Efficacy of Autofluorescence visualization devices in early detection of malignant transformation in Oral Potentially Malignant Disorders (OPMDs): A Systematic Review and Meta-Analysis

doi:10.21203/rs.3.rs-8471378/v1

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Systematic Review

Efficacy of Autofluorescence visualization devices in early detection of malignant transformation in Oral Potentially Malignant Disorders (OPMDs): A Systematic Review and Meta-Analysis

https://doi.org/10.21203/rs.3.rs-8471378/v1

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Background: Autofluorescence visualisation devices have emerged as promising non-invasive adjuncts to conventional oral examinations to identify subtle tissue changes indicative of dysplasia or malignancy. However, their true diagnostic efficacy in detecting malignant transformation in Oral potentially malignant disorders remains debated.

Methods: A search was conducted in PubMed, Scopus, and Web of Science using terms like "oral potentially malignant disorders", "autofluorescence" and "diagnostic accuracy". Diagnostic studies evaluating autofluorescence devices for early detection of OPMDs, with histopathology as the reference standard, were included. Meta-analysis was performed using a random-effects model to estimate pooled sensitivity, specificity, and diagnostic odds ratio (DOR), with heterogeneity assessed by the I² statistic.

Results: Nine diagnostic accuracy studies comprising 1,262 patients were included. The pooled sensitivity of autofluorescence devices was 55.6% (95% CI: 34.6%–74.8%) and pooled specificity was 47.7% (95% CI: 29.2%–66.8%). The diagnostic odds ratio (DOR) was 1.77 (95% CI: 1.04–3.47), with substantial heterogeneity across studies (I² = 78.9%), reflecting inconsistency in diagnostic performance due to differences in lesion types, device models, examiner expertise, and patient demographic.

Conclusion: Autofluorescence visualisation devices offer modest diagnostic value and should be considered as adjuncts, not replacements to conventional oral examinations and histopathological evaluation.

Dentistry

Oncology

Autofluorescence

Oral Potentially Malignant Disorders

Malignant Transformation

Early Detection

Visualization Devices

Oral cancer has been found to be a significant global health burden and has been characterised by high morbidity and mortality rates, especially in cases of late presentation or diagnosis at advanced stages.^1,2 González-Moles et al. (2022) opined that despite advancements in treatment modalities, there is no improvement in the 5-year survival rate for oral cancer, highlighting the urgent need for early detection strategies.³ A group of seemingly harmless lesions like oral leukoplakia, erythroplakia, oral submucous fibrosis, and lichen planus have been found to sometimes undergo malignant transformations.⁴ These lesions are collectively called oral potentially malignant disorders (OPMDs). It is therefore important to carry out regular surveillance and timely intervention for OPMDs in a bid to improve patient outcomes and reduce the devastating impact of oral malignancies, particularly oral squamous cell carcinoma (OSCC).^4,5

Kumari et al. (2022) suggest that the change of OPMDs into cancer is a complicated process that happens when harmful changes build up in a cell’s genes. These changes cause the cells to grow out of control and spread into nearby tissues.^6,7 These transformations can be subtle and challenging to identify during conventional oral examinations. The routine diagnostic approaches are heavily reliant on visual inspection and palpation of the lesion.⁸ In addition, incisional biopsy and histopathological examination, the gold standard, are done to confirm the diagnosis.^9,10 Studies like Rao et al. (2020) noted that this approach is invasive and requires specialised surgical skill. In the same vein, Biggar (2009) argued that there could be a mistake in the collection of the tissue for histologic examination, potentially leading to missed diagnosis or delayed intervention in early-stage lesions.^8,11,12

To address these limitations, Xu et al. (2022) reported that some diagnostic tools have been developed to enhance early detection of malignant transformation in OPMDs, especially with minimal invasion; among these tools are autofluorescence visualisation devices.^13,14 These devices have been found to operate on the principle of tissue autofluorescence, where healthy tissues within the oral mucosa emit light at specific wavelengths when excited by a light source.¹⁵ However, in tissues that are undergoing or have undergone malignant transformation, there is a reduction in autofluorescence. The tissues appear as a dark area.^15,16 These dark areas can then guide the clinicians on possible malignant transformation and further investigation with a biopsy.

Mascitti et al. (2018) stated that autofluorescence visualisation devices like VELscope and Identafi have gained wide popularity in clinical practice and research.^17,18 They offer the advantage of being non-invasive and relatively easy to use; however, their true efficacy in the early detection of malignant transformation in OPMDs remains a subject of ongoing investigation and debate.¹⁹ Existing studies have reported varying sensitivities and specificities; this has led to inconsistencies in recommendations for their routine clinical application. Therefore, a synthesis of the existing evidence is needed to ascertain the true diagnostic accuracy and clinical utility of autofluorescence visualisation devices in identifying malignant transformation within OPMDs.

