Banjar Haneen, Ranasinghe Damith, Brown Fred, Adelson David, Kroger Trent, Leclercq Tamara, White Deborah, Hughes Timothy, Chaudhri Naeem
School of Computer Science, University of Adelaide, Adelaide, South Australia, Australia.
The Department of Computer Science, King AbdulAziz University, Jeddah, Saudi Arabia.
PLoS One. 2017 Jan 3;12(1):e0168947. doi: 10.1371/journal.pone.0168947. eCollection 2017.
Treatment of patients with chronic myeloid leukaemia (CML) has become increasingly difficult in recent years due to the variety of treatment options available and challenge deciding on the most appropriate treatment strategy for an individual patient. To facilitate the treatment strategy decision, disease assessment should involve molecular response to initial treatment for an individual patient. Patients predicted not to achieve major molecular response (MMR) at 24 months to frontline imatinib may be better treated with alternative frontline therapies, such as nilotinib or dasatinib. The aims of this study were to i) understand the clinical prediction 'rules' for predicting MMR at 24 months for CML patients treated with imatinib using clinical, molecular, and cell count observations (predictive factors collected at diagnosis and categorised based on available knowledge) and ii) develop a predictive model for CML treatment management. This predictive model was developed, based on CML patients undergoing imatinib therapy enrolled in the TIDEL II clinical trial with an experimentally identified achieving MMR group and non-achieving MMR group, by addressing the challenge as a machine learning problem. The recommended model was validated externally using an independent data set from King Faisal Specialist Hospital and Research Centre, Saudi Arabia.
The common prognostic scores yielded similar sensitivity performance in testing and validation datasets and are therefore good predictors of the positive group. The G-mean and F-score values in our models outperformed the common prognostic scores in testing and validation datasets and are therefore good predictors for both the positive and negative groups. Furthermore, a high PPV above 65% indicated that our models are appropriate for making decisions at diagnosis and pre-therapy. Study limitations include that prior knowledge may change based on varying expert opinions; hence, representing the category boundaries of each predictive factor could dramatically change performance of the models.
近年来，慢性髓性白血病（CML）患者的治疗变得越来越困难，原因在于可用的治疗方案多种多样，且为个体患者确定最合适的治疗策略颇具挑战。为便于做出治疗策略决策，疾病评估应涵盖个体患者对初始治疗的分子反应。预计接受一线伊马替尼治疗24个月时无法达到主要分子反应（MMR）的患者，可能采用替代一线疗法（如尼罗替尼或达沙替尼）进行更好的治疗。本研究的目的是：i）利用临床、分子和细胞计数观察结果（诊断时收集的预测因素，并根据现有知识进行分类），了解接受伊马替尼治疗的CML患者在24个月时预测MMR的临床预测“规则”；ii）开发用于CML治疗管理的预测模型。该预测模型是基于参加TIDEL II临床试验的接受伊马替尼治疗的CML患者开发的，通过将该挑战作为一个机器学习问题来解决，试验中有一个通过实验确定的达到MMR组和未达到MMR组。推荐的模型使用来自沙特阿拉伯法赫德国王专科医院和研究中心的独立数据集进行了外部验证。
常见的预后评分在测试和验证数据集中产生相似的敏感性表现，因此是阳性组的良好预测指标。我们模型中的G均值和F分数值在测试和验证数据集中优于常见的预后评分，因此是阳性和阴性组的良好预测指标。此外，高于65%的高阳性预测值表明我们的模型适用于在诊断和治疗前做出决策。研究局限性包括，先验知识可能因专家意见不同而改变；因此，每个预测因素的类别边界表示可能会显著改变模型的性能。
