Surgical Extent for Oral Cancer: Emphasis on a Cut-Off Value for the Resection Margin Status: A Narrative Literature Review

Resection status and margin control in intraoperative frozen sectioning analysis of oral squamous cell carcinoma

PURPOSE

Intraoperative frozen section analysis (IFSA) is a well-established procedure for determining the intraoperative soft tissue resection status in patients with oral squamous cell carcinoma (OSCC). Margin status is a major predictor of the patient´s outcome, histologically free margins of ≥ 5 mm are demanded. This study evaluates the accuracy of IFSA, the impact of margin status and the impact of intraoperative margin revision on disease-free survival (DFS) and overall survival (OS).

METHODS

This retrospective study included 213 patients with OSCC. IFSA results were compared with definitive histopathological reports, Kaplan-Meier analysis was performed. Cut-off values were calculated for resection margins considering known risk factors.

RESULTS

IFSA showed positive margins in 8 cases (3.8%). Kaplan-Meier analysis revealed no significant differences for OS or DFS if R0-status was achieved by initial resection or immediate re-resection. Final histopathological evaluation revealed false-positive IFSA in 3/8 cases (37.5%) and false-negative IFSA in 1/205 cases (0.5%). Sensitivity was 83.3% and specificity was 98.6%. Analysis of optimal cut-off values showed no general need for larger resection margins in patients with risk factors. Cut-off values were slightly higher for patients with the risk factor alcohol consumption (7 mm for OS and DFS) or pN + ECS- disease (7 mm for DFS). Optimal cut-off values for tumour-margin-distance were around 6 mm.

CONCLUSION

IFSA provides a valuable assessment method for intraoperative soft tissue resection margins. Risk factors seemingly do not significantly influence the extent of tumour resection.

The modified Polsby-Popper score, a novel quantitative histomorphological biomarker and its potential to predict lymph node positivity and cancer-specific survival in oral tongue squamous cell carcinoma

BACKGROUND

The significance of different histological spreading patterns of tumor tissue in oral tongue squamous cell carcinoma (TSCC) is well known. Our aim was to construct a numeric parameter on a continuous scale, that is, the modified Polsby-Popper (MPP) score, to describe the aggressiveness of tumor growth and infiltration, with the potential to analyze hematoxylin and eosin-stained whole slide images (WSIs) in an automated manner. We investigated the application of the MPP score in predicting survival and cervical lymph node metastases as well as in determining patients at risk in the context of different surgical margin scenarios.

METHODS

We developed a semiautomated image analysis pipeline to detect areas belonging to the tumor tissue compartment. Perimeter and area measurements of all detected tissue regions were derived, and a specific mathematical formula was applied to reflect the perimeter/area ratio in a comparable, observer-independent manner across digitized WSIs. We demonstrated the plausibility of the MPP score by correlating it with well-established clinicopathologic parameters. We then performed survival analysis to assess the relevance of the MPP score, with an emphasis on different surgical margin scenarios. Machine learning models were developed to assess the relevance of the MPP score in predicting survival and occult cervical nodal metastases.

RESULTS

The MPP score was associated with unfavorable tumor growth and infiltration patterns, the presence of lymph node metastases, the extracapsular spread of tumor cells, and higher tumor thickness. Higher MPP scores were associated with worse overall survival (OS) and tongue carcinoma-specific survival (TCSS), both when assessing all pT-categories and pT1-pT2 categories only; moreover, higher MPP scores were associated with a significantly worse TCSS in cases where a cancer-free surgical margin of <5 mm could be achieved on the main surgical specimen. This discriminatory capacity remained constant when examining pT1-pT2 categories only. Importantly, the MPP score could successfully define cases at risk in terms of metastatic disease in pT1-pT2 cancer where tumor thickness failed to exhibit a significant predictive value. Machine learning (ML) models incorporating the MPP score could predict the 5-year TCSS efficiently. Furthermore, we demonstrated that machine learning models that predict occult cervical lymph node involvement can benefit from including the MPP score.

CONCLUSIONS

We introduced an objective, quantifiable, and observer-independent parameter, the MPP score, representing the aggressiveness of tumor growth and infiltration in TSCC. We showed its prognostic relevance especially in pT1-pT2 category TSCC, and its possible use in ML models predicting TCSS and occult lymph node metastases.

Margin to Depth of Invasion Ratio: A Significant Predictor of Survival in Patients With Oral Cancer

PURPOSE

A common definition of a clear margin (≥5 mm) in oral squamous cell carcinoma (OSCC) for all stages is a subject of controversy. Studies have shown that even 1- and 2-mm margins are adequate, and few studies have identified dynamic resection margin as a criterion. We aimed to study the margin to depth of invasion ratio (MDR), margin to tumor thickness ratio (MTR), and margin to tumor size ratio (MSR) as prognostic markers for survival. Notably, to our knowledge, this is the first study to evaluate the role of MDR in OSCC.

METHODS

A prospectively maintained head and neck cancer database was analyzed from January 2017 to February 2023. The MDR, MTR, and MSR were calculated for each patient. Survival outcomes were analyzed using the Cox proportional model and the Kaplan-Meier method. Akaike's information criterion (AIC) and Bayesian information criterion (BIC) were used to compare different ratio models. X-tiles software was used to identify the optimal cutoff value of MDR.

RESULTS

Two hundred eighty patients in the database were assessed, of which 123 eligible patients were enrolled in the study. MDR was an independent predictor of disease-free survival (DFS) on multivariate analysis. The MDR model had the lowest values on AIC and BIC analyses. A cutoff value of 0.5 for MDR showed a significant correlation with DFS and overall survival.

CONCLUSION

MDR was the best predictor of recurrence of all the three ratios studied. The minimum safe surgical margin can be calculated by multiplying the depth of invasion by 0.5. This study signifies the role of dynamic resection margin criteria on the basis of MDR in defining clear margins.