Lasso‑Cox interpretable model of AFP‑negative hepatocellular carcinoma

• Han Li ^[1] ; Chengyuan Zhou ^[1] ; Chenjie Wang ^[1] ; Bo Li ^[1] ; Yanqiong Song ^[2] ; Bo Yang ^[1] ; Yan Zhang ^[3] ; Xueting Li ^[4] ; Mingyue Rao ^[1] ; Jianwen Zhang ^[1] ; Ke Su ^[1] ; Kun He ^[5] ; Yunwei Han ^[1]
  1. [1] Affiliated Hospital of Southwest Medical University

     Affiliated Hospital of Southwest Medical University

     China

     
  2. [2] School of Medicine, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China
  3. [3] Department of Oncology, Affiliated Hospital of Traditional Chinese Medicine of Southwest Medical University, Luzhou 646000, China
  4. [4] Department of Oncology, 363 Hospital, Chengdu, China
  5. [5] Clinical Medical College, Southwest Medical University, Luzhou 646000, China

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• Localización: Clinical & translational oncology, ISSN 1699-048X, Vol. 27, Nº. 1, 2025, págs. 309-318
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • Background In AFP-negative hepatocellular carcinoma patients, markers for predicting tumor progression or prognosis are limited. Therefore, our objective is to establish an optimal predicet model for this subset of patients, utilizing interpretable methods to enhance the accuracy of HCC prognosis prediction.

    Methods We recruited a total of 508 AFP-negative HCC patients in this study, modeling with randomly divided training set and validated with validation set. At the same time, 86 patients treated in different time periods were used as internal validation. After comparing the cox model with the random forest model based on Lasso regression, we have chosen the former to build our model. This model has been interpreted with SHAP values and validated using ROC, DCA. Additionally, we have reconfirmed the model’s effectiveness by employing an internal validation set of independent periods. Subsequently, we have established a risk stratification system.

    Results The AUC values of the Lasso-Cox model at 1, 2, and 3 years were 0.807, 0.846, and 0.803, and the AUC values of the Lasso-RSF model at 1, 2, and 3 years were 0.783, 0.829, and 0.776. Lasso-Cox model was finally used to predict the prognosis of AFP-negative HCC patients in this study. And BCLC stage, gamma-glutamyl transferase (GGT), diameter of tumor, lung metastases (LM), albumin (ALB), alkaline phosphatase (ALP), and the number of tumors were included in the model. The validation set and the separate internal validation set both indicate that the model is stable and accurate. Using risk factors to establish risk stratification, we observed that the survival time of the low-risk group, the middle-risk group, and the high-risk group decreased gradually, with significant differences among the three groups.

    Conclusion The Lasso-Cox model based on AFP-negative HCC showed good predictive performance for liver cancer. SHAP explained the model for further clinical application.