BACKGROUND: This study aimed to develop a prognostic model in patients with early-stage cervical squamous cell carcinoma based on clinicopathological features, including invasive margin characteristics. MATERIALS AND METHODS: Clinicopathological features and outcomes of 190 patients with FIGO stage IB-IIA cervical squamous cell carcinoma treated by surgery were collected and analyzed for factors associated with tumor recurrence. In addition to well-recognized pathological risk factors, the pathological characteristics of invasive margin (type of invasive pattern and degree of stromal desmoplasia and peritumoral inflammatory reaction) were also included in the analysis. Multiple scoring models were made by matching different clinicopathological variables and/ or different weighting of the score for each variable. The model with the best performance in the prediction of recurrence and decreased survival was selected. RESULTS: The model with the best performance was composed of a combined score of invasive pattern, lymphovascular space invasion (LVSI), and degree of inflammatory reaction and stromal desmoplasia (total score =10). Compared to those with score ≤ 8, the patients with score 9-10 had a significantly higher recurrence rate in the overall group (p<0.001) and the subgroup without adjuvant therapy (p<0.001), while the significance was marginal in the subgroup with adjuvant therapy (p=0.069). In addition, the patients with score 9-10 had a higher rate of tumor recurrence at distant sites (p=0.007). The disease-free survival was significantly lower in the patients with score 9-10 than those with score ≤ 8 among the overall patients (p<0.001), in the subgroup without adjuvant therapy (p<0.001), and the subgroup with adjuvant therapy (p=0.047). CONCLUSIONS: In this study, a prognostic model based on a combination of pathological characteristics of invasive margin and LVSI proved to be predictive of tumor recurrence and decreased disease-free survival in patients with early-stage cervical squamous cell carcinoma.
BACKGROUND: This study aimed to develop a prognostic model in patients with early-stage cervical squamous cell carcinoma based on clinicopathological features, including invasive margin characteristics. MATERIALS AND METHODS: Clinicopathological features and outcomes of 190 patients with FIGO stage IB-IIA cervical squamous cell carcinoma treated by surgery were collected and analyzed for factors associated with tumor recurrence. In addition to well-recognized pathological risk factors, the pathological characteristics of invasive margin (type of invasive pattern and degree of stromal desmoplasia and peritumoral inflammatory reaction) were also included in the analysis. Multiple scoring models were made by matching different clinicopathological variables and/ or different weighting of the score for each variable. The model with the best performance in the prediction of recurrence and decreased survival was selected. RESULTS: The model with the best performance was composed of a combined score of invasive pattern, lymphovascular space invasion (LVSI), and degree of inflammatory reaction and stromal desmoplasia (total score =10). Compared to those with score ≤ 8, the patients with score 9-10 had a significantly higher recurrence rate in the overall group (p<0.001) and the subgroup without adjuvant therapy (p<0.001), while the significance was marginal in the subgroup with adjuvant therapy (p=0.069). In addition, the patients with score 9-10 had a higher rate of tumor recurrence at distant sites (p=0.007). The disease-free survival was significantly lower in the patients with score 9-10 than those with score ≤ 8 among the overall patients (p<0.001), in the subgroup without adjuvant therapy (p<0.001), and the subgroup with adjuvant therapy (p=0.047). CONCLUSIONS: In this study, a prognostic model based on a combination of pathological characteristics of invasive margin and LVSI proved to be predictive of tumor recurrence and decreased disease-free survival in patients with early-stage cervical squamous cell carcinoma.
Authors: Joanne K L Rutgers; Andres A Roma; Kay J Park; Richard J Zaino; Abbey Johnson; Isabel Alvarado; Dean Daya; Golnar Rasty; Teri A Longacre; Brigitte M Ronnett; Elvio G Silva Journal: Mod Pathol Date: 2016-06-03 Impact factor: 7.842