Novel nomogram to predict risk of bone metastasis in newly diagnosed thyroid carcinoma: a population-based study

Development and validation of a prognostic nomogram for predicting liver metastasis in thyroid cancer: a study based on the surveillance, epidemiology, and end results database

This study aimed to create a prognostic nomogram to predict the risk of liver metastasis (LM) in thyroid cancer (TC) patients and assess survival outcomes for those with LM. Data were collected from the SEER database, covering TC patients from 2010 to 2020, totaling 110,039 individuals, including 142 with LM. Logistic regression and stepwise regression based on the Akaike information criterion (AIC) identified significant factors influencing LM occurrence: age, histological type, tumor size, bone metastasis, lung metastasis, and T stage (p < 0.05). A nomogram was constructed using these factors, achieving a Cindex of 0.977, with ROC curve analysis showing an area under the curve (AUC) of 0.977. For patients with TCLM, follicular TC, medullary TC, papillary TC, and examined regional nodes were associated with better prognosis (p < 0.001, HR < 1), while concurrent brain metastasis indicated poorer outcomes (HR = 2.747, p = 0.037). In conclusion, this nomogram effectively predicts LM risk and evaluates prognosis for TCLM patients, aiding clinicians in personalized treatment decisions.

Development and validation of a lung metastases-predicting nomogram for intermediate- to high-risk differentiated thyroid carcinoma patients

Aim: This research aimed to construct a clinical model for forecasting the likelihood of lung metastases in differentiated thyroid carcinoma (DTC) with intermediate- to high-risk.Methods: In this study, 375 DTC patients at intermediate to high risk were included. They were randomly divided into a training set (70%) and a validation set (30%). A nomogram was created using the training group and then validated in the validation set using calibration, decision curve analysis (DCA) and receiver operating characteristic (ROC) curve.Results: The calibration curves demonstrated excellent consistency between the predicted and the actual probability. ROC analysis showed that the area under the curve in the training cohort was 0.865 and 0.845 in the validation cohort. Also, the DCA curve indicated that this nomogram had good clinical utility.Conclusion: A user-friendly nomogram was constructed to predict the lung metastases probability with a high net benefit.

Novel Nomograms and Web-Based Tools Predicting Overall Survival and Cancer-specific Survival of Solitary Plasmacytoma of the Spine

STUDY DESIGN

Retrospective analysis.

OBJECTIVE

This study aimed to establish nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) in patients with solitary plasmacytoma of the spine (SPS).

SUMMARY OF BACKGROUND DATA

SPS is a rare type of malignant spinal tumor. A systematic study of prognostic factors associated with survival can provide guidance to clinicians and patients. Consideration of other causes of death (OCOD) in CSS will improve clinical practicability.

METHODS

A total of 1078 patients extracted from the SEER database between 2000 and 2018 were analyzed. Patients were grouped into training and testing data sets (7:3). Factors associated with OS and CSS were identified by Cox regression and competing risk regression, respectively, for the establishment of nomograms on a training data set. The testing data set was used for the external validation of the performance of the nomograms using calibration curves, Brier's scores, C-indexes, time-dependent receiver operating characteristic curves, and decision curve analysis (DCA).

RESULTS

Age and grade were identified as factors associated with both OS and CSS, along with marital status, radiation for OS, and chemotherapy for CSS. Heart disease, cerebrovascular disease, and diabetes mellitus were found to be the 3 most common causes of OCOD. The nomograms showed satisfactory agreement on calibration plots for both training and testing data sets. Integrated Brier score, C-index, and overall area under the curve on the testing data set were 0.162/0.717/0.789 and 0.173/0.709/0.756 for OS and CSS, respectively. DCA curves showed a good clinical net benefit. Nomogram-based web tools were developed for clinical application.

CONCLUSION

This study provides evidence for risk factors and prognostication of survival in SPS patients. The novel nomograms and web-based tools we developed demonstrated good performance and might serve as accessory tools for clinical decision-making and SPS management.

LEVEL OF EVIDENCE

3.

