Development and validation of a nomogram for osteosarcoma-specific survival: A population-based study

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.

Development of nomogram and discussion of radiotherapy effect for osteosarcoma survival

This study aimed to develop a predictive system for prognostic evaluation of osteosarcoma patients. We obtained osteosarcoma sample data from 1998 to 2016 using SEER*Stat software version 8.3.8, and established a multivariable Cox regression model using R-4.0.3 software. Data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The diagnosis of the model was completed through influential cases, proportionality, and multicollinearity. The predictive ability of the model was tested using area under the curve (AUC), calibration curves, and Brier scores. Finally, the bootstrap method was used to internally verify the model. In total, data from 3566 patients with osteosarcoma were included in this study. The multivariate Cox regression model was used to determine the independent prognostic variables. A nomogram and Kaplan-Meier survival curve were established. The AUC and Brier scores indicated that the model had a good predictive calibration. In addition, we found that the radiotherapy appears to be a risk factor of patients with osteosarcoma and made a discussion. We developed a prognostic evaluation system for patients with osteosarcoma for 1-, 3-, and 5-year overall survival with good predictive ability using sample data extracted from the SEER database. This has important clinical significance for the early identification and treatment of high-risk groups of osteosarcoma patients.

Formulation and validation of a baseline prognostic score for osteosarcoma treated uniformly with a non-high dose methotrexate-based protocol from a low middle income healthcare setting: a single centre analysis of 594 patients

Introduction

The outcomes of osteosarcoma in low middle income countries (LMICs) are different due to patients presenting in advanced stages, resource constraints and the use of non-high-dose-methotrexate (HDMTX)-based regimens. This study derived and validated a prognostic score for osteosarcoma that integrates biologic and social factors and is tailored for patients from an LMIC setting using a non-HDMTX-based protocol.

Materials and methods

A retrospective study including osteosarcoma patients enrolled for treatment at a single tertiary care centre in India between 2003-19 was conducted. Baseline biologic and social characteristics were extracted from medical records and survival outcomes were noted. The cohort was randomised into a derivation and validation cohort. Multivariable Cox regression was used to identify baseline characteristics that were independently prognostic for survival outcomes in the derivation cohort. A score was derived from the prognostic factors identified in the derivation cohort and further validated in the validation cohort with estimation of its predictive ability.

Results

594 patients with osteosarcoma were eligible for inclusion in the study. Around one-third of the cohort had metastatic disease with 59% of the patients residing in rural areas. The presence of metastases at baseline (HR 3.39; p<0.001; score=3), elevated serum alkaline phosphatase (SAP) >450 IU/L (HR 1.57; p=0.001; score=1) and baseline tumour size > 10 cm (HR 1.68; p<0.001; score=1) were identified to be independent factors predicting inferior event free survival (EFS) and were included in development of the prognostic score. Patients were categorized as low risk (score 0), intermediate risk (score 1-3) and high risk (4-5). Harrell's c-indices for the score were 0.682, 0.608 and 0.657 respectively for EFS in the derivation, validation and whole cohort respectively. The timed AUC of ROC was 0.67 for predicting 18-month EFS in the derivation, validation and whole cohorts while that for 36-month EFS were 0.68, 0.66 and 0.68 respectively.

Conclusions

The study describes the outcomes among osteosarcoma patients from an LMIC treated uniformly with a non-HDMTX-based protocol. Tumor size, baseline metastases and SAP were prognostic factors used to derive a score with good predictive value for survival outcomes. Social factors did not emerge as determinants of survival.

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.

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.

An External-Validated Prediction Model to Predict Lung Metastasis among Osteosarcoma: A Multicenter Analysis Based on Machine Learning

Background

Lung metastasis greatly affects medical therapeutic strategies in osteosarcoma. This study aimed to develop and validate a clinical prediction model to predict the risk of lung metastasis among osteosarcoma patients based on machine learning (ML) algorithms.

Methods

We retrospectively collected osteosarcoma patients from the Surveillance Epidemiology and End Results (SEER) database and from four hospitals in China. Six ML algorithms, including logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), random forest (RF), decision tree (DT), and multilayer perceptron (MLP), were applied to build predictive models for predicting lung metastasis using patient's demographics, clinical characteristics, and therapeutic variables from the SEER database. The model was internally validated using 10-fold cross-validation to calculate the mean area under the curve (AUC) and the model was externally validated using the Chinese multicenter osteosarcoma data. Relative importance ranking of predictors was plotted to understand the importance of each predictor in different ML algorithms. The correlation heat map of predictors was plotted to understand the correlation of each predictor, selecting the 10-fold cross-validation with the highest AUC value in the external validation ROC curve to build a web calculator.

