A nomogram for predicting overall survival in patients with low‐grade endometrial stromal sarcoma: A population‐based analysis

Abstract

Background Low‐grade endometrial stromal sarcoma (LG‐ESS) is a rare tumor that lacks a prognostic prediction model. Our study aimed to develop a nomogram to predict overall survival of LG‐ESS patients. Methods A total of 1172 patients confirmed to have LG‐ESS between 1988 and 2015 were selected from the Surveillance, Epidemiology and End Results (SEER) database. They were further divided into a training cohort and a validation cohort. The Akaike information criterion was used to select variables for the nomogram. The discrimination and calibration of the nomogram were evaluated using concordance index (C‐index), area under time‐dependent receiver operating characteristic curve (time‐dependent AUC), and calibration plots. The net benefits of the nomogram at different threshold probabilities were quantified and compared with those of the International Federation of Gynecology and Obstetrics (FIGO) criteria‐based tumor staging using decision curve analysis (DCA). Net reclassification index (NRI) and integrated discrimination improvement (IDI) were also used to compare the nomogram's clinical utility with that of the FIGO criteria‐based tumor staging. The risk stratifications of the nomogram and the FIGO criteria‐based tumor staging were compared. Results Seven variables were selected to establish the nomogram for LG‐ESS. The C‐index (0.814 for the training cohort and 0.837 for the validation cohort) and the time‐dependent AUC (> 0.7) indicated satisfactory discriminative ability of the nomogram. The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations in both the training and validation cohorts. The NRI values (training cohort: 0.271 for 5‐year and 0.433 for 10‐year OS prediction; validation cohort: 0.310 for 5‐year and 0.383 for 10‐year OS prediction) and IDI (training cohort: 0.146 for 5‐year and 0.185 for 10‐year OS prediction; validation cohort: 0.177 for 5‐year and 0.191 for 10‐year OS prediction) indicated that the established nomogram performed significantly better than the FIGO criteria‐based tumor staging alone ( P < 0.05). Furthermore, DCA showed that the nomogram was clinically useful and had better discriminative ability to recognize patients at high risk than the FIGO criteria‐based tumor staging. Conclusions A prognostic nomogram was developed and validated to assist clinicians in evaluating prognosis of LG‐ESS patients.

A practical nomogram for predicting cancer-specific survival in patients with clear-cell renal cell carcinoma

“…To compare the discrimination performance of our nomogram with AJCC-7th modeling, we calculated Cindex, AUC which were widely used to evaluate the discrimination of prediction models [13][14][15] , and we also gured out sensitivity, speci city, positive predictive value, negative predictive value and Youden Index. We also evaluated the improvement in the predictive discrimination of our nomogram by calculating the relative integrated discrimination improvement (IDI) and the net reclassi cation improvement (NRI), as described by Pencina et al 16 .…”

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

“…With the improvement of lung cancer diagnosis and treatment technology and standardization of treatment, the survival time of lung cancer patients has been signi cantly extended. However, once brain metastases occur, patients often experience intracranial, limb dysfunction, epilepsy and even delirium, which seriously affect the survival of patients [18]. Brain metastatic from lung cancer has grown up to be a major problem in neurosurgery.…”

Nomograms individually predict the overall survival and cancer-specific survival of adult patients with hemispheres lower-grade gliomas: A SEER-Based Study

“…With the increasingly popularization of the concept of precision medicine, the accurate prediction of patients' prognosis is becoming much more critical [13,14]. Many previous studies have shown the accurate predictive ability of nomograms for predicting overall survival in patients with low-grade endometrial stromal sarcoma [10], adrenocortical carcinoma [15], hepatoblastoma [16], and many other system cancers. So far, only a few sporadic studies have investigated the straightforward application of nomograms in glioma based on the SEER database [17][18][19][20].…”

“…Nomograms have been established for various cancers and are considered reliable tools for individualized survival and risk prediction [8][9][10][11][12]. Given the small sample sizes of retrospective studies on low-grade and high-grade glioma, a large population-based study on LGGs is needed.…”