对比四类癌症预测方法，发现深度影像组学表现最佳，融合模型最准。
Handcrafted vs. Deep Radiomics vs. Fusion vs. Deep Learning: A Comprehensive Review of Machine Learning -Based Cancer Outcome Prediction in PET and SPECT Imaging
- 系统分析226项研究，按59项标准评估模型与数据质量
- 深度影像组学准确率最高（0.862），融合模型AUC最高（0.861）
- 超半数研究未达国际标准，需提升数据质量和可解释性
机器学习（ML），包括深度学习（DL）和基于影像组学的方法，正越来越多地用于基于PET和SPECT成像的癌症预后预测。然而，手工特征（HRF）、深度影像组学特征（DRF）、深度学习模型及融合方法在临床应用中的表现仍不一致。本系统综述分析了2020至2025年间发表的226项相关研究，每项研究均采用包含数据集构建、特征提取、验证方法、可解释性和偏倚风险等59个维度的评估框架。提取的关键信息包括模型类型、癌种、成像模态及准确率、曲线下面积（AUC）等性能指标。基于PET的研究（95%）普遍优于SPECT，可能因其空间分辨率和灵敏度更高。深度影像组学模型平均准确率最高（0.862），而融合模型的AUC最高（0.861）。方差分析显示性能差异显著（准确率：p=0.0006，AUC：p=0.0027）。常见局限包括类别不平衡处理不足（59%）、缺失数据（29%）及人群多样性低（19%）。仅48%的研究遵循IBSI标准。研究强调需建立标准化流程、提升数据质量并发展可解释人工智能以推动临床应用。
原文摘要 · Abstract (English)
Machine learning (ML), including deep learning (DL) and radiomics-based methods, is increasingly used for cancer outcome prediction with PET and SPECT imaging. However, the comparative performance of handcrafted radiomics features (HRF), deep radiomics features (DRF), DL models, and hybrid fusion approaches remains inconsistent across clinical applications. This systematic review analyzed 226 studies published from 2020 to 2025 that applied ML to PET or SPECT imaging for outcome prediction. Each study was evaluated using a 59-item framework covering dataset construction, feature extraction, validation methods, interpretability, and risk of bias. We extracted key details including model type, cancer site, imaging modality, and performance metrics such as accuracy and area under the curve (AUC). PET-based studies (95%) generally outperformed those using SPECT, likely due to higher spatial resolution and sensitivity. DRF models achieved the highest mean accuracy (0.862), while fusion models yielded the highest AUC (0.861). ANOVA confirmed significant differences in performance (accuracy: p=0.0006, AUC: p=0.0027). Common limitations included inadequate handling of class imbalance (59%), missing data (29%), and low population diversity (19%). Only 48% of studies adhered to IBSI standards. These findings highlight the need for standardized pipelines, improved data quality, and explainable AI to support clinical integration.
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