Results

Of all enrolled patients from the SEER database, 17.73% (194/1094) developed lung metastasis. The multiple logistic regression analysis showed that sex, N stage, T stage, surgery, and bone metastasis were all independent risk factors for lung metastasis. In predicting lung metastasis, the mean AUCs of the six ML algorithms ranged from 0.711 to 0.738 in internal validation and 0.697 to 0.729 in external validation. Among the six ML algorithms, the extreme gradient boosting (XGBoost) model had the highest AUC value with an average internal AUC of 0.738 and an external AUC of 0.729. The best performing ML algorithm model was used to build a web calculator to facilitate clinicians to calculate the risk of lung metastasis for each patient.

Conclusions

The XGBoost model may have the best prediction effect and the online calculator based on this model can help doctors to determine the lung metastasis risk of osteosarcoma patients and help to make individualized medical strategies.

A Web-Based Prediction Model for Overall Survival of Elderly Patients With Malignant Bone Tumors: A Population-Based Study

Background: Malignant bone tumors (MBT) are one of the causes of death in elderly patients. The purpose of our study is to establish a nomogram to predict the overall survival (OS) of elderly patients with MBT.

Methods: The clinicopathological data of all elderly patients with MBT from 2004 to 2018 were downloaded from the SEER database. They were randomly assigned to the training set (70%) and validation set (30%). Univariate and multivariate Cox regression analysis was used to identify independent risk factors for elderly patients with MBT. A nomogram was built based on these risk factors to predict the 1-, 3-, and 5-year OS of elderly patients with MBT. Then, used the consistency index (C-index), calibration curve, and the area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model was. Decision curve analysis (DCA) was used to assess the clinical potential application value of the nomogram. Based on the scores on the nomogram, patients were divided into high- and low-risk groups. The Kaplan-Meier (K-M) curve was used to test the difference in survival between the two patients.

Results: A total of 1,641 patients were included, and they were randomly assigned to the training set (N = 1,156) and the validation set (N = 485). The univariate and multivariate analysis of the training set suggested that age, sex, race, primary site, histologic type, grade, stage, M stage, surgery, and tumor size were independent risk factors for elderly patients with MBT. The C-index of the training set and the validation set were 0.779 [0.759–0.799] and 0.801 [0.772–0.830], respectively. The AUC of the training and validation sets also showed similar results. The calibration curves of the training and validation sets indicated that the observed and predicted values were highly consistent. DCA suggested that the nomogram had potential clinical value compared with traditional TNM staging.

Conclusion: We had established a new nomogram to predict the 1-, 3-, 5-year OS of elderly patients with MBT. This predictive model can help doctors and patients develop treatment plans and follow-up strategies.

A predictive model with a risk-classification system for cancer-specific survival in patients with primary osteosarcoma of long bone

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Seven clinical factors were significantly related to the prognosis of patients with long bone osteosarcoma.

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The established nomogram can help surgeons evaluate the prognosis of osteosarcoma patients in the most common sites.

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High-risk individuals can be identified through risk-stratification system.

Background

Osteosarcoma (OS), most commonly occurring in long bone, is a group of malignant tumors with high incidence in adolescents. No individualized model has been developed to predict the prognosis of primary long bone osteosarcoma (PLBOS) and the current AJCC TNM staging system lacks accuracy in prognosis prediction. We aimed to develop a nomogram based on the clinicopathological factors affecting the prognosis of PLBOS patients to help clinicians predict the cancer-specific survival (CSS) of PLBOS patients.

Method

We studied 1199 PLBOS patients from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015 and randomly divided the dataset into training and validation cohorts at a proportion of 7:3. Independent prognostic factors determined by stepwise multivariate Cox analysis were included in the nomogram and risk-stratification system. C-index, calibration curve, and decision curve analysis (DCA) were used to verify the performance of the nomogram.

Results

Age, Histological type, Surgery of primary site, Tumor size, Local extension, Regional lymph node (LN) invasion, and Distant metastasis were identified as independent prognostic factors. C-indexes, calibration curves and DCAs of the nomogram indicating that the nomogram had good discrimination and validity. The risk-stratification system based on the nomogram showed significant differences (P < 0.05) in CSS among different risk groups.

Conclusion

We established a nomogram with risk-stratification system to predict CSS in PLBOS patients and demonstrated that the nomogram had good performance. This model can help clinicians evaluate prognoses, identify high-risk individuals, and give individualized treatment recommendation of PLBOS patients.

