Prognostic nomogram for proliferative verrucous leukoplakia
Proliferative verrucous leukoplakia (PVL) is a special type of leukoplakia with high rate of malignant transformation into oral squamous cell carcinoma (OSCC). PVL is more common in elderly women and non-smokers, frequently involving gingiva, alveolar ridge, buccal mucosa and tongue abdomen. It tends to affect multiple sites, has a wide range, is easy to recur, and exhibits invasive proliferative features. PVL is characterized by a progressive clinical course, constantly changing clinical and histopathological features, and has the highest risk of malignant transformation compared to other potentially malignant disorders (OPMDs). It has been reported a recurrence rate of 67.2% and a malignant transformation rate of 26.7%–72.4%. , , However, compared with OSCC, PVL demonstrates a relatively favored prognosis, especially in terms of survival rates, which indicates that PVL is distinct from typical oral leukoplakia. No specific risk factors have been identified, although viral and bacterial infection have been studied, there is no clear evidence to suggest that PVL is associated with HPV infection. , , There have been limited studies focus on the EB virus infection in PVL. In a study conducted by Bagan et al., the EB virus was detected positive in 40% of PVL cases (6 out of 10 patients). Additionally, PVL patients have been found to have a higher abundance of Campilobacterota and lower Proteobacteria . However, there is still a lack of sufficient data to establish a direct relationship between microbial infection and PVL. Since 1985, several diagnostic criteria have been proposed, , , , , but there are still no unified diagnostic criteria. Among them, the criteria proposed by Cerero-Lapiedra et al., with its quantifiable diagnostic indicators and easy calculation, are relatively easy to implement in clinical practice. The criteria included the disorder affecting more than two different oral sites and the presentation of verrucous appearance. However, it is important to note that the initial manifestation of PVL may be a flat white area or lichenoid appearance, similar to oral leukoplakia, oral lichen planus and other oral white lesions. , , Thus, these cases could be erroneously treated as conventional oral leukoplakia (COL) or oral lichen planus (OLP), ignoring the risk factors, subsequently malignant transformation. Due to the lack of uniform diagnostic criteria for PVL, and few specific risk factors and candidate biomarkers predictive of malignant transformation have been associated with PVL, timely and accurate diagnosis and prediction are challenging in clinical practice. Therefore, we utilized logistic regression to analyze the canceration risk and prognostic factors of PVL and visualized them by nomogram, establishing a system for accurate assessment. 

Patient enrollment This study included data from patients with leukoplakia collected at Peking University School and Hospital of Stomatology from January 2013 to December 2020. The studies involving human participants were reviewed and approved by The Biomedical Ethics Committee of the Department of Peking University (No. PKUSSIRB-201948111). The inclusion criteria were as follows: (a) Histologically confirmed oral leukoplakia, (b) It is not clinically diagnosable for other diseases. Exclusion criteria were: (a) Those with previous or present OSCCs, (b) With tumors of other origins or other serious diseases; (c) Insufficient follow up data. PVL was diagnosed according to the criteria proposed by Cerero-Lapiedra et al.. Major criteria: (a) Leukoplakia lesion with more than two different oral sites; (b) Verrucous appearance; (c) Spreading or engrossing during the disease development; (d) Presence of recurrence in a previously treated area; (e) Histopathological test: oral epithelial hyperkeratosis to verrucous hyperplasia, verrucous carcinoma, or squamous cell carcinoma, whether in situ or infiltrating. Minor criteria: (a) Oral leukoplakia lesion that occupies at least 3 cm when adding all the affected areas; (b) Female patient; (c) Non-smoker regardless of gender; (d) More than 5 years evolution. Diagnostic criteria: Three major criteria (one of which must include the evolution of the histopathological lesions) or two major criteria (one of which must include the evolution of the histopathological lesions) + two minor criteria. The rest patients except those diagnosed with PVL were classified as conventional oral leukoplakia (COL). A minimum of 9-year clinical follow-up was required for COL patients, and 5-year clinical follow up for PVL patients. Data collection and processing Baseline characteristics of PVL patients were collected, including sex, age, local irritants, tobacco and alcohol habits, clinical presentation, primary site, malignant transformation, and oral epithelial dysplasia (OED). Local irritants, such as faulty restorations, residual roots, sharp tooth cusps, dental appliances, silver amalgam, were included in the possible influencing factors accessed in this analysis. The primary lesion sites included oral subsites: dorsum and ventrum of the tongue, buccal mucosa, floor of the mouth, gingiva, lip, soft palate, hard palate. Furthermore, based on the number of subsites involved, the lesion sites were categorized into those affecting more than two subsites and those affecting two or fewer subsites. The involvement of the ventral tongue and floor of the mouth as primary sites was also considered as potential risk factors and prognostic factors. Clinical presentation was considered homogeneous in the presence of predominantly white, flat, thin or wrinkled lesions; and non-homogeneous in the presence of mixed white-and-red, erosion, nodular, granular, and verrucous lesions. Model construction and evaluation All variables underwent univariable logistic analysis, and those with a P -value <0.05 were included in the multivariable logistic analysis to identify the risk and prognostic factors for PVL. Subsequently, diagnostic and prognostic nomogram models were developed based on the factors identified through multivariate logistic regression analysis. The accuracy of the model was assessed using ROC analysis. The calibration curve was employed to assess the agreement between the predicted and actual probability. A closer alignment between the two lines indicates a higher consistency between the predicted and actual incidence rates, suggesting better performance of the nomogram prediction model. Lastly, decision curve analysis (DCA) was employed to evaluate the net benefit and clinical utility for patients by quantifying the net benefit at different threshold probabilities. Statistical analysis Statistical analyses were performed using SPSS software (version 22.0, IBM Corp., Armonk, NY, USA) and R 4.4.0. Continuous variables were expressed as the mean ± SD or median (interquartile range). Categorical variables are presented as numbers (percentages). Independent t-tests or Mann–Whitney U tests were used for analyzing continuous variables, depending on their distribution. For categorical variables, the chi-squared test or Fisher's exact test was employed. A two-sided P -value <0.05 was considered statistically significant. 

