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

Machine learning-based dynamic predictive models for prognosis and treatment decisions in patients with liver metastases from gastric cancer

Gastric cancer with liver metastasis (GCLM) often has a poor prognosis. Therefore, it is crucial to identify risk factors affecting their overall survival (OS) and cancer-specific survival (CSS). This study aimed to construct practical machine learning models to predict survival time and help clinicians choose appropriate treatments. We reviewed the clinical and survival data of GCLM patients from 2010 to 2017 in the Surveillance, Epidemiology, and End Results (SEER) databases and divided the patients into training and testing groups. The risk factors affecting OS and CSS were determined by least absolute shrinkage and selector operator (LASSO), univariate cox regression, best subset regression (BSR) and the stepwise backward regression. Then, five machine learning models, including random survival forest (RSF), Gradient Boosting Machine (GBM), the Cox proportional hazard (CPH), Survival Support Vector Machine (survivalSVM), and eXtreme Gradient Boosting (XGBoost), were built using the identified risk factors. The model with the best predictive ability was determined using concordance index (c-index), area under the curve (AUC), brier score, and decision curve analysis (DCA), and externally verified with data from 233 cases diagnosed with liver metastasis of cancer from The Shijiazhuang People's Hospital, Jinan City People's Hospital, and The Sixth People's Hospital of Huizhou from 2017 to 2018. The study involved a total of 1300 GCLM patients. The prognostic risk factors affecting OS and CSS were the same, including grade, histology, T stage, N stage, surgery, and chemotherapy. The XGBoost model was found to have the best predictive ability for OS, with AUC of 0.891 [95% CI 0.841-0.941], brier score of 0.061 [95% CI 0.046-0.076], and c-index of 0.752 [95% CI 0.742-0.761], as well as for CSS, with AUC of 0.895 [95% CI 0.848-0.942], brier score of 0.064 [95% CI 0.050-0.079], and c-index of 0.746 [95% CI 0.736-0.756]. The AUC score, brier score and c-index all illustrated the accuracy of the model, and the validation using the external datasets further confirmed the reliability of the model. Therefore, the XGBoost model demonstrated significant potential in predicting survival times and selecting appropriate treatment plans.

Successful treatment of osteosarcoma in a pregnant woman with survival of the gestational product: A case report and literature review

Osteosarcoma (OS) is the most prevalent bone neoplasm of mesenchymal origin, accounting for 20% of all bone tumors worldwide. It mainly affects the marrow of long bones, and its diagnosis is more common among adolescents and the geriatric population. Histologically, it is characterized by high cellular variability, abundant osteoid and fibrotic material. In the early stages, it presents with only local symptoms such as pain, edema and limited joint mobility. This neoplasm, when detected promptly, is associated with a favorable prognosis and can be effectively treated through surgical removal and adjuvant therapy. The development of tumors in pregnant women is rare, and the occurrence of osteosarcoma is even more exceptional, with only 10 cases documented in the literature. Given its rarity, the present study describes the case of a female patient with OS diagnosed in the first trimester of pregnancy, where the patient responded well to treatment, resulting in no adverse effects on the pregnancy outcome.

Establishment of a nomogram for potential prediction of lung metastasis in patients with primary limb bone tumors: a study based on the SEER database

Background

The prognosis of lung metastasis in primary limb bone tumors represents a pivotal yet challenging aspect of oncological management. Despite advancements in diagnostic modalities, the predictive accuracy for metastatic spread remains suboptimal. This study aims to bridge this gap by leveraging the Surveillance, Epidemiology, and End Results (SEER) database to construct a nomogram that forecasts the risk of lung metastasis, thereby enhancing clinical decision-making processes.

Methods

A retrospective cohort, including 1,822 patients with primary limb bony tumors from 2010 to 2015 in the SEER database, was extracted. Using precise inclusion and exclusion criteria, variables essential for predicting lung metastasis were identified through univariate and multivariate analyses, along with least absolute shrinkage and selection operator (LASSO) regression. These variables provided a solid basis for creating the multivariable nomogram, of which the discriminating power and utility were verified using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis.

Results

The model incorporated seven key predicting variables, including age, histological type, surgery, radiation, chemotherapy, T stage, and N stage. The nomogram emerged as a cohesive whole with good discriminative power. The area under the curve (AUC) was 0.806 in the training cohort and 0.767 in the validation cohort. The calibration curves demonstrated the model's validity by showing a good match between the actual outcomes and the model-predicted probabilities of lung metastasis.

Conclusions

This study showed for the first time the reliability of the predictive model in translating the hard-to-interpret demographic, clinical, and pathologic data into a very usable predictive model. Thus, it represents a significant step toward demystifying the risk of lung metastasis in primary limb bone tumors. It is an invitation for a paradigm shift of oncology, to evidence-based, person-based oncology that is taking a new metric for cancer prognosis.

Diagnostic value of combined detection of AKP, TSGF, and LDH for pediatric osteosarcoma: a case-control study

OBJECTIVE

To evaluate the diagnostic value of serum alkaline phosphatase (AKP), tumor-supplied growth factor group (TSGF), and lactate dehydrogenase (LDH) for pediatric osteosarcoma.

METHODS

A retrospective analysis of clinical data from 81 pediatric osteosarcoma patients (osteosarcoma group) and 63 patients with benign bone tumors (benign bone tumor group) admitted to Yantaishan Hospital from February 2023 to November 2023 was conducted. Basic and clinical data differences between the two groups of children were compared. A multivariate regression model was established to determine predictive factors for pediatric osteosarcoma, and the diagnostic value of identified indicators for pediatric osteosarcoma was evaluated.

RESULTS

Osteosarcoma group demonstrated significantly higher serum AKP (375.76±73.47 vs 286.12±76.50 U/L), TSGF (69.01±16.30 vs 53.57±16.37 U/mL), and LDH (269.55±66.96 vs 207.46±59.20 U/L) levels as compared to the benign bone tumor group. Correlation analysis suggested significant positive correlations between AKP (rho=0.505), TSGF (rho=406), LDH (rho=0.449) and pediatric osteosarcoma. Multivariate regression analysis showed serum AKP, TSGF, and LDH were independent predictive factor for pediatric osteosarcoma. The AUC value for AKP was 0.794, with a Youden index of 0.459; the AUC value for TSGF was 0.736, with a Youden index of 0.406; and the AUC value for LDH was 0.761, with a Youden index of 0.462. The combined use of these three biomarkers yielded an AUC of 0.886.

CONCLUSION

The combined detection of serum AKP, TSGF, and LDH can enhance the diagnostic accuracy of pediatric osteosarcoma, providing important evidence for clinical treatment.

Clinical Features, Risk Factors, and Prediction Nomogram for Primary Spinal Osteosarcoma: A Large-Cohort Retrospective Study

STUDY DESIGN

Retrospective cohort study.

