Predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes.
Predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes.
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DOI：
10.1186/s12885-021-08122-x
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发表时间：
2021-04-07
期刊：
影响因子：
3.8
通讯作者：
Wu S
中科院分区：
文献类型：
--
作者：
Arefan D;Hausler RM;Sumkin JH;Sun M;Wu S
肿瘤微环境(TME)中免疫细胞和基质细胞的丰富程度反映了炎症、血管生成和结缔组织增生的程度。放射组学是一种从放射成像中提取定量特征来表征疾病的方法，已被证明可预测分子分类、癌症复发风险和许多其他疾病结果。然而，放射组学方法预测TME中各种细胞类型的丰度的能力仍然不清楚。在这项研究中，我们使用放射基因组学方法和机器学习模型，利用从乳腺动态对比增强磁共振成像中提取的放射特征来预测乳腺癌病变中10种细胞类型的渗透。我们利用癌症影像档案(TCIA)和癌症基因组图谱(TCGA)分别提供的影像和基因表达数据，对来自两个独立机构的73名患者进行了回顾性研究。从病变体积中提取了199个放射组学特征，包括基于形状、形态、纹理和动力学的特征。为了捕捉放射组学特征和细胞类型丰度之间的一对一关系，我们对每个放射组学特征/细胞类型丰度组合进行了线性回归。对每个回归模型进行统计学意义检验。此外，还建立了细胞类型渗透状态(即“高”与“低”)预测的多元模型。通过递归特征消除对训练集上的放射学特征进行特征选择。分类模型采用二元Logistic极梯度增强框架的形式。放射学模型的学习和检验采用了留一法交叉验证和外部独立检验两种评价方法。通过接收器工作特性曲线下面积(AUC)来衡量模型的性能。一组放射学特征和成纤维细胞的丰度之间存在单变量关系。对于多种细胞类型的侵袭预测，多变量模型的留一交叉验证AUC范围为0.5-0.83，独立测试AUC范围为0.5-0.68。在两个独立的乳腺癌队列中，根据几种细胞类型的丰度，乳腺MRI衍生的放射组学与肿瘤的微环境有关。需要在更大的队列中进行进一步的评估。网上版载有补充材料，可在10.1186/s12885-021-08122-x查阅。
The abundance of immune and stromal cells in the tumor microenvironment (TME) is informative of levels of inflammation, angiogenesis, and desmoplasia. Radiomics, an approach of extracting quantitative features from radiological imaging to characterize diseases, have been shown to predict molecular classification, cancer recurrence risk, and many other disease outcomes. However, the ability of radiomics methods to predict the abundance of various cell types in the TME remains unclear. In this study, we employed a radio-genomics approach and machine learning models to predict the infiltration of 10 cell types in breast cancer lesions utilizing radiomic features extracted from breast Dynamic Contrast Enhanced Magnetic Resonance Imaging. We performed a retrospective study utilizing 73 patients from two independent institutions with imaging and gene expression data provided by The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA), respectively. A set of 199 radiomic features including shape-based, morphological, texture, and kinetic characteristics were extracted from the lesion volumes. To capture one-to-one relationships between radiomic features and cell type abundance, we performed linear regression on each radiomic feature/cell type abundance combination. Each regression model was tested for statistical significance. In addition, multivariate models were built for the cell type infiltration status (i.e. “high” vs “low”) prediction. A feature selection process via Recursive Feature Elimination was applied to the radiomic features on the training set. The classification models took the form of a binary logistic extreme gradient boosting framework. Two evaluation methods including leave-one-out cross validation and external independent test, were used for radiomic model learning and testing. The models’ performance was measured via area under the receiver operating characteristic curve (AUC). Univariate relationships were identified between a set of radiomic features and the abundance of fibroblasts. Multivariate models yielded leave-one-out cross validation AUCs ranging from 0.5 to 0.83, and independent test AUCs ranging from 0.5 to 0.68 for the multiple cell type invasion predictions. On two independent breast cancer cohorts, breast MRI-derived radiomics are associated with the tumor’s microenvironment in terms of the abundance of several cell types. Further evaluation with larger cohorts is needed. The online version contains supplementary material available at 10.1186/s12885-021-08122-x.
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DOI：
10.1186/s13058-017-0846-1
发表时间：
2017-05-18
期刊：
Breast cancer research : BCR
影响因子：
--
作者：
Braman NM;Etesami M;Prasanna P;Dubchuk C;Gilmore H;Tiwari P;Plecha D;Madabhushi A
通讯作者：
Madabhushi A
影响因子：
4.6
作者：
Tang C;Hobbs B;Amer A;Li X;Behrens C;Canales JR;Cuentas EP;Villalobos P;Fried D;Chang JY;Hong DS;Welsh JW;Sepesi B;Court L;Wistuba II;Koay EJ
通讯作者：
Koay EJ
影响因子：
11.2
作者：
van Griethuysen JJM;Fedorov A;Parmar C;Hosny A;Aucoin N;Narayan V;Beets-Tan RGH;Fillion-Robin JC;Pieper S;Aerts HJWL
通讯作者：
Aerts HJWL
影响因子：
30.8
作者：
Weinstein, John N.;Collisson, Eric A.;Mills, Gordon B.;Shaw, Kenna R. Mills;Ozenberger, Brad A.;Ellrott, Kyle;Shmulevich, Ilya;Sander, Chris;Stuart, Joshua M.
通讯作者：
Stuart, Joshua M.
影响因子：
4.4
作者：
Clark, Kenneth;Vendt, Bruce;Prior, Fred
通讯作者：
Prior, Fred