This study included data from patients with leukoplakia collected at Peking University School and Hospital of Stomatology from January 2013 to December 2020. The studies involving human participants were reviewed and approved by The Biomedical Ethics Committee of the Department of Peking University (No. PKUSSIRB-201948111). The inclusion criteria were as follows: (a) Histologically confirmed oral leukoplakia, (b) It is not clinically diagnosable for other diseases. Exclusion criteria were: (a) Those with previous or present OSCCs, (b) With tumors of other origins or other serious diseases; (c) Insufficient follow up data. PVL was diagnosed according to the criteria proposed by Cerero-Lapiedra et al.. Major criteria: (a) Leukoplakia lesion with more than two different oral sites; (b) Verrucous appearance; (c) Spreading or engrossing during the disease development; (d) Presence of recurrence in a previously treated area; (e) Histopathological test: oral epithelial hyperkeratosis to verrucous hyperplasia, verrucous carcinoma, or squamous cell carcinoma, whether in situ or infiltrating. Minor criteria: (a) Oral leukoplakia lesion that occupies at least 3 cm when adding all the affected areas; (b) Female patient; (c) Non-smoker regardless of gender; (d) More than 5 years evolution. Diagnostic criteria: Three major criteria (one of which must include the evolution of the histopathological lesions) or two major criteria (one of which must include the evolution of the histopathological lesions) + two minor criteria. The rest patients except those diagnosed with PVL were classified as conventional oral leukoplakia (COL). A minimum of 9-year clinical follow-up was required for COL patients, and 5-year clinical follow up for PVL patients. 

Baseline characteristics of PVL patients were collected, including sex, age, local irritants, tobacco and alcohol habits, clinical presentation, primary site, malignant transformation, and oral epithelial dysplasia (OED). Local irritants, such as faulty restorations, residual roots, sharp tooth cusps, dental appliances, silver amalgam, were included in the possible influencing factors accessed in this analysis. The primary lesion sites included oral subsites: dorsum and ventrum of the tongue, buccal mucosa, floor of the mouth, gingiva, lip, soft palate, hard palate. Furthermore, based on the number of subsites involved, the lesion sites were categorized into those affecting more than two subsites and those affecting two or fewer subsites. The involvement of the ventral tongue and floor of the mouth as primary sites was also considered as potential risk factors and prognostic factors. Clinical presentation was considered homogeneous in the presence of predominantly white, flat, thin or wrinkled lesions; and non-homogeneous in the presence of mixed white-and-red, erosion, nodular, granular, and verrucous lesions. 

