深度放射组学模型在多中心前列腺癌检测中表现媲美传统PI-RADS评分。
Deep Radiomics Detection of Clinically Significant Prostate Cancer on Multicenter MRI: Initial Comparison to PI-RADS Assessment
- 基于MRI影像构建深度放射组学模型，融合分割与特征提取自动识别癌症。
- 患者层面性能与PI-RADS相当，AUROC达0.91，敏感性90%、特异性73%。
- 适合临床辅助诊断研发者及影像科医生参考，尤其关注肿瘤定位精度提升。
目的：开发并评估一种深度放射组学模型用于临床上显著前列腺癌（csPCa，grade group ≥2）的检测，并与多中心队列中的前列腺成像报告和数据系统（PI-RADS）评估进行比较。方法：本回顾性研究分析了来自四个数据集（2010–2020年）共615名患者的双参数（T2W和DW）前列腺MRI序列，包括PROSTATEx挑战赛、Prostate158挑战赛、PCaMAP试验及一所院内（NTNU/St. Olavs Hospital）数据集。以专家标注为金标准，训练深度放射组学模型，包含nnU-Net分割前列腺、体素级放射组学特征提取、极端梯度提升分类，以及肿瘤概率图后处理生成csPCa检测图。训练采用5折交叉验证，使用PROSTATEx（n=199）、Prostate158（n=138）和PCaMAP（n=78）数据集，测试在院内数据集（n=200）上进行。患者与病灶层面性能通过受试者工作特征曲线下面积（AUROC [95% CI]）、敏感性和特异性进行对比。结果：在测试数据中，放射科医生在患者层面达到AUROC 0.94 [0.91–0.98]，敏感性94%（75/80），特异性77%（92/120），阈值为PI-RADS ≥3。深度放射组学模型在肿瘤概率阈值≥0.76时，达到AUROC 0.91 [0.86–0.95]，敏感性90%（72/80），特异性73%（87/120），与PI-RADS相比无显著差异（p=0.068）。在病灶层面，PI-RADS ≥3的灵敏度为84%（91/108），每患者假阳性0.2（40/200），而深度放射组学模型在同一假阳性率下灵敏度为68%（73/108）。结论：深度放射组学机器学习模型在患者层面检测csPCa的表现可媲美PI-RADS，但在病灶层面尚有差距。
原文摘要 · Abstract (English)
Objective: To develop and evaluate a deep radiomics model for clinically significant prostate cancer (csPCa, grade group >= 2) detection and compare its performance to Prostate Imaging Reporting and Data System (PI-RADS) assessment in a multicenter cohort. Materials and Methods: This retrospective study analyzed biparametric (T2W and DW) prostate MRI sequences of 615 patients (mean age, 63.1 +/- 7 years) from four datasets acquired between 2010 and 2020: PROSTATEx challenge, Prostate158 challenge, PCaMAP trial, and an in-house (NTNU/St. Olavs Hospital) dataset. With expert annotations as ground truth, a deep radiomics model was trained, including nnU-Net segmentation of the prostate gland, voxel-wise radiomic feature extraction, extreme gradient boost classification, and post-processing of tumor probability maps into csPCa detection maps. Training involved 5-fold cross-validation using the PROSTATEx (n=199), Prostate158 (n=138), and PCaMAP (n=78) datasets, and testing on the in-house (n=200) dataset. Patient- and lesion-level performance were compared to PI-RADS using area under ROC curve (AUROC [95% CI]), sensitivity, and specificity analysis. Results: On the test data, the radiologist achieved a patient-level AUROC of 0.94 [0.91-0.98] with 94% (75/80) sensitivity and 77% (92/120) specificity at PI-RADS >= 3. The deep radiomics model at a tumor probability cut-off >= 0.76 achieved 0.91 [0.86-0.95] AUROC with 90% (72/80) sensitivity and 73% (87/120) specificity, not significantly different (p = 0.068) from PI-RADS. On the lesion level, PI-RADS cut-off >= 3 had 84% (91/108) sensitivity at 0.2 (40/200) false positives per patient, while deep radiomics attained 68% (73/108) sensitivity at the same false positive rate. Conclusion: Deep radiomics machine learning model achieved comparable performance to PI-RADS assessment in csPCa detection at the patient-level but not at the lesion-level.
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