文章：

利用半监督伪标记与多样化PET/CT数据集学习增强肺癌生存预测

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

原文发布日期：17 January 2025

DOI: 10.3390/cancers17020285

类型: Article

开放获取: 是

英文摘要：

Objective: This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets such as head and neck cancer (HNCa) to enhance lung cancer (LCa) survival outcome predictions, analyzing handcrafted and deep radiomic features (HRF/DRF) from PET/CT scans with hybrid machine learning systems (HMLSs). Methods: We collected 199 LCa patients with both PET and CT images, obtained from TCIA and our local database, alongside 408 HNCa PET/CT images from TCIA. We extracted 215 HRFs and 1024 DRFs by PySERA and a 3D autoencoder, respectively, within the ViSERA 1.0.0 software, from segmented primary tumors. The supervised strategy (SL) employed an HMLS–PCA connected with six classifiers on both HRFs and DRFs. The SSL strategy expanded the datasets by adding 408 pseudo-labeled HNCa cases (labeled by the Random Forest algorithm) to 199 LCa cases, using the same HMLS techniques. Furthermore, principal component analysis (PCA) linked with four survival prediction algorithms were utilized in the survival hazard ratio analysis. Results: The SSL strategy outperformed the SL method (p<< 0.001), achieving an average accuracy of 0.85 ± 0.05 with DRFs from PET and PCA + Multi-Layer Perceptron (MLP), compared to 0.69 ± 0.06 for the SL strategy using DRFs from CT and PCA + Light Gradient Boosting (LGB). 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 rankp-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, can significantly achieve high predictive performance.

摘要翻译： 

目的：本研究探讨一种半监督学习策略，通过整合头颈癌等多源数据集构建伪标签，以提升肺癌生存结局预测性能。研究采用混合机器学习系统，分析PET/CT影像中手工及深度放射组学特征。方法：收集199例来自TCIA及本地数据库的肺癌患者PET/CT影像，以及408例TCIA头颈癌PET/CT影像。使用ViSERA 1.0.0软件，分别通过PySERA工具和三维自编码器从原发灶分割区域提取215个手工特征与1024个深度特征。监督学习策略采用混合机器学习系统结合主成分分析，连接六种分类器处理两类特征；半监督策略通过随机森林算法生成伪标签，将408例头颈癌数据与199例肺癌数据融合后采用相同技术体系。生存分析采用主成分分析结合四种生存预测算法进行风险比评估。结果：半监督策略显著优于监督方法（p<<0.001），其中基于PET深度特征结合主成分分析与多层感知器的方案达到0.85±0.05的平均准确率，而基于CT深度特征结合主成分分析与轻量梯度提升的监督策略仅为0.69±0.06。在CT影像中，主成分分析结合分量梯度提升生存分析对两类特征均取得0.80的平均C指数，对数秩检验p值<<0.001，外部验证确认了该结果。结论：在数据有限场景下，从手工特征监督学习转向深度特征半监督学习策略，仅需CT或PET单一模态即可实现显著提升的预测性能。

原文链接：

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