New Insights for Consummate Diagnosis and Management of Oral Submucous Fibrosis Using Reactive and Reparative Fibrotic Parameter Derived Algorithm.

OBJECTIVE: Reproducibility of qualitative changes in histopathological diagnosis involving narrow variation is often challenging. This study aims to characterize the histological fibrotic events in detail so as to derive an in-depth multiparametric algorithm with individually quantified histological parameters for effective monitoring of the. disease process in oral submucous fibrosis and for potential therapeutic targets for early intervention.
METHODS: Formalin fixed paraffin embedded (FFPE) blocks of oral submucous fibrosis (OSMF), were taken and sections were stained with Hematoxylin & Eosin stain and Masson Trichrome stain. Photomicrographs were assessed for various morphometric parameters with Image J software version 1.8. Linear Regression was used to model the relationship using Inflammatory Cell Count, Extent of Inflammation collagen stained area, Epithelial thickness integrated density of collagen, MVPA, Area, Perimeter, were taken as variables. RESULT: Inflammatory cell count and the extent of inflammation also decreased with increasing grades of OSMF. Collagen proportionate area, integrated collagen density and epithelial thickness were compared among different grades of OSMF. Grade IV OSMF had greatest mean collagen proportionate area , highest integrated collagen density and lowest epithelial thickness when compared to other grades of OSMF. Linear regression model revealed smaller variation between Grade I to Grade II. Whereas Grade II to Grade IV exhibited larger variation suggestive of increased growth rate and all the coefficients were found to lie within 95% confidence limits.
CONCLUSION: Diagnostic algorithm with multiparametric regression model were derived and combinatorial therapeutic approaches have been suggested for more effective management of oral submucous fibrosis. Copyright:

INTRODUCTION

Fibrosis is characterized by excess deposition of extracellular core matrisome predominantly collagen.[1] The event of fibrosis is due to an array of chronic inflammatory reactions instigated by a conglomerate of stimuli which includes incessant infections, autoimmune reactions, allergic responses, chemical insults, radiation, and tissue injury.[2] Fibrotic diseases involve wide spectra of organ systems and cause debilitating diseases such as idiopathic pulmonary fibrosis, liver cirrhosis, systemic sclerosis, progressive kidney disease, and cardiovascular fibrosis.[3]

Oral submucous fibrosis (OSF) is a chronic progressive potentially malignant disorder. OSF has a prevalence rate of 0.03%–6.42% with high malignant transformation rate of 7.6%.[4] Etiological factors reported till date are areca nut (AN), capsaicin; Vitamin A, B12, folate, and iron deficiency; defective iron metabolism, high copper content, tobacco, altered genetic, and immunologic processes.[5] Arecoline alkaloid in AN has been reported to be the chief etiological factor causing OSF.[6]

The core pathogenetic event behind OSF is irreversible fibrosis. Fibrosis occurring in OSF is due to defective collagen homeostasis.[7] A decrease in collagen clearance is due to stabilization of collagen, defect in the extracellular matrix (ECM) dynamics, and inhibition of phagocytosis.[8] Collagen Types I and IV and procollagen Type III are the predominant types of collagen present in the ECM of OSF.[9] Differential presence of ECM components in various stages of OSF reveals that tenascin, perlecan, fibronectin, and collagen Type III were common in early stages; elastin in the intermediate stage; and Type I collagen in the advanced stage.[10]

Fibrosis in other organ systems is characterized by reactive or reparative processes that result in the deposition of excess collagen leading to sequential alteration in tissue architecture.[10] Fibrogenic responses encompass the same fundamental mechanisms in all organ systems. Histological segregation of fibrotic events into reactive and reparative has been helpful in the derivation of better treatment protocols. Reactive fibrosis has been proven to be reversible, whereas reparative is irreversible. This phenomenon has been effectively applied in imaging of fibrotic events in vital organs.

This simple histological segmentation could help in defining better treatment algorithms for OSF too. Histopathological grading of OSF encompasses parameters such as epithelial alterations, rete-ridge shapes, subepithelial deposition of dense bands of collagen fibers. The fibrotic event has usually been described as a continuum in OSF. Despite the availability of in-depth molecular evaluation in literature, there has been no sequential analysis of fibrotic events, unlike other fibrotic diseases.

