Prognostic Nomogram and a Risk Classification System for Predicting Overall Survival of Elderly Patients with Fibrosarcoma: A Population-Based Study

A clinical tool to predict overall survival of elderly patients with soft tissue sarcoma after surgical resection

With the aging world population, the incidence of soft tissue sarcoma (STS) in the elderly gradually increases and the prognosis is poor. The primary goal of this research was to analyze the relevant risk factors affecting the postoperative overall survival in elderly STS patients and to provide some guidance and assistance in clinical treatment. The study included 2,353 elderly STS patients from the Surveillance, Epidemiology, and End Results database. To find independent predictive variables, we employed the Cox proportional risk regression model. R software was used to develop and validate the nomogram model to predict postoperative overall survival. The performance and practical value of the nomogram were evaluated using calibration curves, the area under the curve, and decision curve analysis. Age, tumor primary site, disease stage, tumor size, tumor grade, N stage, and marital status, are the risk variables of postoperative overall survival, and the prognostic model was constructed on this basis. In the two sets, both calibration curves and receiver operating characteristic curves showed that the nomogram had high predictive accuracy and discriminative power, while decision curve analysis demonstrated that the model had good clinical usefulness. A predictive nomogram was designed and tested to evaluate postoperative overall survival in elderly STS patients. The nomogram allows clinical practitioners to more accurately evaluate the prognosis of individual patients, facilitates the progress of individualized treatment, and provides clinical guidance.

Distinguishing optimal candidates for primary tumor resection in patients with metastatic lung adenocarcinoma: A predictive model based on propensity score matching

Background

Primary tumor resection is associated with survival benefits in patients with metastatic lung adenocarcinoma (mLUAD). However, there are no established methods to determine which individuals would benefit from surgery. Therefore, we developed a model to predict the patients who are likely to benefit from surgery in terms of survival.

Methods

Data on patients with mLUAD were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Depending on whether surgery was performed on the primary tumor, patients were categorized into two groups: cancer-directed surgery (CDS) and no-cancer-directed surgery (No-CDS). Propensity Score Matching (PSM) was utilized to address bias between the CDS and No-CDS groups. The prognostic impact of CDS was assessed using Kaplan-Meier analysis and Cox proportional hazard models. Subsequently, we constructed a nomogram to predict the potential for surgical benefits based on multivariable logistic regression analysis using preoperative factors.

Results

A total of 89,039 eligible patients were identified, including 6.4% (5705) who underwent surgery. Following PSM, the CDS group demonstrated a significantly longer median overall survival (mOS) compared with the No-CDS group (23 [21-25] vs. 7 [7-8] months; P < 0.001). The nomogram showed robust performance in both the training and validation sets (area under the curve [AUC]: 0.698 and 0.717, respectively), and the calibration curves exhibited high consistency. The nomogram proved clinically valuable according to decision curve analysis (DCA). According to this nomogram, surgical patients were categorized into two groups: no-benefit candidates and benefit candidates groups. Compared with the no-benefit candidate group, the benefit candidate group was associated with longer survival (mOS: 25 vs. 6 months, P < 0.001). Furthermore, no difference in survival was observed between the no-benefit candidates and the no-surgery groups (mOS: 6 vs. 7 months, P = 0.9).

Conclusions

A practical nomogram was developed to identify optimal CDS candidates among patients with mLUAD.

Construction of a Tool to Predict Overall Survival of Patients With Primary Spinal Tumors After Surgical Resection: A Real-World Analysis Based on the Surveillance, Epidemiology, and End Results Database

BACKGROUND

We aim to construct a practical clinical prediction model to accurately evaluate the overall survival (OS) of patients with primary spinal tumors after primary tumor resection, thereby aiding clinical decision-making.

METHODS

A total of 695 patients diagnosed with a primary spinal tumor, selected from the Surveillance, Epidemiology, and End Results (SEER) database, were included in this study. The Cox regression algorithm was applied to the training cohort to build the prognostic nomogram model. The nomogram's performance in terms of discrimination, calibration, and clinical usefulness was also assessed in the internal SEER validation cohort. The fitted prognostic nomogram was then used to create a web-based calculator.

RESULTS

Four independent prognostic factors were identified to establish a nomogram model for patients with primary spinal tumors who had undergone surgical resection. The C-index (.757 for the training cohort and .681 for the validation cohort) and the area under the curve values over time (both >.68) showed that the model exhibited satisfactory discrimination in both the SEER cohort. The calibration curve revealed that the projected and actual survival rates are very similar. The decision curve analysis also revealed that the model is clinically valuable and capable of identifying high-risk patients.

CONCLUSIONS

After developing a nomogram and a web-based calculator, we were able to reliably forecast the postoperative OS of patients with primary spinal tumors. These tools are expected to play an important role in clinical practice, informing clinicians in making decisions about patient care after surgery.

Identification and validation of a nomogram predicting cancer-specific survival for elderly patients with adult fibrosarcoma: a multicenter retrospective study

Background

Due to the low incidence of adult fibrosarcoma (AFS), it is difficult for clinicians to assess cancer-specific survival (CSS) in elderly patients based on this study. The study aimed to develop nomograms capable of accurately predicting 3-, 5-, and 8-year CSS in patients over 40 years of age with AFS.

