Comparison of ALC, LMR and IPS in predicting Prognosis in Hodgkin lymphoma

doi:10.21203/rs.3.rs-1982570/v1

Download PDF

Research Article

Comparison of ALC, LMR and IPS in predicting Prognosis in Hodgkin lymphoma

https://doi.org/10.21203/rs.3.rs-1982570/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Hodgkin lymphoma is a highly treatable malignancy. However, there are many patients who relapse or are refractory to treatment. This explains the amount of studies and attention focused on determining prognosis and predicting the response to the treatment. This study aims to compare between absolute lymphocyte count, lymphocyte/monocyte ratio and international prognostic score in terms of their effectiveness in predicting prognosis of HL in the modern era.

Methods

A prospective cohort study of 84 patients with HL was carried out. Complete blood count and serum Albumin assay were performed at the time of diagnosis and before treatment given. The end points were Response to treatment, Progression Free Survival (PFS) and Overall Survival (OS).

Results

There was a difference in terms of therapeutic response between patients with LMR < 2.9 and LMR ≥ 2.9 when studying the entire patients. When examining the advanced disease group, a difference was found between patients with IPS < 3 and IPS ≥ 3 in terms of their response to treatment. There was a difference in progression-free survival between the high and low LMR group when studying the early disease group and the whole sample. By studying the entire cohort, we found a difference in overall survival between the two groups: LMR < 2.9 and LMR ≥ 2.9.

Conclusions

LMR has efficacy in predicting therapeutic response, 18-month PFS and 18-month OS in the state of the entire cohort. IPS can be used as an effective predictor of therapeutic response in advanced disease.

absolute lymphocyte count

lymphocyte/monocyte ratio and international prognostic score

Hodgkin lymphoma

Classical Hodgkin’s lymphoma cHL accounts for approximately 10%-30% of all lymphomas [1–3] and represents about 1% of all neoplasms occurring worldwide [4]. It is also the most common lymphoma subtype in children and young adults. CHL is a highly treatable malignancy and has high cure rates. However, there are many patients who relapse or are refractory to treatment, especially in adolescents and young adults. Thus, treatment-related toxicity and long-term morbidity remain an ongoing challenge [5]. This explains the amount of studies and attention focused on knowing cHL prognosis and predicting the response to the proposed treatment to avoid overtreatment (that can sometimes overtop the severity of the disease itself) [6]. Several prognostic indicators have been established, such as Positron emission tomography PET, whose prognostic result is delayed until mid-treatment, But it plays an important role in decreasing the escalation of treatment in patients with good prognosis (PET2-negative), and thus reducing the toxic effects of treatment without affecting the patient's recovery, it also has a role in making patients undergo more intensive treatment in case of PET2-positive, which reduces the incidence of relapse, progression and death of patients. We expect that the benefit will be greater if the prognostic indicator is able to determine the prognosis at the time of diagnosing the disease, in order to make the appropriate therapeutic decision. The most common of these indicators are IPS, LMR and ALC. IPS or the International Prognostic Score was developed by HasenClever in 1998 [7]. He was found that the following seven factors: Gender (male), age greater or equal to 45 years, white blood cell count greater than 15000 cells/ mcL, lymphocyte count less than 600 cells/ mcL or lymphocyte percentage less than 8%, hemoglobin value less than 10.5 g / dL, albumin value less than 4 g/ dL and being in stage IV of the disease, are important prognostic factors. IPS is the most commonly prognostic indicator for advanced HL. Since 1998, there have been significant progress in the field of HL management, which made its utility in the modern era into question. LMR or lymphocyte/monocyte ratio is a common indicator of many tumors, including Hodgkin's lymphoma [8]. The problem, however, is that the researchers did not come up with a uniform threshold value, where each study gave different results from other studies which may be due to the difference in ethnic group. ALC or Absolute Lymphocyte Count is the simplest prognostic indicator that reflects the patient's immune status. The aim of this study is to compare between ALC, LMR and IPS in terms of their effectiveness in predicting prognosis of Hodgkin lymphoma.