Chondrocyte-derived Exosomal miR-195 Inhibits Osteosarcoma Cell Proliferation and Anti-Apoptotic by Targeting KIF4A in vitro and in vivo

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Osteosarcoma (OS) chemoresistance and distant metastasis are directly associated with OS recurrence and dismal patient prognosis, which are serious concerns for the medical community.

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However, current knowledge on OS pathogenesis and treatment remains limited. We found that kinesin superfamily protein 4A (KIF4A) acts as a potential OS biomarker.

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KIF4A promoted OS cell proliferation and anti-apoptotic in vitro and enhanced tumor growth in vivo. Our results indicate that miR-195 inhibits the expression of KIF4A by directly targeting its 3’-untranslated region

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Hence, targeting KIF4A could be a novel therapeutic strategy for OS and miR-195 may be a potential KIF4A-targeting drug. Furthermore, this study demonstrates that normal human chondrocytes can be used to produce miR-195-carrying exosomes to successfully deliver miR-195 into OS cells.

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Thus, our results suggest that chondrocyte-derived exosomal miR-195 may be developed into a potential adjuvant chemotherapeutic drug.

Background

Osteosarcoma (OS) is a primary malignant tumor of the bone that occurs in adolescents and is characterized by a young age at onset, high malignancy, high rate of metastasis, and poor prognosis. However, the factors influencing disease progression and prognosis remain unclear.

Methods

In this study, we aimed to investigate the role of chondrocyte-derived exosomal miR-195 in OS. We used normal human chondrocytes to form miR-195-carrying exosomes to deliver miR-195 into OS cells. Xenograft tumor experiments were performed in mice intratumorally injected with exosomal miR-195. We found that kinesin superfamily protein 4A (KIF4A) promoted OS tumor progression and anti-apoptotic.

Resules

We demonstrated that miR-195 inhibited the expression of KIF4A by directly targeting its 3’-untranslated region. Moreover, we observed that exosomal miR-195 successfully inhibited OS cell tumor growth and antiapoptotic in vitro and suppressed tumor growth in vivo.

Conclusion

Collectively, these results demonstrate that normal human chondrocyte-derived exosomal miR-195 can be internalized by OS cells and inhibit tumor growth and antiapoptotic by targeting KIF4A, providing a new direction for clarifying the molecular mechanism underlying OS development.

Pretest models for predicting abnormal stress single-photon emission computed tomography myocardial perfusion imaging

BACKGROUND

The frequency of abnormal stress SPECT myocardial perfusion imaging (MPS) decreased over the past decades despite an increase in the prevalence of cardiovascular risk factors. These findings strengthen the need to develop more effective strategies for appropriately referring patients with suspected coronary artery disease (CAD) to cardiac imaging. The aim of this study was to develop pretest assessment models for predicting abnormal stress MPS.

METHODS

We included 5,601 consecutive patients with suspected CAD, who underwent stress MPS at our academic center. Two different models were considered: a basic model including age, gender, and anginal symptoms; and a clinical model including also diabetes, hypertension, hypercholesterolemia, smoking, and family history of CAD.

RESULTS

In patients with abnormal MPS, the basic model classified more than 75% of patients as intermediate risk, whereas only 23% were incorrectly classified as low risk. In patients with normal MPS, 45% were correctly classified as low risk and none as high risk. Basic and clinical models had a limited discriminating capacity (area under the receiver operating characteristic curve 0.644 for basic model and 0.647 for clinical model) between individuals with and without abnormal stress MPS. The decision curve analysis demonstrates a high net benefit across a range of threshold probabilities from ~ 15% to ~30% for both models.

CONCLUSIONS

A pretest risk stratification based on traditional cardiovascular risk factors has a limited value for predicting an abnormal stress MPS in patients with suspected CAD. However, selecting a proper threshold probability enhances the appropriateness of referral to stress MPS.

Identifying the Risk Factors and Estimating the Prognosis in Patients with Pelvis and Spine Ewing Sarcoma: A Population-Based Study

STUDY DESIGN

Retrospective analysis.

OBJECTIVE

The study was designed to: (1) figure out risk factors of metastasis; (2) explore prognostic factors and develop a nomogram for pelvis and spine Ewing sarcoma (PSES).

SUMMARY OF BACKGROUND DATA

Tools to predict survival of PSES are still insufficient. Nomogram has been widely developed in clinical oncology. Moreover, risk factors of PSES metastasis are still unclear.