Routine histopathological diagnosis involves pattern recognition of the qualitative pathological changes by the observer. Reproducibility of qualitative changes in situations involving narrow variation is often challenging. This study aims to characterize the histological fibrotic events in OSF so as to derive an in-depth multiparametric algorithm with individually quantified histological parameters for effective monitoring of disease process and derive therapeutic targets for early intervention.

MATERIALS AND METHODS

Formalin-fixed paraffin-embedded blocks of OSF were sectioned to 4 μm thickness and stained with H and E and Masson Trichrome stain. Photomicrographs were captured in three high-power fields. Captured images were standardized to maintain uniform dimensions in pixel. Area of interest was selected and morphometric parameters were digitally analyzed and spatially calibrated with Image J software version 1.8 (National Institute of Health Bethesda, Maryland, United states). A calibrated micrometer image at the same magnification as that of the uncalibrated image was opened and the scale was set by drawing a selection line of a known length on the micrometer image. Further, the unit of measurement was set as μm, and calibration was applied to all the uncalibrated images.

Grading of oral submucous fibrosis

Histopathologic grading was done as proposed by Sirsat and Pindborg.[11]

Inflammatory cell infiltrate

Less than 25 inflammatory cells as mild, 25–125 as moderate, and >125 as severe[12] [Figure 1].

Figure 1

Inflammatory cell counting using cell counter > plug in > Image J

Epithelial thickness

Epithelial thickness was measured in μm, by tracing a line from the most superficial point of the epithelium to the interface between epithelium and connective tissue using a straight line tool. Thickness was measured at three different points [Figure 2] with Analyze >Measure tool.[13]

Figure 2

Epithelial thickness determination using straight line tool >> Image J

Collagen proportionate area

Original images were converted into Red, green, blue (RGB) and deconvoluted using color deconvolution. A threshold tool was used to highlight collagen fibers [Figure 3]. Collagen proportionate area (CPA) was calculated as the proportion of collagen stained area to the total area in the region of interest for each image.[14] “Area” and “Limit to Threshold” checkboxes were selected from the Analyze tool and expressed as %. The integrated density of collagen was measured as the product of area and mean intensity of collagen stain. The integrated density of highlighted area was obtained by selecting the “Integrated density” checkbox from the Analyze tool.[1516]

Figure 3

Thresholded collagen stained area (appears red) using color deconvolution plug in >> Image J

Blood vessel parameters

The three most vascular fields in the subepithelial region were captured and blood vessels traced with the freehand tool to measure perimeter and total area [Figure 4]. The mean blood vessel percentage area was calculated from the total area and blood vessel area in %.[17]

Figure 4

Blood vessel parameters' measurement

Statistical analysis

Data analysis was done using SPSS ® software version 21 (SPSS Software version 21, IBM, Newyork, United states). The mean was calculated for all the parameters. Analysis of variance and Kruskal Wallis tests were used to analyze continuous variables. P ≤ 0.05 was considered statistically significant

Graphs were plotted using MATLAB R2019b (Version 9.7). Linear regression was used to model the relationship between variables. As there was more than one explanatory variable, multiple linear regression was applied. Linear regression models were fitted using the least squares approach to find the best fit for a set of data points by minimizing the sum of offsets or residuals of points from the plotted curve. Best fit in least-squares minimized the sum of squared residuals. Least square regression was used to predict the behavior of dependent variables. Grades I, II, III, and IV were considered as dependent variables. Parameters such as collagen stained area, integrated density of collagen, epithelial thickness, MVPA, area, perimeter, inflammatory cell count, and extent of inflammation were taken as independent variables. As there were eight independent variables, using the method of least squares, a polynomial of degree 6 was considered and n−1 = 7 was found to give the best fit.

RESULTS

Inflammatory cells, predominantly lymphocytes, were diffusely distributed in Grade I and II while they were located beneath the epithelium in Grade III and IV OSF. Few plasma cells were observed in Grade III and IV OSF [Table 1]. A statistically significant result was obtained when the inflammatory cell count was compared between different grades of OSF. Inflammatory cell count and the extent of inflammation also decreased with increasing grades of OSF [Table 2].

