多模态融合放射免疫评分模型:准确识别前列腺癌进展。

IF 3.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Zhonglin Zhang, Huan Liu, Xiling Gu, Yang Qiu, Jiangqing Ma, Guangyong Ai, Xiaojing He
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引用次数: 0

摘要

目的:本研究旨在概念化、发展并严格验证一种创新的放射免疫评分(RDIS)模型,以准确区分前列腺癌(PCa)的进展。方法:这项单中心、回顾性队列研究分析了2019年至2022年诊断的PCa患者。本研究采用综合的跨学科方法,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,同时坚持稳健的多相特征选择过程。这包括赤池信息标准(AIC)、最大关联最小冗余(mRMR)和最小绝对收缩和选择算子(LASSO)算法,通过10次交叉验证验证。对放射学、免疫学和联合RDIS模型构建Logistic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)和免疫学模型(AUC: 0.767),支持在早期干预决策中的潜在应用。相关热图显示与前列腺癌进展相关的7对放射学和免疫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)方面具有良好的特异性。结论:RDIS模型有效区分了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的见解。知识进展:免疫学和放射学特征与前列腺癌的进展有关。RDIS多组学综合评分系统在区分前列腺癌是否进展方面显示出巨大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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

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

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.

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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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