METHODS

The data were collected and analyzed from the Surveillance, Epidemiology, and End Results (SEER) database. The optimal cutoff values of continuous variables were identified by X-tile software. The prognostic factors of survival were performed by Kaplan-Meier method and multivariate Cox proportional hazards modeling. Nomograms were further constructed for estimating 3- and 5-year cancer-specific survival (CSS) and overall survival (OS) by using R with rms package. Meanwhile, Pearson χ2 test or Fisher exact test, and logistic regression analysis were used to analyze the risk factors for the metastasis of PSES.

RESULTS

A total of 371 patients were included in this study. The 3- and 5-year CSS and OS rate were 65.8 ± 2.6%, 55.2 ± 2.9% and 64.3 ± 2.6%, 54.1 ± 2.8%, respectively. The year of diagnosis, tumor size, and lymph node invasion were associated with metastasis of patients with PSES. A nomogram was developed based on identified factors including: age, tumor extent, tumor size, and primary site surgery. The concordance index (C-index) of CSS and OS were 0.680 and 0.679, respectively. The calibration plot showed the similar trend of 3-year, 5-year CSS, and OS of PSES patients between nomogram-based prediction and actual observation, respectively.

CONCLUSION

PSES patients with earlier diagnostic year (before 2010), larger tumor size (>59 mm), and lymph node invasion, are more likely to have metastasis. We developed a nomogram based on age, tumor extent, tumor size, and surgical treatments for determining the prognosis for patients with PSES, while more external patient cohorts are warranted for validation.Level of Evidence: 3.

Construction and validation of nomogram to predict distant metastasis in osteosarcoma: a retrospective study

Background

Osteosarcoma is most common malignant bone tumors. OS patients with metastasis have a poor prognosis. There are few tools to assess metastasis; we want to establish a nomogram to evaluate metastasis of osteosarcoma.

Methods

Data from the Surveillance, Epidemiology, and End Results (SEER) database of patients with osteosarcoma were retrieved for retrospective analysis. We identify risk factors through univariate logistic regression and multivariate logistic regression analysis. Based on the results of multivariate analysis, we established a nomogram to predict metastasis of patients with osteosarcoma and used the concordance index (C-index) and calibration curves to test models.

Results

One thousand fifteen cases were obtained from the SEER database. In the univariate and multivariate logistic regression analysis, age, primary site, grade, T stage, and surgery are risk factors. The nomogram for metastasis was constructed based on these factors. The C-index of the training and validation cohort was 0.754 and 0.716. This means that the nomogram predictions of patients with metastasis are correct, and the calibration plots also show the good prediction performance of the nomogram.

Conclusion

We successfully develop the nomogram which can reliably predict metastasis in different patients with osteosarcoma and it only required basic information of patients. The nomogram that we developed can help clinicians better predict the metastasis with OS and determine postoperative treatment strategies.

Overall survival nomogram for patients with spinal bone metastases (SBM)

•Demographic features are essential for a more personalize survival prediction of spinal bone metastasis (SBM).•Women have a relatively better survival chance than men before 75 years, while men have better survival after this age.•SBM survival is not dependent on the number of spinal metastases.

Competing-Risk Nomograms for Predicting the Prognosis of Patients With Infiltrating Lobular Carcinoma of the Breast

BACKGROUND

Infiltrating lobular carcinoma (ILC) is the second most common histologic subtype of breast cancer. We assessed the rates of cause-specific death in ILC patients with the aim of establishing competing-risk nomograms for predicting their prognosis.

PATIENTS AND METHODS

Data on ILC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The cumulative incidence function was used to calculate the cumulative incidence rates of cause-specific death, and Gray's test was applied to test the differences in cumulative incidence rates among groups. We then identified independent prognostic factors by applying the Fine-Gray proportional subdistribution hazard analysis method and established nomograms based on the results. Calibration curves and the concordance index were employed to validate the nomograms.

RESULTS

The study enrolled 11,361 patients. The 3-, 5-, and 10-year overall cumulative incidence rates for those who died of ILC were 3.1%, 6.2%, and 12.2%, respectively, whereas the rates for those who died from other causes were 3.2%, 5.8%, and 14.1%. Age, marriage, grade, size, regional node positivity, American Joint Committee on Cancer M stage, progesterone receptor, and surgery were independent prognostic factors for dying of ILC, whereas the independent prognostic factors for dying of other causes were age, race, marriage, size, radiation, and chemotherapy. The nomograms were well calibrated and had good discrimination ability.

CONCLUSION

We applied competing-risk analysis to ILC patients based on the SEER database and established nomograms that perform well in predicting the cause-specific death rates at 3, 5, and 10 years after the diagnosis.

