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. 2024 Jun 5;19(6):e0303440.
doi: 10.1371/journal.pone.0303440. eCollection 2024.

Development and validation of nomograms to predict survival of neuroendocrine carcinoma in genitourinary system: A population-based retrospective study

Xiangnan Niu  1   2 , Shiwei Sun  3 , Wenjuan Fan  4 , Peng Yue  5 , Wei Yao  5 , Yue Wang  5 , Xiaoqian Deng  5 , Fuyu Guo  5 , Yangang Zhang  1   2   5
Affiliations

Development and validation of nomograms to predict survival of neuroendocrine carcinoma in genitourinary system: A population-based retrospective study

Xiangnan Niu et al. PLoS One. .

Abstract

Neuroendocrine carcinoma (NEC) is a rare yet potentially perilous neoplasm. The objective of this study was to develop prognostic models for the survival of NEC patients in the genitourinary system and subsequently validate these models. A total of 7125 neuroendocrine neoplasm (NEN) patients were extracted. Comparison of survival in patients with different types of NEN before and after propensity score-matching (PSM). A total of 3057 patients with NEC, whose information was complete, were extracted. The NEC influencing factors were chosen through the utilization of the least absolute shrinkage and selection operator regression model (LASSO) and the Fine & Gary model (FGM). Furthermore, nomograms were built. To validate the accuracy of the prediction, the efficiency was verified using bootstrap self-sampling techniques and receiver operating characteristic curves. LASSO and FGM were utilized to construct three models. Confirmation of validation was achieved by conducting analyses of the area under the curve and decision curve. Moreover, the FGS (DSS analysis using FGM) model produced higher net benefits. To maximize the advantages for patients, the FGS model disregarded the influence of additional occurrences. Patients are expected to experience advantages in terms of treatment options and survival assessment through the utilization of these models.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Study flowchart.
Fig 2
Fig 2. Survival rates prior and post- propensity score matching (PSM).
(A) Overall survival [OS] before PSM; (B) Disease-specific survival (DSS) before PSM; (C) OS after PSM; (D). DSS after PSM.
Fig 3
Fig 3. OS model and its performance.
(A) Nomogram; (B) Calibration curves; (C) ROC curves; (D)DCA curves; (E) K-M curves of predicted risk subgroups; (F) Predicted risk scores for each patient.
Fig 4
Fig 4. DSS model and its performance.
(A) Nomogram; (B) Calibration curves; (C) ROC curves; (D)DCA curves; (E) K-M curves of predicted risk subgroups; (F) Predicted risk scores for each patient.
Fig 5
Fig 5. FGS model and its performance.
(A) Nomogram; (B) Calibration curves; (C) ROC curves; (D)DCA curves; (E) K-M curves of predicted risk subgroups; (F) Predicted risk scores for each patient.
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