OBJECTIVES

The goal of this study was to determine the clinical characteristics of patients with primary spinal osteosarcoma and to construct a practical clinical prediction model for patients to achieve an accurate prediction of overall survival.

METHODS

This study included 230 patients diagnosed between 2004-2015 from the Surveillance, Epidemiology, and End Results database. Independent risk factors were screened in the training set using Cox regression algorithms, and a prognostic model was developed. Internal and external validation sets were used to test the nomogram model's calibration, discrimination, and clinical utility. A risk classification system based on the nomogram was developed and validated.

RESULTS

Four independent prognostic factors were identified, and based on this a nomogram model was developed for predicting patient prognosis. The C-index of the training set was .737, while that of the validation set was .693. The time-varying area under the curve values was greater than .720 in both cohorts. The calibration curves proved that the prediction model has high prediction accuracy. The decision curve analysis showed that the nomogram is clinically useful. A risk classification system was established, which allows all patients to be divided into two different risk groups.

CONCLUSIONS

A nomogram and risk classification system was developed for patients with primary spinal osteosarcoma to accurately predict overall patient survival and achieve risk stratification of patient mortality. These tools are expected to play an important role in clinical practice, informing clinicians in making decisions.

Prognostic factors and predictive models for patients with lung large cell neuroendocrine carcinoma: Based on SEER database

BACKGROUND

Lung Large cell neuroendocrine carcinoma (LCNEC) is a rare, aggressive, high-grade neuroendocrine carcinoma with a poor prognosis, mainly seen in elderly men. To date, we have found no studies on predictive models for LCNEC.

METHODS

We extracted data from the Surveillance, Epidemiology, and End Results (SEER) database of confirmed LCNEC from 2010 to 2018. Univariate and multivariate Cox proportional risk regression analyses were used to identify independent risk factors, and then we constructed a novel nomogram and assessed the predictive effectiveness by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

A total of 2546 patients with LCNEC were included, excluding those diagnosed with autopsy or death certificate, tumor, lymph node, metastasis (TNM) stage, tumor grade deficiency, etc., and finally, a total of 743 cases were included in the study. After univariate and multivariate analyses, we concluded that the independent risk factors were N stage, intrapulmonary metastasis, bone metastasis, brain metastasis, and surgical intervention. The results of ROC curves, calibration curves, and DCA in the training and validation groups confirmed that the nomogram could accurately predict the prognosis.

CONCLUSIONS

The nomogram obtained from our study is expected to be a useful tool for personalized prognostic prediction of LCNEC patients, which may help in clinical decision-making.

Molecular characterization of Golgi apparatus-related genes indicates prognosis and immune infiltration in osteosarcoma

BACKGROUND

The Golgi apparatus (GA) is crucial for protein synthesis and modification, and regulates various cellular processes. Dysregulation of GA can lead to pathological conditions like neoplastic growth. GA-related genes (GARGs) mutations are commonly found in cancer, contributing to tumor metastasis. However, the expression and prognostic significance of GARGs in osteosarcoma are yet to be understood.

METHODS

Gene expression and clinical data of osteosarcoma patients were obtained from the TARGET and GEO databases. A consensus clustering analysis identified distinct molecular subtypes based on GARGs. Discrepancies in biological processes and immunological features among the subtypes were explored using GSVA, ssGSEA, and Metascape analysis. A GARGs signature was constructed using Cox regression. The prognostic value of the GARGs signature in osteosarcoma was evaluated using Kaplan-Meier curves and a nomogram.

RESULTS

Two GARG subtypes were identified, with Cluster A showing better prognosis, immunogenicity, and immune cell infiltration than Cluster B. A novel risk model of 3 GARGs was established using the TARGET dataset and validated with independent datasets. High-risk patients had poorer overall survival, and the GARGs signature independently predicted osteosarcoma prognosis. Combining risk scores and clinical characteristics in a nomogram improved prediction performance. Additionally, we discovered Stanniocalcin-2 (STC2) as a significant prognostic gene highly expressed in osteosarcoma and potential disease biomarker.

CONCLUSIONS

Our study revealed that patients with osteosarcoma can be divided into two GARGs subgroups. Furthermore, we have developed a GARGs prognostic signature that can accurately forecast the prognosis of osteosarcoma patients.

Inhibition of anlotinib-induced autophagy attenuates invasion and migration by regulating epithelial-mesenchymal transition and cytoskeletal rearrangement through ATG5 in human osteosarcoma cells

The cure rates for osteosarcoma have remained unchanged in the past three decades, especially for patients with pulmonary metastasis. Thus, a new and effective treatment for metastatic osteosarcoma is urgently needed. Anlotinib has been reported to have antitumor effects on advanced osteosarcoma. However, both the effect of anlotinib on autophagy in osteosarcoma and the mechanism of anlotinib-mediated autophagy in pulmonary metastasis are unclear. The effect of anlotinib treatment on the metastasis of osteosarcoma was investigated by transwell assays, wound healing assays, and animal experiments. Related proteins were detected by western blotting after anlotinib treatment, ATG5 silencing, or ATG5 overexpression. Immunofluorescence staining and transmission electron microscopy were used to detect alterations in autophagy and the cytoskeleton. Anlotinib inhibited the migration and invasion of osteosarcoma cells but promoted autophagy and increased ATG5 expression. Furthermore, the decreases in invasion and migration induced by anlotinib treatment were enhanced by ATG5 silencing. In addition, Y-27632 inhibited cytoskeletal rearrangement, which was rescued by ATG5 overexpression. ATG5 overexpression enhanced epithelial-mesenchymal transition (EMT). Mechanistically, anlotinib-induced autophagy promoted migration and invasion by activating EMT and cytoskeletal rearrangement through ATG5 both in vitro and in vivo. Our results demonstrated that anlotinib can induce protective autophagy in osteosarcoma cells and that inhibition of anlotinib-induced autophagy enhanced the inhibitory effects of anlotinib on osteosarcoma metastasis. Thus, the therapeutic effect of anlotinib treatment can be improved by combination treatment with autophagy inhibitors, which provides a new direction for the treatment of metastatic osteosarcoma.

Hypoxia inducible factor-1ɑ as a potential therapeutic target for osteosarcoma metastasis

Osteosarcoma (OS) is a malignant tumor originating from mesenchymal tissue. Pulmonary metastasis is usually present upon initial diagnosis, and metastasis is the primary factor affecting the poor prognosis of patients with OS. Current research shows that the ability to regulate the cellular microenvironment is essential for preventing the distant metastasis of OS, and anoxic microenvironments are important features of solid tumors. During hypoxia, hypoxia-inducible factor-1α (HIF-1α) expression levels and stability increase. Increased HIF-1α promotes tumor vascular remodeling, epithelial-mesenchymal transformation (EMT), and OS cells invasiveness; this leads to distant metastasis of OS cells. HIF-1α plays an essential role in the mechanisms of OS metastasis. In order to develop precise prognostic indicators and potential therapeutic targets for OS treatment, this review examines the molecular mechanisms of HIF-1α in the distant metastasis of OS cells; the signal transduction pathways mediated by HIF-1α are also discussed.

