Machine Learning Allows for Distinguishing Precancerous and Cancerous Human Epithelial Cervical Cells Using High-Resolution AFM Imaging of Adhesion Maps.
Machine Learning Allows for Distinguishing Precancerous and Cancerous Human Epithelial Cervical Cells Using High-Resolution AFM Imaging of Adhesion Maps.
复制标题
机器学习允许使用粘附图的高分辨率AFM成像来区分癌前病变和癌变的宫颈上皮细胞。
作者：
此前，原子力显微镜(AFM)图像的分析使我们能够仅使用分维参数来区分正常和癌/癌前病变的人宫颈上皮细胞。在这项研究中使用了原子力显微镜探针和细胞表面之间的高分辨率粘附图。然而，癌和癌前细胞的分离较差(曲线下面积(AUC)仅为0.79，而准确性、敏感性和特异性分别为74%、58%和84%)。同时，从临床角度来看，癌前细胞和恶性细胞的分离是最有意义的。在这里，我们表明，引入机器学习方法来分析粘附图，使我们能够以相当高的精度区分癌前和癌前宫颈细胞(AUC，准确度，敏感度和特异度分别为0.93，83%，92%和78%)。灵敏度的显著提高是因为临床实践中没有满足提高宫颈癌筛查的需要(通过将该方法与其他现有的筛查方法相结合，可以弥补相对较低的特异性)。本研究采用随机森林决策树算法。这项分析使用了6个癌前原代细胞系和6个癌原代细胞系的数据，每个细胞系来自不同的人。用K-折叠交叉验证(K=500)验证了分类的稳健性。在P<；0.0001，这一结果具有统计学意义。以随机洗牌法作为对照，确定统计学意义。
Previously, the analysis of atomic force microscopy (AFM) images allowed us to distinguish normal from cancerous/precancerous human epithelial cervical cells using only the fractal dimension parameter. High-resolution maps of adhesion between the AFM probe and the cell surface were used in that study. However, the separation of cancerous and precancerous cells was rather poor (the area under the curve (AUC) was only 0.79, whereas the accuracy, sensitivity, and specificity were 74%, 58%, and 84%, respectively). At the same time, the separation between premalignant and malignant cells is the most significant from a clinical point of view. Here, we show that the introduction of machine learning methods for the analysis of adhesion maps allows us to distinguish precancerous and cancerous cervical cells with rather good precision (AUC, accuracy, sensitivity, and specificity are 0.93, 83%, 92%, and 78%, respectively). Substantial improvement in sensitivity is significant because of the unmet need in clinical practice to improve the screening of cervical cancer (a relatively low specificity can be compensated by combining this approach with other currently existing screening methods). The random forest decision tree algorithm was utilized in this study. The analysis was carried out using the data of six precancerous primary cell lines and six cancerous primary cell lines, each derived from different humans. The robustness of the classification was verified using K-fold cross-validation (K = 500). The results are statistically significant at p < 0.0001. Statistical significance was determined using the random shuffle method as a control.
登录
查看更多内容
影响因子：
7.5
作者：
Ishwaran H
通讯作者：
Ishwaran H
影响因子：
3.1
作者：
Azuri I;Rosenhek-Goldian I;Regev-Rudzki N;Fantner G;Cohen SR
通讯作者：
Cohen SR
影响因子：
1.3
作者：
Hoda, RS;Colello, C;Houser, PM
通讯作者：
Houser, PM
影响因子：
38.3
作者：
通讯作者：
--
影响因子：
9.5
作者：
Makarova, Nadezda;Lekka, Malgorzata;Gnanachandran, Kajangi;Sokolov, Igor
通讯作者：
Sokolov, Igor