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多模态融合放射组学-免疫组学评分模型:准确识别前列腺癌进展

Multimodal fusion radiomic-immunologic scoring model: accurate identification of prostate cancer progression.

作者信息

Zhang Zhonglin, Liu Huan, Gu Xiling, Qiu Yang, Ma Jiangqing, Ai Guangyong, He Xiaojing

机构信息

Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 76 Linjiang Rd, Chongqing, 400010, China.

State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China.

出版信息

BMC Med Imaging. 2025 Aug 12;25(1):324. doi: 10.1186/s12880-025-01869-w.

Abstract

OBJECTIVES

This study aims to conceptualize, develop, and rigorously validate an innovative Radiomic-Immunologic Score (RDIS) model for accurately distinguishing prostate cancer (PCa) progression.

METHODS

This single-center, retrospective cohort study analyzed PCa patients diagnosed between 2019 and 2022. This study employed a comprehensive interdisciplinary approach, integrating CD3+/CD8 + T cell immunoanalysis with Multiparametric Magnetic Resonance Imaging (mpMRI) analysis, while adhering to a robust multi-phase feature selection process. This included the Akaike Information Criterion (AIC), Maximum Relevance Minimum Redundancy (mRMR), and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, validated through 10-fold cross-validation. Logistic regression models were constructed for radiomic, immunologic, and combined RDIS models, with predictive performance rigorously evaluated using Receiver Operating Characteristic (ROC) curve analysis, calibration curve assessments, and Decision Curve Analysis (DCA).

RESULTS

The RDIS model achieved an Area Under the Curve (AUC) of 0.874 in the validation cohort, outperforming traditional single-omics models, including the radiomic model (AUC: 0.844) and the immunologic model (AUC: 0.767), supporting potential use in early intervention decision-making. The correlation heatmap reveals weak to moderate correlations among 7 pairs of radiomic and immunologic features associated with PCa progression. The RDIS model demonstrates good specificity in further predicting bone metastases and castration-resistant prostate cancer (CRPC).

CONCLUSIONS

The RDIS model effectively distinguished the progression status of PCa, with its multi-omics integrative attributes likely providing comprehensive insights into the factors influencing disease progression.

ADVANCES IN KNOWLEDGE

The immunologic and radiologic characteristics are associated with prostate cancer progression. The RDIS multi-omics integrative scoring system shows great potential in distinguishing whether prostate cancer has progressed.

摘要

目的

本研究旨在概念化、开发并严格验证一种创新的放射组学-免疫组学评分(RDIS)模型,以准确区分前列腺癌(PCa)进展情况。

方法

本单中心回顾性队列研究分析了2019年至2022年期间确诊的PCa患者。本研究采用了综合的跨学科方法,将CD3+/CD8 + T细胞免疫分析与多参数磁共振成像(mpMRI)分析相结合,同时遵循稳健的多阶段特征选择过程。这包括赤池信息准则(AIC)、最大相关最小冗余(mRMR)和最小绝对收缩与选择算子(LASSO)算法,并通过10倍交叉验证进行验证。构建了放射组学、免疫组学和联合RDIS模型的逻辑回归模型,并使用受试者工作特征(ROC)曲线分析、校准曲线评估和决策曲线分析(DCA)对预测性能进行了严格评估。

结果

RDIS模型在验证队列中的曲线下面积(AUC)为0.874,优于传统的单组学模型,包括放射组学模型(AUC:0.844)和免疫组学模型(AUC:0.767),支持其在早期干预决策中的潜在应用。相关热图显示了与PCa进展相关的7对放射组学和免疫组学特征之间存在弱至中度相关性。RDIS模型在进一步预测骨转移和去势抵抗性前列腺癌(CRPC)方面表现出良好的特异性。

结论

RDIS模型有效地区分了PCa的进展状态,其多组学整合属性可能为影响疾病进展的因素提供全面见解。

知识进展

免疫和放射学特征与前列腺癌进展相关。RDIS多组学整合评分系统在区分前列腺癌是否进展方面显示出巨大潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35d7/12341337/f85a72cfeebe/12880_2025_1869_Fig1_HTML.jpg

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