Abstract：
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.
摘要：
肺癌分为两种主要类型：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。对于没有癌基因成瘾的晚期NSCLC患者，首选的治疗方法是免疫疗法和化疗的结合.然而,无进展生存期（PFS）通常只有大约6到8个月，伴有某些不良事件。为了更有效地进行个体化治疗,在该治疗方案下,迫切需要准确筛查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附属第一医院60例确诊为非小细胞肺癌患者的肺部CT图像。它开发了一个机器学习模型，指定为bSGSRIME-SVM，该算法将雾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,该模型通过采用SGSRIME算法识别关键图像特征来启动其过程。随后，它利用SVM分类器来评估这些特征，旨在提高模型的预测精度。最初，验证了SGSRIME在IEEECEC2017基准测试函数中的卓越优化能力和鲁棒性。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。然后利用开发的模型进行分析。结果表明，该模型在预测免疫治疗联合化疗治疗NSCLC的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预测和个性化治疗计划奠定了基础。
