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Deep-learning-based survival prediction of patients with lower limb melanoma

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机构： [1]Department of Dermatology, The First Afliated Hospital of Jinan University and Jinan University Institute of Dermatology, Guangzhou 510630, China [2]Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China [3]Centro de Hospitalar Conde de Januario, Macau, China [4]Primax Biotech Company, Hong Kong, China [5]Hong Kong Medical and Education, Hong Kong, China [6]Shanghai Aige Medical Beauty Clinic Co., Ltd.(Agge), Shanghai, China [7]Department of Clinical Research, The First Afliated Hospital of Jinan University, Guangzhou, China [8]Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, China [9]Department of Dermatology, The Fifth Afliated Hospital of Jinan University, Heyuan, China

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For the purpose to examine lower limb melanoma (LLM) and its long-term survival rate, we used data from the Surveillance, Epidemiology and End Results (SEER) database. To estimate the prognosis of LLM patients and assess its efficacy, we used a powerful deep learning and neural network approach called DeepSurv.We gathered data on those who had an LLM diagnosis between 2000 and 2019 from the SEER database. We divided the people into training and testing cohorts at a 7:3 ratio using a random selection technique. To assess the likelihood that LLM patients would survive, we compared the results of the DeepSurv model with those of the Cox proportional-hazards (CoxPH) model. Calibration curves, the time-dependent area under the receiver operating characteristic curve (AUC), and the concordance index (C-index) were all used to assess how accurate the predictions were.In this study, a total of 26,243 LLM patients were enrolled, with 7873 serving as the testing cohort and 18,370 as the training cohort. Significant correlations with age, gender, AJCC stage, chemotherapy status, surgery status, regional lymph node removal and the survival outcomes of LLM patients were found by the CoxPH model. The CoxPH model's C-index was 0.766, which signifies a good degree of predicted accuracy. Additionally, we created the DeepSurv model using the training cohort data, which had a higher C-index of 0.852. In addition to calculating the 3-, 5-, and 8-year AUC values, the predictive performance of both models was evaluated. The equivalent AUC values for the CoxPH model were 0.795, 0.767, and 0.847, respectively. The DeepSurv model, in comparison, had better AUC values of 0.872, 0.858, and 0.847. In comparison to the CoxPH model, the DeepSurv model demonstrated greater prediction performance for LLM patients, as shown by the AUC values and the calibration curve.We created the DeepSurv model using LLM patient data from the SEER database, which performed better than the CoxPH model in predicting the survival time of LLM patients.© 2023. The Author(s).

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大类 | 4 区医学

小类 | 4 区内分泌学与代谢 4 区肿瘤学

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第一作者机构： [1]Department of Dermatology, The First Afliated Hospital of Jinan University and Jinan University Institute of Dermatology, Guangzhou 510630, China

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通讯机构： [1]Department of Dermatology, The First Afliated Hospital of Jinan University and Jinan University Institute of Dermatology, Guangzhou 510630, China [7]Department of Clinical Research, The First Afliated Hospital of Jinan University, Guangzhou, China [8]Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, China [9]Department of Dermatology, The Fifth Afliated Hospital of Jinan University, Heyuan, China

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