Deep-learning-based survival prediction of patients with cutaneous malignant melanoma

Hai Yu; Wei Yang; Shi Wu; Shaohui Xi; Xichun Xia; Qi Zhao; Wai-Kit Ming; Lifang Wu; Yunfeng Hu; Liehua Deng; Jun Lyu

doi:10.3389/fmed.2023.1165865

Deep-learning-based survival prediction of patients with cutaneous malignant melanoma

Front Med (Lausanne). 2023 Mar 27:10:1165865. doi: 10.3389/fmed.2023.1165865. eCollection 2023.

Authors

Hai Yu¹, Wei Yang², Shi Wu¹, Shaohui Xi³, Xichun Xia⁴, Qi Zhao⁵, Wai-Kit Ming⁶, Lifang Wu⁷, Yunfeng Hu¹, Liehua Deng^{1

7}, Jun Lyu^{8

9}

Affiliations

¹ Department of Dermatology, The First Affiliated Hospital of Jinan University and Jinan University Institute of Dermatology, Guangzhou, China.
² Office of Drug Clinical Trial Institution, The First Affiliated Hospital of Jinan University, Guangzhou, China.
³ School of Mechatronical Engineering, Guangdong Polytechnic Normal University, Guangzhou, China.
⁴ Institute of Biomedical Transformation, Jinan University, Guangzhou, China.
⁵ Cancer Centre, Faculty of Health Sciences, University of Macau, Macau, China.
⁶ Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China.
⁷ Department of Dermatology, The Fifth Affiliated Hospital of Jinan University, Heyuan, China.
⁸ Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China.
⁹ Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, China.

Abstract

Background: This study obtained data on patients with cutaneous malignant melanoma (CMM) from the Surveillance, Epidemiology, and End Results (SEER) database, and used a deep learning and neural network (DeepSurv) model to predict the survival rate of patients with CMM and evaluate its effectiveness.

Methods: We collected information on patients with CMM between 2004 and 2015 from the SEER database. We then randomly divided the patients into training and testing cohorts at a 7:3 ratio. The likelihood that patients with CMM will survive was forecasted using the DeepSurv model, and its results were compared with those of the Cox proportional-hazards (CoxPH) model. The calibration curves, time-dependent area under the receiver operating characteristic curve (AUC), and concordance index (C-index) were used to assess the prediction abilities of the model.

Results: This study comprised 37,758 patients with CMM: 26,430 in the training cohort and 11,329 in the testing cohort. The CoxPH model demonstrated that the survival of patients with CMM was significantly influenced by age, sex, marital status, summary stage, surgery, radiotherapy, chemotherapy, postoperative lymph node dissection, tumor size, and tumor extension. The C-index of the CoxPH model was 0.875. We also constructed the DeepSurv model using the data from the training cohort, and its C-index was 0.910. We examined how well the aforementioned two models predicted outcomes. The 1-, 3-, and 5-year AUCs were 0.928, 0.837, and 0.855, respectively, for the CoxPH model, and 0.971, 0.947, and 0.942 for the DeepSurv model. The DeepSurv model presented a greater predictive effect on patients with CMM, and its reliability was better than that of the CoxPH model according to both the AUC value and the calibration curve.

Conclusion: The DeepSurv model, which we developed based on the data of patients with CMM in the SEER database, was found to be more effective than the CoxPH model in predicting the survival time of patients with CMM.

Keywords: DeepSurv; SEER; cutaneous malignant melanoma; neural network; survival prediction.