The shift towards efficient and automated data analysis through Machine Learning (ML) has notably impacted healthcare systems, particularly Radiomics. Radiomics leverages ML to analyze medical images accurately and efficiently for precision medicine. Current methods rely on Deep Learning (DL) to improve performance and accuracy (Deep Radiomics). Given the sensitivity of medical images, ensuring privacy throughout the Deep Radiomics pipeline-from data generation and collection to model training and inference-is essential, especially when outsourced. Thus, Privacy-Enhancing Technologies (PETs) are crucial tools for Deep Radiomics. Previous studies and systematization efforts have either broadly overviewed PETs and their applications or mainly focused on subsets of PETs for ML algorithms. In Deep Radiomics, where efficiency, accuracy, and privacy are crucial, many PETs, while theoretically applicable, may not be practical without specialized optimizations or hybrid designs. Additionally, not all DL models are suitable for Radiomics. Consequently, there is a need for specialized studies that investigate and systematize the effective and practical integration of PETs into the Deep Radiomics pipeline. This work addresses this research gap by (1) classifying existing PETs, presenting practical hybrid PETS constructions, and a taxonomy illustrating their potential integration with the Deep Radiomics pipeline, with comparative analyses detailing assumptions, architectural suitability, and security, (2) Offering technical insights, describing potential challenges and means of combining PETs into the Deep Radiomics pipeline, including integration strategies, subtilities, and potential challenges, (3) Proposing potential research directions, identifying challenges, and suggesting solutions to enhance the PETs in Deep Radiomics.

翻译：机器学习（ML）向高效自动化数据分析的转变已显著影响医疗系统，尤其在影像组学领域。影像组学利用ML对医学影像进行精准高效分析，以支持精准医疗。现有方法依赖深度学习（DL）提升性能与准确度（深度影像组学）。鉴于医学影像的敏感性，在深度影像组学全流程——从数据生成与采集到模型训练与推理——中确保隐私至关重要，尤其在流程外包时。因此，隐私增强技术（PETs）成为深度影像组学的关键工具。既往研究与体系化工作或广泛概述PETs及其应用，或主要聚焦于ML算法的PETs子集。在深度影像组学中，效率、准确性与隐私性均至关重要，许多PETs虽理论可行，但若无专门优化或混合设计则难以实用。此外，并非所有DL模型均适用于影像组学。因此，亟需开展专门研究，以探索并系统化PETs在深度影像组学流程中的有效实用集成。本研究通过以下方式填补该研究空白：（1）对现有PETs进行分类，提出实用混合PETs架构，并构建分类体系阐明其与深度影像组学流程的潜在集成方式，通过比较分析详述其假设条件、架构适用性与安全性；（2）提供技术见解，描述PETs融入深度影像组学流程的潜在挑战与结合途径，包括集成策略、技术细节与潜在难点；（3）提出未来研究方向，识别挑战并给出解决方案以增强深度影像组学中的PETs。