This study aims to evaluate the current body of literature by focusing on studies that assess the diagnostic performance of autofluorescence visualisation devices against histopathological diagnosis as the gold standard. It looks to provide a robust and evidence-based answer to the question of whether autofluorescence visualisation devices can significantly improve the early detection of malignant transformation in OPMDs. Thereby informing clinical guidelines and enhancing patient care in the fight against oral cancer.

2.1 Study Design

This study was reported as per PRISMA 2020 checklist for diagnostic test accuracy (DTA) studies. The review was registered in PROSPERO (CRD42025648118) (Supplementary file.1). The objective was to evaluate the diagnostic performance of autofluorescence visualization devices in detecting oral cancer in OPMDs.

2.2 Research question

How accurate are autofluorescence visualization devices in detecting OSCC in patients with OPMDs compared to histopathological examination?

Population (P)

Patients with oral potentially malignant disorders (leukoplakia, erythroplakia, OSMF, etc.)

Index test

Autofluorescence technique

Reference test

Histopathology

Outcome (O)

Sensitivity, specificity, diagnostic odds ratio for detection of OSCC

2.3 Search Strategy

The search was carried out in the PubMed, Scopus, and Web of Science, covering literature published up to 29 Feb 2025. The search strategy (Appendix-1,Table.1) was curated using combinations of Medical Subject Headings (MeSH), free-text terms, and synonyms which included "oral potentially malignant disorders", "OPMD", "autofluorescence", "VELscope", "diagnostic accuracy", "oral cancer", "early detection", "sensitivity", "specificity".

2.4 Eligibility Criteria

Inclusion criteria were diagnostic studies evaluating autofluorescence devices (e.g., VELscope) for early detection of OPMDs with histopathology as the reference standard, Studies reporting sensitivity and specificity, or providing sufficient data to construct a 2×2 contingency table (TP, FP, FN, TN). Exclusion criteria included Case reports, reviews, editorials, animal studies, or in vitro studies, studies not using histopathological confirmation, and studies using other adjunctive diagnostic aids without autofluorescence.

2.5 Screening and Selection

The records were exported in Rayyan software [20]. The duplicates were removed. The remaining records were initially screened by reading title, abstract, and keywords. After removing the non-relevant records, full-text of records were retrieved and assessed for eligibility based on eligibility criteria. In case of any discrepancy, a third reviewer was contacted and discrepancies were resolved with discussion. Final included records taken for data extraction.

2.6 Data Extraction

Two reviewers independently screened titles, abstracts, and full texts. Data extracted included: Author, year, country, sample size, autofluorescence test or technique including device used, Reference standard (Clinical oral examination or histopathology), True positives (TP), false positives (FP), false negatives (FN), true negatives (TN), Reported sensitivity, specificity, and area under the curve (AUC), if available. Further, required data was extrapolated in studies where there is limited data, using the available information in text in articles. Discrepancies were resolved through discussion or by consulting a third reviewer.

2.7 Risk of Bias Assessment

The quality and risk of bias of the included DTA studies were evaluated independently by two reviewers using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool. The domains assessed included: 1. Patient selection, 2. Index test (autofluorescence), 3. Reference standard (histopathology), 4. Flow and timing. Each domain was rated as “low,” “high,” or “unclear” risk of bias. Four of the studies were rated as having a low risk of bias while 5 of the included studies achieved an uncertain overall risk of bias.

2.8 Summary Measures and Statistical Analysis

Meta-analysis was performed using the random-effects model due to anticipated clinical and methodological heterogeneity. Sensitivity, Specificity, Diagnostic Odds Ratio (DOR), Positive and Negative Likelihood Ratios (PLR, NLR) were estimated. Logit transformation was applied to sensitivity and specificity to stabilize variance. Standard errors were computed for each estimate based on reconstructed 2×2 tables using reported sensitivity, specificity, and sample sizes. Heterogeneity was assessed using the I² statistic, and sources of heterogeneity were explored descriptively or via subgroup analysis if data permitted. All analysis were performed using the metafor and mada packages in R-Studio.