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

BACKGROUND

Osteosarcoma is the most common bone cancer, mainly occurring in children and adolescents, among which distant metastasis (DM) still leads to a poor prognosis. Although nomogram has recently been used in tumor areas, there are no studies focused on diagnostic and prognostic evaluation of DM in primary osteosarcoma patients.

METHODS

The data of osteosarcoma patients diagnosed between 2004 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for DM in osteosarcoma patients, and univariate and multivariate Cox proportional hazards regression analyses were used to determine independent prognostic factors of osteosarcoma patients with DM. We then established two novel nomograms and the results were evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULT

A total of 1,657 patients with osteosarcoma were included, and 267 patients (16.11%) had DM at the time of diagnosis. The independent risk factors for DM in patients with osteosarcoma include age, grade, T stage, and N stage. The independent prognostic factors for osteosarcoma patients with DM are age, chemotherapy and surgery. The results of ROC curves, calibration, DCA, and Kaplan-Meier (K-M) survival curves in the training, validation, and expanded testing sets, confirmed that two nomograms can precisely predict occurrence and prognosis of DM in osteosarcoma patients.

CONCLUSION

Two nomograms are expected to be effective tools for predicting the risk of DM for osteosarcoma patients and personalized prognosis prediction for patients with DM, which may benefit clinical decision-making.

Nomogram for predicting cancer-specific survival in undifferentiated pleomorphic sarcoma: A Surveillance, Epidemiology, and End Results -based study

INTRODUCTION

The purpose of this study was to construct and validate a nomogram for predicting cancer-specific survival (CSS) in undifferentiated pleomorphic sarcoma (UPS) patients at 3, 5, and 8 years after the diagnosis.

METHODS

Data for UPS patients were extracted from the SEER (Surveillance, Epidemiology, and End Results) database. The patients were randomly divided into a training cohort (70%) and a validation cohort (30%). The backward stepwise Cox regression model was used to select independent prognostic factors. All of the factors were integrated into the nomogram to predict the CSS rates in UPS patients at 3, 5, and 8 years after the diagnosis. The nomogram' s performance was then validated using multiple indicators, including the area under the time-dependent receiver operating characteristic curve (AUC), consistency index (C-index), calibration curve, decision-curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification improvement (NRI).

RESULTS

This study included 2,009 UPS patients. Ten prognostic factors were identified after analysis of the Cox regression model in the training cohort, which were year of diagnosis, age, race, primary site, histological grade, T, N, M stage, surgery status, and insurance status. The nomogram was then constructed and validated internally and externally. The relatively high C-indexes and AUC values indicated that the nomogram has good discrimination ability. The calibration curves revealed that the nomogram was well calibrated. NRI and IDI values were both improved, indicating that our nomogram was superior to the AJCC (American Joint Committee on Cancer) system. DCA curves demonstrated that the nomogram was clinically useful.

CONCLUSIONS

The first nomogram for predicting the prognosis of UPS patients has been constructed and validated. Its usability and performance showed that the nomogram can be applied to clinical practice. However, further external validation is still needed.

A novel risk score model based on eight genes and a nomogram for predicting overall survival of patients with osteosarcoma

BACKGROUND

This study aims to identify a predictive model to predict survival outcomes of osteosarcoma (OS) patients.

METHODS

A RNA sequencing dataset (the training set) and a microarray dataset (the validation set) were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, respectively. Differentially expressed genes (DEGs) between metastatic and non-metastatic OS samples were identified in training set. Prognosis-related DEGs were screened and optimized by support vector machine (SVM) recursive feature elimination. A SVM classifier was built to classify metastatic and non-metastatic OS samples. Independent prognosic genes were extracted by multivariate regression analysis to build a risk score model followed by performance evaluation in two datasets by Kaplan-Meier (KM) analysis. Independent clinical prognostic indicators were identified followed by nomogram analysis. Finally, functional analyses of survival-related genes were conducted.

RESULT

Totally, 345 DEGs and 45 prognosis-related genes were screened. A SVM classifier could distinguish metastatic and non-metastatic OS samples. An eight-gene signature was an independent prognostic marker and used for constructing a risk score model. The risk score model could separate OS samples into high and low risk groups in two datasets (training set: log-rank p < 0.01, C-index = 0.805; validation set: log-rank p < 0.01, C-index = 0.797). Tumor metastasis and RS model status were independent prognostic factors and nomogram model exhibited accurate survival prediction for OS. Additionally, functional analyses of survival-related genes indicated they were closely associated with immune responses and cytokine-cytokine receptor interaction pathway.

CONCLUSION

An eight-gene predictive model and nomogram were developed to predict OS prognosis.