Evidence map›Paper›PMID 37755576›Full record

ArticleJournal of cancer research and clinical oncology2023

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

Siyu Cai, Wei Li, Cong Deng, Qiao Tang, Zhou Zhou

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Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Siyu Cai *Dermatology Department, General Hospital of Western Theater Command PLA, No. 270, Rongdu Avenue, Chengdu, 610083, Sichuan, China.
Wei Li *Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, No. 9 Beiguan Street, Tongzhou District, Beijing, 101149, China.
Cong DengDepartment of Respiratory and Critical Care Medicine, General Hospital of Western Theater Command, No. 270 Rongdu Avenue, Chengdu, 610083, Sichuan, China.
Qiao TangDermatology Department, Medical Center Hospital of Qionglai City, No. 172 Xinglin Road, Qionglai City, Chengdu, 611500, Sichuan, China.
Zhou ZhouDermatology Department, General Hospital of Western Theater Command PLA, No. 270, Rongdu Avenue, Chengdu, 610083, Sichuan, China. doctorzzhou@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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

methodsThe 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.

resultsDeepCMM 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.

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

Indexed as

Deep LearningMelanomaSkin NeoplasmsCutaneous Malignant MelanomaHumansRetrospective StudiesCutaneous malignant melanomaDeep learningNeural networkSurvival

Identifiers

PMID37755576
PMCPMC11798153

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