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

• Published in: PloS one 2024-06, Vol.19 (6), p.e0303440-e0303440
• Main Authors: Niu, Xiangnan, Sun, Shiwei, Fan, Wenjuan, Yue, Peng, Yao, Wei, Wang, Yue, Deng, Xiaoqian, Guo, Fuyu, Zhang, Yangang
• Format: Article
• Language: English
• Subjects: Adult; Age; Aged; Cancer; Carcinoma; Carcinoma, Neuroendocrine - diagnosis; Carcinoma, Neuroendocrine - mortality; Carcinoma, Neuroendocrine - pathology; Chemotherapy; Development and progression; Evaluation; Female; Health aspects; Humans; Kidney cancer; Male; Males; Marital status; Medical prognosis; Medicine and Health Sciences; Middle Aged; Neoplasia; Neuroendocrine tumors; Nomograms; Nomography (Mathematics); Patient outcomes; Patients; Population studies; Prognosis; Radiation therapy; Regression models; Retrospective Studies; ROC Curve; Sampling techniques; Surgery; Survival; Survival analysis; Testicular cancer; Tumors; Urogenital Neoplasms - diagnosis; Urogenital Neoplasms - mortality; Urogenital Neoplasms - pathology; Urogenital system; Variables
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0303440

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.