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. 2022 Sep 21:2022:9778555.
doi: 10.1155/2022/9778555. eCollection 2022.

Prognostic Nomogram of Osteocarcinoma after Surgical Treatment

Qiuli Wu  1 , Canchun Yang  2 , Haolin Yan  2 , Zheyu Wang  2 , Zhilei Zhang  2 , Qiwei Wang  2 , Renyuan Huang  2 , Xumin Hu  2 , Bo Li  2
Affiliations

Prognostic Nomogram of Osteocarcinoma after Surgical Treatment

Qiuli Wu et al. J Oncol. .

Abstract

Purpose: This study aimed to establish a valid prognostic nomogram for osteocarcinoma after surgical management.

Methods: Based on the SEER database, we retrieved the clinical variables of patients confirmed to have osteocarcinoma between 1975 and 2016. Then, we performed univariate and multivariate analyses and constructed a nomogram of overall survival.

Results: Multivariate analysis of the primary cohort revealed that the independent factors for survival were age, grade, pathologic stage, T stage, and surgery performed. All these factors were showed by the nomogram. The correction curve of survival probability showed that the prediction results of nomogram well agreed with the actual observation results. The C index of the nomogram used to predict survival was 0.82; the AUC of 1-year, 3-year, and 5-year survival rates in the training cohort were 0.9, 0.819, and 0.80631, respectively, indicating that the model was accurate and reliable; whether the operation was performed or not; T stage; grade; and age were the main factors affecting the survival of patients. The AUC of the validation cohort for 1 year, 3 years, and 5 years were 0.8, 0.831, and 0.80023, respectively.

Conclusion: The proposed nomogram can more accurately predict the prognosis of patients with osteocarcinoma after surgical management. This could be a potential method that services clinical work.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Osteosarcoma survival diagram. When using a nomogram, the value of each patient is on each variable axis, and a line is drawn up to determine the number of points received by each variable value. The sum of these numbers is on the total point axis, and a line is drawn down to the survival axis to determine the possibility of 1-, 3-, or 5-year survival.
Figure 2
Figure 2
Calibration curves for training cohort to predict patient survival: (a–c) 1, 3, and 5 years. The nomogram (the predicted probability of overall survival (OS)) is plotted on the X-axis. The actual OS is plotted on the Y-axis.
Figure 3
Figure 3
Comparison of receiving operating characteristic curve (ROC): (a) 1-year ROC of the training cohort, (b) 3-year ROC of the training cohort, and (c) 5-year ROC of the training cohort.
Figure 4
Figure 4
Kaplan–Meier survival analysis for patients with clinical features: (a) risk-score-dependent survival curve, (b) age-dependent survival curve, (c) grade-dependent survival curve, (d) race-dependent survival curve, (e) gender-dependent survival curve, (f) stage-dependent, (g) stage M-dependent survival curve, (h) stage N-dependent survival curve, (i) stage T-dependent survival curve, and (j) surgery-performed-dependent survival curve.
Figure 5
Figure 5
Calibration curves for validation cohort to predict patient survival: (a–c) 1, 3, and 5 years. The nomogram (the predicted probability of overall survival) is plotted on the X-axis. The actual overall survival is plotted on the Y-axis.
Figure 6
Figure 6
Comparison of receiving operating characteristic curve (ROC): (a) 1-year ROC of the validation cohort, (b) 3-year ROC of the validation cohort, and (c) 5-year ROC of the validation cohort.
See this image and copyright information in PMC

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