A Novel Tool to Predict Early Death in Uterine Sarcoma Patients: A Surveillance, Epidemiology, and End Results-Based Study

Song, Zixuan; Wang, Yizi; Zhang, Dandan; Zhou, Yangzi

doi:10.3389/fonc.2020.608548

Cited by 16 publications

(12 citation statements)

References 22 publications

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“…This implies that stage is an important independent in uencing factor. This is consistent with the results of previous studies on prognostic evaluation models for pancreatic cancer and uterine sarcoma 13,14 . The prognosis of stage III and IV patients is generally poor and mainly depends on the size of intraperitoneal metastases 15 .…”

Section: Discussionsupporting

confidence: 93%

Nomogram for Predicting Postoperative Cancer-specific Early Death in Patients with Epithelial Ovarian Cancer based on the SEER Database: A Large Cohort Study

Zhang

Zhu

2021

Preprint

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Background: Ovarian cancer is a common gynecological malignant tumor. Poor prognosis is strongly associated with early death, but there is no effective tool to predict this. This study aimed to construct a nomogram for predicting cancer-specific early death in ovarian cancer patients.Methods: Our study used data from the Surveillance, Epidemiology, and End Results (SEER) database of ovarian cancer patients registered from 1988 to 2016. Important independent prognostic factors were determined by univariate and multivariate logistic regression and LASSO Cox regression. Several risk factors were considered in constructing the nomogram. Nomogram discrimination and calibration were evaluated using C-index, internal validation, and receiver operating characteristic (ROC) curves.Results: A total of 4769 patients were included. Patients were assigned to the training set (n = 3340; 70%) and validation set (n = 1429; 30%). Based on the training set, eight variables were shown to be significant factors for early death and were incorporated in the nomogram: AJCC (American Joint Committee on Cancer) stage, residual lesion size, chemotherapy, serum CA125 level, tumor size, number of lymph nodes examined, surgery of primary site, and age. The C-indices and ROC curves showed that the nomogram had better predictive ability than the AJCC staging system and good clinical practicability. Internal validation based on validation set showed good consistency between predicted and observed values for early death. Conclusions: Compared with predictions made using AJCC stage or residual lesion size, the nomogram was able to provide more robust predictions for early death in ovarian cancer patients.

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Section: Discussionsupporting

confidence: 93%

Nomogram for Predicting Postoperative Cancer-specific Early Death in Patients with Epithelial Ovarian Cancer based on the SEER Database: A Large Cohort Study

Zhang

Zhu

2021

Preprint

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“…The study on early death has been applied to advanced cancers in other systems and has shown important clinical significance. Song et al established a nomogram chart to predict the early mortality of uterine sarcoma, which was significantly better than FIGO stage system [ 24 ]. Yang et al established a model to predict the early mortality of stage IV gastric cancer, and the AUC was as high as 0.847 [ 25 ].…”

Section: Discussionmentioning

confidence: 99%

The incidence, risk factors and predictive nomograms for early death of lung cancer with synchronous brain metastasis: a retrospective study in the SEER database

et al. 2021

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Background The prognosis of lung cancer with synchronous brain metastasis (LCBM) is very poor, and patients often die within a short time. However, little is known about the early mortality and related factors in patients with LCBM. Methods Patients diagnosed with LCBM between 2010 and 2016 were enrolled from the Surveillance, Epidemiology, and End Result (SEER) database. Univariate and multivariate logistic regression analysis were used to identify significant independent prognostic factors, which were used to construct nomograms of overall and cancer-specific early death. Then, the prediction ability of the model was verified by receiver operating characteristic (ROC) curve. At last, the clinical application value of the model was tested through decision curve analysis (DCA). Results A total of 29,902 patients with LCBM were enrolled in this study. Among them, 13,275 (44.4%) patients had early death, and 11,425 (38.2%) cases died of lung cancer. The significant independent risk factors for overall and cancer-specific early death included age, race, gender, Gleason grade, histological type, T stage, N stage, bone metastasis, liver metastasis and marital status, which were used to construct the nomogram. The ROC curve demonstrated good predictive ability and clinical application value. The areas under the curve (AUC) of the training group was 0.793 (95% CI: 0.788–0.799) and 0.794 (95% CI: 0.788–0.799), in the model of overall and cancer-specific early death respectively. And the AUC of the validation group were 0.803 (95% CI: 0.788–0.818) and 0.806 (95% CI: 0.791–0.821), respectively. The calibration plots of the model showed that the predicted early death is consistent with the actual value. The DCA analysis indicated a good clinical application value of this model. Conclusions We established a comprehensive nomogram to predict early death in lung cancer patients with synchronous brain metastases. Nomograms may help oncologists develop better treatment strategies, such as clinical trials and hospice care.

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“…Figure 1 illustrates a flowchart of patient selection. According to past research ( 8 , 9 ), death within 3 months of initial diagnosis was defined as early death.…”

Section: Methodsmentioning

confidence: 99%

A Practical Nomogram to Predict Early Death in Advanced Epithelial Ovarian Cancer

et al. 2021

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Background: Ovarian cancer is a common gynecological malignancy, most of which is epithelial ovarian cancer (EOC). Advanced EOC is linked with a higher incidence of premature death. To date, no effective prognostic tools are available to evaluate the possibility of early death in patients with advanced EOC.Methods: Advanced (FIGO stage III and IV) EOC patients who were enrolled in the Surveillance, Epidemiology, and End Results database between 2004 and 2015 were regarded as subjects and studied. We aimed to construct a nomogram that can deliver early death prognosis in patients with advanced EOC by identifying crucial independent factors using univariate and multivariate logistic regression analyses to help deliver accurate prognoses.Results: In total, 13,403 patients with advanced EOC were included in this study. Three hundred ninety-seven out of a total of 9,379 FIGO stage III patients died early. There were 4,024 patients with FIGO stage IV, 414 of whom died early. Nomograms based on independent prognostic factors have the satisfactory predictive capability and clinical pragmatism. The internal validation feature of the nomogram demonstrated a high level of accuracy of the predicted death.Conclusions: By analyzing data from a large cohort, a clinically convenient nomogram was established to predict premature death in advanced EOC. This tool can aid clinicians in screening patients who are at higher risk for tailoring treatment plans.

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A Novel Tool to Predict Early Death in Uterine Sarcoma Patients: A Surveillance, Epidemiology, and End Results-Based Study

Cited by 16 publications

References 22 publications

Nomogram for Predicting Postoperative Cancer-specific Early Death in Patients with Epithelial Ovarian Cancer based on the SEER Database: A Large Cohort Study

Nomogram for Predicting Postoperative Cancer-specific Early Death in Patients with Epithelial Ovarian Cancer based on the SEER Database: A Large Cohort Study

The incidence, risk factors and predictive nomograms for early death of lung cancer with synchronous brain metastasis: a retrospective study in the SEER database

A Practical Nomogram to Predict Early Death in Advanced Epithelial Ovarian Cancer

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