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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1495329
This article is part of the Research Topic Protein-Protein Interaction and Therapeutic Immunomodulation View all 9 articles

The Established of A Machine Learning Model for Predicting the Efficacy of Adjuvant Interferon Alpha1b in Patients with Advanced Melanoma

Provisionally accepted
Linhan Jiang Linhan Jiang 1Ke Su Ke Su 2,3*Jing Wang Jing Wang 1*Yitong Lin Yitong Lin 1*Xianya Zhao Xianya Zhao 1*Hengxiang Zhang Hengxiang Zhang 1*Yu Liu Yu Liu 1*
  • 1 Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China
  • 2 Department of Radiation Oncology, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
  • 3 Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China

The final, formatted version of the article will be published soon.

    Background: Interferon-alpha1b (IFN-α1b) has shown remarkable therapeutic potential as adjuvant therapy for melanoma. This study aimed to develop five machine learning models to evaluate the efficacy of postoperative IFN-α1b in patients with advanced melanoma. Methods: We retrospectively analyzed 113 patients with the American Joint Committee on Cancer (AJCC) stage III-IV melanoma who received postoperative IFN-α1b therapy between July 2009 and February 2024. Recurrence-free survival (RFS) and overall survival (OS) were assessed using Kaplan-Meier analysis. Five machine learning models (Decision Tree, Cox Proportional Hazards, Random Forest, Support Vector Machine, and LASSO regression) were developed and compared for their capacity to predict the outcomes of patients. Model performance was evaluated using concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and decision curve analysis. Results: The 1-year, 2-year, and 3-year RFS rates were 71.10%, 43.10%, and 31.10%, respectively. For OS, the 1-year, 2year, and 3-year OS rates were 99.10%, 82.30%, and 75.00%, respectively. The Decision Tree (DT) model demonstrated superior predictive performance with the highest C-index of 0.792. Time-dependent ROC analysis for predicting 1-, 2-, and 3-year RFS based on the DT model is 0.77, 0.79 and 0.76, respectively. Serum albumin emerged as the important predictor of RFS. Conclusions: Our study demonstrates the considerable efficacy DT model for predicting the efficacy of adjuvant IFN-α1b in patients with advanced melanoma. Serum albumin was identified as a key predictive factor of the treatment efficacy.

    Keywords: Immunotherapy, machine learning, Melanoma, Interferon-alpha, adjuvant therapy, prognostic factors

    Received: 12 Sep 2024; Accepted: 22 Oct 2024.

    Copyright: © 2024 Jiang, Su, Wang, Lin, Zhao, Zhang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Ke Su, Department of Radiation Oncology, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
    Jing Wang, Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China
    Yitong Lin, Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China
    Xianya Zhao, Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China
    Hengxiang Zhang, Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China
    Yu Liu, Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China

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