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LETTERS TO EDITOR  
Year : 2020  |  Volume : 63  |  Issue : 1  |  Page : 156-158
Can peripheral blood findings predict bone marrow infiltration in Hodgkin lymphoma?


1 Department of Hematology, Post Graduate Institute of Medical Education and Research, Chandigarh, India
2 Unit of Clinical Hematology, Department of Internal Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, India
3 Department of Histopathology, Post Graduate Institute of Medical Education and Research, Chandigarh, India

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Date of Web Publication31-Jan-2020
 

How to cite this article:
Sreedharanunni S, Nampoothiri RV, Rajpal S, Prakash G, Das A, Malhotra P, Varma N. Can peripheral blood findings predict bone marrow infiltration in Hodgkin lymphoma?. Indian J Pathol Microbiol 2020;63:156-8

How to cite this URL:
Sreedharanunni S, Nampoothiri RV, Rajpal S, Prakash G, Das A, Malhotra P, Varma N. Can peripheral blood findings predict bone marrow infiltration in Hodgkin lymphoma?. Indian J Pathol Microbiol [serial online] 2020 [cited 2020 Feb 17];63:156-8. Available from: http://www.ijpmonline.org/text.asp?2020/63/1/156/277380




Editor,

The detection of bone marrow involvement (BMI) is important for the prognostication as well as planning of therapy in Hodgkin lymphoma (HL). Several studies have shown the superior sensitivity or negative predictive value (NPV) of fluorodeoxyglucose positron emission tomography (FDG-PET) in detecting BMI,[1] especially in patients with multifocal uptake, though a bone marrow biopsy (BMB) is needed to confirm the same. Despite several limitations, BMB is currently not recommended in the staging of HL if PET-CT is available.[2] However, in most of the resource-limited settings, the nonavailability of FDG-PET, its high cost, and the long-waiting periods even if it is available make it imperative to depend on BMB in the initial workup of HL. BMB is also useful in patients with focal or diffuse FDG-PET or with borderline SUVmax values. In this background, we retrospectively evaluated the utility of peripheral blood findings in predicting BMI in patients with HL. A simplified scoring system was developed based on baseline complete blood count (CBC) and peripheral blood film (PBF) findings. The scoring system was tested prospectively in a cohort of 50 patients of HL who underwent bone marrow (BM) examination.

A retrospective analysis was performed in consecutive 250 HL patients who underwent BMB over a period of 32 months from January 2016 to August 2018. All cases of HL were staged by bilateral BMB, and the infiltration was reported on the basis of the presence of Reed Sternberg cells or mononuclear cells or equivalent cells reconfirmed by immunohistochemistry. The CBC, PBF, and BMB findings were collected from the laboratory records. A BMI was detected in 66 patients (26.4%). A comparison of various hematological parameters in cases with and without BMI is summarised in [Table 1]. Univariate analysis using Man–Whitney test showed a significant difference between two groups in terms of hemoglobin (P = 0.000), platelet count (P = 0.004), total leukocyte count (P = 0.011), absolute lymphocyte count (P = 0.003), and absolute eosinophil count (P = 0.000). Chi-square test showed significant association of anemia (<11g/l) (P = 0.000), leukopenia (<4 × 106/L) (P = 0.000), thrombocytopenia (<150 × 109/L) (P = 0.000), neutropenia (<1.5 × 106/L) (P = 0.000), lymphocytopenia (<1.5 × 106/L) (P = 0.002) and the presence of immature cells (metamyelocytes, promyelocytes, blasts, and nucleated red cells) (P = 0.002) with the presence of BMI. The presence of anemia, thrombocytopenia, leucopenia, and immature cells in PBF was found to be independent predictors of BMI in multivariate analysis. By giving weightage to the predictive capacity (based on multivariate analysis) of each significant parameter, a formula was developed as follows based on a method described previously[3]: Z = 1 × immature cells (IC) + 1 × anaemia (A) + 2 × leukopenia (LP) + 2 × thrombocytopenia (TP); and the total score (Z) ranged from 0 to 6. The sensitivity, specificity, positive predictive value (PPV), and NPV of each score were calculated. A score of zero showed a NPV of 90.53%, while a score >3 showed specificity, PPV, and NPV of 97.28%, 73.68%, and 77.49%, respectively.
Table 1: Comparison of demographic and hematological parameters in patients with and without BMI

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The scoring system was further tested prospectively in 50 consecutive patients of HL who underwent staging BM examination between September 2018 and February 2019. A total of 11 patients (22%) had BMI. The male–female ratio was 2.3:1. The age of the patients ranged from 14 to 82 years (median: 29 years). None of the patients with a score of zero (n = 21) showed BMI. Score >3 was seen only in one patient and he had BMI. Thus, the score of zero showed a NPV of 100%, while a score >3 showed a specificity, PPV, and NPV of 100%, 100%, and 76%, respectively, comparable to the original cohort reassuring the utility of the scoring system.

