Development and validation of a prognostic nomogram for neuroendocrine prostate cancer, based on the SEER database

Prognostic nomogram to predict cancer-specific survival with small-cell carcinoma of the prostate: a multi-institutional study

OBJECTIVE

The aim of this study is to examine the predictive factors for cancer-specific survival (CSS) in patients diagnosed with Small-Cell Carcinoma of the Prostate (SCCP) and to construct a prognostic model.

METHODS

Cases were selected using the Surveillance, Epidemiology, and End Results (SEER) database. The Kaplan-Meier method was utilized to calculate survival rates, while Lasso and Cox regression were employed to analyze prognostic factors. An independent prognostic factor-based nomogram was created to forecast CSS at 12 and 24 months. The model's predictive efficacy was assessed using the consistency index (C-index), calibration curve, and decision curve analysis (DCA) in separate tests.

RESULTS

Following the analysis of Cox and Lasso regression, age, race, Summary stage, and chemotherapy were determined to be significant risk factors (P < 0.05). In the group of participants who received training, the rate of 12-month CSS was 44.6%, the rate of 24-month CSS was 25.5%, and the median time for CSS was 10.5 months. The C-index for the training cohort was 0.7688 ± 0.024. As for the validation cohort, it was 0.661 ± 0.041. According to the nomogram, CSS was accurately predicted and demonstrated consistent and satisfactory predictive performance at both 12 months (87.3% compared to 71.2%) and 24 months (80.4% compared to 71.7%). As shown in the external validation calibration plot, the AUC for 12- and 24-month is 64.6% vs. 56.9% and 87.0% vs. 70.7%, respectively. Based on the calibration plot of the CSS nomogram at both the 12-month and 24-month marks, it can be observed that both the actual values and the nomogram predictions indicate a predominantly stable CSS. When compared to the AJCC staging system, DCA demonstrated a higher level of accuracy in predicting CSS through the use of a nomogram.

CONCLUSION

Clinical prognostic factors can be utilized with nomograms to forecast CSS in Small-Cell Carcinoma of the Prostate (SCCP).

External validation of a nomogram predicting conditional survival after tri-modality treatment of esophageal cancer

BACKGROUND

A conditional survival nomogram was developed at a single high-volume center to predict 5-year overall survival for esophageal cancer patients after neoadjuvant chemoradiation and esophagectomy. The aim of this study was to externally validate the nomogram in a cohort of patients with esophageal adeno- or squamous cell carcinoma from another high-volume center.

METHODS

Consecutive patients with an esophageal adeno- or squamous cell carcinoma who had undergone esophagectomy after being treated with preoperative chemoradiation between 2004 and 2016 were selected from a prospectively maintained institutional database. The level of discrimination for prediction of 5-year overall survival was quantified by Harrell's C statistic. Calibration of the conditional survival nomogram was visualized by plotting predicted 5-year survival and observed 5-year survival for comparison.

RESULTS

Of the 296 patients examined, the probability of 5-year overall survival directly after surgery was 45% and increased to 51%, 68%, 78%, and 89% for each additional year survived. The predicted 5-year overall survival differed from the observed survival, with a calibration slope of 0.54, 0.55, 0.59, 0.73, and 1.09 directly after surgery and 1, 2, 3, and 4 years of survival after surgery, respectively. The nomogram's discrimination level for 5-year survival was moderate, with a C statistic of 0.65 compared to the 0.70 reported in the original study.

CONCLUSION

The nomogram model has moderate predictive discrimination and accuracy, supporting its applicability to external cohorts to predict conditional survival. Further validation studies should empirically assess the model for predictive performance.