All variables underwent univariable logistic analysis, and those with a P -value <0.05 were included in the multivariable logistic analysis to identify the risk and prognostic factors for PVL. Subsequently, diagnostic and prognostic nomogram models were developed based on the factors identified through multivariate logistic regression analysis. The accuracy of the model was assessed using ROC analysis. The calibration curve was employed to assess the agreement between the predicted and actual probability. A closer alignment between the two lines indicates a higher consistency between the predicted and actual incidence rates, suggesting better performance of the nomogram prediction model. Lastly, decision curve analysis (DCA) was employed to evaluate the net benefit and clinical utility for patients by quantifying the net benefit at different threshold probabilities. 

Statistical analyses were performed using SPSS software (version 22.0, IBM Corp., Armonk, NY, USA) and R 4.4.0. Continuous variables were expressed as the mean ± SD or median (interquartile range). Categorical variables are presented as numbers (percentages). Independent t-tests or Mann–Whitney U tests were used for analyzing continuous variables, depending on their distribution. For categorical variables, the chi-squared test or Fisher's exact test was employed. A two-sided P -value <0.05 was considered statistically significant. 

Clinical and pathological characteristics A total of 467 patients were enrolled in this study with oral leukoplakia (OLK) confirmed by two pathologists, including 207 males and 260 females. The age ranged from 13 to 88 years, with a mean of 55 years. In these OLK patients, 170 cases were diagnosed with PVL according the criteria. As shown in , patients with PVL were predominantly female (81.76%), non-smokers (95.29%), and non-alcohol users (94.71%). They also had a higher likelihood of presenting with multifocal lesions initially (73.53%). Additionally, OED was observed in 53.53% of patients with PVL. Lesions located on the ventral tongue or floor of the mouth were more commonly observed in this patient population ( ). In the diagnostic nomogram model, all samples were divided into training set (n = 280), validation set (n = 93) and testing set (n = 94) according to the 6:2:2 random allocation principle. And in the prognostic nomogram model, PVL samples were divided into a training set (n = 118), and validation set (n = 52). The details of cohort inclusion were shown in . Diagnostic nomogram model for PVL Univariable logistic analysis on 9 potential factors, and the result determined PVL-related risk factors, including sex, number of lesion sites, clinical presentation, smoker, alcohol use, OED and ventral tongue or floor of mouth involved. Additionally, the multivariable logistic regression analysis revealed that independent risk factor of PVL were sex, number of lesion sites, clinical presentation, smoker, and OED ( ). Based on the combined model, we developed an intuitive, simple-to-use nomogram for individual risk prediction of PVL ( ). Hosmer–Lemeshow test indicated a great calibration performance ( P = 0.628). ROC analysis revealed that AUC value of the nomogram reached 0.945, indicating that this model has excellent discriminant ability ( A). The calibration plot revealed good predictive accuracy between the actual probability and predicted probability ( B). In addition, DCA showed that the nomogram model is effective in clinical practice ( C). To further validate the model, ROC analysis of the nomogram revealed that AUC reached 0.898 and 0.931 in the validation set and testing set respectively, suggesting that the nomogram has excellent accuracy. Calibration curve and DCA were employed to verify the effectiveness of nomogram model ( D–I). Prognostic nomogram model for PVL As shown in , a total of 170 PVL patients was utilized to analyze the prognostic factor. univariable logistic analysis on 9 potential factors, and the result determined PVL malignant transformation related variables, including sex, clinical presentation, local irritants and OED. Additionally, the multivariable logistic regression analysis revealed that independent predictors of PVL were sex, clinical presentation, local irritants and OED ( ). Based on the independent predictors in the training cohort, we constructed a predictive nomogram model of PVL ( ). Hosmer–Lemeshow test indicated a great calibration performance ( P = 0.796). ROC analysis revealed that AUC value of the nomogram reached 0.893, indicating that this model has preferable discriminant ability ( A). The calibration plot revealed good predictive accuracy between the actual probability and predicted probability ( B). In addition, DCA showed that the nomogram model was effective in clinical practice ( C). To further validate the model, ROC analysis of the nomogram revealed that AUC reached 0.723 in the validation cohort. Calibration curve and DCA were employed to verify the effectiveness of nomogram model training cohort ( D–F). 