Identifying the Best Candidate for Primary Tumor Resection in Patients With Advanced Osteosarcoma

OBJECTIVES

Not all patients with stage III and IV osteosarcoma who undergo surgery to remove the primary tumor will benefit from surgery; therefore, we developed a nomogram model to test the hypothesis that only a subset of patients will benefit from surgery.

METHODS

412 patients were screened from the Surveillance, Epidemiology and End Results (SEER) database. Subsequently, 1:1 propensity score matching (PSM) was used to screen and balance confounders. We first made the hypothesis that patients who underwent the procedure would benefit more. A multivariate Cox model was used to explore the independent influencing factors of CSS in two groups (benefit group and non-benefit group) and constructed nomograms with predicted prognosis. Finally, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to verify the performance of the nomogram.

RESULTS

Of these patients, approximately 110 did not undergo primary tumour resection. After passing PSM, they were divided into a surgical group and a non-surgical group. Age, primary site and chemotherapy as calculated independent factors were used to construct a nomogra. The predicted nomogram showed good consistency in terms of the ROC curve and the calibration curve, and the DCA curve showed a certain clinical utility. Finally, dividing the surgical patients into surgical beneficiaries and surgical non-beneficiaries, a Kaplan-Meier analysis showed that the nomogram can identify patients with osteosarcoma who can benefit from surgery.

CONCLUSION

A practical predictive model was established to determine whether patients with stage III or IV osteosarcoma would benefit from surgery.

Risk factors for distant metastasis of Chondrosarcoma in the middle-aged and elderly people

Chondrosarcoma is the second most common primary bone malignancy with the highest incidence in middle-aged and elderly people, where distant metastasis (DM) still leads to poor prognosis. The purpose of this study was to construct a nomogram for studying the diagnosis of DM in middle-aged and elderly patients with chondrosarcoma. Data on chondrosarcoma patients aged ≥ 40 years diagnosed from 2004 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The data were divided into a training set and an internal validation set according to a 7:3 ratio, and the training set data were screened for independent risk factors for DM in chondrosarcoma patients using univariate and multivariate logistic regression analysis. The screened independent risk factors were then used to build a nomogram. In addition, data from 144 patients with chondrosarcoma aged ≥ 40 years diagnosed in a tertiary hospital in China from 2012 to 2021 were collected as the external validation set. The results were evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis in the training set, internal validation set, and external validation set. A total of 1462 middle-aged and elderly patients with chondrosarcoma were included, and 92 (6.29%) had DM at the time of diagnosis. Independent risk factors for DM in middle-aged and elderly patients with chondrosarcoma included being married (OR: 2.119, 95% CI: 1.094-4.105), histological type of dedifferentiated chondrosarcoma (OR: 1.290, 95% CI: 1.110-1.499), high-grade tumor (OR: 1.511, 95% CI: 1.079-2.115), T3 stage (OR: 4.184, 95% CI: 1.977- 8.858), and N1 staging (OR: 5.666, 95% CI: 1.964-16.342). The area under the receiver operating characteristic curve (AUC) was 0.857, 0.820, and 0.859 in the training set, internal validation set, and external validation set, respectively. The results of the calibration curve and decision curve analysis also confirmed that the established nomogram could accurately predict DM in middle-aged and elderly patients with chondrosarcoma. Married, histological type of dedifferentiated chondrosarcoma, high-grade tumor, T3 stage, and N1 stage are independent risk factors for DM in middle-aged and elderly chondrosarcoma patients, and clinicians should see more attention.

Construction of novel predictive tools for post-surgical cancer-specific survival probability in patients with primary chondrosarcoma and external validation in Chinese cohorts: a large population-based retrospective study

BACKGROUND

Surgery is the predominant treatment modality for chondrosarcoma. This study aims to construct a novel clinic predictive tool that accurately predicts the 3-, 5-, and 8-year probability of cancer-specific survival (CSS) for primary chondrosarcoma patients who have undergone surgical treatment.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) database was used to identify 982 primary chondrosarcoma patients after surgery, who were randomly divided into two sets: training set (60%) and internal validation set (40%). Cox proportional regression analyses were used to screen post-surgical independent prognostic variables in primary chondrosarcoma patients. These identified variables were used to construct a nomogram to predict the probability of post-surgical CSS of primary chondrosarcoma patients. The k-fold cross-validation method (k = 10), Harrell's concordance index (C-index), receiver operating characteristic curve (ROC) and area under curve (AUC) were used to assess the predictive accuracy of the nomogram. Calibration curve and decision curve analysis (DCA) were used to validate the clinical application of the nomogram.

RESULTS

Age, tumor size, disease stage and histological type were finally identified post-surgical independent prognostic variables. Based the above variables, a nomogram was constructed to predict the 3-, 5- and 8-year probability of post-surgical CSS in primary chondrosarcoma patients. The results of the C-index showed excellent predictive performance of the nomogram (training set: 0.837, 95% CI: 0.766-0.908; internal validation set: 0.835, 95% CI: 0.733-0.937; external validation set: 0.869, 95% CI: 0.740-0.998). The AUCs of ROC were all greater than 0.830 which again indicated that the nomogram had excellent predictive performance. The results of calibration curve and DCA indicated that the clinical applicability of this nomogram was outstanding. Finally, the risk classification system and online access version of the nomogram was developed.

CONCLUSION

We constructed the first nomogram to accurately predict the 3-, 5- and 8-year probability of post-surgical CSS in primary chondrosarcoma patients. This nomogram would assist surgeons to provide individualized post-surgical survival predictions and clinical strategies for primary chondrosarcoma patients.

Predicting diagnosis and survival of bone metastasis in breast cancer using machine learning

This study aimed at establishing more accurate predictive models based on novel machine learning algorithms, with the overarching goal of providing clinicians with effective decision-making assistance. We retrospectively analyzed the breast cancer patients recorded in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2016. Multivariable logistic regression analyses were used to identify risk factors for bone metastases in breast cancer, whereas Cox proportional hazards regression analyses were used to identify prognostic factors for breast cancer with bone metastasis (BCBM). Based on the identified risk and prognostic factors, we developed diagnostic and prognostic models that incorporate six machine learning classifiers. We then used the area under the receiver operating characteristic (ROC) curve (AUC), learning curve, precision curve, calibration plot, and decision curve analysis to evaluate performance of the machine learning models. Univariable and multivariable logistic regression analyses showed that bone metastases were significantly associated with age, race, sex, grade, T stage, N stage, surgery, radiotherapy, chemotherapy, tumor size, brain metastasis, liver metastasis, lung metastasis, breast subtype, and PR. Univariate and multivariate Cox regression analyses revealed that age, race, marital status, grade, surgery, radiotherapy, chemotherapy, brain metastasis, liver metastasis, lung metastasis, breast subtype, ER, and PR were closely associated with the prognosis of BCBM. Among the six machine learning models, the XGBoost algorithm predicted the most accurate results (Diagnostic model AUC = 0.98; Prognostic model AUC = 0.88). According to the Shapley additive explanations (SHAP), the most critical feature of the diagnostic model was surgery, followed by N stage. Interestingly, surgery was also the most critical feature of prognostic model, followed by liver metastasis. Based on the XGBoost algorithm, we could effectively predict the diagnosis and survival of bone metastasis in breast cancer and provide targeted references for the treatment of BCBM patients.