Table 1

Comparison of location and type of inflammatory cells among different grades of oral submucous fibrosis

	Location	Lymphocytes	Neutrophils	Eosinophils	Plasma cells
Grade I	Diffuse	3	1	0	0
Grade II	Diffuse	3	0	0	0
Grade III	Subepithelial	3	0	0	1
Grade IV	Subepithelial	3	0	0	1

Table 2

Quantification of inflammatory cell infiltrate among different grades of oral submucous fibrosis

	Inflammatory cell count		Extent of inflammation (μm2)		Inflammatory cell area (%)
	Mean±SD	P	Mean±SD	P	Mean±SD
Grade I	419±54.51	0.0003*	404.4±36.91	0.0004*	1.6±0.56
Grade II	399.88±39.09		392.85±14.40		0.9±3.42
Grade III	218.25±42.99		170±10.80		0.5±2.15
Grade IV	91.75±31.28		33.55±14.85		0.2±0.98
Normal	24.6±10.29		21±6.192		0.1±0.54

*P≤0.05 was considered significant, Kruskal–Wallis test. SD: Standard deviation

CPA, integrated collagen density, and epithelial thickness were compared among different grades of OSF [Table 3]. Grade IV OSF had the greatest mean CPA (66%), highest integrated collagen density (5133565.51 ± 4458223.49 μm2), and lowest epithelial thickness (36.90 ± 9.27 μm) when compared to other grades of OSF. The difference between groups was statistically significant.

Table 3

Comparison of collagen proportionate area, integrated collagen density, and epithelial thickness among different grades of oral submucous fibrosis cases

	Collagen proportionate area (%)		Integrated density of collagen (μm2)	Epithelial thickness (μm)
	Mean±SD	P	Mean±SD	Mean±SD	P
Grade I	52.92±12.36	0.000615**	2,772,056.56±1,015,787.66	163.97±64.22	0.0009*
Grade II	55.99±11.68		2,793,419.55±900,848.011	71.80±17.40
Grade III	62.90±9.856		3,361,771.30±763,835.50	46.27±7.37
Grade IV	65.88±13.84		5,133,565.51±4,458,223.49	36.90±9.27
Normal	29.78±6.93		1,589,827±592,560.57	56.17±10.37

*P≤0.05 was considered significant, Kruskal–Wallis test. **P≤0.05 was considered significant, ANOVA test. SD: Standard deviation

Grade I OSF (4808.85 μm2, 11.41%, 43.68 μm) had the highest MVA, MVPA, and MVP and Grade IV OSF (2099.63 μm2, 1.42%, 13.87 μm) had the lowest values of MVA, MVPA, and MVP. The difference was statistically significant among the groups (P < 0.05) [Table 4].

Table 4

Comparison of mean vascular area, mean vascular percentage area, and mean vascular perimeter among different grades of oral submucous fibrosis cases

	MVA (µm²)		MVPA (%)		MVP (µm)
	Mean±SD	P	Mean±SD	P	Mean±SD	P
Grade I	4808.85±422.61	0.0002*	11.41±0.01	0.0001*	43.68±4.18	0.0002*
Grade II	3485.07±360.50		9.02±0.01		34.84±3.01
Grade III	2793.82±143.92		3.21±0.01		22.60±0.73
Grade IV	2099.63±388.08		1.42±0.01		13.87±4.06
Normal	5176.41±100.55		11.41±0.6		43.68±0.76

*P≤0.05 was considered significant, Kruskal–Wallis test. MVA: Mean vascular area, MVPA: Mean vascular percentage area, MVP: Mean vascular perimeter, SD: Standard deviation

Table 5 represents the mean value of collagen stained area, integrated density of collagen, epithelial thickness, MVPA, MVA, MVP, inflammatory cell count, and extent of inflammation. Table 6 represents the values of grades for different values of x. Table 7 proposes therapeutic approaches based on histopathologic rationale.

Table 5

Mean value of collagen stained area, integrated density of collagen, epithelial thickness, mean vascular percentage area, area, perimeter, inflammatory cell count, and extent of inflammation

Parameters	Mean
	1	2	3	4	5	6	7	8
Grade
Grade I	52.9185	2,772,056.564	163.9681	0.1141	4808.853	43.68	419	404.4
Grade II	55.99275	2,793,419.548	71.79821	0.09025	3485.07	34.83625	399.875	392.85
Grade III	62.906	3,361,771.296	46.27358	0.03225	2793.815	22.6025	218.25	170
Grade IV	65.88375	5,133,565.511	36.89717	0.01425	2099.678	13.87	91.75	33.55

Table 6

Values of grades for different values of x

x	1	2	3	4	5
Grade I	−49,795.57121	58,672.85759	495,609.3	1,470,816	3,194,094
Grade II	−49,769.14241	59,978.71517	500,824.6	1,484,244	3,221,714
Grade III	−60,059.14241	71,882.71517	602,364.6	1,785,936	3,877,148
Grade IV	−91,740.71362	109,786.5728	920,065.9	2,727,903	5,922,104