A total of 467 patients were enrolled in this study with oral leukoplakia (OLK) confirmed by two pathologists, including 207 males and 260 females. The age ranged from 13 to 88 years, with a mean of 55 years. In these OLK patients, 170 cases were diagnosed with PVL according the criteria. As shown in , patients with PVL were predominantly female (81.76%), non-smokers (95.29%), and non-alcohol users (94.71%). They also had a higher likelihood of presenting with multifocal lesions initially (73.53%). Additionally, OED was observed in 53.53% of patients with PVL. Lesions located on the ventral tongue or floor of the mouth were more commonly observed in this patient population ( ). In the diagnostic nomogram model, all samples were divided into training set (n = 280), validation set (n = 93) and testing set (n = 94) according to the 6:2:2 random allocation principle. And in the prognostic nomogram model, PVL samples were divided into a training set (n = 118), and validation set (n = 52). The details of cohort inclusion were shown in . 

Univariable logistic analysis on 9 potential factors, and the result determined PVL-related risk factors, including sex, number of lesion sites, clinical presentation, smoker, alcohol use, OED and ventral tongue or floor of mouth involved. Additionally, the multivariable logistic regression analysis revealed that independent risk factor of PVL were sex, number of lesion sites, clinical presentation, smoker, and OED ( ). Based on the combined model, we developed an intuitive, simple-to-use nomogram for individual risk prediction of PVL ( ). Hosmer–Lemeshow test indicated a great calibration performance ( P = 0.628). ROC analysis revealed that AUC value of the nomogram reached 0.945, indicating that this model has excellent discriminant ability ( A). The calibration plot revealed good predictive accuracy between the actual probability and predicted probability ( B). In addition, DCA showed that the nomogram model is effective in clinical practice ( C). To further validate the model, ROC analysis of the nomogram revealed that AUC reached 0.898 and 0.931 in the validation set and testing set respectively, suggesting that the nomogram has excellent accuracy. Calibration curve and DCA were employed to verify the effectiveness of nomogram model ( D–I). 

As shown in , a total of 170 PVL patients was utilized to analyze the prognostic factor. univariable logistic analysis on 9 potential factors, and the result determined PVL malignant transformation related variables, including sex, clinical presentation, local irritants and OED. Additionally, the multivariable logistic regression analysis revealed that independent predictors of PVL were sex, clinical presentation, local irritants and OED ( ). Based on the independent predictors in the training cohort, we constructed a predictive nomogram model of PVL ( ). Hosmer–Lemeshow test indicated a great calibration performance ( P = 0.796). ROC analysis revealed that AUC value of the nomogram reached 0.893, indicating that this model has preferable discriminant ability ( A). The calibration plot revealed good predictive accuracy between the actual probability and predicted probability ( B). In addition, DCA showed that the nomogram model was effective in clinical practice ( C). To further validate the model, ROC analysis of the nomogram revealed that AUC reached 0.723 in the validation cohort. Calibration curve and DCA were employed to verify the effectiveness of nomogram model training cohort ( D–F). 