Risk factors for distant metastasis of chondrosarcoma: A population-based study

Chondrosarcoma is the second largest bone malignancy after osteosarcoma and mainly affects middle-aged adults, where patients with distant metastasis (DM) often have a poor prognosis. Although nomograms have been widely used to predict distant tumor metastases, there is a lack of large-scale data studies for the diagnostic evaluation of DM in chondrosarcoma. Data on patients diagnosed with chondrosarcoma from 2004 to 2015 were obtained from the Surveillance, Epidemiology, and End Results database. Independent risk factors for having DM from chondrosarcoma were screened using univariate and multivariate logistics regression analysis. A nomogram was created to predict the probability of DM from the screened independent risk factors. The nomogram was then validated using receiver operating characteristic curves and calibration curves. A total of 1870 chondrosarcoma patients were included in the study after data screening, of which 157 patients (8.40%) had DM at the time of diagnosis. Univariate and multivariate logistic regression analysis screened four independent risk factors, including grade, tumor number, T stage, and N stage. receiver operating characteristic curves and calibration curves showed good accuracy of the nomogram in both training and validation sets. The current study screened for independent risk factors for DM from chondrosarcoma, which will help clinicians evaluate patients.

Machine learning for predicting the survival in osteosarcoma patients: Analysis based on American and Hebei Province cohort

Osteosarcoma, a rare malignant tumor, has a poor prognosis. This study aimed to find the best prognostic model for osteosarcoma. There were 2912 patients included from the SEER database and 225 patients from Hebei Province. Patients from the SEER database (2008-2015) were included in the development dataset. Patients from the SEER database (2004-2007) and Hebei Province cohort were included in the external test datasets. The Cox model and three tree-based machine learning algorithms (survival tree [ST], random survival forest [RSF] and gradient boosting machine [GBM]) were used to develop the prognostic models by 10-fold cross-validation with 200 iterations. Additionally, performance of models in the multivariable group was compared with the TNM group. The 3-year and 5-year cancer specific survival (CSS) were 72.71% and 65.92% in the development dataset, respectively. The predictive ability in the multivariable group was superior to that in the TNM group. The calibration curves and consistency in the multivariable group were superior to those in the TNM group. The Cox and RSF models performed better than the ST and GBM models. A nomogram was constructed to predict the 3-year and 5-year CSS of osteosarcoma patients. The RSF model can be used as a nonparametric alternative to the Cox model. The constructed nomogram based on the Cox model can provide reference for clinicians to formulate specific therapeutic decisions both in America and China.

Predicting metastasis at initial diagnosis and radiotherapy effectiveness in patients with metastatic osteosarcoma

Osteosarcoma is a primary malignant bone tumor affecting mostly children and adolescents. The overall 10 year survivals of patients with metastatic osteosarcoma are typically less than 20% in the literature and remain concerning. We aimed to develop a nomogram for predicting the risk of metastasis at initial diagnosis in patients with osteosarcoma and evaluate the effectiveness of radiotherapy in patients with metastatic osteosarcoma. Clinical and demographic data of patients with osteosarcoma were collected from the surveillance, epidemiology, and end results database. We randomly split our analytical sample into the training and validation cohorts, then established and validated a nomogram for predicting the risk of osteosarcoma metastasis at initial diagnosis. The effectiveness of radiotherapy was evaluated by performing propensity score matching in patients underwent surgery + chemotherapy and those underwent surgery + chemotherapy + radiotherapy, among patients with metastatic osteosarcoma. 1439 patients met the inclusion criteria and were included in this study. 343 of 1439 had osteosarcoma metastasis by the time of initial presentation. A nomogram for predicting the likelihood of osteosarcoma metastasis by the time of initial presentation was developed. In both unmatched and matched samples, the radiotherapy group demonstrated a superior survival profile comparing with the non-radiotherapy group. Our study established a novel nomogram to evaluate the risk of osteosarcoma with metastasis, and demonstrated that radiotherapy combined with chemotherapy and surgical resection could improve 10-year survival in patients with metastasis. These findings may guide the clinical decision-making for orthopedic surgeons.

Prognostic Impact of Pulmonary Metastasectomy in Bone Sarcoma Patients: A Retrospective, Single-Centre Study

This retrospective study aimed at analyzing the impact of metastasectomy on post-metastasis survival (PMS) in bone sarcoma patients with lung metastases. Altogether, 47 bone sarcoma patients (24 males, median age at diagnosis of lung metastases: 21.8 (IQR: 15.6–47.3) years) with primary (n = 8) or secondary (n = 39) lung metastases treated at a single university hospital were retrospectively included. Based on a propensity score, inverse probability of treatment weight (IPTW) was calculated to account for selection bias whether patients had undergone metastasectomy or not. The most common underlying histology was osteosarcoma (n = 37; 78.7%). Metastasectomy was performed in 39 patients (83.0%). Younger patients (p = 0.025) with singular (p = 0.043) and unilateral lesions (p = 0.024), as well as those with an interval ≥ 9 months from primary diagnosis to development of lung metastases (p = 0.024) were more likely to undergo metastasectomy. Weighted 1- and 3-year PMS after metastasectomy was 80.8% and 58.3%, compared to 88.5% and 9.1% for patients who did not undergo metastasectomy. Naive Cox-regression analysis demonstrated a significantly prolonged PMS for patients with metastasectomy (HR: 0.142; 95%CI: 0.045–0.450; p = 0.001), which was confirmed after IPTW-weighting (HR: 0.279; 95%CI: 0.118–0.662; p = 0.004), irrespective of age, time to metastasis, and the number of lesions. In conclusion, metastasectomy should be considered in bone sarcoma patients with lung metastases, after carefully considering the individual risks, to possibly improve PMS.

A clinical prediction model for lung metastasis risk in osteosarcoma: A multicenter retrospective study

Background

Lung metastases (LM) have a poor prognosis of osteosarcoma. This study aimed to predict the risk of LM using the nomogram in patients with osteosarcoma.

Methods

A total of 1100 patients who were diagnosed as osteosarcoma between 2010 and 2019 in the Surveillance, Epidemiology and End Results (SEER) database were selected as the training cohort. Univariate and multivariate logistic regression analyses were used to identify independent prognostic factors of osteosarcoma lung metastases. 108 osteosarcoma patients from a multicentre dataset was as valiation data. The predictive power of the nomogram model was assessed by receiver operating characteristic curves (ROC) and calibration plots, and decision curve analysis (DCA) was utilized to interpret the accurate validity in clinical practice.

