Risk Factors, Prognostic Factors, and Nomograms for Distant Metastasis in Patients With Newly Diagnosed Osteosarcoma: A Population-Based Study

Chen, Bin; Zeng, Yuan; Liu, Bo; Lu, Gaoxiang; Xiang, Zhouxia; Chen, Jian-Zhang; Yu, Yan; Zuo, Ziyi; Lin, Yujing; Ma, Jinfeng

doi:10.3389/fendo.2021.672024

Cited by 38 publications

(53 citation statements)

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“…Older persons had a higher proportion of lung metastases in PCLM patients regarding age at diagnosis. This finding was consistent with many studies, which show age as an independent risk factor for distant metastases ( 29 – 31 ). We suspected that it might be due to various changes that have taken place in the body's metabolism and development with age.…”

Section: Discussionsupporting

confidence: 93%

Two Novel Nomograms Predicting the Risk and Prognosis of Pancreatic Cancer Patients With Lung Metastases: A Population-Based Study

Zhang

Zhong

et al. 2022

Front. Public Health

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BackgroundPancreatic cancer (PC) is one of the most common malignant types of cancer, with the lung being the frequent distant metastatic site. Currently, no population-based studies have been done on the risk and prognosis of pancreatic cancer with lung metastases (PCLM). As a result, we intend to create two novel nomograms to predict the risk and prognosis of PCLM.MethodsPC patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database from 2010 to 2016. A multivariable logistic regression analysis was used to identify risk factors for PCLM at the time of diagnosis. The multivariate Cox regression analysis was carried out to assess PCLM patient's prognostic factors for overall survival (OS). Following that, we used area under curve (AUC), time-dependent receiver operating characteristics (ROC) curves, calibration plots, consistency index (C-index), time-dependent C-index, and decision curve analysis (DCA) to evaluate the effectiveness and accuracy of the two nomograms. Finally, we compared differences in survival outcomes using Kaplan-Meier curves.ResultsA total of 803 (4.22%) out of 19,067 pathologically diagnosed PC patients with complete baseline information screened from SEER database had pulmonary metastasis at diagnosis. A multivariable logistic regression analysis revealed that age, histological subtype, primary site, N staging, surgery, radiotherapy, tumor size, bone metastasis, brain metastasis, and liver metastasis were risk factors for the occurrence of PCLM. According to multivariate Cox regression analysis, age, grade, tumor size, histological subtype, surgery, chemotherapy, liver metastasis, and bone metastasis were independent prognostic factors for PCLM patients' OS. Nomograms were constructed based on these factors to predict 6-, 12-, and 18-months OS of patients with PCLM. AUC, C-index, calibration curves, and DCA revealed that the two novel nomograms had good predictive power.ConclusionWe developed two reliable predictive models for clinical practice to assist clinicians in developing individualized treatment plans for patients.

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Section: Discussionsupporting

confidence: 93%

Two Novel Nomograms Predicting the Risk and Prognosis of Pancreatic Cancer Patients With Lung Metastases: A Population-Based Study

Zhang

Zhong

et al. 2022

Front. Public Health

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“…Some attempts have been made to establish a metastasis predictive model; however, most of them merely focused on a single inflammatory factor or clinical feature, which is difficult to represent the disease state of those patients (28,(31)(32)(33). In this study, we also verified that the LIPI had an advantage over predicting the metastasis status over other single inflammation factors and clinical features (Figures 2A, 5).…”

Section: Discussionsupporting

confidence: 69%

Lung Immune Prognostic Index Could Predict Metastasis in Patients With Osteosarcoma

Wang

et al. 2022

Front. Surg.

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BackgroundThe 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.MethodsWe 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.ResultsThe 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.ConclusionWe 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.

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“…Nomograms are rarely reported in predicting metastasis in osteosarcoma. Most of the nomograms are established by integrating clinical parameters and genes associated with metastasis (48)(49)(50)(51). However, due to the imprecision of clinical features and the clinical inapplicability of genes, the clinical application of these nomograms was limited.…”

Section: Discussionmentioning

confidence: 99%

Osteosarcoma immune prognostic index can indicate the nature of indeterminate pulmonary nodules and predict the metachronous metastasis in osteosarcoma patients

et al. 2022

Front. Oncol.

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PurposeThe relationship between indeterminate pulmonary nodules (IPNs) and metastasis is difficult to determine. We expect to explore a predictive model that can assist in indicating the nature of IPNs, as well as predicting the probability of metachronous metastasis in osteosarcoma patients.Patients and methodsWe conducted a retrospective study including 184 osteosarcoma patients at West China Hospital from January 2016 to January 2021. Hematological markers and clinical features of osteosarcoma patients were collected and analyzed.ResultsIn this study, we constructed an osteosarcoma immune prognostic index (OIPI) based on the lung immune prognostic index (LIPI). Compared to other hematological markers and clinical features, OIPI had a better ability to predict metastasis. OIPI divided 184 patients into four groups, with the no-OIPI group (34 patients), the light-OIPI group (35 patients), the moderate-OIPI group (75 patients), and the severe-OIPI group (40 patients) (P < 0.0001). Subgroup analysis showed that the OIPI could have a stable predictive effect in both the no-nodule group and the IPN group. Spearman’s rank correlation test and Kruskal–Wallis test demonstrated that the OIPI was related to metastatic site and metastatic time, respectively. In addition, patients with IPNs in high-OIPI (moderate and severe) groups were more likely to develop metastasis than those in low-OIPI (none and light) groups. Furthermore, the combination of OIPI with IPNs can more accurately identify patients with metastasis, in which the high-OIPI group had a higher metastasis rate, and the severe-OIPI group tended to develop metastasis earlier than the no-OIPI group. Finally, we constructed an OIPI-based nomogram to predict 3- and 5-year metastasis rates. This nomogram could bring net benefits for more patients according to the decision curve analysis and clinical impact curve.ConclusionThis study is the first to assist chest CT in diagnosing the nature of IPNs in osteosarcoma based on hematological markers. Our findings suggested that the OIPI was superior to other hematological markers and that OIPI can act as an auxiliary tool to determine the malignant transformation tendency of IPNs. The combination of OIPI with IPNs can further improve the metastatic predictive ability in osteosarcoma patients.

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Risk Factors, Prognostic Factors, and Nomograms for Distant Metastasis in Patients With Newly Diagnosed Osteosarcoma: A Population-Based Study

Cited by 38 publications

References 50 publications

Two Novel Nomograms Predicting the Risk and Prognosis of Pancreatic Cancer Patients With Lung Metastases: A Population-Based Study

Two Novel Nomograms Predicting the Risk and Prognosis of Pancreatic Cancer Patients With Lung Metastases: A Population-Based Study

Lung Immune Prognostic Index Could Predict Metastasis in Patients With Osteosarcoma

Osteosarcoma immune prognostic index can indicate the nature of indeterminate pulmonary nodules and predict the metachronous metastasis in osteosarcoma patients

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