用伪标签提升肺癌生存预测，仅靠少量数据也能达到高准确率。
Enhanced Lung Cancer Survival Prediction using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets
- 用无监督伪标签扩展数据集，结合深度影像特征提升预测能力。
- 在仅199例患者数据下，预测准确率达0.85，显著优于传统方法。
- 适合数据稀缺场景，尤其适用于临床中难以获取大量标注数据的疾病预测。
本研究探索了一种基于半监督学习（SSL）和伪标签策略的方法，利用多样化的数据集增强肺癌（LCa）生存预测性能。从TCIA及本地数据库收集了199例肺癌患者的PET/CT图像，并引入408例头颈部癌（HNCa）的PET/CT图像作为伪标签数据源。通过ViSERA软件提取215个手工影像特征（HRF）和1024个深度影像特征（DRF），分别采用PySERA与3D自编码器。在监督学习（SL）中，使用主成分分析（PCA）连接4种分类器处理HRF和DRF；而半监督学习（SSL）则将408例伪标签的HNCa病例加入原始199例数据中，保持相同建模流程。结果表明，SSL策略优于SL（p<0.05），在仅使用PET的深度特征时，平均准确率达0.85（对应PCA+MLP模型），而传统方法使用CT的深度特征时仅为0.65（对应PCA+KNN）。此外，基于CT图像的HRF与DRF，经组件式梯度提升生存分析（Component-wise Gradient Boosting Survival Analysis）处理后，平均c-index达0.80，对数秩检验p值<<0.001，且在外部测试中验证有效。结论：转向深度影像特征与半监督学习，在数据有限情况下，可实现单模态（如仅用CT或仅用PET）下的高预测性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。