Results

A total of 1208 patients with osteosarcoma from both the SEER database(n=1100) and the multicentre database (n=108) were analyzed. Univariate and multivariate logistic regression analyses showed that Survival time, Sex, T-stage, N-stage, Surgery, Radiation, and Bone metastases were independent risk factors for lung metastasis. We combined these factors to construct a nomogram for estimating the risk of lung metastasis. Internal and external validation showed significant predictive differences (AUC 0.779, 0.792 respectively). Calibration plots showed good performance of the nomogram model.

Conclusions

In this study, a nomogram model for predicting the risk of lung metastases in osteosarcoma patients was constructed and turned out to be accurate and reliable through internal and external validation. Moreover we built a webpage calculator (https://drliwenle.shinyapps.io/OSLM/) taken into account nomogram model to help clinicians make more accurate and personalized predictions.

Lung and bone metastases patterns in osteosarcoma: Chemotherapy improves overall survival

Osteosarcoma (OS) is a malignant tumor originating from the mesenchymal tissue. Simultaneous reports of lung and bone metastases (BM) in OS are rare in the literature. A total of 353 new cases of lung metastases (LM), 93 new cases of BM, and 59 new cases of LM and BM were diagnosed in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2019. Univariate and multivariate logistic regression analyses were used to identify risk factors for LM and/or BM, and Cox regression analyses were performed to identify the prognostic factors for LM and/or BM. Kaplan-Meier (K-M) curves and log-rank tests were used to analyze the overall survival of patients with LM and/or BM. LM was diagnosed in 353 patients. Female sex, tumor size >100 mm, telangiectatic OS type, central OS type, N1 stage, other locations, BM, surgical treatments, radiotherapy and chemotherapy were significantly correlated with LM. 93 patients were diagnosed with BM. 25 to 59 years old, T1 stage, presence of LM, liver metastases, radiotherapy, and surgical treatments were significantly correlated with the BM. 59 patients were diagnosed with LM and BM. The chondroblastic OS type, small cell OS type, T1 stage, N1 stage, other locations, liver metastases, radiotherapy, and surgical treatments were significantly correlated with LM and BM. Metastases, radiotherapy, and surgery at the primary site were significantly associated with LM and/or BM. Chemotherapy at the primary site has been shown to be effective in improving the survival rate of LM and/or BM. Of the OS patients with LM, 61.47% died, and older age, BM, no surgery, and no chemotherapy were harmful to survival. 72.04% of OS patients with BM died, and N1 stage, no surgery, and no chemotherapy were harmful for survival. 69.49% of OS patients with LM and BM died, and older age and no chemotherapy were harmful for survival.

A new model of preoperative systemic inflammatory markers predicting overall survival of osteosarcoma: a multicenter retrospective study

BACKGROUND

The purpose of this study was to investigate the significance of preoperative C-reactive protein-to-albumin ratio (CAR), neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in predicting overall survival (OS) of osteosarcoma, to establish a nomogram of an individualized prognostic prediction model for osteosarcoma.

METHODS

Two hundred thirty-five patients with osteosarcoma from multiple centers were included in this study. Receiver operating characteristic (ROC) and Youden index were used to determine the optimal cutoff values ​​for CAR, NLR, and PLR. Univariate analysis using COX proportional hazards model to identify factors associated with OS in osteosarcoma, and multivariate analysis of these factors to identify independent prognostic factors. R software (4.1.3-win) rms package was used to build a nomogram, and the concordance index (C-index) and calibration curve were used to assess model accuracy and discriminability.

RESULTS

Univariate analysis revealed that the OS of osteosarcoma is significantly correlated (P < 0.05) with CAR, NLR, PLR, Enneking stage, tumor size, age, neoadjuvant chemotherapy (NACT), and high alkaline phosphatase. Multivariate analysis confirmed that CAR, NLR, Enneking stage, NACT and tumor size are independent prognostic factors for OS of osteosarcoma. The calibration curve shows that the nomogram constructed from these factors has acceptable consistency and calibration capability.

CONCLUSION

Preoperative CAR and NLR were independent predictors of osteosarcoma prognosis, and the combination of nomogram model can realize individualized prognosis prediction and guide medical practice.

LINC00629 protects osteosarcoma cell from ER stress-induced apoptosis and facilitates tumour progression by elevating KLF4 stability

BACKGROUND

Escaping from ER stress-induced apoptosis plays an important role in the progression of many tumours. However, its molecular mechanism in osteosarcoma remains incompletely understood.

METHODS

The molecular mechanism was investigated using RNA sequencing, qRT-PCR and Western blot assays. The relationship between LINC00629 and KLF4 was investigated using RNA pulldown and ubiquitylation assays. The transcriptional regulation of laminin subunit alpha 4 (LAMA4) by KLF4 was identified using bioinformatic analysis, a luciferase assay, and a chromatin immunoprecipitation assay.

RESULTS

Here, we demonstrated that LINC00629 was increased under ER stress treatment. Elevated LINC00629 inhibited ER stress-induced osteosarcoma cell apoptosis and promoted clonogenicity and migration in vitro and in vivo. Further mechanistic studies indicated that LINC00629 interacted with KLF4 and suppressed its degradation, which led to a KLF4 increase in osteosarcoma. In addition, we also found that KLF4 upregulated LAMA4 expression by directly binding to its promoter and that LINC00629 inhibited ER stress-induced apoptosis and facilitated osteosarcoma cell clonogenicity and metastasis by activating the KLF4-LAMA4 pathway.

CONCLUSION

Collectively, our data indicate that LINC00629 is a critical long noncoding RNA (lncRNA) induced by ER stress and plays an oncogenic role in osteosarcoma cell by activating the KLF4-LAMA4 axis.

Lung metastases pattern in limb osteosarcoma: A population-based study from 2010 to 2018

Osteosarcoma (OS) is one of the most prevalent malignant bone tumors. The proportion of limb OS is relatively high, and lung metastases (LM) are one of the most prevalent metastatic types. A total of 1694 new cases of limb OS were identified in the surveillance, epidemiology and end results (SEER) database from 2010 to 2018. Cox regression analyze was performed to identify prognostic factors for limb OS with LM, and univariate and multivariate logistic regression analyses were used to assess risk factors for LM. Kaplan-Meier analysis was performed to calculate overall survival for LM, and a log-rank test was used for comparison. A total of 287 patients (16.94%) were diagnosed with limb OS with LM. 25 to 59 years old (odds ratio, OR 0.68; 95% confidence interval, CI: 0.46-0.99), larger than 100 mm tumors (OR 3.65, 95% CI: 1.54-8.64), telangiectatic osteosarcoma type (OR 0.24, 95% CI: 0.07-0.81), central osteosarcoma type (OR 0.44, 95% CI: 0.19-0.99), T2 stage (OR 2.59, 95% CI: 1.18-5.69), N1 stage (OR 7.79, 95% CI: 3.90-15.56), presence of bone metastases (OR 4.58, 95% CI: 2.43-8.63) and surgical treatments of primary site (OR 0.22, 95% CI: 0.14-0.33) were significant correlations with lung metastases. Elderly age, black race and absence of surgery were harmful for survival. Age between 25 and 59 years, telangiectatic osteosarcoma and central osteosarcoma were identified as high-risk factors in limb OS patients with LM, and surgical treatment of the primary site significantly increased the survival rate of LM in these patients.

