Development and external validation of two nomograms for predicting brain metastases and brain metastasis-free survival in primary small cell carcinoma of the esophagus: a retrospective multicenter analysis
Liang Yi, Wei Huang, Qifeng Wang, Qingwu Du, Cihui Yan, Yaowen Zhang, Wenbin Shen, Xiaolin Ge, Ning Yang, Xiujun Su, Feng Wang, Jian Zhang, Wencheng Zhang, Zhiyong Yuan, Meng WangBackground:
Reliably predicting brain metastasis (BM) and brain metastasis‑free survival (BMFS) in patients with primary small cell carcinoma of the esophagus (PSCCE) remains a clinical challenge.
Objectives:
To develop and externally validate predictive nomograms for assessing BM probability and BMFS in patients with PSCCE.
Design:
A retrospective model development and external validation study.
Methods:
Using a training cohort, we constructed two separate nomograms. The first was developed using univariable and multivariable logistic regression to predict the probability of BM. The second was built using a Fine‑Gray competing-risk model to estimate BMFS, treating death without BM as a competing event. The performance of both nomograms was evaluated using the area under the receiver operating characteristic (ROC) curve/time-dependent ROC curve, calibration plots, and decision curve analysis (DCA). External validation was subsequently performed using an independent validation cohort.
Results:
The study sample included 492 patients in the training cohort (mean age at diagnosis 61.97 ± 8.86 years; 133 (27%) female) and 344 patients in the external validation cohort (mean age at diagnosis 62.97 ± 8.14 years; 108 (31%) female). Age, N stage, and M stage emerged as independent predictors and were included in a nomogram to estimate BM risk. Adding lesion length and initial treatments (radiotherapy, chemotherapy, and surgery) to these three factors allowed the model to predict BMFS. Both nomograms showed strong predictive performance in the training cohort, with area under the ROC curve values of 0.76 and 0.843, respectively, supported by calibration plots and DCA. These results were confirmed in the external validation cohort. The logistic regression‑based nomogram accurately predicted BM probability, while the Fine–Gray model provided reliable estimates of BMFS.
Conclusion:
We successfully developed and externally validated two complementary nomograms that reliably predict BM probability and BMFS in patients with PSCCE. These tools may aid in risk stratification and inform personalized surveillance strategies.