Table 7

Therapeutic approaches based on histopathologic rationale

Histopathological variables	Grade 1 and Grade 2		Grade 3 and 4
	Score	Therapeutic approaches	Score	Therapeutic approaches
Inflammatory cell count	++++	Topical anti-inflammatory agents (Tariq et al. 2016)	+	Systemic anti-inflammatory agents (Tariq et al. 2016)
Extent of inflammation	++++		++
Epithelial thickness (μm)	++++	Topical immune response modifiers Levamisole–(Jirge et al. 2008) Topical and systemic antioxidants Systemic micronutrients Vitamins A, B, C, D, and E minerals Fe, Zn, Mg (Maher et al. 1987)	++	Intralesional and systemic immune response modifiers Corticosteroids ( Lai et al. 1995) Levamisole (Jirge et al. 2008)
Collagen proportionate area	++	Topical fibrinolytic agents Pirfenidone–(Shesha et al. 2015)	+++	Intralesional and systemic fibrinolytic agents
Integrated density of collagen	++		+++	Hyaluronidase (Singh et al. 2010), collagenase (Lin 2007) Intralesional and systemic fibrinolytic agents Interferon-gamma Colchicine (Krishnamurthy 2013) Physiotherapy (Gupta 1980)
MVA	++++	Topical vasodilators	++	Systemic vasodilators
MVPA (%)	++++	Buflomedil hydrochloride-(Tyagi et al. 2019)	++	Pentoxifylline (Mehrotrra et al. 2011) Nylidrin hydrochloride (Sharma et al. 1987)
MVP (µm)	++++		++	Buflomedial hydrochloride (Lai. 1995) Isoxsuprine (Bhadage et al. 2013)

MVA: Mean vascular area, MVPA: Mean vascular percentage area, MVP: Mean vascular perimeter

Figure 5 represents the comparison of CPA, MVA, and inflammatory cell area among different grades of OSF.

Figure 5

Comparison of area occupied by collagen, blood vessel, and inflammatory cells

Figure 6 represents the sum of squares due to error (SSE) very closer to zero indicating that the fit is best and is suitable for prediction. R2 (coefficient of determination) value = 0.984 indicates 98.4% of the data fit the regression model. In general, a higher R2 value is considered a better fit for the model. Adjusted R2 = 0.8877 closer to 1 indicates that this fit is best. Root mean squared error (RMSE) value indicates how the data are concentrated around the line of best fit. It is an absolute measure of fit.

Figure 6

Linear polynomial of degree 6: Line of best fit y = -9642 x6 + 2.808e + 05 x5 - 3.247e + 06 x4 + 1.886e + 07 x3 - 5.0706e + 07 x2 +8.245e + 07 x - 4.128e + 07

Figure 7 depicts that SSE = 1.1e+11 is very close to zero indicative of the best fit and suitable for prediction. R2 = 0.9839 indicates 98.39% of data fitted the regression model. Adjusted R2 = 0.8872 closer to 1 is indicative of the best fit. RMSE = 3.316e+05 indicates that the line is an absolute measure of fit.

Figure 7

Linear polynomial of degree 6: Line of best fit y = -9712 x6 + 2.828e + 05 x5 – 3.27e+06 x4 + 1.9e + 07 x3 -5.7486e + 07 x2 + 8.306e + 07 x - 4.159e + 07

Figure 8 illustrates SSE = 1.6e+11 very closer to zero indicating that fit is best and suitable for prediction. R2 = 0.9838 indicates 98.38% of data fit the regression model. Adjusted R2 = 0.8867 closer to 1 indicates that this fit is best. RMSE = 4e+05 indicates that this line is an absolute measure of fit.

Figure 8

Linear polynomial of degree 6: Line of best fit y = -2.523e + 06 x6 + 2.186e + 06 x5 + 6.206e + 06 x4 - 5.146e + 06 x3 - 2.191e + 06 x2 + 1.3886e + 06 x + 8.21e + 04

Figure 9 represents SSE = 3.747e+11 close to zero indicating a best fit and suitable for prediction. R2 = 0.9837 indicates 98.37% data fit the regression model. Adjusted R2 = 0.8862 closer to 1 indicates fit is best. RMSE = 6.121e+05 indicates that this line is an absolute measure of fit.

