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A Tentative Staging of Multiple Myeloma by Utilizing Respective Coefficients of Prognostic Factors
Author(s) -
Hasan Jalaeikhoo,
Morteza Sharifzadeh,
Mohsen Rajaeinejad
Publication year - 2018
Publication title -
multidisciplinary cancer investigation
Language(s) - English
Resource type - Journals
eISSN - 2538-1911
pISSN - 2476-4922
DOI - 10.30699/acadpub.mci.2.1.22
Subject(s) - multiple myeloma , medicine , oncology
Multiple myeloma (MM) is considered to be the second most common hematologic cancer [1]. Different staging systems have been proposed for risk stratification of MM. However, current staging systems have limitations and therefore are in the process of developing. A suitable staging system should consist of a reliable method of prognosis prediction based on widely obtained, reproducible parameters, and segregate patients into roughly equal groups [2]. The proposed staging method of this study fulfills these criteria. One major problem in current staging systems is the different number of cases in each stage, especially in revised international staging system (R-ISS) [3, 4]. Furthermore, main prognostic factors in MM consist of continuous variables that are generally grouped as a single prognostic parameter. For instance, in a group of cases where albumin concentrations are less than 3.5 g/dL, different albumin values that are classified under the same group, i.e., albumin < 3.5 g/dL group may be related to different prognosis. According to Bradburn et al. [5], one simple and possibly underused quantity for a given risk group is gamma value that is derived from the proportional Abstract Introduction: Multiple myeloma is a heterogeneous disease with different survival times among patients. Accurate prediction of prognosis in multiple myeloma is essential, as patients with a shorter survival time may require early bone marrow transplantation (BMT) and more advanced chemotherapy as a part of their first-line treatment. In the present study, a parameter, depicted by gamma (γ) symbol, was utilized to categorize patients into different stages. Gamma value is equal to the summation of each prognostic factor multiplied by its corresponding beta coefficient. This parameter has been previously studied for the staging of some malignancies, such as “Nottingham Prognostic Index” for breast cancer and “Prognostic Score” for parotid carcinoma. Methods: One hundred forty-three cases were randomly divided into two groups. Beta coefficients for prognostic factors, including creatinine, calcium, and albumin, were obtained from multivariate Cox analysis in the first group. In this group, a staging system based on patients’ gamma parameters was defined followed by the evaluation of the accuracy of this staging system in the second group. Results: The staging system that developed from the first group was suitable for the prediction of outcomes in the second group. The patients of the second group were divided into approximately equal numbers in each stage comprising 29, 24, and 18 cases in stage 1, 2, and 3, respectively. In this group, the median overall survival (OS) values for patients in each stage were 92, 57, and 22 months, respectively, with log-rank = 0.002. Conclusions: The proposed method demonstrated promising results for myeloma prognostication. The authors believe this approach would increase the strength and validity of staging of multiple myeloma.

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