技术上可行但临床误导？患者个性化合成前列腺MRI的专家评估
Technically Plausible but Clinically Misleading? Expert Evaluation of Patient-Personalized Synthetic Prostate MRI
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中文总结 AI 辅助
本研究用3D扩散模型合成患者个性化前列腺MRI，验证其技术可行性后开展专家研究，发现其虽技术可行但存在误导解读风险，提示需评估其临床应用安全性。
中文摘要 AI 辅助
磁共振成像（MRI）是前列腺癌评估的核心手段，但其采集成本高、耗时长，成为患者诊疗路径中的主要瓶颈。个性化MRI合成是一个有前景的方向，因为它可生成适配个体患者的临床真实图像，同时减少对扫描仪采集影像的依赖。本研究探究患者个性化MRI合成是否能生成专家认为临床可行的图像，以及这种个性化如何影响专家的解读。我们使用基于常规预成像临床变量年龄和PSA的3D扩散模型合成患者个性化前列腺MRI。利用一个大型公开数据集，我们首先通过标准图像相似度指标验证合成图像在技术上可与扫描仪采集的MRI相媲美。随后，我们针对反映患者临床特征差异的抽样病例子集开展了一项单盲、随机的专家预试验研究。一名放射科医生采用配对评估和李克特量表评分，对个性化及无约束的合成MRI的临床可行性、置信度和误导解读风险进行比较，并辅以定性反馈。尽管个性化合成MRI在技术上看似可行，但专家解读显示，当常规临床数据嵌入图像生成过程时，置信度存在差异，且存在误导线索的潜在风险。这些发现表明，个性化合成在技术上可行，但在这类系统可安全整合至临床 workflow 之前，需仔细评估生成的线索如何影响临床医生的解读。
英文摘要
Magnetic resonance imaging (MRI) is central to prostate cancer assessment, yet its acquisition is costly and time-consuming, making it a major bottleneck in the patient care pathway. Personalized MRI synthesis is a promising direction because it may allow the generation of clinically realistic images tailored to individual patients while reducing the dependence on scanner-acquired imaging. In this work, we in-vestigate whether patient-personalized MRI synthesis produces images that experts perceive as clinically plausible and how such personaliza-tion influences expert interpretation. We synthesize patient-personalized prostate MRI using a 3D diffusion model conditioned on routine pre-imaging clinical variables age and PSA. Using a large publicly available dataset, we first verify that synthesized images are technically compara-ble to scanner-acquired MRIs using standard image similarity metrics. We then conduct a pilot, single-blinded, randomized expert study on a subset of cases sampled to reflect variation in patient clinical pro-files. A radiologist compared personalized and unconditioned synthetic MRIs using paired assessments and Likert-scale ratings of clinical plausibility, confidence, and risk of misleading interpretation, supplemented by qualitative feedback. Although personalized synthetic MRIs appeared technically plausible, expert interpretation highlighted variability in con-fidence and potential risks of misleading cues when routine clinical data was embedded into image generation. These findings suggest that while personalized synthesis may be technically feasible, careful assessment is needed to understand how generated cues influence clinician interpreta-tion before such systems can be safely integrated into clinical workflows.