Developing and validating a prognostic nomogram for ovarian clear cell carcinoma patients: A retrospective comparison of lymph node staging schemes with competing risk analysis

Purpose

Lymph node (LN) involvement is a key factor in ovarian clear cell carcinoma (OCCC) although, there several indicators can be used to define prognosis. This study examines the prognostic performances of each indicator for OCCC patients by comparing the number of lymph nodes examined (TNLE), the number of positive lymph nodes (PLN), lymph node ratio (LNR), and log odds of metastatic lymph nodes (LODDS).

Methods

1,300 OCCC patients who underwent lymphadenectomy between 2004 and 2015 were extracted from the Surveillance Epidemiology and End Results (SEER) database. Primary outcomes were Overall Survival (OS) and the cumulative incidence of Cancer-Specific Survival (CSS). Kaplan–Meier’s and Fine-Gray’s tests were implemented to assess OS and CSS rates. After conducting multivariate analysis, nomograms using OS and CSS were constructed based upon an improved LN system. Each nomograms’ performance was assessed using Receiver Operating Characteristics (ROC) curves, calibration curves, and the C-index which were compared to traditional cancer staging systems.

Results

Multivariate Cox’s regression analysis was used to assess prognostic factors for OS, including age, T stage, M stage, SEER stage, and LODDS. To account for the CSS endpoint, a proportional subdistribution hazard model was implemented which suggested that the T stage, M stage, SEER stage, and LNR are all significant. This enabled us to develop a LODDS-based nomogram for OS and a LNR-based nomogram for CSS. C-indexes for both the OS and CSS nomograms were higher than the traditional American Joint Committee on Cancer (AJCC), 8th edition, staging system. Area Under the Curve (AUC) values for predicting 3- and 5-year OS and CSS between nomograms also highlighted an improvement upon the AJCC staging system. Calibration curves also performed with consistency, which was verified using a validation cohort.

Conclusions

LODDS and LNR may be better predictors than N stage, TNLE, and PLNs. For OCCC patients, both the LODDS-based and LNR-based nomograms performed better than the AJCC staging system at predicting OS and CSS. However, further large sample, real-world studies are necessary to validate the assertion.

Construction and validation of web-based nomograms for detecting and prognosticating in prostate adenocarcinoma with bone metastasis

Bone metastasis (BM) is one of the most common sites of metastasis in prostate adenocarcinoma (PA). PA with BM can significantly diminish patients' quality of life and result in a poor prognosis. The objective of this study was to establish two web-based nomograms to estimate the risk and prognosis of BM in PA patients. From the Surveillance, Epidemiology, and End Results (SEER) database, data on 308,332 patients diagnosed with PA were retrieved retrospectively. Logistic and Cox regression, respectively, were used to determine independent risk and prognostic factors. Then, We constructed two web-based nomograms and the results were validated by receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA) , and the Kaplan-Meier analyses. The independent risk factors for BM in PA patients included race, PSA, ISUP, T stage, N stage, brain, liver, lung metastasis, surgery, radiation and chemotherapy. The independent prognostic predictors for overall survival (OS) were age, marital status, PSA, ISUP and liver metastasis. Both nomograms could effectively predict risk and prognosis of BM in PA patients according to the results of ROC curves, calibration, and DCA in the training and validation sets. And the Kaplan-Meier analysis illustrated that the prognostic nomogram could significantly distinguish the population with different survival risks. We successfully constructed the two web-based nomograms for predicting the incidence of BM and the prognosis of PA patients with BM, which may assist clinicians in optimizing the establishment of individualized treatment programs and enhancing patients' prognoses.

A nomogram for predicting cancer-specific survival for elderly patients with gallbladder cancer

Background

Gallbladder cancer (GBC) is a highly aggressive malignancy in elderly patients. Our goal is aimed to construct a novel nomogram to predict cancer-specific survival (CSS) in elderly GBC patients.

Method

We extracted clinicopathological data of elderly GBC patients from the SEER database. We used univariate and multivariate Cox proportional hazard regression analysis to select the independent risk factors of elderly GBC patients. These risk factors were subsequently integrated to construct a predictive nomogram model. C-index, calibration curve, and area under the receiver operating curve (AUC) were used to validate the accuracy and discrimination of the predictive nomogram model. A decision analysis curve (DCA) was used to evaluate the clinical value of the nomogram.

Result

A total of 4241 elderly GBC patients were enrolled. We randomly divided patients from 2004 to 2015 into training cohort (n = 2237) and validation cohort (n = 1000), and patients from 2016 to 2018 as external validation cohort (n = 1004). Univariate and multivariate Cox proportional hazard regression analysis found that age, tumor histological grade, TNM stage, surgical method, chemotherapy, and tumor size were independent risk factors for the prognosis of elderly GBC patients. All independent risk factors selected were integrated into the nomogram to predict cancer-specific survival at 1-, 3-, and 5- years. In the training cohort, internal validation cohort, and external validation cohort, the C-index of the nomogram was 0.763, 0.756, and 0.786, respectively. The calibration curves suggested that the predicted value of the nomogram is highly consistent with the actual observed value. AUC also showed the high authenticity of the prediction model. DCA manifested that the nomogram model had better prediction ability than the conventional TNM staging system.

Conclusion

We constructed a predictive nomogram model to predict CSS in elderly GBC patients by integrating independent risk factors. With relatively high accuracy and reliability, the nomogram can help clinicians predict the prognosis of patients and make more rational clinical decisions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02544-y.

Nomogram for Predicting Distant Metastasis of Pancreatic Ductal Adenocarcinoma: A SEER-Based Population Study

(1) Background: The aim of this study was to identify risk factors for distant metastasis of pancreatic ductal adenocarcinoma (PDAC) and develop a valid predictive model to guide clinical practice; (2) Methods: We screened 14328 PDAC patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. Lasso regression analysis combined with logistic regression analysis were used to determine the independent risk factors for PDAC with distant metastasis. A nomogram predicting the risk of distant metastasis in PDAC was constructed. A receiver operating characteristic (ROC) curve and consistency-index (C-index) were used to determine the accuracy and discriminate ability of the nomogram. A calibration curve was used to assess the agreement between the predicted probability of the model and the actual probability. Additionally, decision curve analysis (DCA) and clinical influence curve were employed to assess the clinical utility of the nomogram; (3) Results: Multivariate logistic regression analysis revealed that risk factors for distant metastasis of PDAC included age, primary site, histological grade, and lymph node status. A nomogram was successfully constructed, with an area under the curve (AUC) of 0.871 for ROC and a C-index of 0.871 (95% CI: 0.860-0.882). The calibration curve showed that the predicted probability of the model was in high agreement with the actual predicted probability. The DCA and clinical influence curve showed that the model had great potential clinical utility; (4) Conclusions: The risk model established in this study has a good predictive performance and a promising potential application, which can provide personalized clinical decisions for future clinical work.

