Predict overall survival of spinal conventional chordoma: Development and assessment of a new predictive nomogram

Prognostic factors and overall survival in pelvic Ewing's sarcoma and chordoma: A comparative SEER database analysis

Background

This study aimed to develop and validate nomograms to predict overall survival (OS) for pelvic Ewing's sarcoma (EWS) and chordoma, identify prognostic factors, and compare outcomes between the two conditions.

Methods

We identified patients diagnosed with pelvic EWS or chordoma from the SEER database (2001-2019). Independent risk factors were identified using univariate and multivariate Cox regression analyses, and these factors were used to construct nomograms predicting 3-, 5-, and 10-year OS. Validation methods included AUC, calibration plots, C-index, and decision curve analysis (DCA). Kaplan-Meier curves and log-rank tests compared survival differences between low- and high-risk groups.

Results

The study included 1175 patients (EWS: 611, chordoma: 564). Both groups were randomly divided into training (70 %) and validation (30 %) cohorts. OS was significantly higher for chordoma. Multivariate analysis showed year of diagnosis, income, stage, and surgery were significant for EWS survival, while age, time to treatment, stage, and surgery were significant for chordoma survival. Validation showed the nomograms had strong predictive performance and clinical utility.

Conclusions

The nomograms reliably predict overall survival (OS) in pelvic EWS and chordoma, helping to identify high-risk patients early and guide preventive measures. The study also found that survival rates are significantly higher for chordoma, highlighting different prognostic profiles between EWS and chordoma.

Deep-Learning-Based Model for the Prediction of Cancer-Specific Survival in Patients with Spinal Chordoma

OBJECTIVE

Spinal chordomas are locally aggressive and frequently recurrent tumors with a poor prognosis. Previous studies focused on a Cox regression model to predict the survival of patients with spinal chordoma. We aimed to develop a more effective model based on deep learning for prognosis prediction in spinal chordoma.

METHODS

Patients with spinal chordoma were gathered from the SEER database. Cox regression analysis was conducted to compare the influence of different clinical characteristics on cancer-specific survival. Two deep learning models, namely, DeepSurv and NMTLR, were developed, alongside 2 classic models, for the purpose of comparison. Performance of these models was evaluated by concordance index, Integrated Brier Score, receiver operating characteristic curves, Kaplan-Meier curves, and calibration curves.

RESULTS

A total of 258 spinal chordoma patients were included in the current study. The median follow-up time was 94 ± 52 months. Variables used for prognosis prediction consisted of age, primary site, tumor size, histologic grade, extension of surgery, tumor invasion, and metastasis. Comparing with conventional models, each deep learning model showed superior predictive performance, the C-index on the test cohort is 0.830 for DeepSurv and 0.804 for NMTLR, respectively. The DeepSurv model represented the best performance, with area under the curve of 0.843 in predicting 5-year survival and 0.880 in predicting 10-year survival.

CONCLUSIONS

We successfully constructed a deep learning model to predict the CSS of spinal chordoma patients and proved that it was more accurate and practical than conventional prediction model. Our deep learning model has the potential to guide clinicians in better care planning and decision-making.

Predicting overall survival in chordoma patients using machine learning models: a web-app application

OBJECTIVE

The goal of this study was to evaluate the efficacy of machine learning (ML) techniques in predicting survival for chordoma patients in comparison with the standard Cox proportional hazards (CoxPH) model.

METHODS

Using a Surveillance, Epidemiology, and End Results database of consecutive newly diagnosed chordoma cases between January 2000 and December 2018, we created and validated three ML survival models as well as a traditional CoxPH model in this population-based cohort study. Randomly, the dataset was divided into training and validation datasets. Tuning hyperparameters on the training dataset involved a 1000-iteration random search with fivefold cross-validation. Concordance index (C-index), Brier score, and integrated Brier score were used to evaluate the performance of the model. The receiver operating characteristic (ROC) curves, calibration curves, and area under the ROC curves (AUC) were used to assess the reliability of the models by predicting 5- and 10-year survival probabilities.

RESULTS

A total of 724 chordoma patients were divided into training (n = 508) and validation (n = 216) cohorts. Cox regression identified nine significant prognostic factors (p < 0.05). ML models showed superior performance over CoxPH model, with DeepSurv having the highest C-index (0.795) and the best discrimination for 5- and 10-year survival (AUC 0.84 and 0.88). Calibration curves revealed strong correlation between DeepSurv predictions and actual survival. Risk stratification by DeepSurv model effectively discriminated high- and low-risk groups (p < 0.01). The optimized DeepSurv model was implemented into a web application for clinical use that can be found at https://hust-chengp-ml-chordoma-app-19rjyr.streamlitapp.com/ .

