用新型扩散MRI与AI提升前列腺癌无创检测准确率
Non-Invasive Detection of PROState Cancer with Novel Time-Dependent Diffusion MRI and AI-Enhanced Quantitative Radiological Interpretation: PROS-TD-AI
- 结合时间依赖扩散MRI与机器学习，实现区域特异性风险预测
- 相比传统PI-RADS v2.1，显著降低误诊率，提高诊断准确性
- 适合临床医生、影像科医师及前列腺癌筛查研究者参考
前列腺癌（PCa）是男性中最常见的恶性肿瘤，也是全球癌症死亡的第八大原因。多参数MRI（mpMRI）已成为中等风险男性诊断路径的核心，可提高对临床显著性前列腺癌（csPCa）的检出率，同时减少不必要的活检和过度诊断。然而，mpMRI仍受限于假阳性、假阴性以及观察者间一致性中到高度不一致的问题。时间依赖扩散（TDD）MRI是一种新型序列，可表征组织微结构，在区分临床显著性与非显著性前列腺癌方面表现出良好的前期性能。将TDD衍生指标与机器学习结合，有望提供更稳健、区域特异性的风险预测，减少对阅片者培训的依赖，并优于当前标准诊疗方案。本研究方案阐述了自主研发的AI增强TDD-MRI软件（PROSTDAI）在常规诊断中的前瞻性评估依据，旨在评估其相对于PI-RADS v2.1的附加价值，并通过磁共振引导下前列腺活检验证结果。
原文摘要 · Abstract (English)
Prostate cancer (PCa) is the most frequently diagnosed malignancy in men and the eighth leading cause of cancer death worldwide. Multiparametric MRI (mpMRI) has become central to the diagnostic pathway for men at intermediate risk, improving de-tection of clinically significant PCa (csPCa) while reducing unnecessary biopsies and over-diagnosis. However, mpMRI remains limited by false positives, false negatives, and moderate to substantial interobserver agreement. Time-dependent diffusion (TDD) MRI, a novel sequence that enables tissue microstructure characterization, has shown encouraging preclinical performance in distinguishing clinically significant from insignificant PCa. Combining TDD-derived metrics with machine learning may provide robust, zone-specific risk prediction with less dependence on reader training and improved accuracy compared to current standard-of-care. This study protocol out-lines the rationale and describes the prospective evaluation of a home-developed AI-enhanced TDD-MRI software (PROSTDAI) in routine diagnostic care, assessing its added value against PI-RADS v2.1 and validating results against MRI-guided prostate biopsy.
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。