3.1 Search results

A literature search was conducted in Scopus (n = 25), PubMed (n = 12), and Web of Science (n = 13), yielding a total of 50 diagnostic test accuracy (DTA) studies (Figure.1). After removing 21 duplicate DTAs, 29 DTAs selected for screening. During the screening phase, 24 DTA studies were excluded after reading titles, abstracts, and keywords. The remaining 5 DTA studies were sought for full-text retrieval. After full-text assessment, all 5 records met eligibility criteria and were included in the review. In addition, 11 DTA studies were identified through citation and manual search. Of these, 4 studies were eligible for full-text review, and 7 studies were excluded due to lack of relevant data. All 4 eligible studies were included in the final synthesis. In total, 9 DTA studies were included in the systematic review and meta-analysis for qualitative and quantitative synthesis.

3.2 Characteristics of studies

Following a thorough literature search and review, individual study characteristics (author, year of publication, study design, sample size, population, location, gender, habit history, follow-up duration, index test details, primary findings, and study limitations) of the included studies were extracted (Supplementary file 2). A total of nine studies met our predefined criteria, including two clinical trials (CTs) ^21,22, one pilot cohort study²³, one prospective observational study²⁴ two retrospective studies^25,26, and three cross-sectional studies^27–29. The total patient population ranged from 32 in the prospective observational study ⁴ to 517 in the cohort study ²³, totalling 1,262 participants. Autofluorescence interventions ranged from four months to forty-four months. Studies were performed in individuals aged 17 years or older. Only a few studies conducted a follow-up period that ranged from 12 months to 5 years. Three of these studies were conducted in Italy^22,24,26, two in China^23,25, two in India^28,29, one in Germany²¹, and one in Iran²⁷. Appendix-1- Table 2 further provides meta-analytic details extracted from the included studies.

3.3 Clinical Presentations

Lajolo et al. classified lesions based on clinical presentations, including lichenoid, erythroplakia (red patches), leukoplakia (white patches), erythroleukoplakia (red and white patches), and verrucous leukoplakia.²⁶ Amirchaghmaghi et al. conducted a cross-sectional study, categorizing clinical presentations as positive or negative conventional oral cavity examinations (COE+/COE-).²⁷ COE + lesions were those with exophytic (mass-forming polypoid or verruciform and papillary lesions), endophytic (crater-like and destructive ulcers), leukoplakia-like features, erythroplakia-like features, and erythroleukoplakia-like features. In addition to being categorized as COE+, lesions must also exhibit dysplasia signs, such as stiffness and induration on palpation, surface roughness, non-healing ulceration, and a progressive course of growth.²⁷ All the studies in this review had predefined inclusion criteria combining all or some of the criteria above with important background data such as history of alcohol/tobacco smoking/areca nut chewing and surgical/radiotherapeutic interventions. ^23,29

3.4. Quality Assessment

Among the nine studies assessed (Supplementary file 3), three (Li et al., Hanken et al., Wang et al.) had low risk across all domains.^21,23,25 The remaining studies exhibited varying degrees of bias, most commonly in patient selection and reference standard domains. Notably, two studies (Moro et al., Moro A et al.)^22,24 showed high risk in patient selection, and Lajolo et al. had high bias in flow/timing.²⁶ Overall, while a subset of studies maintained methodological rigor, several presented with unclear or high-risk elements that may influence pooled estimates and should be interpreted with caution.

3.5 Diagnostic Approaches and Findings

The explored studies included the different market available fluorescence products such as ViziLite (Visual Light enhanced diagnostic test)²⁸, GOCCLES (Glasses for Oral Cancer Curing Light)^22,24,26 and VELscope (Visually Enhanced Lesion scope)^21,3,5,7,9 thus ensuring unique perspectives on outcomes of autofluorescence techniques across various clinical settings. All the diagnostic interventional studies combined autofluorescence interventions with conventional oral examinations (COE) or white light ^21,29, and histopathological biopsy results as standard reference points.^21–29

In a cross-sectional comparative study involving 150 patients, Jain et al. evaluated the diagnostic performance of VELscope autofluorescence versus conventional white light examination in detecting oral lesions. The VELscope demonstrated a significantly higher diagnostic accuracy of 95% (95% CI: 90%–98%), compared to 75.7% (95% CI: 67.8%–82.6%) achieved with white light examination. Similarly, Hanken et al also showed higher sensitivity, 97.9% (CI: 94%-100%) in the VELscope group compared to the conventional white light group with 75.9% (65%-87%).²¹