Osseous Metastases in Thyroid Cancer: Unveiling Risk Factors, Disease Outcomes, and Treatment Impact

Bone is the second most common site of metastasis in patients with thyroid cancer (TC) and dramatically impacts overall survival and quality of life with no definitive cure, yet there is no extensive study of the demographic and clinical risk factors in the recent literature. Data regarding 120,754 TC patients with bone metastasis were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate analyses were used to identify the risk factors of bone metastasis occurring in various histologies of TC. Cox regression was performed to analyze the influence of bone metastasis on overall survival. Hazard ratios were computed to analyze the association between bone metastasis and the primary outcomes. Of the 120,754 records collected from the SEER database from 2000 to 2019, 976 (0.8%) presented with bone metastasis, with occurrence being the greatest in patients of age ≥ 55 years (OR = 5.63, 95%CI = 4.72-6.71), males (OR = 2.60, 95%CI = 2.27-2.97), Blacks (OR = 2.38, 95%CI = 1.95-2.9) and Asian or Pacific Islanders (OR = 1.90, 95%CI = 1.58-2.27), and single marital status. TC patients presenting with bone metastasis (HR = 2.78, 95%CI = 2.34-3.3) or concurrent bone and brain metastases (HR = 1.62, 95%CI = 1.03-2.55) had a higher mortality risk. Older age, gender, race, and single marital status were associated with bone metastasis and poorer prognosis in TC patients at initial diagnosis. Understanding such risk factors can potentially assist clinicians in making early diagnoses and personalized treatment plans, as well as researchers in developing more therapeutic protocols.

Two simple-to-use web-based nomograms to predict overall survival and cancer-specific survival in patients with extremity fibrosarcoma

Background

Fibrosarcoma is a rare sarcoma of the soft tissue in adults, occurring most commonly in the extremities. This study aimed to construct two web-based nomograms to predict overall survival (OS) and cancer-specific survival (CSS) in patients with extremity fibrosarcoma (EF) and validate it with multicenter data from the Asian/Chinese population.

Method

Patients with EF in the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 were included in this study and were randomly divided into a training cohort and a verification cohort. The nomogram was developed based on the independent prognostic factors determined by univariate and multivariate Cox proportional hazard regression analyses. The predictive accuracy of the nomogram was validated with the Harrell's concordance index (C-index), receiver operating curve, and calibration curve. Decision curve analysis (DCA) was utilized to compare the clinical usefulness between the novel model and the existing staging system.

Result

A total of 931 patients finally were obtained in our study. Multivariate Cox analysis determined five independent prognostic factors for OS and CSS, namely, age, M stage, tumor size, grade, and surgery. The nomogram and the corresponding web-based calculator were developed to predict OS (https://orthosurgery.shinyapps.io/osnomogram/) and CSS (https://orthosurgery.shinyapps.io/cssnomogram/) probability at 24, 36, and 48 months. The C-index of the nomogram was 0.784 in the training cohort and 0.825 in the verification cohort for OS and 0.798 in the training cohort and 0.813 in the verification cohort for CSS, respectively, indicating excellent predictive performance. The calibration curves showed excellent agreement between the prediction by the nomogram and actual outcomes. Additionally, the results of DCA showed that the newly proposed nomogram was significantly better than the conventional staging system with more clinical net benefits. The Kaplan-Meier survival curves showed that patients assigned into the low-risk group had a more satisfactory survival outcome than the high-risk group.

Conclusion

In this study, we constructed two nomograms and web-based survival calculators including five independent prognostic factors for the survival prediction of patients with EF, which could help clinicians make personalized clinical decisions.

Machine learning algorithms are comparable to conventional regression models in predicting distant metastasis of follicular thyroid carcinoma

OBJECTIVE

Distant metastasis often indicates a poor prognosis, so early screening and diagnosis play a significant role. Our study aims to construct and verify a predictive model based on machine learning (ML) algorithms that can estimate the risk of distant metastasis of newly diagnosed follicular thyroid carcinoma (FTC).

DESIGN

This was a retrospective study based on the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015.

PATIENTS

A total of 5809 FTC patients were included in the data analysis. Among them, there were 214 (3.68%) cases with distant metastasis.

METHOD

Univariate and multivariate logistic regression (LR) analyses were used to determine independent risk factors. Seven commonly used ML algorithms were applied for predictive model construction. We used the area under the receiver-operating characteristic (AUROC) curve to select the best ML algorithm. The optimal model was trained through 10-fold cross-validation and visualized by SHapley Additive exPlanations (SHAP). Finally, we compared it with the traditional LR method.

RESULTS

In terms of predicting distant metastasis, the AUROCs of the seven ML algorithms were 0.746-0.836 in the test set. Among them, the Extreme Gradient Boosting (XGBoost) had the best prediction performance, with an AUROC of 0.836 (95% confidence interval [CI]: 0.775-0.897). After 10-fold cross-validation, its predictive power could reach the best [AUROC: 0.855 (95% CI: 0.803-0.906)], which was slightly higher than the classic binary LR model [AUROC: 0.845 (95% CI: 0.818-0.873)].

