.

Objective: Laryngeal chondritis (LC) is a rare complication of carbon dioxide transoral laser microsurgery (CO2 TOLMS) for laryngeal tumours and can pose a diagnostic challenge. Its magnetic resonance (MR) features have not been previously described. This study aims to characterise a cohort of patients who developed LC after CO2 TOLMS and describe its clinical and MR findings. Methods: Clinical records and MR images of all patients presenting with LC after CO2 TOLMS between 2008 and 2022 were reviewed. Results: Seven patients were analysed. Timing of LC diagnosis ranged from 1 to 8 months after CO2 TOLMS. Four patients were symptomatic. Abnormal endoscopic findings included suspected tumour recurrence in 4 patients. MR documented focal or extensive signal changes involving the thyroid lamina and para-laryngeal space with T2 hyperintensity, T1 hypointensity and intense contrast enhancement (n = 7), and minimally reduced mean apparent diffusion coefficient (ADC) values (1.0-1.5 x 10-3 mm2/s) (n = 6). A favourable clinical outcome was achieved in all patients. Conclusions: LC after CO2 TOLMS has a distinctive MR pattern. When tumour recurrence cannot be confidently excluded based on imaging, antibiotic therapy, close clinical and radiological follow-up and/or biopsy are recommended.

The role of adjuvant therapy in pT4N0 laryngectomized patients: Multicentric observational study.

BACKGROUND: To retrospectively evaluate oncological outcomes in two groups of patients with pT4aN0 glottic SCC treated with total laryngectomy (TL) and neck dissection (ND) who underwent postoperative radiotherapy or exclusive clinical and radiological follow-up. METHODS: It includes patients with pT4N0 glottic SCC who underwent TL and unilateral or bilateral ND with or without PORT. Divided in two comparison groups: the first group underwent adjuvant RT (TL-PORT); the second group referred to clinical and radiological follow-up (TL). RESULTS: PORT was associated with a better OS while no differences were found in terms of DSS. A better local control is achieved when PORT is administered while no differences in terms of regional and distant control rates were found. Bilateral ND positively impacts on the regional control while the PNI negatively impact the regional control. CONCLUSIONS: A tailored PORT protocol might be considered for pT4N0 glottic SCC treated with TL and ND, both considering the ND's extent and presence of PNI.

Thyroid cartilage infiltration in advanced laryngeal cancer: prognostic implications and predictive modelling.

Objective: Detection of laryngeal cartilage invasion is of great importance in staging of laryngeal squamous cell carcinoma (LSCC). The role of prognosticators in locally advanced laryngeal cancer are still widely debated. This study aimed to assess the impact of volume of thyroid cartilage infiltration, as well as other histopathologic variables, on patient survival. Materials and methods: We retrospectively analysed 74 patients affected by pT4 LSCC and treated with total laryngectomy between 2005 and 2021 at the Department of Otorhinolaryngology - Head and Neck Surgery of the University of Brescia, Italy. We considered as potential prognosticators histological grade, perineural (PNI) and lympho-vascular invasion (LVI), thyroid cartilage infiltration, and pTN staging. Pre-operative CT or MRI were analysed to quantify the volume of cartilage infiltration using 3D Slicer software. Results: The 1-, 3-, and 5-year disease free survivals (DFS) were 76%, 66%, and 64%, respectively. Using machine learning models, we found that the volume of thyroid cartilage infiltration had high correlation with DFS. Patients with a higher volume (> 670 mm3) of infiltration had a worse prognosis compared to those with a lower volume. Conclusions: Our study confirms the essential role of LVI as prognosticator in advanced LSCC and, more innovatively, highlights the volume of thyroid cartilage infiltration as another promising prognostic factor.

Artificial intelligence in clinical endoscopy: Insights in the field of videomics.

Artificial intelligence is being increasingly seen as a useful tool in medicine. Specifically, these technologies have the objective to extract insights from complex datasets that cannot easily be analyzed by conventional statistical methods. While promising results have been obtained for various -omics datasets, radiological images, and histopathologic slides, analysis of videoendoscopic frames still represents a major challenge. In this context, videomics represents a burgeoning field wherein several methods of computer vision are systematically used to organize unstructured data from frames obtained during diagnostic videoendoscopy. Recent studies have focused on five broad tasks with increasing complexity: quality assessment of endoscopic images, classification of pathologic and nonpathologic frames, detection of lesions inside frames, segmentation of pathologic lesions, and in-depth characterization of neoplastic lesions. Herein, we present a broad overview of the field, with a focus on conceptual key points and future perspectives.

