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基于使用FOS-3NN分类器的首个周期血细胞计数预测中性粒细胞减少症

Neutropenia Prediction Based on First-Cycle Blood Counts Using a FOS-3NN Classifier.

作者信息

Shirdel Elize A, Korenberg Michael J, Madarnas Yolanda

机构信息

Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada K7L 3N6.

出版信息

Adv Bioinformatics. 2011;2011:172615. doi: 10.1155/2011/172615. Epub 2012 Feb 20.

Abstract

Background. Delivery of full doses of adjuvant chemotherapy on schedule is key to optimal breast cancer outcomes. Neutropenia is a serious complication of chemotherapy and a common barrier to this goal, leading to dose reductions or delays in treatment. While past research has observed correlations between complete blood count data and neutropenic events, a reliable method of classifying breast cancer patients into low- and high-risk groups remains elusive. Patients and Methods. Thirty-five patients receiving adjuvant chemotherapy for early-stage breast cancer under the care of a single oncologist are examined in this study. FOS-3NN stratifies patient risk based on complete blood count data after the first cycle of treatment. All classifications are independent of breast cancer subtype and clinical markers, with risk level determined by the kinetics of patient blood count response to the first cycle of treatment. Results. In an independent test set of patients unseen by FOS-3NN, 19 out of 21 patients were correctly classified (Fisher's exact test probability P < 0.00023 [2 tailed], Matthews' correlation coefficient +0.83). Conclusions. We have developed a model that accurately predicts neutropenic events in a population treated with adjuvant chemotherapy in the first cycle of a 6-cycle treatment.

摘要

背景。按计划给予全剂量辅助化疗是实现最佳乳腺癌治疗效果的关键。中性粒细胞减少是化疗的严重并发症,也是实现这一目标的常见障碍,会导致剂量减少或治疗延迟。虽然过去的研究观察到全血细胞计数数据与中性粒细胞减少事件之间的相关性,但将乳腺癌患者可靠地分为低风险和高风险组的方法仍然难以捉摸。

患者与方法。本研究对在一名肿瘤学家照料下接受早期乳腺癌辅助化疗的35名患者进行了检查。FOS-3NN根据治疗第一周期后的全血细胞计数数据对患者风险进行分层。所有分类均独立于乳腺癌亚型和临床标志物,风险水平由患者血细胞计数对第一周期治疗的反应动力学决定。

结果。在FOS-3NN未见过的独立患者测试集中,21名患者中有19名被正确分类(费舍尔精确检验概率P < 0.00023[双侧],马修斯相关系数+0.83)。

结论。我们开发了一种模型,该模型能够准确预测在6周期治疗的第一周期接受辅助化疗的人群中的中性粒细胞减少事件。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fada/3290820/bf6da864765e/ABI2011-172615.001.jpg

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