Development of a Nomogram for Predicting the Efficacy of Preoperative Chemotherapy in Osteosarcoma

Due to the obvious heterogeneity of osteosarcoma, many patients are not sensitive to neoadjuvant chemotherapy. In this study, the clinical characteristics and auxiliary examinations of patients with osteosarcoma were used to predict the effect of preoperative chemotherapy, so as to guide the clinical adjustment of the treatment plan to improve the prognosis of patients.

In this study, 90 patients with pathologically confirmed osteosarcoma were included, and they were randomly divided into training cohort (n=45) and validation cohort (n=45). A prediction model of preoperative chemotherapy efficacy for osteosarcoma was established by multivariate logistic regression analysis, and a nomogram was used as the visualization of the model. The ROC curve and C-index were used to evaluate the accuracy of the nomogram. Decision curve analysis (DCA) was used to evaluate the net benefit of the nomogram in predicting the efficacy of neoadjuvant chemotherapy under different threshold probabilities.

In the study, the age, gender, location, tumor volume, metastasis at the first visit, MSTS staging, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), alkaline phosphatase (ALP), and lactate dehydrogenase (LDH) were used in the multivariate logistic regression analysis and the construction of the nomogram. The AUC and C-index of the training cohort were 0.793 (95% CI: 0.632, 0.954) and 0.881 (95% CI: 0.776, 0.986), respectively. The AUC and C-index in the validation cohort were 0.791 (95% CI: 0.644, 0.938) and 0.813 (95% CI: 0.679, 0.947), respectively, which were close to the training cohort. DCA showed that the model had good clinical application value.

Based on the clinical characteristics of patients and auxiliary examinations, the nomogram can be good used to predict the efficacy of preoperative chemotherapy for osteosarcoma.