Methods

Data were collected from The Surveillance, Epidemiology, and End Results (SEER) registry. 586 patients were included in this study. Univariate as well as multivariate Cox regression analyses were applied to identify independent risk factors. A nomogram was constructed and validated to predict the 3-, 5-, and 8-year CSS of patients.

Results

Five variables including age, sex, stage, grade, and chemotherapy status were considered independent risk factors and were used to construct the nomogram. The nomogram was well validated. The C-indexes of the training cohort and the validation cohort are 0.766 and 0.780, respectively. In addition, the area under the curves for 3-, 5- and 8-year CSS are 0.824, 0.846 and 0.840 in the training cohort, 0.835, 0.806 and 0.829 in the validation cohort. Calibration curves were also plotted to show that predicted endings have a well fit for the true endings. Finally, decision curve analysis demonstrates that the nomogram can bring a high benefit to patients.

Conclusion

We successfully constructed a highly accurate nomogram to predict the CSS of AFS patients at 3-, 5-, and 8 years. The nomogram can greatly help clinicians and patients with AFS.

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 Prognostic Nomogram and Risk Classification System for Predicting Cancer-Specific Survival of Postoperative Fibrosarcoma Patients: A Large Cohort Retrospective Study

Background

Fibrosarcoma (FS) is a typically invasive sarcoma formed by fibroblasts and collagen fibers. Currently, the standard treatment for FS is the surgical resection, but the high recurrence rate and poor prognosis limit the benefits of postoperative patients. Exploring what factors affect the benefit of postoperative patients is significant for guiding the implementation of surgical resection. Therefore, this study aims to construct a novel nomogram to predict the cancer-specific survival (CSS) of postoperative fibrosarcoma (POFS) patients.

Methods

The included patients were randomly assigned to the training and validation sets at a ratio of 7 : 3. CSS was indexed as the research endpoint. Firstly, univariate and multivariate Cox regression analyses were used on the training set to determine independent prognostic predictors and build a nomogram for predicting the 1-, 3-, and 5-year CSS of POFS patients. Secondly, the nomogram's discriminative power and prediction accuracy were evaluated by receiver operating characteristic (ROC) and the calibration curve, and a risk classification system for POFS patients was constructed. Finally, the nomogram's clinical utility was evaluated using decision curve analysis (DCA).

Results

Our study included 346 POFS patients, divided into the training (244) and validation sets (102). Multivariate Cox regression analysis demonstrated that tumor size, SEER stage, and tumor grade were independent prognostic predictors of CSS for POFS patients. They were used to create a nomogram. In the training and validation sets, the ROC curve showed that the 1-, 3-, and 5-year area under the curve (AUC) were higher than 0.700, indicating that the nomogram had good reliability and accuracy. DCA also showed that the nomogram has high application value in clinical practice.

Conclusion

The larger tumor size, higher tumor grade, and distant metastasis were independently related to the poor prognosis of POFS patients. The nomogram constructed based on the above variables could accurately predict the 1-, 3-, and 5-year CSS of POFS patients. So, the nomogram and risk classification system we built might help make accurate judgments in clinical practice, optimize patient treatment decisions, maximize postoperative benefits, and ultimately improve the prognosis of POFS patients.

The nomogram for the prediction of overall survival in patients with metastatic lung adenocarcinoma undergoing primary site surgery: A retrospective population-based study

Background

Lung adenocarcinoma (LUAD) is the most common type of Non-small-cell lung cancer (NSCLC). Distant metastasis of lung adenocarcinoma reduces the survival rate. we aim to develop a nomogram in order to predict the survival of patients with metastatic lung adenocarcinoma.

Methods

We retrospectively collected patients who were initially diagnosed as metastatic LUAD from 2010 to 2015 from SEER database. Based on the multivariate and univariate Cox regression analysis of the training cohorts, independent prognostic factors were assessed. The nomogram prediction model was then constructed based on these prognostic factors to predict the overall survival at 12, 24 and 36 months after surgery. Nomogram were identified and calibrated by c-index, time-dependent receiver operating characteristic curve (time-dependent AUC) and calibration curve. Decision curve analysis (DCA) was used to quantify the net benefit of the nomogram at different threshold probabilities, and to better compare with the TNM staging system, we calculated the c-index of this nomogram as well as the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI).

Result

A total of 1102 patients with metastatic LUAD who met the requirements were included for analysis. They were randomly divided into 774 in the training cohorts and 328 in the validation cohorts. As can be seen from the calibration plots, the predicted nomogram and the actual observations in both of the training and validation cohorts were generally consistent. The time dependent AUC values of 12 months, 24 months and 36 months were 0.707, 0.674 and 0.686 in the training cohorts and 0.690, 0.680 and 0.688 in the verification cohorts, respectively. C-indexes for the training and validation cohorts were 0.653 (95%CI 0.626-0.68)and 0.663 (95%CI 0.626-1), respectively. NRI and IDI show that the model is more clinical applicable than the existing staging system. In addition, our risk scoring system based on Kaplan Meier (K-M) survival curve can accurately divide patients into three hierarchy risk groups.

Conclusion

This has led to the development and validation of a prognostic nomogram to assist clinicians in determining the prognosis of patients with metastatic lung adenocarcinoma after primary site surgery.