基于多参数MRI的可解释性habitat和瘤周影像组学用于术前高风险前列腺癌预测：一项多机构研究
Interpretable habitat and peritumoral radiomics from multiparametric MRI for preoperative high-risk prostate cancer prediction: a multi-institutional study.
摘要(中文译文)
目前的术前评估面临局限性，包括PI-RADS评分的主观性和诊断的不确定性，无法区分高危前列腺癌症与良性和低风险病变。开发一个可解释的集合学习框架，整合基于习惯的放射组学和多参数MRI的肿瘤周围分析，用于术前高危前列腺癌症预测。这项回顾性多机构研究包括三个中心的896名疑似前列腺病变和组织病理学确诊的患者（2018年1月至2024年12月）。瘤内栖息地分析采用K-means聚类；瘤周分析评估了1mm、3mm和5mm膨胀环。特征选择使用最小冗余最大相关性（mRMR）和LASSO回归。模型通过SHAP分析进行了外部验证，以确保其可解释性。该队列包括398名培训患者、171名内部验证患者和327名外部验证患者。在外部验证队列中，栖息地特征的AUC为0.827（95%CI:0.768-0.886）和0.855（95%CI:0.795-0.915），表现优异，显著优于肿瘤内特征（AUC:0.774和0.629，p < 0.001) 和临床特征（AUC:0.791和0.712，p < 0.001). 3mm瘤周特征表现最佳（AUC:0.782-0.793）。组合模型达到了最高的性能（AUC:0.860-0.876）。SHAP分析显示，ADC衍生特征的重要性占主导地位，栖息地区域H3起到了重要作用 > 70% 选定的功能。综合栖息地和肿瘤周围放射组学为前列腺癌症提供了强大的术前风险分层，ADC衍生的栖息地特征具有优异的性能。不适用。这是一项没有前瞻性试验注册的回顾性观察性研究。
Abstract
Current preoperative assessment faces limitations, including PI-RADS scoring subjectivity and diagnostic uncertainty in distinguishing high-risk prostate cancer from benign and low-risk lesions. To develop an interpretable ensemble learning framework integrating habitat-based radiomics and peritumoral analysis from multiparametric MRI for preoperative high-risk prostate cancer prediction. This retrospective, multi-institutional study included 896 patients with suspected prostate lesions and histopathologically confirmed diagnoses across three centers (January 2018-December 2024). Intratumoral habitat analysis used K-means clustering; peritumoral analysis evaluated 1 mm, 3 mm, and 5 mm expansion rings. Feature selection used minimum Redundancy Maximum Relevance (mRMR) and LASSO regression. Models were validated externally with SHAP analysis for interpretability. The cohort comprised 398 training, 171 internal validation, and 327 external validation patients. The habitat signature achieved superior performance with AUCs of 0.827 (95% CI: 0.768-0.886) and 0.855 (95% CI: 0.795-0.915) in external validation cohorts, significantly outperforming intratumoral signatures (AUCs: 0.774 and 0.629, p < 0.001) and clinical signatures (AUCs: 0.791 and 0.712, p < 0.001). The 3 mm peritumoral signature performed best (AUC: 0.782-0.793). The combined model achieved the highest performance (AUC: 0.860-0.876). SHAP analysis showed ADC-derived features dominated importance, with habitat region H3 contributing > 70% of selected features. Integrated habitat and peritumoral radiomics provide robust preoperative risk stratification for prostate cancer, with superior performance from ADC-derived habitat features. Not applicable. This was a retrospective observational study without prospective trial registration.
如何引用
AMA
Mengxuan Yuan, Di Chang, Wanjun Lu, Kaiyang Ma, Yue Gu, Tianyi Xia, et al. Interpretable habitat and peritumoral radiomics from multiparametric MRI for preoperative high-risk prostate cancer prediction: a multi-institutional study.. Journal of translational medicine. 2026; doi:10.1186/s12967-026-07848-1.
APA
Mengxuan Yuan, Di Chang, Wanjun Lu, Kaiyang Ma, Yue Gu, Tianyi Xia, et al (2026). Interpretable habitat and peritumoral radiomics from multiparametric MRI for preoperative high-risk prostate cancer prediction: a multi-institutional study.. Journal of translational medicine. https://doi.org/10.1186/s12967-026-07848-1
GB/T 7714
Mengxuan Yuan, Di Chang, Wanjun Lu, Kaiyang Ma, Yue Gu, Tianyi Xia, et al. Interpretable habitat and peritumoral radiomics from multiparametric MRI for preoperative high-risk prostate cancer prediction: a multi-institutional study.[J]. Journal of translational medicine, 2026 doi:10.1186/s12967-026-07848-1.
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