Classification of cancer cells using computational analysis of dynamic morphology
Classification of cancer cells using computational analysis of dynamic morphology
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DOI：
10.1016/j.cmpb.2017.12.003
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发表时间：
2018-03-01
影响因子：
6.1
通讯作者：
Iqbal, Samir M.
中科院分区：
文献类型：
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作者：
Hasan, Mohammad R.;Hassan, Naeemul;Iqbal, Samir M.
背景和目的：转移性肿瘤细胞的检测对于癌症的早期诊断和分期非常重要。然而，在疾病发作时从血液或活检样本中检测到此类细胞极其困难。据报道，与健康细胞相比，癌细胞，特别是转移性肿瘤细胞在适体功能化基质上表现出非常独特的形态行为。快速分析数据并量化细胞形态以获得即时实时反馈的能力无疑有助于早期癌症诊断。提出了一种监督机器学习方法，用于对癌细胞姿态进行识别和分类，以进行早期诊断。方法：我们量化了从延时光学显微照片中在适配体功能化玻璃基板上捕获的转移细胞及其健康对应细胞的形态学独特行为。作为概念证明，使用了人胶质母细胞瘤 (hGBM) 和星形胶质细胞的形态。用光学显微镜捕获细胞并成像。提取多个特征向量来量化和区分癌细胞和非癌细胞的复杂物理姿势。使用机器学习算法使用已知数据集训练三种不同的分类器模型：支持向量机 (SVM)、随机森林树 (RFT) 和朴素贝叶斯分类器 (NBC)。使用五倍交叉验证技术比较分类器的准确度、精确度和召回率测量结果。结果：所有分类器模型检测癌细胞的平均准确度至少为 82%。在现有数据集的精确度 (0.91)、召回率 (0.9) 和 F-1 分数 (0.89) 方面，NBC 的表现是三个分类器中最好的。 结论：本文提出了一个基于机器学习技术构建的独立系统，用于基于细胞手势的癌症筛查。该系统可快速、高效、新颖地识别 hGBM 脑肿瘤细胞，并可扩展以定义许多其他类型肿瘤细胞的单细胞分析指标。 (C) 2017 Elsevier B.V. 保留所有权利。
Background and Objective: Detection of metastatic tumor cells is important for early diagnosis and staging of cancer. However, such cells are exceedingly difficult to detect from blood or biopsy samples at the disease onset. It is reported that cancer cells, and especially metastatic tumor cells, show very distinctive morphological behavior compared to their healthy counterparts on aptamer functionalized substrates. The ability to quickly analyze the data and quantify the cell morphology for an instant real-time feedback can certainly contribute to early cancer diagnosis. A supervised machine learning approach is presented for identification and classification of cancer cell gestures for early diagnosis.& para;& para;Methods: We quantified the morphologically distinct behavior of metastatic cells and their healthy counterparts captured on aptamer-functionalized glass substrates from time-lapse optical micrographs. As a proof of concept, the morphologies of human glioblastoma (hGBM) and astrocyte cells were used. The cells were captured and imaged with an optical microscope. Multiple feature vectors were extracted to quantify and differentiate the complex physical gestures of cancerous and non-cancerous cells. Three different classifier models, Support Vector Machine (SVM), Random Forest Tree (RFT), and Naive Bayes Classifier (NBC) were trained with the known dataset using machine learning algorithms. The performances of the classifiers were compared for accuracy, precision, and recall measurements using five-fold cross-validation technique.& para;& para;Results: All the classifier models detected the cancer cells with an average accuracy of at least 82%. The NBC performed the best among the three classifiers in terms of Precision (0.91), Recall (0.9), and F-1-score (0.89) for the existing dataset.& para;& para;Conclusions: This paper presents a standalone system built on machine learning techniques for cancer screening based on cell gestures. The system offers rapid, efficient, and novel identification of hGBM brain tumor cells and can be extended to define single cell analysis metrics for many other types of tumor cells. (C) 2017 Elsevier B.V. All rights reserved.