CONCLUSION

ML algorithms based on time-to-event results are effective in chordoma prediction, with DeepSurv having the best discrimination performance and calibration.

A Simple-to-Use Nomogram for Predicting Postoperative Early Death Risk in Elderly Patients with Spinal Tumors: A Population-Based Study

Background

For elderly patients with primary spinal tumors, surgery is the best option for many elderly patients, in addition to palliative care. However, due to the unique physical function of elderly patients, the short-term prognosis is often unpredictable. It is therefore essential to develop a novel nomogram as a clinical aid to predict the risk of early death for elderly patients with primary spinal tumors who undergo surgery.

Materials and Methods

In this study, clinical data were obtained from 651 patients through the SEER database, and they were retrospectively analyzed. Logistic regression analyses were used for risk-factor screening. Predictive modeling was performed through the R language. The prediction models were calibrated as well as evaluated for accuracy in the validation cohort. The receiver operating characteristic (ROC) curve and the decision curve analysis (DCA) were used to evaluate the functionality of the nomogram.

Results

We identified four separate risk factors for constructing nomograms. The area under the receiver operating characteristic curve (training set 0.815, validation set 0.815) shows that the nomogram has good discrimination ability. The decision curve analysis demonstrates the clinical use of this nomogram. The calibration curve indicates that this nomogram has high accuracy. At the same time, we have also developed a web version of the online nomogram for clinical practitioners to apply.

Conclusions

We have successfully developed a nomogram that can accurately predict the risk of early death of elderly patients with primary spinal tumors undergoing surgery, which can provide a reference for clinicians.

Clinical characteristics, prognostic factors, and predictive model for elderly primary spinal tumor patients who are difficult to tolerate surgery or refuse surgery

Background

As a rare tumor, surgery is the best treatment for primary spinal tumors. However, for elderly patients who cannot undergo surgery, the prognosis is often difficult to evaluate. The purpose of this study was to identify the risk factors that may lead to death and predict the prognosis of elderly patients with primary spinal tumors who have not undergone surgical treatment.

Methods

In this study, 426 patients aged 60 years or older diagnosed with a primary spinal tumor between 1975 and 2015 were selected and included from the Surveillance, Epidemiology, and End Results database. A retrospective analysis was performed by using the Cox regression algorithm to identify independent prognostic factors. A nomogram model was developed based on the results. Multiple evaluation methods (calibration curve, receiver operating characteristic curve, and decision curve analyses) were used to evaluate and validate the performance of the nomogram.

Results

A nomogram was developed, with age, histological type, and stage as independent prognostic factors. The results indicated that the prognostic risk tended to increase significantly with age and tumor spread. Osteosarcoma was found to have the most prominent risk prognosis in this patient group, followed by chondrosarcoma and chordoma. The area under the curve and the C-index of the model were both close to or greater than 0.7, which proved the high-differentiation ability of the model. The calibration curve and decision curve analyses showed that the model had high predictive accuracy and application value.

Conclusions

We successfully established a practical nomogram to assess the prognosis of elderly patients with primary spinal tumors who have not undergone surgical treatment, providing a scientific basis for clinical management.

A Novel Nomogram for Predicting Cancer-Specific Survival in Patients With Spinal Chordoma: A Population-Based Analysis

Background:

Chordoma is a rare malignant bone tumor, and the survival prediction for patients with chordoma is difficult. The objective of this study was to construct and validate a nomogram for predicting cancer-specific survival (CSS) in patients with spinal chordoma.

Methods:

A total of 316 patients with spinal chordoma were identified from the SEER database between 1998 and 2015. The independent prognostic factors for patients with spinal chordoma were determined by univariate and multivariate Cox analyses. The prognostic nomogram was established for patients with spinal chordoma based on independent prognostic factors. Furthermore, we performed internal and external validations for this nomogram.

Results:

Primary site, disease stage, histological type, surgery, and age were identified as independent prognostic factors for patients with spinal chordoma. A nomogram for predicting CSS in patients with spinal chordoma was constructed based on the above 5 variables. In the training cohort, the area under the curve for predicting 1-, 3-, and 5-year CSS were 0.821, 0.856, and 0.920, respectively. The corresponding area under the curve in the validation cohort were 0.728, 0.804, and 0.839, respectively. The calibration curves of the nomogram showed a high degree of agreement between the predicted and the actual results, and the decision curve analysis further demonstrated the satisfactory clinical utility of the nomogram.

Conclusions:

The prognostic nomogram provides a considerably more accurate prediction of prognosis for patients with spinal chordoma. Clinicians can use it to categorize patients into different risk groups and make personalized treatment methods.