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interest roi  (Bruker Corporation)


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    Bruker Corporation interest roi
    Interest Roi, supplied by Bruker Corporation, used in various techniques. Bioz Stars score: 97/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/interest+roi/MM2/pm41810563-197-42-36
    Average 97 stars, based on 1 article reviews
    interest roi - by Bioz Stars, 2026-10
    97/100 stars

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    Related Articles

    other:

    Article Title: Proanthocyanidin enhances the endogenous regeneration of alveolar bone by elevating the autophagy of PDLSCs.
    Article Snippet: Funding information National Natural Science Foundation of China, Grant/Award Number: 81771121; Natural Science Foundation of Hubei Province, Grant/Award Number: 2021CFB367 and 2022CFB293 Abstract Objective: This study aimed to investigate the effect of proanthocyanidin (PA) on osteogenesis mediated by periodontal ligament stem cells (PDLSCs) and endogenous alveolar bone regeneration.. Background: Leveraging the osteogenic potential of resident stem cells is a promising strategy for alveolar bone regeneration.. PA has been reported to be effective in osteogenesis.

    Software:

    Article Title: A thermosensitive hydrogel achieves sustained co-delivery of two therapeutic agents with distinct properties for preventing aseptic loosening.
    Article Snippet: Then, the bone volume to tissue volume ratio (percent bone volume, BV/TV), bone surface to volume ratio (BS/BV) and bone surface to tissue volume ratio (bone surface density, BS/TV) were determined using CTAn software (version 1.17.7, Bruker) within a cylindrical region of interest (ROI) measuring 2 mm × 2 mm × 1 mm. .. Then, the bone volume to tissue volume ratio (percent bone volume, BV/TV), bone surface to volume ratio (BS/BV) and bone surface to tissue volume ratio (bone surface density, BS/TV) were determined using CTAn software (version 1.17.7, Bruker) within a cylindrical region of interest (ROI) measuring 2 mm × 2 mm × 1 mm. ..



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    Image Search Results


    模型预测 EGFR 基因突变的 ROC 曲线 Figure 3 ROC curve of the model for predicting EGFR gene mutations ROC: Receiver operator characteristic; AUC: Area under the curve.

    Journal: Journal of Central South University Medical Sciences

    Article Title: 基于放射基因组学预测非小细胞癌患者的 KRAS 和 EGFR 基因突变

    doi: 10.11817/j.issn.1672-7347.2025.250026

    Figure Lengend Snippet: 模型预测 EGFR 基因突变的 ROC 曲线 Figure 3 ROC curve of the model for predicting EGFR gene mutations ROC: Receiver operator characteristic; AUC: Area under the curve.

    Article Snippet: 基于美国癌症影像档案(The Cancer Imaging Archive,TCIA)公开数据集中的Lung-PET-CT-Dx数据,纳入符合条件的NSCLC患者影像学及基因突变信息。采用三维卷积神经网络模型从感兴趣区(regions of interest,ROI)提取影像学特征。使用LightGBM算法对影像学特征进行建模,并分别构建预测 EGFR 和 KRAS 基因突变的分类模型。采用5折交叉验证评估模型的性能,并采用受试者操作特征(receiver operator characteristic,ROC)曲线及曲线下面积(area under the curve,AUC)、准确度、敏感度和特异度等指标进行性能验证。

    Techniques:

    模型预测 EGFR 基因突变的 DCA 结果 Figure 4 DCA result of the model for predicting EGFR gene mutations DCA: Decision curve analysis.

    Journal: Journal of Central South University Medical Sciences

    Article Title: 基于放射基因组学预测非小细胞癌患者的 KRAS 和 EGFR 基因突变

    doi: 10.11817/j.issn.1672-7347.2025.250026

    Figure Lengend Snippet: 模型预测 EGFR 基因突变的 DCA 结果 Figure 4 DCA result of the model for predicting EGFR gene mutations DCA: Decision curve analysis.

    Article Snippet: 基于美国癌症影像档案(The Cancer Imaging Archive,TCIA)公开数据集中的Lung-PET-CT-Dx数据,纳入符合条件的NSCLC患者影像学及基因突变信息。采用三维卷积神经网络模型从感兴趣区(regions of interest,ROI)提取影像学特征。使用LightGBM算法对影像学特征进行建模,并分别构建预测 EGFR 和 KRAS 基因突变的分类模型。采用5折交叉验证评估模型的性能,并采用受试者操作特征(receiver operator characteristic,ROC)曲线及曲线下面积(area under the curve,AUC)、准确度、敏感度和特异度等指标进行性能验证。

    Techniques:

    模型预测 KRAS 基因突变的 ROC 曲线 Figure 5 ROC curve of the model for predicting KRAS gene mutations

    Journal: Journal of Central South University Medical Sciences

    Article Title: 基于放射基因组学预测非小细胞癌患者的 KRAS 和 EGFR 基因突变

    doi: 10.11817/j.issn.1672-7347.2025.250026

    Figure Lengend Snippet: 模型预测 KRAS 基因突变的 ROC 曲线 Figure 5 ROC curve of the model for predicting KRAS gene mutations

    Article Snippet: 基于美国癌症影像档案(The Cancer Imaging Archive,TCIA)公开数据集中的Lung-PET-CT-Dx数据,纳入符合条件的NSCLC患者影像学及基因突变信息。采用三维卷积神经网络模型从感兴趣区(regions of interest,ROI)提取影像学特征。使用LightGBM算法对影像学特征进行建模,并分别构建预测 EGFR 和 KRAS 基因突变的分类模型。采用5折交叉验证评估模型的性能,并采用受试者操作特征(receiver operator characteristic,ROC)曲线及曲线下面积(area under the curve,AUC)、准确度、敏感度和特异度等指标进行性能验证。

    Techniques: