Department of Pulmonary and Critical Care Medicine, The First Affliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Wenzhou University Library, Wenzhou, 325035, China.
Comput Biol Med. 2024 Aug;178:108638. doi: 10.1016/j.compbiomed.2024.108638. Epub 2024 May 21.
Lung cancer is categorized into two main types: non-small cell lung cancer (NSCLC) and small cell lung cancer. Of these, NSCLC accounts for approximately 85% of all cases and encompasses varieties such as squamous cell carcinoma and adenocarcinoma. For patients with advanced NSCLC that do not have oncogene addiction, the preferred treatment approach is a combination of immunotherapy and chemotherapy. However, the progression-free survival (PFS) typically ranges only from about 6 to 8 months, accompanied by certain adverse events. In order to carry out individualized treatment more effectively, it is urgent to accurately screen patients with PFS for more than 12 months under this treatment regimen. Therefore, this study undertook a retrospective collection of pulmonary CT images from 60 patients diagnosed with NSCLC treated at the First Affiliated Hospital of Wenzhou Medical University. It developed a machine learning model, designated as bSGSRIME-SVM, which integrates the rime optimization algorithm with self-adaptive Gaussian kernel probability search (SGSRIME) and support vector machine (SVM) classifier. Specifically, the model initiates its process by employing the SGSRIME algorithm to identify pivotal image features. Subsequently, it utilizes an SVM classifier to assess these features, aiming to enhance the model's predictive accuracy. Initially, the superior optimization capability and robustness of SGSRIME in IEEE CEC 2017 benchmark functions were validated. Subsequently, employing color moments and gray-level co-occurrence matrix methods, image features were extracted from images of 60 NSCLC patients undergoing immunotherapy combined with chemotherapy. The developed model was then utilized for analysis. The results indicate a significant advantage of the model in predicting the efficacy of immunotherapy combined with chemotherapy for NSCLC, with an accuracy of 92.381% and a specificity of 96.667%. This lays the foundation for more accurate PFS predictions and personalized treatment plans.
非小细胞肺癌（NSCLC）和小细胞肺癌。其中，NSCLC 约占所有病例的 85%，包括鳞状细胞癌和腺癌等多种类型。对于没有致癌基因成瘾的晚期 NSCLC 患者，首选的治疗方法是免疫治疗和化疗的联合。然而，无进展生存期（PFS）通常只有约 6 到 8 个月，同时伴有一定的不良反应。为了更有效地进行个体化治疗，迫切需要在这种治疗方案下准确筛选出 PFS 超过 12 个月的患者。因此，本研究回顾性收集了温州医科大学第一附属医院 60 例 NSCLC 患者的肺部 CT 图像，建立了一个机器学习模型，称为 bSGSRIME-SVM，该模型将rime 优化算法与自适应高斯核概率搜索（SGSRIME）和支持向量机（SVM）分类器相结合。具体来说，该模型首先使用 SGSRIME 算法识别关键图像特征，然后使用 SVM 分类器评估这些特征，旨在提高模型的预测准确性。首先，在 IEEE CEC 2017 基准函数中验证了 SGSRIME 在优化能力和鲁棒性方面的优势。随后，采用颜色矩和灰度共生矩阵方法，从 60 例接受免疫治疗联合化疗的 NSCLC 患者的图像中提取图像特征，然后对开发的模型进行分析。结果表明，该模型在预测 NSCLC 免疫治疗联合化疗疗效方面具有显著优势，准确率为 92.381%，特异性为 96.667%。这为更准确的 PFS 预测和个性化治疗方案奠定了基础。
