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

作者：Jinrong Zhang, Hai Yu, Xinkai Zheng, Wai‐Kit Ming, Yau Sun Lak, Kong Ching Tom, Alice W. Lee, Hui Huang, Wenhui Chen, Jun Lyu, Liehua Deng · 发表于：Discover Oncology · 年份：2023 · DOI：10.1007/s12672-023-00823-y · 被引用次数：7 · 研究领域：Cutaneous Melanoma Detection and Management、AI in cancer detection、Lymphatic System and Diseases

BACKGROUND: 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. METHODS: 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. RESULTS: 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, t...