AUTHOR=He Xuanhong , Wang Yitian , Ye Qiang , Wang Yang , Min Li , Luo Yi , Zhou Yong , Tu Chongqi 

TITLE=Lung Immune Prognostic Index Could Predict Metastasis in Patients With Osteosarcoma

JOURNAL=Frontiers in Surgery

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2022.923427

DOI=10.3389/fsurg.2022.923427

ISSN=2296-875X

ABSTRACT=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 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 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, and 0.59 and 158 and 0.607 and 2.05 for ALP, LMR, NLR, PLR, LDH and dNLR, respectively. 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, initial CT report of suspicious nodule, NLR, PLR, ALP, and LIPI were significantly associated with metastasis, and multivariate analysis showed that initial CT report of suspicious nodule, PLR, ALP, and LIPI were dependent risk factors for metastasis. Metastasis predictive factors were selected and incorporated into the nomogram construction, including LIPI, ALP, PLR, initial CT report and pathological fracture. The C-index of our model was 0.72. This predictive nomogram exhibited close performance compared with the ideal model on the calibration plot and was practical in the clinic, referring to the decision curve and clinical impact curve.
Conclusion: We first demonstrated the metastatic predictive effect of LIPI in 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.