Follicular Lymphoma: Diagnostic and Prognostic Considerations in Initial Treatment Approach
Purpose of Review Follicular lymphoma is a diverse disease, with a diverse variable biology, clinical presentation, and prognosis. The identification of factors that can predict the specific outcome of follicular lymphoma patients remains an area of need. Here, we review the significant advances mad...
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Published in | Current oncology reports Vol. 21; no. 7; pp. 63 - 9 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
New York
Springer US
01.07.2019
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | Purpose of Review
Follicular lymphoma is a diverse disease, with a diverse variable biology, clinical presentation, and prognosis. The identification of factors that can predict the specific outcome of follicular lymphoma patients remains an area of need. Here, we review the significant advances made in follicular lymphoma prognosis in the last two decades, particularly with the advent of new genetic models.
Recent Findings
Tumor burden remains an important predictor of prognosis and is still a standard upon which treatment initiation decisions are made. Clinical prognostic indices, including the follicular lymphoma international prognostic index (FLIPI) and FLIPI-2, are validated to predict overall survival and progression-free survival, respectively. However, clinical decisions are rarely made based on these and other indices. Recently described molecular abnormalities in follicular lymphoma include those involving epigenetic regulation, cell-surface receptor signaling, and those controlling the interactions of the follicular lymphoma cell with the microenvironment. Clinicogenetic indices, such as the m7-FLIPI, take into account molecular information and provide a more accurate prognostic prediction than clinical indices.
Summary
Significant advances have been made in the development of predictive models of risk in follicular lymphoma, in particular with the incorporation of genetic information to predict risk. New models are capable of identifying groups of patients at highest risk of progression. However, it is not yet possible to predict the specific clinical course of a particular patient based on baseline factors. Continued research is needed to identify patients at highest risk of failing initial therapy and the therapeutic approaches to improve their outcomes. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-1 |
ISSN: | 1523-3790 1534-6269 |
DOI: | 10.1007/s11912-019-0808-0 |