Lung Immune Prognostic Index Could Predict Metastasis in Patients With Osteosarcoma

Abstract

Background: The lung immune prognostic index (LIPI), composed of serum lactate dehydrogenase (LDH) and the derived neutrophil to lymphocyte ratio (dNLR), is a novel prognostic factor of lung cancer. The prognostic effect of the LIPI has never been verified in osteosarcoma.

Methods: We retrospectively reviewed the osteosarcoma patients with metachronous metastasis from January 2016 to January 2021 in West China Hospital. We collected and analyzed the clinical data and constructed the LIPI for osteosarcoma. The correlation between the LIPI and metastasis was analyzed according to the Kaplan-Meier method and Cox regression analysis with hazard ratios (HRs) and 95% confidence intervals (CIs). Univariate analysis and multivariate analysis were conducted to clarify the independent risk factors of metastasis. The nomogram model was established by R software, version 4.1.0.

Results: The area under the curve (AUC) and best cutoff value were 0.535 and 91, 0.519, and 5.02, 0.594 and 2.77, 0.569 and 227.14, 0.59 and 158, and 0.607 and 2.05 for ALP, LMR, NLR, PLR, LDH, and dNLR, respectively. The LIPI was composed of LDH and dNLR and showed a larger AUC than other hematological factors in the time-dependent operator curve (t-ROC). In total, 184 patients, 42 (22.8%), 96 (52.2%), and 46 (25.0%) patients had LIPIs of good, moderate, and poor, respectively (P < 0.0001). Univariate analysis revealed that pathological fracture, the initial CT report of suspicious nodule, and the NLR, PLR, ALP, and the LIPI were significantly associated with metastasis, and multivariate analysis showed that the initial CT report of suspicious nodule and the PLR, ALP, and LIPI were dependent risk factors for metastasis. Metastatic predictive factors were selected and incorporated into the nomogram construction, including the LIPI, ALP, PLR, initial CT report, and pathological fracture. The C-index of our model was 0.71. According to the calibration plot, this predictive nomogram could accurately predict 3- and 5-year metachronous metastasis. Based on the result of decision curve and clinical impact curve, this predictive nomogram could also help patients obtain significant net benefits.

Conclusion: We first demonstrated the metastatic predictive effect of the LIPI on osteosarcoma. This LIPI-based model is useful for clinicians to predict metastasis in osteosarcoma patients and could help conduct timely intervention and facilitate personalized management of osteosarcoma patients.

Keywords: derived neutrophil to lymphocyte ratio (dNLR); lactate dehydrogenase (LDH); lung immune prognostic index (LIPI); metastasis; osteosarcoma.

Figure 1

ROC analysis of different hematological biomarkers. (A–F) The AUC and best cutoff values of ALP, LMR, NLR, PLR, LDH, and dNLR are shown, respectively. The vertical axis represents the sensitivity and the horizontal axis represents the 1-specificity.

Figure 2

Comparison of different hematological factors in predicting the metastatic probability. (A) The difference of predictive ability is shown in a time-dependent ROC curve, in which a larger AUC value means a better metastatic predictive ability. (B,C) Metastatic predictive ability of different hematological factors in 184 osteosarcoma patients.

Figure 3

Univariate analysis and multivariate analysis. (A) Univariate analysis of clinical features and hematological factors. (B) Multivariate analysis of significant clinical factors and hematological factors.

Figure 4

Construction and validation of the osteosarcoma metachronous metastases nomogram. (A) The nomogram was constructed by combining the LIPI, ALP, initial CT report, and pathological fracture, and the sum of the scores for each covariate was the nomogram total score. (B–D) This nomogram was validated by the calibration curve, decision curve analysis, and clinical impact curve.

Figure 5

Comparison of the metastatic predictive effect between LIPI and clinical features. A larger AUC in the t-ROC means a better predictive ability.