Prognosis and Nomogram Prediction for Patients with Oral Squamous Cell Carcinoma: A Cohort Study

Impact of histopathological parameters in prognosis of oral squamous cell carcinoma

OBJECTIVE

Squamous cell carcinomas comprise approximately 90% of all oral malignancies. There is a wide geographical variation in the incidence of oral cancer, with South and South East Asia (SSEA) accounting for almost two third of new cases. The prognosis of oral cancer is influenced by a vast array of factors including demographic, clinical, histopathological and molecular factors. The objective this review is to analyse the impact of histopathological features assessed in hematoxylin and eosin stained sections on the prognosis of OSCC.

MATERIALS AND METHODS

Medline and Scopus data base search was performed in order to identify related articles on histopathological parameters in predicting prognosis of oral squamous cell carcinoma. The primary emphasis is on the studies conducted in SSEA, with an accompanying comparison of their findings with those from research conducted in other parts of the world.

RESULTS

It has been shown that the number of studies conducted in SSEA is not proportionate to the high prevalence of Oral Cancer in the region. There is no significant difference between the findings from SSEA compared to the rest of the world. It is clearly shown that most histopathological parameters can be accurately used to predict nodal metastasis and prognosis.

CONCLUSIONS

Histopathological parameters can be used reliably in planning treatment of Oral cancer. Clinicians should combine clinical and histopathological parameters in drawing treatment plan for Oral Cancer.

A nomogram prediction of implant apical non-coverage on bone-added transcrestal sinus floor elevation: A retrospective cohort study

OBJECTIVES

To identify the risk indicators and develop and validate a nomogram prediction model of implant apical non-coverage by comprehensively analyzing clinical and radiographic factors in bone-added transcrestal sinus floor elevation (TSFE).

MATERIAL AND METHODS

A total of 260 implants in 195 patients receiving bone-added TSFE were included in the study. The population was divided into a development (180 implants) and a validation (80 implants) cohort. According to 6 months post-surgery radiographic images, implants were categorized as "apical non-coverage" or "apical covered." The association of risk factors including clinical and radiographic parameters with implant apical non-coverage was assessed using regression analyses. A nomogram prediction model was developed, and its validation and discriminatory ability were analyzed.

RESULTS

The nomogram predicting bone-added TSFE's simultaneously placed implant's apex non-coverage after 6 months. This study revealed that sinus angle, endo-sinus bone gain, implant protrusion length, graft contact walls, and distal angle were predictors of implant apical non-coverage. The generated nomogram showed a strong predictive capability (area under the curve [AUC] = 0.845), confirmed by internal validation using 10-fold cross-validation (Median AUC of 0.870) and temporal validation (AUC = 0.854). The calibration curve and decision curve analysis demonstrated good performance and high net benefit of the nomogram, respectively.

CONCLUSIONS

The clinical implementation of the present nomogram is suitable for predicting the apex non-coverage of implants placed simultaneously with bone-added TSFE after 6 months.

Prognostic Nomogram for Lymph-Node Metastasis in Oral Squamous Cell Carcinoma (OSCC) Using Immunohistochemical Marker D2-40

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.