发现前列腺癌影像假阳性与真肿瘤特征相似，可借轻量后处理提升识别准确率。
A multi-architecture study of specificity refinement and false-positive mechanism analysis in prostate MRI
- 通过对比影像特征分析假阳性成因，发现其与真实病灶在信号强度上高度相似。
- 引入轻量化后处理头，在保持检出率前提下将特异性提升17.2%。
- 结论适用于多种模型架构，但效果受数据划分方式影响，需谨慎应用。
目的：表征前列腺MRI检测中的残余假阳性现象，并评估一种轻量级后处理精炼头在病例级特异性上的表现。方法：本回顾性研究采用PI-CAI（5折交叉验证）和Prostate158（n=158；外部验证）数据集。在冻结的检测主干网络上训练了一个上下文感知证据头与一个含89,216个参数的精炼头；证据头还额外在四种主干网络（bare nnU-Net、bare U-Net、bare Mamba、MIGF-Mamba）上训练。对每个假阳性区域，比较其在T2加权、表观扩散系数及高b值对比度上相对于周围环状区域的差异，与真实病灶及对侧良性组织进行对比。结果：假阳性在证据和原始T2、ADC对比度上均更接近真实癌症而非良性组织，在五种架构中均复现35/35（Cohen's d=1.10；FP/良性证据比值2.38倍），在模态扰动场景中复现105/105。在PI-CAI fold-0上，精炼使病例级特异性从0.469升至0.549（+17.2%），敏感性维持在0.943；5折交叉验证显示折叠条件依赖性（15次观察中9次为正，范围-22%至+28%）。在Prostate158上，两模型均达饱和（McNemar合并p=0.69），但假阳性对比度匹配现象仍复现。结论：残余假阳性与癌症在影像特征上存在对比度匹配，反映数据层面的成像属性而非模型特异性伪影；后处理精炼可在域内提升实际特异性，但具有折叠条件依赖性。
原文摘要 · Abstract (English)
Objectives: To characterize residual false positives in prostate MRI detection, and to evaluate a lightweight post-hoc refinement head for case-level specificity. Materials and Methods: This retrospective study used PI-CAI (5-fold cross-validation) and Prostate158 (n=158; external). A context-aware evidence head and an 89,216-parameter refinement head were trained on a frozen detection backbone; the evidence head was also trained on four further backbones (bare nnU-Net, bare U-Net, bare Mamba, MIGF-Mamba). For each false-positive region, T2-weighted, apparent-diffusion-coefficient, and high-b-value contrast ratios versus peri-lesional rings were compared against ground-truth lesions and contralateral benign regions. Results: False positives were closer to true cancers than to benign tissue in evidence and raw T2-weighted and apparent-diffusion-coefficient contrast, reproducing 35/35 across five architectures (Cohen's d 1.10; FP/benign evidence ratio 2.38x) and 105/105 across modality-perturbation scenarios. On PI-CAI fold-0, refinement raised case-level specificity from 0.469 to 0.549 (+17.2%) at preserved sensitivity (0.943); 5-fold cross-validation showed fold-conditional behavior (9/15 observations positive; range -22% to +28%). On Prostate158, both models saturated (McNemar pooled p=0.69), while the false-positive contrast-matching finding replicated. Conclusion: Residual false positives are contrast-matched to cancer (sharing raw imaging features rather than histologically confirmed mimicry), reproducing across five architectures -- a data-level imaging property, not model-specific artifacts; post-hoc refinement adds practical specificity in-domain but is fold-conditional.
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