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Erschienen in: Journal of Cancer Research and Clinical Oncology 19/2023

27.09.2023 | Research

Predicting cutaneous malignant melanoma patients’ survival using deep learning: a retrospective cohort study

verfasst von: Siyu Cai, Wei Li, Cong Deng, Qiao Tang, Zhou Zhou

Erschienen in: Journal of Cancer Research and Clinical Oncology | Ausgabe 19/2023

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Abstract

Background

Cutaneous malignant melanoma (CMM) has the worst prognosis among skin cancers, especially metastatic CMM. Predicting its prognosis accurately could direct clinical decisions.

Methods

The Surveillance, Epidemiology, and End Results database was screened to collect CMM patients’ data. According to diagnosed time, patients were subdivided into three cohorts, train cohort (diagnosed between 2010 and 2013), validation cohort (diagnosed in 2014), and test cohort (diagnosed in 2015). Train cohort was used to train deep learning survival model for cutaneous malignant melanoma (DeepCMM). DeepCMM was then evaluated in train cohort and validation cohort internally, and validated in test cohort externally.

Results

DeepCMM showed 0.8270 (95% CI, confidence interval, CI 0.8260–0.8280) as area under the receiver operating characteristic curve (AUC) in train cohort, 0.8274 (95% CI 0.8286–0.8298) AUC in validation cohort, and 0.8303 (95% CI 0.8289–0.8316) AUC in test cohort. Then DeepCMM was packaged into a Windows 64-bit software for doctors to use.

Conclusion

Deep learning survival model for cutaneous malignant melanoma (DeepCMM) can offer a reliable prediction on cutaneous malignant melanoma patients’ overall survival.

Graphical abstract

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Metadaten
Titel
Predicting cutaneous malignant melanoma patients’ survival using deep learning: a retrospective cohort study
verfasst von
Siyu Cai
Wei Li
Cong Deng
Qiao Tang
Zhou Zhou
Publikationsdatum
27.09.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Cancer Research and Clinical Oncology / Ausgabe 19/2023
Print ISSN: 0171-5216
Elektronische ISSN: 1432-1335
DOI
https://doi.org/10.1007/s00432-023-05421-7

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