A targeted and timely offered treatment can be a benefitting tool for patients with acute promyelocytic leukemia (APML). Current round of study made use of potential morphological and immature fraction–related parameters (cell population data) generated during complete blood cell count (CBC), through artificial neural network (ANN) predictive modeling for early flagging of APML cases. We collected classical CBC items along with cell population data (CPD) from hematology analyzer at diagnosis of 1067 study subjects with hematological neoplasms. For morphological assessment, peripheral blood films were examined. Statistical and machine learning tools including principal component analysis (PCA) helped in the evaluation of predictive capacity of routine and CPD items. Then selected CBC item–driven ANN predictive modeling was developed to smartly use the hidden trend by increasing the auguring accuracy of these parameters in differentiation of APML cases. We found a characteristic triad based on lower (53.73) platelet count (PLT) with decreased/normal (4.72) immature fraction of platelet (IPF) with addition of significantly higher value (65.5) of DNA/RNA content–related neutrophil (NE-SFL) parameter in patients with APML against other hematological neoplasm's groups. On PCA, APML showed exceptionally significant variance for PLT, IPF, and NE-SFL. Through training of ANN predictive modeling, our selected CBC items successfully classify the APML group from non-APML groups at highly significant (0.894) AUC value with lower (2.3 percent) false prediction rate. Practical results of using our ANN model were found acceptable with value of 95.7% and 97.7% for training and testing data sets, respectively. We proposed that PLT, IPF, and NE-SFL could potentially be used for early flagging of APML cases in the hematology-oncology unit. CBC item–driven ANN modeling is a novel approach that substantially strengthen the predictive potential of CBC items, allowing the clinicians to be confident by the typical trend raised by these studied parameters.

及时提供有针对性的治疗可以成为急性早幼粒细胞白血病（APML）患者的有益工具。本轮研究利用全血细胞计数 (CBC) 期间生成的潜在形态学和未成熟分数相关参数（细胞群数据），通过人工神经网络 (ANN) 预测模型对 APML 病例进行早期标记。我们收集了经典 CBC 项目以及来自血液分析仪的细胞群数据 (CPD)，用于诊断 1067 名患有血液肿瘤的研究对象。为了进行形态学评估，检查了外周血涂片。包括主成分分析 (PCA) 在内的统计和机器学习工具有助于评估常规和 CPD 项目的预测能力。然后开发了选定的 CBC 项目驱动的 ANN 预测模型，通过提高这些参数在区分 APML 病例时的预测准确性，巧妙地使用隐藏趋势。我们发现了一个特征三联体，其基础是血小板计数 (PLT) 较低 (53.73)、血小板未成熟分数 (IPF) 减少/正常 (4.72)，以及 DNA/RNA 含量相关中性粒细胞 (NE-) 值显着升高 (65.5)。 APML 患者与其他血液肿瘤组的 SFL）参数。在 PCA 上，APML 对 PLT、IPF 和 NE-SFL 显示出异常显着的方差。通过 ANN 预测模型的训练，我们选择的 CBC 项目成功地将 APML 组与非 APML 组分类，AUC 值非常显着（0.894），错误预测率较低（2.3%）。使用我们的 ANN 模型的实际结果是可以接受的，训练和测试数据集的值分别为 95.7% 和 97.7%。 我们提出 PLT、IPF 和 NE-SFL 有可能用于血液肿瘤科 APML 病例的早期标记。 CBC 项目驱动的 ANN 建模是一种新颖的方法，可大大增强 CBC 项目的预测潜力，使临床医生对这些研究参数提出的典型趋势充满信心。