Nomogram for Preoperative Prediction of Adjuvant Therapy Requirement Following Radical Surgery in Stage IB Cervical Squamous Cell Carcinoma With Tumor Size ≤ 4 cm: A Retrospective Study.
This study aimed to construct a nomogram incorporating preoperative laboratory parameters and clinical pathological factors for the first time to predict the probability of adjuvant therapy requirement following radical surgery in IB stage cervical squamous cell carcinoma (SCC) with tumor size ≤ 4 cm.
Clinical pathological parameters and relevant laboratory indicators were collected from IB stage cervical SCC with tumor size ≤ 4 cm patients who underwent radical surgery at our hospital. Patients included in the study were randomly divided into a training set and a validation set in a 7:3 ratio. In the training set, the least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to determine the final variables for constructing the nomogram. Finally, the performance of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) in both the training and validation sets.
The nomogram ultimately included seven predictive variables. The area under the ROC curve (AUC) for the nomogram in the training and validation sets was 0.863 and 0.767, respectively. Moreover, the calibration curve of the nomogram was relatively close to the ideal curve. The DCA showed that using the nomogram to predict the probability of adjuvant therapy requirement over a wide range of threshold values is beneficial.
This study constructed and validated a model for predicting adjuvant therapy requirement in IB stage cervical SCC with tumor size ≤ 4 cm. The model can help clinicians determine the risk of postoperative adjuvant therapy before surgery, promoting personalized treatment choices and patient management.
Clinical pathological parameters and relevant laboratory indicators were collected from IB stage cervical SCC with tumor size ≤ 4 cm patients who underwent radical surgery at our hospital. Patients included in the study were randomly divided into a training set and a validation set in a 7:3 ratio. In the training set, the least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to determine the final variables for constructing the nomogram. Finally, the performance of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) in both the training and validation sets.
The nomogram ultimately included seven predictive variables. The area under the ROC curve (AUC) for the nomogram in the training and validation sets was 0.863 and 0.767, respectively. Moreover, the calibration curve of the nomogram was relatively close to the ideal curve. The DCA showed that using the nomogram to predict the probability of adjuvant therapy requirement over a wide range of threshold values is beneficial.
This study constructed and validated a model for predicting adjuvant therapy requirement in IB stage cervical SCC with tumor size ≤ 4 cm. The model can help clinicians determine the risk of postoperative adjuvant therapy before surgery, promoting personalized treatment choices and patient management.