Individualized assessment of risk and overall survival in patients newly diagnosed with primary osseous spinal neoplasms with synchronous distant metastasis

Background

The prognosis of patients with primary osseous spinal neoplasms (POSNs) presented with distant metastases (DMs) is still poor. This study aimed to evaluate the independent risk and prognostic factors in this population and then develop two web-based models to predict the probability of DM in patients with POSNs and the overall survival (OS) rate of patients with DM.

Methods

The data of patients with POSNs diagnosed between 2004 and 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistics regression analyses were used to study the risk factors of DM. Based on independent DM-related variables, we developed a diagnostic nomogram to estimate the risk of DM in patients with POSNs. Among all patients with POSNs, those who had synchronous DM were included in the prognostic cohort for investigating the prognostic factors by using Cox regression analysis, and then a nomogram incorporating predictors was developed to predict the OS of patients with POSNs with DM. Kaplan-Meier (K-M) survival analysis was conducted to study the survival difference. In addition, validation of these nomograms were performed by using receiver operating characteristic (ROC) curves, the area under curves (AUCs), calibration curves, and decision curve analysis (DCA).

Results

A total of 1345 patients with POSNs were included in the study, of which 238 cases (17.70%) had synchronous DM at the initial diagnosis. K-M survival analysis and multivariate Cox regression analysis showed that patients with DM had poorer prognosis. Grade, T stage, N stage, and histological type were found to be significantly associated with DM in patients with POSNs. Age, surgery, and histological type were identified as independent prognostic factors of patients with POSNs with DM. Subsequently, two nomograms and their online versions (https://yxyx.shinyapps.io/RiskofDMin/ and https://yxyx.shinyapps.io/SurvivalPOSNs/) were developed. The results of ROC curves, calibration curves, DCA, and K-M survival analysis together showed the excellent predictive accuracy and clinical utility of these newly proposed nomograms.

Conclusion

We developed two well-validated nomograms to accurately quantify the probability of DM in patients with POSNs and predict the OS rate in patients with DM, which were expected to be useful tools to facilitate individualized clinical management of these patients.

Risk stratification system and web-based nomogram constructed for predicting the overall survival of primary osteosarcoma patients after surgical resection

Background

Previous prediction models of osteosarcoma have not focused on survival in patients undergoing surgery, nor have they distinguished and compared prognostic differences among amputation, radical and local resection. This study aimed to establish and validate the first reliable prognostic nomogram to accurately predict overall survival (OS) after surgical resection in patients with osteosarcoma. On this basis, we constructed a risk stratification system and a web-based nomogram.

Methods

We enrolled all patients with primary osteosarcoma who underwent surgery between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. In patients with primary osteosarcoma after surgical resection, univariate and multivariate cox proportional hazards regression analyses were utilized to identify independent prognostic factors and construct a novel nomogram for the 1-, 3-, and 5-year OS. Then the nomogram's predictive performance and clinical utility were evaluated by the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

Result

This study recruited 1,396 patients in all, with 837 serving as the training set (60%) and 559 as the validation set (40%). After COX regression analysis, we identified seven independent prognostic factors to develop the nomogram, including age, primary site, histological type, disease stage, AJCC stage, tumor size, and surgical method. The C-index indicated that this nomogram is considerably more accurate than the AJCC stage in predicting OS [Training set (HR: 0.741, 95% CI: 0.726–0.755) vs. (HR: 0.632, 95% CI: 0.619–0.645); Validation set (HR: 0.735, 95% CI: 0.718–0.753) vs. (HR: 0.635, 95% CI: 0.619–0.652)]. Moreover, the area under ROC curves, the calibration curves, and DCA demonstrated that this nomogram was significantly superior to the AJCC stage, with better predictive performance and more net clinical benefits.

Conclusion

This study highlighted that radical surgery was the first choice for patients with primary osteosarcoma since it provided the best survival prognosis. We have established and validated a novel nomogram that could objectively predict the overall survival of patients with primary osteosarcoma after surgical resection. Furthermore, a risk stratification system and a web-based nomogram could be applied in clinical practice to assist in therapeutic decision-making.

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

Purpose

The 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 methods

We 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.

Results

In 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.

Conclusion

This 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.

Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges

Deep learning is a subfield of state-of-the-art artificial intelligence (AI) technology, and multiple deep learning-based AI models have been applied to musculoskeletal diseases. Deep learning has shown the capability to assist clinical diagnosis and prognosis prediction in a spectrum of musculoskeletal disorders, including fracture detection, cartilage and spinal lesions identification, and osteoarthritis severity assessment. Meanwhile, deep learning has also been extensively explored in diverse tumors such as prostate, breast, and lung cancers. Recently, the application of deep learning emerges in bone tumors. A growing number of deep learning models have demonstrated good performance in detection, segmentation, classification, volume calculation, grading, and assessment of tumor necrosis rate in primary and metastatic bone tumors based on both radiological (such as X-ray, CT, MRI, SPECT) and pathological images, implicating a potential for diagnosis assistance and prognosis prediction of deep learning in bone tumors. In this review, we first summarized the workflows of deep learning methods in medical images and the current applications of deep learning-based AI for diagnosis and prognosis prediction in bone tumors. Moreover, the current challenges in the implementation of the deep learning method and future perspectives in this field were extensively discussed.

Lung Immune Prognostic Index Could Predict Metastasis in Patients With Osteosarcoma

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 &lt; 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.

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

Background

Pancreatic 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.

Methods

PC 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.

Results

A 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.

Conclusion

We developed two reliable predictive models for clinical practice to assist clinicians in developing individualized treatment plans for patients.

