Research Article: Neutropenia Prediction Based on First-Cycle Blood Counts Using a FOS-3NN Classifier

Date Published: February 20, 2011

Publisher: Hindawi Publishing Corporation

Author(s): Elize A. Shirdel, Michael J. Korenberg, Yolanda Madarnas.

http://doi.org/10.1155/2011/172615

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.

Partial Text

Maintenance of dose intensity in adjuvant (curative) chemotherapy is associated with improved outcome in early-stage breast cancer [1, 2]. Myelosuppression is the main dose-limiting factor of cytotoxic chemotherapy and a barrier to maintenance of dose intensity. Retrospective data from a very mature study of adjuvant chemotherapy for early-stage breast cancer suggested that patients receiving less than 65% of the intended dose did not benefit from adjuvant chemotherapy, highlighting the importance of dose intensity maintenance throughout treatment [3]. Neutropenia is the most common type of myelosuppression and often prompts dose reductions or delays. Use of hematopoietic growth factors can reduce the incidence, severity, and duration of established neutropenia. However, these agents can cause bone pain, fever and require administration by subcutaneous injection over several consecutive days. They are also costly, and not all chemotherapy regimens carry the same risk of neutropenia, thus not warranting their use for all patients preemptively [4]. However, Chang does note that there would be a marked benefit in being able to identify high-risk patients prior to beginning chemotherapy in order to rationally dispense growth factor support and avoid the occurrence of both dose reduction and delay [5]. Given the cost of these agents, there is also an economic argument to enhanced patient selection that would enable more rational resource allocation [6].

The FOS-3NN classifier correctly classified 19 of the 21 patients in these two sets combined. None of the low-risk and only 2 of the high-risk patients were misclassified. Fisher’s exact test probability is P < 0.00019 (1 tailed) and P < 0.00023 (2 tailed). Fisher's exact test was conservatively used due to the small sample sizes in this study and is similar to the chi-square statistic for larger studies. The corresponding Matthews' correlation coefficient is phi = +0.83. Matthews' correlation coefficient is used for binary-valued classifications and ranges from +1 for a perfect prediction set to −1 for a completely incorrect prediction set. As an added test, the model was rebuilt switching the initial 14-patient testing and training sets but leaving the independent 7 patients as part of the testing procedure. Identifying the optimal classification terms on this new training set resulted in 11 chosen terms, 3 of which were also chosen the first time this model was built based on the original training set. With these 11 chosen terms in the 3-NN classifier, on the 21 patients reserved for testing 17 out of 21 were correctly classified. Four of the 10 low-risk patients were misclassified and 0 out of the 11 high-risk patients were misclassified resulting in Fisher's exact test probability of P < 0.0039 (1 or 2 tailed) and Matthews' correlation coefficient of phi = +0.66. Recalling that all of these classifications were made based on blood marker values available in the first 4 weeks of a 24-week chemotherapy regimen, we can see just how clinically valuable this type of risk prediction can be. FOS has been used elsewhere for feature selection, predicting heat-related emergency department visits, where FOS searched about 140,000 candidate terms to find within minutes a concise 3-term model, each term a cross-product of multiple predictors [24]. While the role of FOS in feature selection has similarity to other feature selection methods such as principal component analysis (PCA) and partial least squares, there are important differences. For example, FOS finds features that have physical meaning, whereas PCA finds a few linear combinations (eigenvectors) of all the candidates, and these linear combinations do not have physical meaning. In a recent application to WiFi indoor positioning, FOS was significantly faster, and also more accurate, than PCA [23]. Here, we lay the groundwork for a tool that might be applied in the future to prospectively identify patients at high risk for neutropenia. Many authors have observed that incorporating a model such as the one that this paper presents into clinical practice would allow the early identification of high-risk patients to target for preventative interventions and would provide a cost-effective way to distribute expensive resources. There is little doubt that many nonlinear models will surface in future biological signaling prediction work. This paper gives us a glimpse of the clinical utility of a nonlinear model able to determine risk status for neutropenia based on early blood count data.   Source: http://doi.org/10.1155/2011/172615

 

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