PVL typically exhibits a slow and persistent progression over an extended period, followed by sudden rapid growth or malignant transformation. Hyperkeratosis without dysplasia is the most common histopathological diagnosis during the initial visit. These making it challenging to provide an accurate diagnosis during the first clinical visit. However, PVL is acknowledged as an aggressive lesion with frequent recurrences, and nearly 50% of cases undergo malignant transformation, primarily towards OSCC. Therefore, early diagnosis and prognosis assessment are critical challenges that need to be addressed. According to the existing diagnostic criteria, we adopted the criteria proposed by Cerero-Lapiedra et al. as the inclusion criteria for this study. More than two different oral sites involved and the existence of a verrucous area were included in the criteria. As shown in this study, 101 cases of PVL (59.41%) presented as homogeneous leukoplakia without a verrucous area during the initial visit. This highlights the complexity of the clinical presentation of PVL, emphasizing the need to consider multiple factors in the diagnostic and predictive models for this condition. Unlike COL, PVL appears to have no clear association with factors such as smoking or alcohol consumption. The analysis conducted in this study revealed no specific relationship between tobacco or alcohol consumption and PVL. Among the PVL patients, 162 cases (95.29%) were non-smokers, and there were 87 cases (92.55%) of non-smokers among the PVL malignant transformation cases. However, local irritants, such as faulty restorations, residual roots, sharp tooth cusps, dental appliances, silver amalgam, were identified as prognostic factors for malignant transformation in PVL ( P < 0.05). Previous case reports have shown that PVL is more commonly observed in elderly female. Initially, PVL lesions manifest unifocal, homogeneous, slowly and persistent growth lesion, these lesions tend to become multifocal and may exhibit an exophytic, verrucous, or erythematous area. , In this study, according to the incidence population, elderly female (median age was 57 years) is a risk factor for the diagnosis of PVL, but the male patients are more likely to suffer malignant transformation ( P < 0.05). The lesion presented non-homogeneous clinical manifestation was independent risk factor and prognostic factor for malignant transformation of PVL. In addition, multifocal lesion was a risk factor for the diagnosis of PVL, but was not associated with its malignant outcome. The histological morphological appearances of PVL are varied, ranging from simple hyperplasia, hyperkeratosis, epithelial dysplasia, verrucous carcinoma, carcinoma in situ, to squamous cell carcinoma, but lacking specific manifestations. In 2021, American Academy of Oral and Maxillofacial Pathology and the North American Society of Head and Neck Pathologists published consensus guidelines on the histological characteristics of PVL, including three categories of lesions within PVL for standardized reporting as following: (1) ‘corrugated ortho(para)hyperkeratotic lesion, not reactive; ’ (2) ‘bulky hyperkeratotic epithelial proliferation, not reactive; ’ and (3) ‘suspicious for,’ or ‘squamous cell carcinoma’. In the 5th edition of the WHO Classification of Head and Neck tumors, the newly added architectural and cytological features of OED, such as premature keratinization, sharp lateral margins, skip keratoses, and increased keratin, were demonstrated in the early lesions of PVL. However, due to the limited number of cases, no feature as independent risk factor associated with PVL has been found. Moreover, there is considerable interobserver variation among pathologists in the grading of epithelial dysplasia of OPMD, , especially inter-observer variability in the diagnosis of PVL. In this study, cases were classified according to the presence or absence of epithelial dysplasia, and multivariate regression analysis was performed. The results revealed that the presence of OED, regardless of the degree of dysplasia, was a risk factor for early diagnosis of PVL. Additionally, it was identified as an independent prognostic factor for its malignant transformation ( P < 0.05). The etiology of PVL remains unknown. Single-cell RNA sequencing of a histopathological continuum of PVL progression reveals that cellular and microenvironment remodeling, including immunosuppressive phenotypes, angiogenesis, and stromal fibrosis, are involved in the process of malignant transformation. Furthermore, DNA methylation could potentially serve as a regulatory mechanism in the PVL progression. Researchers have focused on investigating molecular markers as potential predictive biomarker in early stages of PVL. Studies have explored the relationship between P53, Ki-67, Mcm-2, and Mcm-5 and PVL. It has been suggested that high expression of Mcm-2 and Mcm-5 in cases of mild and moderate dysplasia could potentially serve as helpful indicators to predict the malignant transformation of PVL. Aberrations in p16INK4a and p14ARF have been observed in 45% of PVL patients, while aneuploidy and TERT promoter mutations have shown promise as new biomarkers for PVL, , , However, further validation is needed, as no biomarkers have been fully validated for PVL currently. In this study, independent risk factors and prognostic factors of PVL were analyzed by multivariate logistic regression, and a visualization prediction model was established by nomogram. And some limitations should be considered. Firstly, owing to the low incidence of PVL, the study included a limited sample size, however, this is the largest sample size compared to previous studies. Secondly, the study included a demographic group from only one institution. Due to the unclearly histological definition and unified diagnostic criteria, it is difficult to conduct multicenter studies because of the heterogeneity among different investigators. Thirdly, nomograms were constructed in a retrospective manner, uncertainty might arise when the models are being applied clinically. Thus, we adopted DCA to help evaluate the model effectiveness and determine whether the model is applicable in the situation. In spite of the limitations, the nomogram models showed satisfactory predictive value not only in training and validation cohort but also in test cohort, which is more helpful for early diagnosis and precise treatment. In addition, Due to the complex etiology and variable course of PVL, it is still necessary for clinicians and pathologists’ collaboration to enhance understanding and management of the disease. In summary, we identified the risk and prognostic factors of PVL based on univariate and multivariate logistic regression analysis, and establishing early diagnosis and prognosis prediction models. As diagnostic decision support tools, they are expected to provide support to clinicians and patients, improve the accuracy of diagnosis and medical outcomes for patients. 

During the preparation of this work the authors used ChatGPT in order to improve readability and make the language expression more in line with the native English expression habits. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. 

The authors have no conflicts of interest relevant to this article. 