Prevalence, Risk Factors, and Prognostic Factors of Primary Malignant Bone Neoplasms with Bone Metastasis at Initial Diagnosis: A Population-Based Study

Background. Bone metastasis (BM) has been proven to be responsible for the poor prognosis of primary malignant bone neoplasms (PMBNs). We aimed to identify the prevalence, risk factors, and prognostic factors for PMBNs patients with BM based on the Surveillance, Epidemiology, and End Results (SEER) database. Methods. 4,758 patients diagnosed with PMBNs from 2010 to 2018 were selected from the SEER database. All patients were divided into two groups: the BM group or the non-BM group. Pearson’s chi-square test and Fisher’s exact method were used to assess baseline characteristics, and logistic regression analysis was applied to assess risk factors. In addition, a nomogram was constructed based on the results of Cox regression analysis among 227 patients with BM. The good performance and clinical applicability of the nomogram were tested by the concordance index, operating characteristic curve, area under the curve, calibration curves, and decision curve analysis. Results. 227 (4.8%) patients had metastasis to bone at diagnosis. Primary site outside the extremities (axial: odds ratio, $\text{OR}=1.770$ ; others: $\text{OR}=1.951$ ), Ewing sarcoma ( $\text{OR}=2.845$ ), larger tumor size (5–8 cm: $\text{OR}=3.403$ ; >8 cm: $\text{OR}=5.562$ ), tumor extension beyond the periosteum ( $\text{OR}=2.477$ ), and regional lymph node metastasis ( $\text{OR}=2.900$ ) were associated with a higher risk of BM at the initial diagnosis of PMBNs. Five independent prognostic factors were found in the survival analysis: pathological type (chondrosarcoma vs. osteosarcoma: hazard ratio, $\text{HR}=0.342$ ; Ewing sarcoma vs. osteosarcoma: $\text{HR}=0.592$ ; and chordoma vs. osteosarcoma: $\text{HR}=0.015$ ), marital status ( $\text{HR}=2.457$ ), pulmonary metastasis ( $\text{HR}=1.934$ ), surgery at the primary site ( $\text{HR}=0.164$ ), and chemotherapy ( $\text{HR}=0.084$ ). A nomogram based on these prognostic factors could be a good predictor of cancer-specific survival. Conclusions. We identified the prevalence, risk factors, and prognostic factors correlated with BM in PMBNs patients. The related nomogram could be a practical tool for therapeutic decision-making and individual counseling.

Development and Validation of Nomograms to Assess Risk Factors and Overall Survival Prediction for Lung Metastasis in Young Patients with Osteosarcoma: A SEER-Based Study

BACKGROUND

To establish two nomograms to quantify the diagnostic factors of lung metastasis (LM) and their role in assessing prognosis in young patients with LM osteosarcoma.

METHODS

A total of 618 osteosarcoma young patients from 2010 to 2015 were included from the Surveillance, Epidemiology, and End Results (SEER) database. Another 131 patients with osteosarcoma from local hospitals were also collected as an external validation set. Patients were randomized into training sets (n = 434) and validation sets (n = 184) with a ratio of 7:3. Univariate and multivariate logistic regression analyses were used to identify the risk factor for LM and were used to construct the nomogram. Risk variables for the overall survival rate of patients with LM were evaluated by Cox regression. Another nomogram was also constructed to predict survival rates. The results were validated using bootstrap resampling and retrospective research on 131 osteosarcoma young patients from 2010 to 2019 at three local hospitals.

RESULTS

There were 114 (18.45%) patients diagnosed as LM at initial diagnosis. The multivariate logistic regression analysis suggested that T stage, N stage, and bone metastasis were independent risk factors for LM in newly diagnosed young osteosarcoma patients (P < 0.001). The ROC analysis revealed that area under the curve (AUC) values were 0.751, 0.821, and 0.735 in the training set, internal validation set, and external validation set, respectively, indicating good predictive discrimination. The multivariate Cox proportional hazard regression analysis suggested that age, surgery, chemotherapy, primary site, and bone metastasis were prognostic factors for young osteosarcoma patients with LM. The time-dependent ROC curves showed that the AUCs for predicting 1-year, 2-year, and 3-year survival rates were 0.817, 0.792, and 0.815 in the training set and 0.772, 0.807, and 0.804 in the internal validation set, respectively. As for the external validation set, the AUCs for predicting 1-year, 2-year, and 3-year survival rates were 0.787, 0.818, and 0.717.

CONCLUSIONS

The nomograms can help clinicians strengthen their personal decision-making and can improve the prognosis of osteosarcoma patients.

Risk factors, survival analysis, and nomograms for distant metastasis in patients with primary pulmonary large cell neuroendocrine carcinoma: A population-based study

INTRODUCTION

Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a rapidly progressive and easily metastatic high-grade lung cancer, with a poor prognosis when distant metastasis (DM) occurs. The aim of our study was to explore risk factors associated with DM in LCNEC patients and to perform survival analysis and to develop a novel nomogram-based predictive model for screening risk populations in clinical practice.

METHODS

The study cohort was derived from the Surveillance, Epidemiology, and End Results database, from which we selected patients with LCNEC between 2004 to 2015 and formed a diagnostic cohort (n = 959) and a prognostic cohort (n = 272). The risk and prognostic factors of DM were screened by univariate and multivariate analyses using logistic and Cox regressions, respectively. Then, we established diagnostic and prognostic nomograms using the data in the training group and validated the accuracy of the nomograms in the validation group. The diagnostic nomogram was evaluated using receiver operating characteristic curves, decision curve analysis curves, and the GiViTI calibration belt. The prognostic nomogram was evaluated using receiver operating characteristic curves, the concordance index, the calibration curve, and decision curve analysis curves. In addition, high- and low-risk groups were classified according to the prognostic monogram formula, and Kaplan-Meier survival analysis was performed.

RESULTS

In the diagnostic cohort, LCNEC close to bronchus, with higher tumor size, and with higher N stage indicated higher likelihood of DM. In the prognostic cohort (patients with LCNEC and DM), men with higher N stage, no surgery, and no chemotherapy had poorer overall survival. Patients in the high-risk group had significantly lower median overall survival than the low-risk group.

CONCLUSION

Two novel established nomograms performed well in predicting DM in patients with LCNEC and in evaluating their prognosis. These nomograms could be used in clinical practice for screening of risk populations and treatment planning.

A predictive web-based nomogram for the early death of patients with lung adenocarcinoma and bone metastasis: a population-based study

Objective

To identify risk factors and develop predictive web-based nomograms for the early death of patients with bone metastasis of lung adenocarcinoma (LUAD).

Methods

Patients in the Surveillance, Epidemiology, and End Results database diagnosed with bone metastasis of LUAD between 2010 and 2016 were included and randomly divided into training and validation sets. Early death-related risk factors (survival time ≤7 months) were evaluated by logistic regression. Two predictive nomograms were established and validated by calibration curves, receiver operating characteristic curves, and decision curve analysis.

Results

A total of 9189 patients (56.59%) died from all causes within 7 months of being diagnosed, including 8585 patients (56.67%) who died from cancer-specific causes. Age >65 years, sex (men), T stage (T3 and T4), N stage (N2 and N3), brain metastasis, and liver metastasis were risk factors for all-cause and cancer-specific early death. The area under the curves of the nomograms for all-cause and cancer-specific early death prediction were 0.754 and 0.753 (training set) and 0.747 and 0.754 (validation set), respectively. Further analysis showed that the two nomograms performed well.

Conclusions

Our two web-based nomograms for all-cause and cancer-specific early death provide valuable tools for predicting early death in these patients.