Compared to the reported frequency of BMI, which ranges from 2% to 32% and an average frequency of 13%,[4] our study shows a frequency of 26.4% possibly related to bilateral BMB rather than unilateral BMB and late presentation of patients related to their poor socio-economic status.

FDG-PET has been shown to have 96.9% sensitivity and 99.7% specificity in predicting BMI in HL.[5] Hence, BMB is not a replacement for FDG-PET in ideal situations. However, BMB continues to be a cost-effective and easily available procedure in the developing countries and will continue to be performed until the widespread availability of FDG-PET. In such a situation, it is imperative to develop alternative strategies to predict cases where BMB is unlikely to be positive and situations where BMB is highly likely to be positive. An efficient strategy should help to avoid painful BMB in both situations in the absence of FDG-PET studies. Vassilakopoulos et al.[3] evaluated 15 parameters of which six parameters were significantly related to BMI in multivariate analysis. The clinical predictive score developed based on these six parameters (B symptoms, anemia, clinical stage III/IV, leukocytes <6 × 109/L, age ≥35 years, and inguinal/iliac involvement) were found to have a good NPV in excluding BMI. In our study, we included only CBC/PBF findings in developing a prediction system.

The peripheral blood findings in HL are quite varied possibly related to the complex endocrine effects of the cytokines released from tumor cells and their microenvironment. However, the current study shows that the presence of anemia, leukopenia, thrombocytopenia, and immature cells in PBF should be seen with caution as they could be indicators of BMI. The absence of any of these findings/score 0 has a NPV of 91%, while a score >3 was found to have a specificity of 97.3% and PPV of 74%. The specificity of the cut-off values increases proportionately as the score increases with nearly 100% specificity and a PPV of nearly 80% if a cut off >5 is used. Overall, our scoring system is comparable to the previous study[3] in predicting BMI in patients with HL with the advantage of having only objective, easily available, and cost-effective parameters.

In future, the questions like whether patients with higher score need careful study of the BMB by deeper serial sections and immunohistochemistry to avoid the possibility of false-negative reporting; whether BMB can be safely excluded in patients with score zero or >5; and what is the utility of score in patients with homogeneous or borderline FDG-PET uptake can be studied prospectively. Despite being a retrospective analysis with unintentional or unavoidable selection bias, our study shows the utility of peripheral blood findings in identifying patients at risk of BMI in HL.

Author contributions

Sreejesh Sreedharanunni: Proposed the concept, designed the study, performed the statistical analysis, and wrote the manuscript. Corresponding author and “guarantor” of the work.

Ram Nampoothiri: Collected the data, performed the statistical analysis and the literature search, and assisted in writing the manuscript.

Sweta Rajpal: Collected the data and helped in manuscript editing and review.

Gaurav Prakash: Helped in data acquisition, manuscript editing, and review.

Ashim Das: Helped in data acquisition, manuscript editing, and review.

Pankaj Malhotra: Helped in data acquisition, manuscript editing, and review.

Neelam Varma: Helped in data acquisition, manuscript editing, and review.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
   References Top

1.
Muzahir S, Mian M, Munir I, K Nawaz M, S Faruqui Z, A Mufti K, et al. Clinical utility of 18F FDG-PET/CT in the detection of bone marrow disease in Hodgkin's lymphoma. Br J Radiol 2012;85:e490-6.  Back to cited text no. 1
    
2.
Eichenauer DA, Aleman BM, André M, Federico M, Hutchings M, Illidge T, et al. Hodgkin lymphoma: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol 2018;29:iv19-29.  Back to cited text no. 2
    
3.
Vassilakopoulos TP, Angelopoulou MK, Constantinou N, Karmiris T, Repoussis P, Roussou P, et al. Development and validation of a clinical prediction rule for bone marrow involvement in patients with Hodgkin lymphoma. Blood 2005;105:1875-80.  Back to cited text no. 3
    
4.
Spector N, Nucci M, Oliveira De Morais JC, Maiolino A, Portugal RD, Costa MA, et al. Clinical factors predictive of bone marrow involvement in Hodgkin's disease. Leuk Lymphoma 1997;26:171-6.  Back to cited text no. 4
    
5.
Adams HJ, Kwee TC, de Keizer B, Fijnheer R, de Klerk JM, Littooij AS, et al. Systematic review and meta-analysis on the diagnostic performance of FDG-PET/CT in detecting bone marrow involvement in newly diagnosed Hodgkin lymphoma: Is bone marrow biopsy still necessary? Ann Oncol 2014;25:921-7.  Back to cited text no. 5
    

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Correspondence Address:
Sreejesh Sreedharanunni
Department of Hematology, Post Graduate Institute of Medical Education and Research, Chandigarh - 160 012
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/IJPM.IJPM_381_19

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