循环肿瘤细胞和白细胞的图像分析可预测乳腺癌患者的生存情况和转移模式。
Image Analysis of Circulating Tumor Cells and Leukocytes Predicts Survival and Metastatic Pattern in Breast Cancer Patients.
作者信息
Da Col Giacomo, Del Ben Fabio, Bulfoni Michela, Turetta Matteo, Gerratana Lorenzo, Bertozzi Serena, Beltrami Antonio Paolo, Cesselli Daniela
机构信息
Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy.
Department of Medicine, University of Udine, Udine, Italy.
出版信息
Front Oncol. 2022 Feb 10;12:725318. doi: 10.3389/fonc.2022.725318. eCollection 2022.
BACKGROUND
The purpose of the present work was to test whether quantitative image analysis of circulating cells can provide useful clinical information targeting bone metastasis (BM) and overall survival (OS >30 months) in metastatic breast cancer (MBC).
METHODS
Starting from cell images of epithelial circulating tumor cells (eCTC) and leukocytes (CD45pos) obtained with DEPArray, we identified the most significant features and applied single-variable and multi-variable methods, screening all combinations of four machine-learning approaches (Naïve Bayes, Logistic regression, Decision Trees, Random Forest).
RESULTS
Best predictive features were circularity (OS) and diameter (BM), in both eCTC and CD45pos. Median difference in OS was 15 vs. 43 (months), p = 0.03 for eCTC and 19 vs. 36, p = 0.16 for CD45pos. Prediction for BM showed low accuracy (64%, 53%) but strong positive predictive value PPV (79%, 91%) for eCTC and CD45, respectively. Best machine learning model was Naïve Bayes, showing 46 vs 11 (months), p <0.0001 for eCTC; 12.5 vs. 45, p = 0.0004 for CD45pos and 11 vs. 45, p = 0.0003 for eCTC + CD45pos. BM prediction reached 91% accuracy with eCTC, 84% with CD45pos and 91% with combined model.
CONCLUSIONS
Quantitative image analysis and machine learning models were effective methods to predict survival and metastatic pattern, with both eCTC and CD45pos containing significant and complementary information.
背景
本研究的目的是测试循环细胞的定量图像分析是否能为转移性乳腺癌（MBC）的骨转移（BM）和总生存期（OS>30个月）提供有用的临床信息。
方法
从通过DEPArray获得的上皮循环肿瘤细胞（eCTC）和白细胞（CD45阳性）的细胞图像开始，我们确定了最显著的特征，并应用单变量和多变量方法，筛选了四种机器学习方法（朴素贝叶斯、逻辑回归、决策树、随机森林）的所有组合。
结果
在eCTC和CD45阳性细胞中，最佳预测特征分别是圆形度（OS）和直径（BM）。eCTC的OS中位数差异为15个月对43个月，p = 0.03；CD45阳性细胞的OS中位数差异为19个月对36个月，p = 0.16。BM预测的准确率较低（分别为64%、53%），但eCTC和CD45阳性细胞的阳性预测值（PPV）较高（分别为79%、91%）。最佳机器学习模型是朴素贝叶斯，eCTC的结果显示为46个月对11个月，p <0.0001；CD45阳性细胞的结果显示为12.5个月对45个月，p = 0.0004；eCTC + CD45阳性细胞的结果显示为11个月对45个月，p = 0.0003。BM预测在eCTC中准确率达到91%，在CD45阳性细胞中为84%，在联合模型中为91%。
结论
定量图像分析和机器学习模型是预测生存期和转移模式的有效方法，eCTC和CD45阳性细胞均包含重要且互补的信息。
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