标题： 使用半监督伪标签和从多样化PET/CT数据集学习的增强型肺癌生存预测 显示英文标题

标题： Enhanced Lung Cancer Survival Prediction using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets

摘要： 目标：本研究探索了一种使用多样化数据集的半监督学习（SSL）伪标签策略，以提升肺癌（LCa）生存预测性能，并分析来自PET/CT扫描的手工特征和深度影像组学特征（HRF/DRF），通过混合机器学习系统（HMLS）。 方法：我们收集了199名同时拥有PET和CT图像的LCa患者，这些数据来自癌症影像档案库（TCIA）和本地数据库，同时从TCIA获得了408名头颈癌（HNCa）的PET/CT图像。 我们在ViSERA软件中，通过PySERA和3D自动编码器，分别提取了215个HRF和1024个DRF，这些特征来自分割后的原发肿瘤。 监督策略（SL）采用HMLSs： PCA连接到HRF和DRF上的4个分类器。 SSL策略通过添加由随机森林算法标记的408个伪标签HNCa病例（共199个LCa病例），扩展了数据集，使用相同的HMLS技术。 此外， 主成分分析（PCA）结合4个生存预测算法用于生存风险比分析。 结果：SSL策略优于SL方法（p值<0.05），使用PET的DRF和PCA+多层感知机（MLP）实现了平均准确率0.85，而SL策略使用CT的DRF和PCA+K最近邻（KNN）则为0.65。 此外， PCA结合从CT提取的HRF和DRF上的分量梯度提升生存分析，具有平均c指数0.80，Log Rank p值<<0.001，外部测试确认。 结论：从HRF和SL转向DRF和SSL策略，特别是在数据点有限的情况下，使单独的CT或PET能够显著实现高预测性能。 显示英文摘要

摘要： Objective: This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets to enhance lung cancer (LCa) survival predictions, analyzing Handcrafted and Deep Radiomic Features (HRF/DRF) from PET/CT scans with Hybrid Machine Learning Systems (HMLS). Methods: We collected 199 LCa patients with both PET & CT images, obtained from The Cancer Imaging Archive (TCIA) and our local database, alongside 408 head&neck cancer (HNCa) PET/CT images from TCIA. We extracted 215 HRFs and 1024 DRFs by PySERA and a 3D-Autoencoder, respectively, within the ViSERA software, from segmented primary tumors. The supervised strategy (SL) employed a HMLSs: PCA connected with 4 classifiers on both HRF and DRFs. SSL strategy expanded the datasets by adding 408 pseudo-labeled HNCa cases (labeled by Random Forest algorithm) to 199 LCa cases, using the same HMLSs techniques. Furthermore, Principal Component Analysis (PCA) linked with 4 survival prediction algorithms were utilized in survival hazard ratio analysis. Results: SSL strategy outperformed SL method (p-value<0.05), achieving an average accuracy of 0.85 with DRFs from PET and PCA+ Multi-Layer Perceptron (MLP), compared to 0.65 for SL strategy using DRFs from CT and PCA+ K-Nearest Neighbor (KNN). Additionally, PCA linked with Component-wise Gradient Boosting Survival Analysis on both HRFs and DRFs, as extracted from CT, had an average c-index of 0.80 with a Log Rank p-value<<0.001, confirmed by external testing. Conclusions: Shifting from HRFs and SL to DRFs and SSL strategies, particularly in contexts with limited data points, enabling CT or PET alone to significantly achieve high predictive performance.