Score for the Overall Survival Probability Scores of Fibrosarcoma Patients after Surgery: A Novel Nomogram-Based Risk Assessment System

Prediction of histopathologic grades of myxofibrosarcoma with radiomics based on magnetic resonance imaging

PURPOSE

To develop a radiomics-based model from preoperative magnetic resonance imaging (MRI) for predicting the histopathological grades of myxofibrosarcoma.

METHODS

This retrospective study included 54 patients. The tumors were classified into high-grade and low-grade myxofibrosarcoma. The tumor size, signal intensity heterogeneity, margin, and surrounding tissue were evaluated on MRI. Using the least absolute shrinkage and selection operator (LASSO) algorithms, 1037 radiomics features were obtained from fat-suppressed T2-weighted images (T2WI), and a radiomics signature was established. Using multivariable logistic regression analysis, three models were built to predict the histopathologic grade of myxofibrosarcoma. A radiomics nomogram represents the integrative model. The three models' performance was evaluated using the receiver operating characteristics (ROC) and calibration curves.

RESULTS

The high-grade myxofibrosarcoma had greater depth (P = 0.027), more frequent heterogeneous signal intensity at T2WI (P = 0.015), and tail sign (P = 0.014) than the low-grade tumor. The area under curve (AUC) of these conventional MRI features models was 0.648, 0.656, and 0.668, respectively. Seven radiomic features were selected by LASSO to construct the radiomics signature model, with an AUC of 0.791. The AUC of the integrative model based on radiomics signature and conventional MRI features was 0.875. The integrative model's calibration curve and insignificant Hosmer-Lemeshow test statistic (P = 0.606) revealed good calibration.

CONCLUSION

An integrative model using radiomics signature and three conventional MRI features can preoperatively predict low- or high-grade myxofibrosarcoma.

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.

Development and validation of novel risk prediction models of breast cancer based on stanniocalcin‐1 level

PURPOSE

The function of stanniocalcin-1 (STC-1) in the oncogenesis and progression of tumors has been extensively studied. The purpose of this study was to investigate the relationship between secreted STC-1 and prognosis in patients with breast cancer (BC) and to determine whether STC-1 could be a key prognostic factor in BC.

METHODS

The STC-1 level was measured by ELISA and clinical data from 1210 female patients with BC were used to develop and validate nomograms. We then verified the models through the plotting of ROC curves and calibration curves, calculating the C-index, and performing decision curve analyses (DCA).

RESULTS

The level of STC-1 in the peripheral plasma was significantly correlated with the T stage, N stage, clinical stage, grade, hormone receptors, HER-2 status, and tumor subtype. Cox regression analyses revealed that estrogen receptor(ER) status, N stage, and STC-1 level were risk factors for overall survival (OS), whereas T stage, N stage, and STC-1 level were independent prognostic factors for distant disease-free survival (DDFS) and disease-free survival (DFS). Both the ROC curve and the C-index confirmed the high resolution of these models, while the DCA identified the feasibility of their practical application. In addition, the calibration curves indicated good consistency between the predicted and actual survival rates.

CONCLUSION

Nomograms were created based on STC-1 levels for 3-, 5-, and 7-year OS, DDFS, and DFS of patients with BC respectively. As a key prognostic factor for BC, peripheral blood STC-1 level can be used clinically as a liquid biopsy indicator.