ArticleAmerican journal of translational research2022
Risk factors for additional postoperative adjuvant therapy in patients with locally advanced cervical cancer and construction of a risk model.
Article in American journal of translational research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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3 authors at 1 institution in 1 country.
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Abstract
objectiveTo investigate the influencing factors of postoperative adjuvant therapy for stage IB1-IIA2 cervical cancer, and establish a nomogram model to predict the risk of postoperative adjuvant therapy for locally advanced cervical cancer (LACC).
methodsA retrospective analysis was conducted on 144 patients with stage IB1-IIA2 cervical squamous cell carcinoma treated in Wuhan No.1 Hospital from June 2015 to January 2017, and their clinical data were analyzed. The clinical application value of the nomogram risk model was evaluated by receiver operating characteristic curve (ROC).
resultsThrough logistic regression analysis, we found that squamous cell carcinoma antigen (SCC-Ag), International Federation of Gynecology and Obstetrics (FIGO) stage ≥ IIA1, and laparoscopic surgery were independent influencing factors for additional adjuvant therapy after laparoscopic surgery. The nomogram model for predicting the risk of postoperative adjuvant therapy for cervical cancer constructed according to the selected variables had good predictive performance (with C-index of 0.798) and conformity. The area under the curve of established model in predicting 1-, 3- and 5-year survival time was 0.730, 0.810 and 0.830, respectively, indicating that the model has good performance.
conclusionHistory of diabetes, tumor size, FIGO stage ≥ IIA1, and SCC-Ag >1.5 are independent influencing factors for additional adjuvant therapy after laparoscopic surgery of LACC patients. In addition, the constructed risk model is effective in predicting the postoperative risk of additional adjuvant therapy, which is expected to provide a reference for clinical treatment selection.
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