Survival of Patients With Cervical Cancer in Rural India
- Jissa Vinoda Thulaseedharana
The purpose of this study was to investigate the prognostic factors, such as clinical, histological and socio-demographic features affecting the event-free and overall survival of the patients with stage I-III carcinoma of the cervix. Eighty-nine patients with International FIGO stage I-III cervical cancer were treated radiation therapy and follow-up of 5-7 years were analyzed for various clinical, histopathological and socio-demographic factors influencing prognosis. Survival estimations were performed using the Kaplan-Meier method, and were compared using the un-weighted log-rank test and multivariable analysis using the Cox proportional hazards model. The median age was 46 years (range, 28-65 years). The 5-year event-free survival (EFS) and overall survival (OAS), along with standard error (SE), were 65.2% (7.0%) and 81.4% (6.1%), respectively. Significant prognostic factors for EFS include, stage (P=0.019), pelvic lymph node metastasis (P=0.013), parametrial (PMT) involvement (P=0.025), number of parametria involved (P=0.000) and tumor size (P=0.034). However, number of parametrial invasion was only significant prognostic factors for overall survival (P=0.015); 5-year survival rate was significantly lower in patients with both PMT involved (58%) than with one PMT involved (>85%). Using a multivariable analysis, we found that number of PMT involved being the only independent significant factor for the development of recurrent disease. None of the socio-demographic factors analyzed were of prognostic importance on event-free and overall survival in cervical cancer patients. Several clinicopathological factors were of prognostic significance but none of the socio-demographic factors analyzed had any role in determining patient outcome. Hence, in cervical cancer, prognosis is more likely dependent on clinical than socio-demographic factors unlike several other cancers where their significant role is well documented. Study of clinical and demographic characteristics for their influence on patient survival could help design better patient management strategies.