Videomics of the Upper Aero-Digestive Tract Cancer: Deep Learning Applied to White Light and Narrow Band Imaging for Automatic Segmentation of Endoscopic Images.

Introduction: Narrow Band Imaging (NBI) is an endoscopic visualization technique useful for upper aero-digestive tract (UADT) cancer detection and margins evaluation. However, NBI analysis is strongly operator-dependent and requires high expertise, thus limiting its wider implementation. Recently, artificial intelligence (AI) has demonstrated potential for applications in UADT videoendoscopy. Among AI methods, deep learning algorithms, and especially convolutional neural networks (CNNs), are particularly suitable for delineating cancers on videoendoscopy. This study is aimed to develop a CNN for automatic semantic segmentation of UADT cancer on endoscopic images. Materials and Methods: A dataset of white light and NBI videoframes of laryngeal squamous cell carcinoma (LSCC) was collected and manually annotated. A novel DL segmentation model (SegMENT) was designed. SegMENT relies on DeepLabV3+ CNN architecture, modified using Xception as a backbone and incorporating ensemble features from other CNNs. The performance of SegMENT was compared to state-of-the-art CNNs (UNet, ResUNet, and DeepLabv3). SegMENT was then validated on two external datasets of NBI images of oropharyngeal (OPSCC) and oral cavity SCC (OSCC) obtained from a previously published study. The impact of in-domain transfer learning through an ensemble technique was evaluated on the external datasets. Results: 219 LSCC patients were retrospectively included in the study. A total of 683 videoframes composed the LSCC dataset, while the external validation cohorts of OPSCC and OCSCC contained 116 and 102 images. On the LSCC dataset, SegMENT outperformed the other DL models, obtaining the following median values: 0.68 intersection over union (IoU), 0.81 dice similarity coefficient (DSC), 0.95 recall, 0.78 precision, 0.97 accuracy. For the OCSCC and OPSCC datasets, results were superior compared to previously published data: the median performance metrics were, respectively, improved as follows: DSC=10.3% and 11.9%, recall=15.0% and 5.1%, precision=17.0% and 14.7%, accuracy=4.1% and 10.3%. Conclusion: SegMENT achieved promising performances, showing that automatic tumor segmentation in endoscopic images is feasible even within the highly heterogeneous and complex UADT environment. SegMENT outperformed the previously published results on the external validation cohorts. The model demonstrated potential for improved detection of early tumors, more precise biopsies, and better selection of resection margins.

Clinical and Histological Prognostic Factors of Recurrence and Malignant Transformation in a Large Series of Oral Potentially Malignant Disorders.

Background: Oral potentially malignant disorders (OPMDs) represent a heterogeneous set of different histological lesions, characterized by the capacity to transform in oral squamous cell carcinoma (OSCC). Despite optimal surgical treatment, approximately 20%-30% of OPMDs may evolve into OSCC. No clear clinical/histological factors are able to identify OPMDs at higher risk of malignant transformation. Materials and Methods: We considered surgically treated patients with a diagnosis of OPMDs, enrolled from 1996 to 2019 at ASST Spedali Civili of Brescia without a diagnosis of OSCC within the previous 2 years. Clinical and histological characteristics were recorded. Outcomes of interest were recurrence-free survival (RFS), defined as the time from surgery for primary OPMD to any relapse of OPMD or malignant transformation, whichever occurred first, and carcinoma-free survival (CFS), defined as the time from surgery for OPMD to malignant transformation. Results: We retrospectively reviewed 106 OPMDs cases. Median age at first diagnosis was 64 years old (IQR = 18.75); female patients comprise 51.9% of the cases. During a median follow-up of 30.5 months (IQR = 44), in 23.5% of patients, malignant transformation occurred. RFS at 1, 5, and 10 years was 92.4%, 60.9%, and 43.2%, respectively. Female sex and history of previous OSCC were independent risk factors for RFS. CFS at 1, 5, and 10 years of follow-up was 97.1%, 75.9%, and 64.4%, respectively. Previous OSCC was an independent risk factor for CFS. Conclusions: In this large series of OPMDs, only previous diagnosis of OSCC was a prognostic factor for further OSCC occurrence. Given the lack of additional clinical/pathological prognostic factors, we advocate further studies into molecular characterization of OPMDs to better stratify the risk of malignant transformation.