RESUMO
Jeyashanth RijuObjectives The study was aimed to (1) evaluate the effectiveness of clinical examination, intraoperative finding, and contrast-enhanced computed tomography (CECT) to detect extranodal extension (ENE) in buccoalveolar complex squamous cell carcinoma (BAOSCC), (2) to know various factors influencing ENE, and (3) to evaluate survival outcome in patients with ENE. Materials and Methods This was a retrospective cohort study, which included 137 patients with BAOSCC who underwent curative treatment between May 2019 and April 2021. Collaborative findings suggestive of ENE were noted during preoperative clinical examination, CECT, and intraoperatively, and their efficacy was compared with postoperative histopathology. Also, the various factors associated with ENE were evaluated and compared. Statistical Analysis Univariate and multivariate analysis of parameters was done using multiple logistic regression analysis and significant correlation was determined using chi-square test between ENE positive and negative categories. Analysis of prognosis and survival was done by Kaplan-Meier curve plotting using regression analysis and its significance was compared. Results The overall prevalence of ENE was 18.98% and that of lymph node involvement was 40.88%. CECT (73.1%) was found to be more sensitive in detecting ENE compared to intraoperative examination (46.2%) and clinical examination (34.6%).In comparison with clinical examination (91.9%) or CECT (78.38%), intraoperative examination (93.7%) showed the highest specificity in detecting ENE. Clinical nodal size ≥ 3 cm ( p ≤ 0.001), fixity ( p ≤ 0.001), and clinical number of nodes ( p ≤ 0.001) had significant association with ENE. The presence of thick nodal walls on CECT increased the probability of predicting ENE 15 times ( p = 0.180, confidence interval: 0.3-765.4). After a mean follow-up of 18 months, subjects without nodal positivity had a survival advantage over patients with positive lymph nodes (86.4% vs. 53.3%) and those with ENE (86.4% vs. 23.2%), respectively. Conclusion The results demonstrated that clinical examination can be used as an adjuvant to radiological imaging for prediction of ENE preoperatively. Clinical finding suggesting size of node ≥ 3 cm and ≥ 2 nodes are strong predictor of ENE, in addition to other known predictors. Patients with ENE had an unfavorable prognosis when compared with subjects with metastatic nodes without ENE. Presence of ENE remains one of the strongest factors predicting recurrence and thus poor prognosis.
RESUMO
Although Worst pattern of invasion (WPOI) is one of the histopathological (HP) markers that has been utilized in risk stratification of oral squamous cell carcinoma (OSCC) patients, its potential as an independent predictive factor for lymph node metastasis (LNM) and prognosis is least analyzed. Aim of the study is to analyze the relationship of various HP parameters to WPOI, their propensity for lymph node metastasis and prognostic value. This retrospective study included 140 patients diagnosed with resectable OSCC who underwent definitive surgery. Multiparametric HP risk assessment was done on the postoperative specimen and patients were categorized as low-risk WPOI (Type 1-3), and high-risk group (type 4 and 5). After categorization, 36.1% patients had low-risk WPOI and 63.9% had high-risk WPOI. Significant association was noted between WPOI and patient's age (p = 0.001), nodal stage (p = 0.001), lymphovascular invasion (LVI) (p = 0.006) and neural invasion (p = 0.001). 87% patients with nodal metastasis had high risk WPOI. LVI (p = 0.014) and WPOI (p < 0.001) had significant predictive role in LNM. High-risk WPOI and bone involvement were found to be predictive factors for overall survival, and only high risk WPOI had strong correlation with disease free survival having significant poor prognosis. Analyzing WPOI is essential in reporting HP specimens in OSCC. High-risk WPOI can act as an independent predictor for LNM, early recurrence and poor prognosis. Incorporation of WPOI into TNM staging is recommended to improve clinician's ability to prognosticate and individualize treatment strategies.