Figure 9

Linear polynomial of degree 6: Line of best fit y = -1.783e + 04 x6 + 5.193e + 05 x5 - 6.005e + 06 x4 + 3.489e + 07 x3 - 1.056e + 08 x2 + 1.526e + 08 x - 7.638e + 07

DISCUSSION

The process of fibrosis is dynamic in nature with sequential events from induction of fibroblasts to differentiation into myofibroblasts. Fibroblasts lead to dysregulated synthesis and secretion of ECM due to modulation of glycoproteins effectuating posttranslational transfiguration and cross linking of ECM proteins. This eventually causes imbalance in ECM production and breakdown by matrix metalloproteinases and inhibitors (TIMPs). These diverse sequences of fibrosis suggest that a manifold of prospects in the derivation of multiple targets or therapeutic opportunities exists and that therapies could be customized and targeted based on the type of etiological agent, frequency of use, coexistent local environmental factors, and specificity of the fibrotic events.

Categorization of the myriad presentation of fibrosis has been attempted differently in different organ systems. Fundamentally fibrotic events can be descriptively phased as reactive and reparative. Despite the fact that this descriptive grouping sounds elementary, it has been well established and validated in other organ fibrosis for effective determination of treatment protocols. It has not been attempted in OSF till date. Challenge in using this categorization in OSF lies in the difficulty to differentiate the reactive and reparative reactions in histopathological sections. Fibrogenesis evolves from being dynamic during the initial phases, followed by becoming static during later stages. Sequential transition into stages is not clearly demarcated both in clinical and histopathological perspectives. Since the identification of the specific and probably dominant type of fibrosis has been proven to generate individualized antifibrotic approaches, the present study aimed to delineate the reactive and reparative events in OSF and to derive an in-depth algorithm based on multiple histological parameters.

Preliminary inflammatory reactions were characterized by infiltration of defense cells in the connective tissue. The sequence of cell ingress occurs in the order of neutrophils, macrophages, plasma cells, and lymphocytes. Aggregation of cellular elements leads to the release of chemical mediators causing damaging effects on ECM resulting in vasodilatation, increased vascular permeability, and hyperplasia. The low-grade chronic inflammatory process keeps persisting in the mucosa depending on the degree of the stimulus.[18] Reactive pathologic events are described with the distribution and type of inflammatory infiltrate, type of fibroblastic proliferation, presence of vascular proliferation, and pattern of gingival lining epithelium.[1920]

AN metabolites and trauma from chewing of AN-related products trigger a cascade of inflammatory process which progresses to fibrotic changes. Key components that trigger inflammation are due to increased alkaloid content in AN.[21] Early inflammation triggered due to AN chewing acts as an initiating factor for fibrosis in OSF.[21] The concerted inflammatory process results in the production of reactive oxygen species which are further activated and enhanced by the presence of Fe2+, Fe3+, and Cu2+ ions.[22] Permeation of the areca alkaloids through the epithelium into the connective tissue has been assessed by diffusion kinetics in the oral mucosal membrane and established that the nonionized form permeates through buccal mucosa more readily than the ionized form.[23] Metabolites were found to permeate through the epithelial layer and caused damage to the plasma membrane by ATP production, to enter into the submucosal region. ATP further generates a chemotactic signal for inflammatory cell recruitment.[24]

Our findings related to the type of inflammatory cells and their distribution in different grades suggested that the predominant inflammatory cells were lymphocytes. The pattern of distribution of inflammatory cells in Grades I and II was diffuse, whereas Grades III and IV exhibited localization of cells in the lamina propria region. Few plasma cells were also observed in Grades III and IV OSF. Further, in our results, inflammatory cell count was found to reduce with increasing grades of OSF. OSF has been reported to exhibit a diverse pattern of inflammation ranging from eosinophils, monocytes, lymphocytes, and plasma cells.[25] Our findings were similar to Haque et al.[26] who demonstrated that the presence of T lymphocytes surpassed the presence of other inflammatory cells such as B-lymphocytes, macrophages, and Langerhans cells. The presence of immunocompetent T-cells was the predominant cause of higher expression of cytokines in OSF. Inflammatory events continually reflect its presence in body fluids as chemokines and other molecules. Although they are mostly nonspecific in nature, some inflammatory chemokines have well-ascertained roles as diagnostic markers. These molecules aid in monitoring early inflammation and prognostic prediction.[27]

