Affiliation:
1. Department of Radiation Oncology, Department of Gynecology,Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital
2. Department of Radiation Oncology, First Hospital of Quanzhou Affiliated to Fujian Medical University
3. Department of Radiotherapy, Cancer Center,The First Affiliated Hospital of Fujian Medical University
Abstract
Abstract
Objective: This retrospective study identified prognostic factors to help guide the clinical treatment of elderly patients (≥65 y) with cervical cancer who had undergone radiotherapy. A personalized model to predict 3- and 5-year survival was developed.
Methods: A review was conducted of 367 elderly women with cervical cancer (staged II-III) who had undergone radiotherapy in our hospital between January 2012 and December 2016. The Cox proportional hazards regression model was used for survival analysis that considered age, hemoglobin, squamous cell carcinoma antigen, pathologic type, stage, pelvic lymph node metastasis status, and others. A nomogram was constructed to predict the survival rates.
Results: The median follow-up time was 71 months (4-118 mo). The 3- (5-) year overall, progression-free, local recurrence-free, and distant metastasis-free survival rates were, respectively, 91.0% (84.4%), 92.3% (85.9%), 99.18% (99.01%), and 99.18% (97.82%). The following were significant independent prognostic factors for overall survival: tumor size, pre-treatment hemoglobin, chemotherapy, and pelvic lymph node metastasis. The C-index of the line chart was 0.699 (95% CI: 0.652-0.746). The areas under the receiver operating characteristic curves for 3- and 5-year survival were 0.751 and 0.724. The nomogram was in good concordance with the actual survival rates.
Conclusions: The independent prognostic factors for overall survival in elderly patients with cervical cancer after radiotherapy were: tumor size, pre-treatment hemoglobin, chemotherapy, and pelvic lymph node metastasis. The novel prognostic nomogram based on these factors can be an asset for personalized clinical management.
Publisher
Research Square Platform LLC