CONCLUSIONS

The XGBoost approach was comparable to the conventional LR method for predicting the risk of distant metastasis for FTC.

Early distinction of lymph node metastasis in patients with soft tissue sarcoma and individualized survival prediction using the online available nomograms: A population-based analysis

Background

The presence of metastatic tumor cells in regional lymph nodes is considered as a significant indicator for inferior prognosis. This study aimed to construct some predictive models to quantify the probability of lymph node metastasis (LNM) and survival rate of patients with soft tissue sarcoma (STS) with LNM.

Methods

Research data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2017, and data of patients with STS from our medical institution were collected to form an external testing set. Univariate and multivariate logistic regression analyses were used to determine the independent risk factors for developing LNM. On the basis of the identified variables, we developed a diagnostic nomogram to predict the risk of LNM in patients with STS. Those patients with STS presenting with LNM were retrieved to build a cohort for identifying the independent prognostic factors through univariate and multivariate Cox regression analysis. Then, two nomograms incorporating the independent prognostic predictors were developed to predict the overall survival (OS) and cancer-specific survival (CSS) for patients with STS with LNM. Kaplan-Meier (K-M) survival analysis was conducted to study the survival difference. Moreover, validations of these nomograms were performed by the receiver operating characteristic curves, the area under the curve, calibration curves, and the decision curve analysis (DCA).

Results

A total of 16,601 patients with STS from the SEER database were enrolled in our study, of which 659 (3.97%) had LNM at the initial diagnosis. K-M survival analysis indicated that patients with LNM had poorer survival rate. Sex, histology, primary site, grade, M stage, and T stage were found to be independently related with development of LNM in patients with STS. Age, grade, histology, M stage, T stage, chemotherapy, radiotherapy, and surgery were identified as the independent prognostic factors for OS of patients with STS with LNM, and age, grade, M stage, T stage, radiotherapy, and surgery were determined as the independent prognostic factors for CSS. Subsequently, we constructed three nomograms, and their online versions are as follows: https://tyxupup.shinyapps.io/probabilityofLNMforSTSpatients/, https://tyxupup.shinyapps.io/OSofSTSpatientswithLNM/, and https://tyxupup.shinyapps.io/CSSofSTSpatientswithLNM/. The areas under the curve (AUCs) of diagnostic nomogram were 0.839 in the training set, 0.811 in the testing set, and 0.852 in the external testing set. For prognostic nomograms, the AUCs of 24-, 36-, and 48-month OS were 0.820, 0.794, and 0.792 in the training set and 0.759, 0.728, and 0.775 in the testing set, respectively; the AUCs of 24-, 36-, and 48-month CSS were 0.793, 0.777, and 0.775 in the training set and 0.775, 0.744, and 0.738 in the testing set, respectively. Furthermore, calibration curves suggested that the predicted values were consistent with the actual values. For the DCA, our nomograms showed a superior net benefit across a wider scale of threshold probabilities for the prediction of risk and survival rate for patients with STS with LNM.

Conclusion

These newly proposed nomograms promise to be useful tools in predicting the risk of LNM for patients with STS and individualized survival prediction for patients with STS with LNM, which may help to guide clinical practice.

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.

Bone metastases in newly diagnosed patients with thyroid cancer: A large population-based cohort study

Background

Population-based estimates of the incidence and prognosis of bone metastases (BM) stratified by histologic subtype at diagnosis of thyroid cancer are limited.

Methods

Using multivariable logistic and Cox regression analyses, we identified risk factors for BM and investigated the prognostic survival of BM patients between 2010 and 2015 via the Surveillance, Epidemiology, and End Results (SEER) database.

Results

Among 64,083 eligible patients, a total of 347 patients with BM at the time of diagnosis were identified, representing 0.5% of the entire cohort but 32.4% of the subset with metastases. BM incidence was highest (11.6%) in anaplastic thyroid cancer (ATC), which, nevertheless, was highest (61.5%) in follicular thyroid cancer (FTC) among the subset with metastases. The median overall survival among BM patients was 40.0 months, and 1-, 3-, and 5-year survival rates were 65.2%, 51.3%, and 38.7%, respectively. Compared with papillary thyroid cancer (PTC), FTC (aOR, 6.33; 95% CI, 4.72–8.48), medullary thyroid cancer (MTC) (aOR, 6.04, 95% CI, 4.09–8.92), and ATC (aOR, 6.21; 95% CI, 4.20–9.18) significantly increased the risk of developing BM. However, only ATC (aHR, 6.07; 95% CI, 3.83–9.60) was independently associated with worse survival in multivariable analysis. Additionally, patients with BM alone (56.5%) displayed the longest median survival (66.0 months), compared with those complicated with one extraskeletal metastatic site (lung, brain, or liver) (35.2%; 14.0 months) and two or three sites (8.3%; 6.0 months). The former 5-year overall survival rate was 52.6%, which, however, drastically declined to 23.0% in patients with one extraskeletal metastatic site and 9.1% with two or three sites.