Li et al also utilized VELscope in an extensive prospective cohort study. They achieved a 15%-47% positive predictive value (PPV) in detecting malignant transformation via autofluorescence in oral lichenoid lesions over five years [23]. Additionally, there was a nine-time (9x) risk of malignant transformation with a loss of autofluorescence (LAF) compared with retention of autofluorescence (RAF) (HR 9.18, CI: 1.22–68.79).²³ Lajolo et al, in a retrospective analysis of 24 patients with oral potentially malignant disorders, reported that GOCCLES was better (75%-100% accuracy) at identifying dysplastic areas in red/erythroplakic lesions as compared to proliferative verrucous lesions (0% accuracy).²⁶ Moro et al (2015) reported similar findings after using GOCCLES attached to different curing light sources (LED, Elipar, Optilux) in a non-randomized multicenter clinical trial of 61 patients.²² It was reported that the specificity of GOCCLES in detecting low-risk lesions is higher than in detecting high-risk lesions. Additionally, the three different light sources showed no significant differences (p-value 0.488).² Wang et al (2022), using VELscope for a retrospective analysis of 59 potentially malignant disorders, also reported a higher specificity (82.1%) for low-risk lesions compared to (50.0%) for high-risk lesions.²⁵

3.6 Diagnostic efficacy of Autoflourences in detecting the malignancy or malignant transformation in OPMDs.

The sensitivity meta-analysis reveals the variation across included studies in detecting OSCC, with a wide prediction interval (7.8%–94.9%). The pooled sensitivity is 55.6% (95% CI: 34.6% − 74.8%) (Figure.2), indicating that the autofluorescence technique accurately diagnosed slightly more than 50% of OSCC cases in OPMD. Furthermore, the high heterogeneity (I² = 78.9%) suggests substantial differences in the sensitivity estimates between studies, indicating that the diagnostic performance of autofluorescence may be inconsistent and unreliable. Similarly, the specificity meta-analysis also shows variation in correctly diagnosing OPMDs without OSCC. The pooled specificity is 47.7% (95% CI: 29.2%–66.8%) with a wide prediction interval (7.9%–90.6%), suggesting that the autofluorescence correctly rules out OSCC in only 50% (Figure.3). The diagnostic odds ratio (DOR) analysis yielded a value of 1.77 (95% CI: 1.04–3.47), indicating low diagnostic effectiveness of autofluorescence (Figure.4). While the autofluorescence technique shows limited ability to detect OSCC in OPMD, its poor specificity and low DOR suggest that it is not reliable as a standalone diagnostic tool.

This systematic review provides a synthesis of autofluorescence visualization devices in the early detection of malignant transformation in oral premalignant and malignant lesions. Several histopathological changes (hallmarks) have been described in the literature for an oral lesion to become malignant. These changes, such as, the acquisition of sustained proliferative signalling, evasion of growth suppressor cells, resistance to cell death, replicative immortality (immortalization), induction of angiogenesis, activation of tissue invasion and metastasis, deregulation of cellular energy metabolism, evasion of immune destruction are difficult/impossible to accurately detect with conventional oral examinations through the naked eye.^30–32

Over the past few decades, different biotechnological companies have developed devices (diagnostic tools) that claim to detect the histological changes that occur during carcinogenesis.^33–38 When these changes occur, they appear as a LOF or as a dark view during the autofluorescence examination.^36,38 Multiple studies have investigated the diagnostic value of using these diagnostic devices to assist in adjunctive diagnosis of oral cancers from OPMDs.^{33–35,37,39} Despite the technological promise of autofluorescence-based adjunctive diagnostics, our findings suggest that their overall clinical reliability is limited.

From our pooled meta-analysis, the overall sensitivity of autofluorescence devices was estimated at 55.6% (95% CI: 34.6%–74.8%), indicating that almost half of truly malignant or dysplastic lesions could be missed using these tools alone. Similarly, the pooled specificity was 47.7% (95% CI: 29.2%–66.8%), suggesting a mid ability to identify those without the disease, which may lead to unnecessary biopsies, anxiety, and overtreatment. The diagnostic odds ratio (DOR) of 1.77 (95% CI: 1.04–3.47) underscores the modest discriminative power of these tools when compared to the gold standard of histopathology. Additionally, variations in lesion types (e.g., erythroplakia, leukoplakia, verrucous leukoplakia), examiner expertise, device type, light source, and patient demographics (e.g., habits such as tobacco or areca nut chewing) likely contributed to the high heterogeneity (I² = 78.9%) observed across studies. This underlines the need for standardized protocols in clinical assessment and interpretation of autofluorescence findings.

These results align with the current consensus in the literature that autofluorescence tools should not be used in isolation but rather serve as adjuncts to conventional oral examination (COE).^{34–36, 38–40} For instance, the study by Jain et al. highlighted that while autofluorescence showed improved diagnostic accuracy over white light examination (95% vs. 75.7%)²⁹, the broad variation in sensitivity and specificity among included studies, as evidenced by the wide prediction intervals, suggests that diagnostic performance is highly context-dependent.

