Introduction: Nomograms are proven in “individualized risk prediction” in sarcomas and breast and prostate cancers. Incorporating immunohistochemical markers and histopathological parameters can enhance accuracy of these graphical representations of statistical predictive models concerning metastasis. D2-40, a monoclonal antibody to podoplanin (regulator of motility expressed in malignant epithelial cells), dually predicts metastatic potential of tumour by estimating the motile tumour phenotype and by detecting lymphatic vessels/density, both essential to metastasis in OSCC. Thus, we propose a model that incorporates D2-40 immunostaining of individual tumour cells (ITC) too with other variables (seen in H+E staining) as a predictive nomogram. Methods: Sixty cases of OSCC were selected with equal number of cases (n=30) of pN0 and pN+ status. Bryne’s grading of invasive front of tumour (ITF) was done on H+E-stained slides followed by D2-40 immunostaining for ITCs at ITF and lymphatic vessels. Multivariate regression analysis was used to generate the nomogram of LNM where the predictive contribution of each covariate, namely depth of invasion, D2-40-stained ITCs, gender, histological grade, and worst pattern of invasion (WPOI), was plotted on a scale of 1–100 points. Results: The nomogram showed that the strongest variable in OSCC was the WPOI in H+E-stained section followed by D2-40-positive ITCs and gender. Discussion: Our predictive nomogram for LNM in OSCC surprisingly showed that a tumour with lower score of WPOI (islands vs ITC) showed numerous D2-40-positive ITCs, drastically increasing the probability of metastasis. The concept of “individualized risk prediction” can be used to predict lymph node metastasis using a variety of histopathological criteria that can be visualized in routine and immunohistochemical staining in OSCC with the aid of a nomogram.
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