Conclusion

Closer bone surveillance should be required for patients with FTC, MTC, and ATC, and extraskeletal metastases at initial diagnosis frequently predict a poorer prognosis.

Nomogram individually predicts the risk for distant metastasis and prognosis value in female differentiated thyroid cancer patients: A SEER-based study

Objective

Distant metastasis (DM) is an important prognostic factor in differentiated thyroid cancer (DTC) and determines the course of treatment. This study aimed to establish a predictive nomogram model that could individually estimate the risk of DM and analyze the prognosis of female DTC patients (FDTCs).

Materials and methods

A total of 26,998 FDTCs were retrospectively searched from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2018 and randomly divided into validation and training cohorts. Univariate and multivariate analyses were performed to screen for prognostic factors and construct a prediction nomogram. The performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC), concordance index (C-index), and a calibration curve. The overall survival (OS) and cancer-specific survival (CSS) were evaluated by Kaplan-Meier (K-M) analysis.

Results

A total of 263 (0.97%) FDTCs were reported to have DM. K-M analysis showed the association of multiple-organ metastases and brain involvement with lower survival rates (P < 0.001) in patients. Tumor size, age at diagnosis, thyroidectomy, N1 stage, T3-4 stage, and pathological type were independent predictive factors of DM in FDTCs (all P < 0.001). Similarly, age at diagnosis, Black, DM, T3-4 stage, thyroidectomy, and lung metastasis were determined as independent prognostic factors for FDTCs (all P < 0.001). Several predictive nomograms were established based on the above factors. The C-index, AUC, and calibration curves demonstrated a good performance of these nomogram models.

Conclusion

Our study was successful in establishing and validating nomograms that could predict DM, as well as CSS and OS in individual patients with FDTC based on a large study cohort. These nomograms could enable surgeons to perform individualized survival evaluation and risk stratification for FDTCs.

A Nomogram for Predicting the Risk of Bone Metastasis in Newly Diagnosed Head and Neck Cancer Patients: A Real-World Data Retrospective Cohort Study From SEER Database

Background: Bone metastasis (BM) is one of the typical metastatic types of head and neck cancer (HNC). The occurrence of BM prevents the HNC patients from obtaining a long survival period. Early assessment of the possibility of BM could bring more therapy options for HNC patients, as well as a longer overall survival time. This study aims to identify independent BM risk factors and develop a diagnostic nomogram to predict BM risk in HNC patients.

Methods: Patients diagnosed with HNC between 2010 and 2015 were retrospectively evaluated in the Surveillance, Epidemiology, and End Results (SEER) database, and then eligible patients were enrolled in our study. First, those patients were randomly assigned to training and validation sets in a 7:3 ratio. Second, univariate and multivariate logistic regression analyses were used to determine the HNC patients’ independent BM risk factors. Finally, the diagnostic nomogram’s risk prediction capacity and clinical application value were assessed using calibration curves, receiver operating characteristic (ROC), and decision curve analysis (DCA) curves.

Results: 39,561 HNC patients were enrolled in the study, and they were randomly divided into two sets: training (n = 27,693) and validation (n = 11,868). According to multivariate logistic regression analysis, race, primary site, tumor grade, T stage, N stage, and distant metastases (brain, liver, and lung) were all independent risk predictors of BM in HNC patients. The diagnostic nomogram was created using the above independent risk factors and had a high predictive capacity. The training and validation sets’ area under the curves (AUC) were 0.893 and 0.850, respectively. The AUC values of independent risk predictors were all smaller than that of the constructed diagnostic nomogram. Meanwhile, the calibration curve and DCA also proved the reliability and accuracy of the diagnostic nomogram.

Conclusion: The diagnostic nomogram can quickly assess the probability of BM in HNC patients, help doctors allocate medical resources more reasonably, and achieve personalized management, especially for HNC patients with a potentially high BM risk, thus acquiring better early education, early detection, and early diagnosis and treatment to maximize the benefits of patients.

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.