The inaccuracies associated with using autofluorescence are described in the literature for lesions with hyperkeratosis or proliferative growths, which can result from additional cellular layers (e.g., keratin).^26,41 These additional cellular layers can cause an increase in fluorescence that may conceal dysplastic and/or neoplastic areas, leading to a masked or umbrella effect.^42,43 While advancements and alternative autofluorescence tools have emerged to address these shortcomings, diagnostic energy sources must ideally operate within a specific wavelength range (400–460 nm) to ensure optimal interaction with oral tissues.⁴⁴ While some studies have utilized devices with higher wavelength filters (up to 525nm)^44,45, deviations from this range risk inducing oxidative stress and potential cellular damage, underscoring the need for both spectral precision and biological safety in the development of next-generation diagnostic adjuncts.^25,45,46

Despite the methodological quality of this systematic review and meta-analysis, it is not without limitations. There is considerable heterogeneity among the studies due to differences in study designs, sample sizes, autofluorescence devices, and variations in population groups, which may limit the generalizability of the findings. Additionally, we did not account for the subjective nature of autofluorescence interpretation, which may lead to operator-dependent variabilities.

Autofluorescence tools offer a non-invasive and rapid adjunct to conventional oral examination, however, our systematic review reports a modest pooled sensitivity (55.6%) and specificity (47.7%), alongside a minimal diagnostic odds ratio (1.77), indicating that they cannot be relied upon as standalone diagnostic modalities.. Significant heterogeneity across studies stemming from variability in device types, lesion characteristics, examiner expertise, and patient habits further diminishes their clinical consistency and generalizability. Despite these limitations, autofluorescence may still play a supportive role in the screening process, particularly in guiding biopsy site selection and raising clinical suspicion in ambiguous cases. However, its use must be integrated with conventional oral examination and confirmed by histopathological assessment to ensure diagnostic accuracy and optimal patient outcomes.

We call for further research on developing standardized protocols. Additionally, there is a need to enhance current autofluorescent models, by improving their ability to distinguish proliferative non-cancerous lesions from oral cancers. Until such advancements are achieved, clinicians are advised to exercise caution in interpreting autofluorescence findings and to avoid substituting it for gold-standard diagnostic approaches.

OPMDs: Oral Potentially Malignant Disorders
QUADAS-2: Quality Assessment of Diagnostic Accuracy Studies-2
DOR: Diagnostic Odds Ratio
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
CTs: Clinical Trials
VELscope: Visually Enhanced Lesion scope
ViziLite: Visual Light enhanced diagnostic test)
GOCCLES: Glasses for Oral Cancer Curing Light
LAF: Loss of AutoFluorescence
RAF: Retention of AutoFluorescence
OR: Odds Ratio
TP: True Positive
FP: False Positive
FN: False Negative
TN: True Negative
PPV: Positive Predictive Value
COE: Conventional Oral Examination
AUC: Area Under the Curve
PLR: Positive Likelihood Ratio
NLR: Negative Likelihood Ratio
DTA: Diagnostic Test Accuracy

Acknowledgments

Not Applicable

Declaration of conflicting 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.

Funding statement

Authors did not receive any funding for this work

Ethical approval and informed consent statements

Not Applicable

Data availability statement

All data generated or analysed during this study are included in this published article and its supplementary information files.

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The authors declare no competing interests.

Appendix1.docx
Appendix-1 Legends · Table 1-Search strategy for each database searched · Table 2- Meta-analysis of data extracted from included studies
Supplementaryfile1.docx
Supplementary file 1: PRISMA checklist
Supplementaryfile2.docx
Supplementary file 2: Characteristics of included studies
Supplementaryfile3.docx
Supplementary file 3: QUADAS-2 assessment of included studies

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Efficacy of Autofluorescence visualization devices in early detection of malignant transformation in Oral Potentially Malignant Disorders (OPMDs): A Systematic Review and Meta-Analysis

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Abstract

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1. Introduction

2. Methods

2.1 Study Design

2.2 Research question

2.3 Search Strategy

2.4 Eligibility Criteria

2.5 Screening and Selection

2.6 Data Extraction

2.7 Risk of Bias Assessment

2.8 Summary Measures and Statistical Analysis

3. Results

3.1 Search results

3.2 Characteristics of studies

3.3 Clinical Presentations

3.4. Quality Assessment

3.5 Diagnostic Approaches and Findings

3.6 Diagnostic efficacy of Autoflourences in detecting the malignancy or malignant transformation in OPMDs.

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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