Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis.
Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis.
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DOI：
10.1038/s41523-018-0078-2
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发表时间：
2018
影响因子：
5.9
通讯作者：
Seo Y
中科院分区：
文献类型：
--
作者：
Huang SY;Franc BL;Harnish RJ;Liu G;Mitra D;Copeland TP;Arasu VA;Kornak J;Jones EF;Behr SC;Hylton NM;Price ER;Esserman L;Seo Y
放射组学是一种新兴的成像生物标志物发现和疾病特异性个性化治疗管理技术。本文旨在确定使用PET和MR图像的多模态放射组学数据在表征乳腺癌表型和预后中的益处。从113例乳腺癌患者的PET和MR图像中提取了84个特征。基于PET和MRI放射组学特征的无监督聚类创建了三个亚组。这些衍生的亚组与肿瘤分级（p = 2.0 × 10−6）、肿瘤总体分期（p = 0.037）、乳腺癌亚型（p = 0.0085）和疾病复发状态（p = 0.0053）在统计学上显著相关。PET衍生的一阶统计量和灰度共生矩阵（GLCM）纹理特征可区分乳腺癌肿瘤等级，这通过L2正则化逻辑回归（重复嵌套交叉验证）的结果得到证实，估计的受试者工作特征曲线下面积（AUC）为0.76（95%置信区间（CI）= [0.62，0.83]）。ElasticNet逻辑回归的结果表明，PET和MR放射组学区分了无复发生存期，1年和2年的平均AUC分别为0.75（95% CI = [0.62，0.88]）和0.68（95% CI = [0.58，0.81]）。MRI衍生的GLCM逆差矩归一化（IDMN）和PET衍生的GLCM簇显著性是无复发生存预测模型中的关键特征。总之，PET和MR图像的放射组学特征可能有助于解读乳腺癌表型，并可能作为预测乳腺癌无复发生存率的成像生物标志物。使用两种类型的医学成像技术进行的乳腺扫描的自动分析可以帮助肿瘤学家解码临床相关特征，这一发现可以帮助个性化癌症诊断和治疗。来自加州大学旧金山分校弗朗西斯科的Youngho Seo及其同事从对113名乳腺癌妇女进行的正电子发射断层扫描和磁共振成像扫描中提取了84个定量特征。然后，研究人员应用数据表征和模式识别算法（包括机器学习方法和专家编码的工程特征）来创建分类模型，帮助发现肉眼不明显的疾病特征。这些模型成功地根据肿瘤分级、总体分期、癌症亚型和疾病复发风险对患者进行了细分，为这种放射组学分析可以为乳腺癌的个性化管理提供有价值的信息提供了原则证明。
Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization breast cancer phenotype and prognosis. Eighty-four features were extracted from PET and MR images of 113 breast cancer patients. Unsupervised clustering based on PET and MRI radiomic features created three subgroups. These derived subgroups were statistically significantly associated with tumor grade (p = 2.0 × 10−6), tumor overall stage (p = 0.037), breast cancer subtypes (p = 0.0085), and disease recurrence status (p = 0.0053). The PET-derived first-order statistics and gray level co-occurrence matrix (GLCM) textural features were discriminative of breast cancer tumor grade, which was confirmed by the results of L2-regularization logistic regression (with repeated nested cross-validation) with an estimated area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval (CI) = [0.62, 0.83]). The results of ElasticNet logistic regression indicated that PET and MR radiomics distinguished recurrence-free survival, with a mean AUC of 0.75 (95% CI = [0.62, 0.88]) and 0.68 (95% CI = [0.58, 0.81]) for 1 and 2 years, respectively. The MRI-derived GLCM inverse difference moment normalized (IDMN) and the PET-derived GLCM cluster prominence were among the key features in the predictive models for recurrence-free survival. In conclusion, radiomic features from PET and MR images could be helpful in deciphering breast cancer phenotypes and may have potential as imaging biomarkers for prediction of breast cancer recurrence-free survival. Automated analyses of breast scans taken with two types of medical imaging technologies can help oncologists decode clinically relevant features, a finding that could help personalize cancer diagnosis and treatment. Youngho Seo from the University of California, San Francisco, USA, and coworkers extracted 84 quantitative features from positron emission tomography and magnetic resonance imaging scans performed on 113 women with breast cancer. The researchers then applied data-characterization and pattern-recognition algorithms—which included machine-learning methods and engineered features coded by experts—to create classification models that helped uncover disease characteristics that were not obvious to the naked eye. These models successfully subdivided patients according to tumor grade, overall stage, cancer subtype and disease recurrence risk, providing proof of principle that radiomic analyses of this kind could provide valuable information for personalized management of breast cancer.
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DOI：
10.1016/j.neurad.2014.02.006
发表时间：
2015-07
期刊：
Journal of neuroradiology = Journal de neuroradiologie
影响因子：
--
作者：
Nicolasjilwan M;Hu Y;Yan C;Meerzaman D;Holder CA;Gutman D;Jain R;Colen R;Rubin DL;Zinn PO;Hwang SN;Raghavan P;Hammoud DA;Scarpace LM;Mikkelsen T;Chen J;Gevaert O;Buetow K;Freymann J;Kirby J;Flanders AE;Wintermark M;TCGA Glioma Phenotype Research Group
通讯作者：
TCGA Glioma Phenotype Research Group
影响因子：
2.7
作者：
LIU, DC;NOCEDAL, J
通讯作者：
NOCEDAL, J
影响因子：
4.6
作者：
Parmar C;Grossmann P;Bussink J;Lambin P;Aerts HJWL
通讯作者：
Aerts HJWL
影响因子：
4.6
作者：
Parmar C;Leijenaar RT;Grossmann P;Rios Velazquez E;Bussink J;Rietveld D;Rietbergen MM;Haibe-Kains B;Lambin P;Aerts HJ
通讯作者：
Aerts HJ
影响因子：
19.7
作者：
Li H;Zhu Y;Burnside ES;Drukker K;Hoadley KA;Fan C;Conzen SD;Whitman GJ;Sutton EJ;Net JM;Ganott M;Huang E;Morris EA;Perou CM;Ji Y;Giger ML
通讯作者：
Giger ML