&lt;p&gt;Prognostic Factors and Local Treatment Modalities of Small-Cell Carcinoma of the Cervix: An Analysis According to the International Federation of Gynecology and Obstetrics Stage [Corrigendum]&lt;/p&gt;

Huang, Ru Chih C.; Gan, Qiyu; Cheng, Jianhua

doi:10.2147/cmar.s272111

Cited by 1 publication

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“…There are only seven nomograms constructed on single-organ small-scale data. [41][42][43][44][45][46][47] None of the nomograms has external validation. Thus, this study explores predicting the survival of EPSCC throughout the body with a machine-learning ensemble algorithm and large-scale data.…”

Section: Discussionmentioning

confidence: 99%

“…2), compared to the nomograms of seven singlesite EPSCC studies, whose AUCs range from 0.656 to 0.75. [41][42][43][44][45][46][47] It may be because of the same pathological nature, the large sample size, and the merits of the decision tree-based algorithm. Thus, EPSCC of multiple sites was included in this study to increase the applicable range of the established model.…”

Section: Discussionmentioning

confidence: 99%

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An Online Tool for Survival Prediction of Extrapulmonary Small Cell Carcinoma with Random Forest

Zhang

2022

Preprint

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Purpose Extrapulmonary small cell carcinoma (EPSCC) is rare, and its knowledge is mainly extrapolated from small cell lung carcinoma. Reliable survival prediction tools are lacking. Methods 7813 cases of EPSCC were collected from the Surveillance Epidemiology and End Results (SEER) database as the train and internal validation cohort of the survival prediction model. The endpoints were overall survivals of 0.5-5 years. Internal validation performances of machine learning algorithms were compared, and the best model was selected. External validation was performed to evaluate the generalization ability of the selected model. Results Among machine learning algorithms, the random forest model performs best on internal validation, whose area under the curve (AUC) is 0.734-0.811. The net benefit is higher than the TNM classification in decision curve analysis. The AUC of this model on the external validation cohort is 0.779-0.823. This model was then deployed online as a free, publicly available prediction tool of EPSCC. (http://42.192.80.13:4399/). Conclusions This study provides a well-performed online survival prediction tool for EPSCC with machine learning and large-scale data.

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