Our findings of MVA, MVPA, and MVP were in accordance with Sirsat et al. 1967 and Fang et al. 2000[28] who demonstrated changes in vascular characteristics in the initial stages of OSF. Dilatation and congestion of blood vessels were found noticeable in the early stages and reduction in diameter with obliteration at later stages. These findings were contradictory to those of Rajendran et al.[29] who indicated that the mean vascular percentage area and the mean vascular luminal diameter progressively increased with higher grades of OSF. Modulations in vessel traits were attributed to dilatation as an adaptive mechanism to compensate for tissue ischemia/hypoxia by Tilakaratne et al.[30] These findings were further endorsed by Garg et al. as their results also did not show a consistent increase or decrease with different grades of OSF. Fibrosis has an augmented association with tissue vasculature. Most of the fibrotic diseases report the occurrence of ECM expansion and basement membrane thickening of blood vessels. These changes have a significant impact in organ systems.[31] Reduction of perfusion to epithelial tissues occurs due to hampered vascularization of the underlying connective tissue. This gradually leads to cell alterations in epithelium resulting in atrophic changes.[32]

CPA is a quantitative indicator of collagen deposited in proportion to the total biopsy area. It has been established as an accurate measure of fibrosis and an independent predictor of clinical outcomes in nonalcoholic fatty liver disease.[33] The integrated density of collagen was high in OSF with increasing grades. This was in accordance with Ceena et al.[34] who demonstrated that the density of collagen fibers in OSF was found to progressively increase with disease progression. Collagen fibers were thinner in the early stages in the zones immediately adjacent to the epithelium. As disease progressed, the fibers increased in thickness and involved the deeper submucosal region[35] and collagen III and IV are replaced with Type I.[36]

Epithelial thickness decreased with increasing grades of OSF as pathological changes in the connective tissue have reciprocative action on the overlying epithelium. ECM remodeling initiates an array of molecular events that causes a significant alteration in the epithelium as the disease progresses leading to atrophy.[32]

Mathematical modeling in pathology has made a significant impact with precise diagnostics and treatment decisions. Modeling is based on the consideration that nonequilibrium steady states occur in biological systems and derivation of numerical solution of linear equations aids in improved precision. Modeling of pathological fibrosis has been attempted in other organ systems and has been found to predict the long-term prognosis of the disease process.[37] The derived regression model had regression coefficients which were perfect and were well within higher confidence limits. From the results, it is evident that variation from Grade I to Grade II was lesser, whereas Grade II to III and Grade III to Grade IV were found to have larger variation. This is indicative that the pathogenetic mechanisms of fibrosis are slow in the early stages and strengthen in intensity as the grades become severe.

Various attempts have been made to standardize the classification systems of OSF in terms of clinical staging and grading. More et al. 2012 proposed a classification based on clinical observations and degree of severity. Association of other PMD lesions and oral malignancy were also considered. Lambade et al.[38] proposed a system which included mouth opening, site of involvement, association with other premalignant conditions, malignant transformation, and severity of fibrosis. Hameed et al.[39] established a new clinical staging and evaluated the relationship between the proposed staging and traditional histopathological grading. Passi et al., 2017,[40] also attempted to propose a newer classification which includes functional, clinical, histopathological, treatment, and prognostic components which were not attempted earlier. However, there were no quantified derivations of the components.

Stringent quantification by use of such regression models will make diagnosis more precise and improve accuracy. It can further be used to generate effective treatment strategies. This study recommends combinatorial therapeutic approaches and the rationale for therapeutic approaches relies on the sequential histopathological events. Grades I and II which had similar changes reflecting more on a reactive basis could be treated with topical agents than systematic therapies except for antioxidants and micronutrients. Grades III and IV were more representative of reparative events which needed invasive intralesional application of drugs for management of the established lesion.

CONCLUSION

Ability to detect and quantify fibrosis is pivotal in derivation of pertinent therapeutic approaches. This system gives an in-depth assessment of histopathological events which are more reliable than the clinical scoring alone as it reflects the acuity of underlying pathogenic processes. Biopsy in the oral submucous fibrosis has always been indicated to monitor malignant transformation considering higher transformation rates amongst the oral potentially malignant disorders. Reinforcing the role of biopsy the proposed system will offer a more reliable solution in management of OSMF.

Though not prototypical in presentation as distinctive reactive and reparative patterns, they offer a scope for utilisation as elaborate fibrotic patterns. Translation as a diagnostic marker to guide and substantiate the suggested therapeutic approaches lies in establishing the same with larger samples in correlation with clinical staging.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.