利用拉曼光谱和机器学习对hdr近距离放射治疗前列腺癌的免疫浸润进行建模。

IF 3.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Reports Pub Date : 2025-10-16 DOI:10.1038/s41598-025-20107-5

Sandra N Popescu, Kirsty Milligan, Mitchell Wiebe, Alejandra Fuentes, Joan M Brewer, Christina K Haston, Julian J Lum, Samantha Punch, Alejandra Raudales, Alexandre G Brolo, Juanita M Crook, Jeffrey L Andrews, Andrew Jirasek

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引用次数: 0

摘要

前列腺癌的特点是免疫抑制肿瘤环境。这项工作将拉曼光谱与基团和碱基限制性非负矩阵分解（GBR-NMF）和机器学习相结合，在接受高剂量率近距离放射治疗（HDR-BT）的患者的针芯活检中组装免疫细胞密度模型。对HDR-BT第一部分注射前和注射后2周采集的活检细胞进行拉曼光谱采集和CD68[公式：见文]、CD3[公式：见文]和CD3[公式：见文]细胞的免疫组织化学染色。使用GBR-NMF评分构建的回归技术，通过根均方误差（RMSE）和R[公式：见文]的指标，产生了最准确的免疫细胞密度预测，这是[公式：见文]密度的梯度增强树模型（RMSE: 163个计数[公式：见文]，[公式：见文]:0.65）和[公式：见文]/[公式：见文]的弹性网模型（RMSE: 0.25，[公式：见文]:0.82）。这些模型的准确性，这里定义为患者预测在其测量值标准差内的比例，CD68[公式：见文]CD3[公式：见文]和CD68[公式：见文]/ CD8[公式：见文]模型分别为11/16和12/16。为了进一步描述哪些代谢物在CD68 / CD8模型中最重要，在活检的间质和上皮组织中进一步预测了这一比例，由此产生的模型利用谷胱甘肽、胶原蛋白、棕榈酸和hdr - bt前后标记的GBR-NMF评分，根据RMSE和R（公式：见文本）产生最佳性能水平。总之，本研究说明了一种新的方法，其中监督机器学习技术用于模拟免疫细胞，这是疾病进展的预后指标。

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Modelling of immune infiltration in prostate cancer treated with HDR-brachytherapy using Raman spectroscopy and machine learning.

Prostate cancer is characterized by an immunosuppressive tumour environment. This work combines Raman spectroscopy with group-and-bases-restricted non-negative matrix factorization (GBR-NMF) and machine learning to assemble models of immune cell densities within the needle-core biopsies of patients undergoing high-dose-rate brachytherapy (HDR-BT). Raman spectral acquisition, as well as immunohistochemistry staining of CD68[Formula: see text], CD3[Formula: see text], and [Formula: see text] cells, was completed for biopsies collected before and 2 weeks following the first fraction of HDR-BT. Regression techniques, constructed using GBR-NMF scores, that produced the most accurate predictions of immune cell density by metrics of root mean-squared error (RMSE) and R[Formula: see text] were the gradient-boosted trees model of [Formula: see text] density (RMSE: 163 counts[Formula: see text], [Formula: see text]: 0.65) and the elastic net model of [Formula: see text]/ [Formula: see text] (RMSE: 0.25, [Formula: see text]: 0.82). The accuracy of these models, herein defined as the fraction of patient predictions within [Formula: see text] standard deviation of their measured values was 11/16 and 12/16, for CD68[Formula: see text] CD3[Formula: see text] and CD68[Formula: see text]/ CD8[Formula: see text] models, respectively. To further delineate which metabolites were most important in the CD68[Formula: see text]/ CD8[Formula: see text] model, this ratio was further predicted in stromal and epithelial tissues within the biopsies, and resulting models utilized the GBR-NMF scores of glutathione, collagen, palmitic acid, and the pre- or post-HDR-BT label to produce an optimal performance level according to RMSE and R[Formula: see text]. In summary, this study illustrates a novel methodology in which supervised machine learning techniques are used to model immune cells, which are prognostic indicators of disease progression.

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来源期刊

Scientific Reports Natural Science Disciplines-

CiteScore

7.50

自引率

4.30%

发文量

19567

审稿时长

3.9 months

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