PO-1391 Curative radiotherapy of prostate cancer: a risk stratification system based on prognostic factors

Abstract BackgroundEffective tools evaluating the prognosis for patients with upper thoracic esophageal carcinoma is lacking. We aimed to develop a nomogram model to predict overall survival (OS) and construct a risk stratification system of upper thoracic esophageal squamous cell carcinoma (ESCC) patients.MethodsNewly diagnosed 568 patients with upper thoracic ESCC at Fujian Medical University Cancer Hospital between February 2004 and December 2016 was taken as a training cohort, and additional 155 patients with upper ESCC from Sichuan Cancer Hospital Institute between January 2011 and December 2013 were used as a validation cohort. A nomogram was established using Cox proportional hazard regression to identify prognostic factors for OS. The predictive power of nomogram model was evaluated by using 4 indices: concordance statistics (C-index), time-dependent ROC (ROCt) curve, net reclassification index (NRI) and integrated discrimination improvement (IDI). Decision curve analysis (DCA) was used to evaluate clinical usefulness of prediction models. Patients were categorized into three risk groups by X-tile software on the survival scores of the training cohort.ResultsMultivariate analysis revealed that gender, clinical T stage, clinical N stage and primary gross tumor volume (GTVp) were independent prognostic factors for OS in the training cohort. The nomogram based on these factors showed favorable prognostic efficacy in the both training and validation cohorts, with C-index of 0.622, 0.713, and AUC value of 0.709, 0.739, respectively, which appeared superior to those of the American Joint Committee on Cancer (AJCC) staging system. In addition, NRI and IDI of the nomogram presented better discrimination ability to predict survival than those of AJCC staging. Furthermore, DCA curve of the nomogram exhibited greater clinical performance than that of AJCC staging. Finally, the nomogram fairly distinguished the OS rates among low, moderate, and high risk groups, whereas the OS curves of clinical stage could not be well separated among clinical AJCC stage. ConclusionsWe built an effective nomogram model for predict OS of upper thoracic ESCC, which may improve clinicians’ abilities to predict individualized survival and facilitate to further stratify the management of patients at risk.

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A Nomogram for Predicting Survival in Patients With Breast Cancer Liver Metastasis: A Population-Based Study

Frontiers in Oncology ◽

10.3389/fonc.2021.600768 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yu Xiong ◽

Xia Shi ◽

Qi Hu ◽

Xingwei Wu ◽

Enwu Long ◽

...

Keyword(s):

Breast Cancer ◽

Prognostic Factors ◽

Liver Metastasis ◽

Risk Stratification ◽

Staging System ◽

Tnm Staging ◽

Tnm Staging System ◽

Stratification System ◽

Breast Cancer Liver Metastasis ◽

Risk Stratification System

ObjectiveThe prognosis of patients with breast cancer liver metastasis (BCLM) was poor. We aimed at constructing a nomogram to predict overall survival (OS) for BCLM patients using the SEER (Surveillance Epidemiology and End Results) database, thus choosing an optimized therapeutic regimen to treat.MethodsWe identified 1173 patients with BCLM from the SEER database and randomly divided them into training (n=824) and testing (n=349) cohorts. The Cox proportional hazards model was applied to identify independent prognostic factors for BCLM, based on which a nomogram was constructed to predict 1-, 2-, and 3-year OS. Its discrimination and calibration were evaluated by the Concordance index (C-index) and calibration plots, while the accuracy and benefits were assessed by comparing it to AJCC-TNM staging system using the decision curve analysis (DCA). Kaplan-Meier survival analyses were applied to test the clinical utility of the risk stratification system.ResultsGrade, marital status, surgery, radiation therapy, chemotherapy, CS tumor size, tumor subtypes, bone metastatic, brain metastatic, and lung metastatic were identified to be independent prognostic factors of OS. In comparison with the AJCC-TNM staging system, an improved C-index was obtained (training group: 0.701 vs. 0.557, validation group: 0.634 vs. 0.557). The calibration curves were consistent between nomogram-predicted survival probability and actual survival probability. Additionally, the DCA curves yielded larger net benefits than the AJCC-TNM staging system. Finally, the risk stratification system can significantly distinguish the ones with different survival risk based on the different molecular subtypes.ConclusionWe have successfully built an effective nomogram and risk stratification system to predict OS in BCLM patients, which can assist clinicians in choosing the appropriate treatment strategies for individual BCLM patients.

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