Subjects:

A prospective cohort study of 84 cHL patients was carried out. All patients were met the following criteria: pathologically confirmed cHL based on immunohistochemistry IHC),age more than 16 years, they haven’t previous treatment, no previous history of malignancy, transplantation or immunosuppression, no human immune deficiency virus infection and treatment with combination Chemotherapy (ABVD) with Radiation. Therapeutic decision was made based on the EORTC classification, patients with early stage favorable HL (stages I,II without risk factors) treated with 2 cycles of ABVD and radiotherapy therapy (20-30 Gy), patients with early stage unfavorable HL (stages I,II with risk factors) treated with 4 cycles of ABVD and radiotherapy therapy (20-30 Gy) and patients with advanced stage HL (stages III,IV) treated with 6 cycles of ABVD and radiotherapy therapy (20-30 Gy).

The data were prospectively collected from June 1^st, 2020 to January 31^st, 2021. Ethical approval was obtained from institutional review board of Tishreen University Hospital Number (2374) and written consents were obtained from patients. For patients aged 16 and 17 years, the consents were signed by the parents. The study was performed in accordance with the 1964 Declaration of Helsinki and its later amendments. And was registered on the website (https://clinicaltrials.gov/) under number (NCT04893538).

Examination and analyses:

CBC and serum Albumin assay were performed at the time of diagnosis and before treatment given. CBC with 5 parts differential counts was measured using hematology analyzer, Sysmex XT-1800i (Sysmex, Kobe, Jaban), while the Alb was measured using a chemistry analyzer, Mindray BS-380 (Mindray, Shenzhen, P.R. China). ALC and LMR were obtained from CBC. IPS was obtained from general information about the patient (age, gender, Ann arbor staging) and from CBC results (Hb level, ALC, WBC) and serum Albumin results.

The patient cohorts consisted 3 subgroups: ALC group, LMR group and IPS group. For ALC group the exposed subjects were whose have ALC less than 1500 cell/mcL and control subjects were whose have ALC of 1500 cell/mcL or more. For LMR group subjects whose have LMR less than 2.9 were considered exposed group and who have LMR of 2.9 or more were the control group. For IPS group the exposed subjects were who have score of 3 or more and the control subjects were who have score less than 3.

Pilot study:

There were differences between previous study in terms of cut-off values of ALC and LMR as the table (1) shows.

Table (1): Difference in cut-off points between studies

researcher	indicator	Cut-off point	country	date
Hancock BW	ALC	1500 cell/ mcL	United Kingdom	1982
Hasenclever D	ALC	600 cell/ mcL	Germany	1998
Koh YW	ALC	1100 cell/ mcL	South Korea	2012
Tarsheen K	ALC	1000 cell/ mcL	USA	2015
Porrata L	LMR	1.1	USA	2011
Koh YW	LMR	2.8	South Korea	2012
Simon Z	LMR	2.11	Hungary	2015
Jakovic R	LMR	2	Serbia	2016
Romano A	LMR	2	Italy	2018
Tao J	LMR	2.5	China	2019

In our study, there was a really need to conduct a pilot study to determine the appropriate cut-off values for patients in this study. This pilot study was also used in order to determine the appropriate simple size. This pilot study was retrospective case control analysis. It included 136 HL patients. receiver operating characteristics curve analysis showed that: the ALC of 1500 cell/mcL represents the best cut-off point between good and bad prognosis patients. Likewise, LMR of 2.9 was showed as the best cut-off point. For IPS: we could not study the best cut-off point suitable for our patients because the pilot study data did not include Albumin analysis results. Therefore, we relied on what is found in the medical literature, we considered that IPS of 3 represent the best cut-off point [9, 10].

Sample size was calculated using G power (3.1.9.4). The total sample size of 73 patients gives the least acceptable power for the study of 80%.

End points and Follow-up:

Patients were examined every two weeks during treatment period. After completion of the treatment, all patients were followed up every 3 months for 18 months. CT scan was carried out in the middle and at the end of treatment and every 3 months afterward. The end points were Response to treatment, Progression Free Survival (PFS) and Overall Survival (OS). Response to treatment was defined as achieving Complete Remission (CR) after receiving the prescribed treatment plan. Complete Remission was defined according Computed Tomography-Based Lugano's classification [11]. PFS was defined as the length of time during and after the treatment of HL, that a patient lives with the disease without getting worse. OS was defined as the length of time from the date of diagnosis the HL, that patients are still alive.

Statistical analysis:

Chi_Squared test (or Fisher's exact test, if indicated) was used for categorical variables. Survival Curves for PFS and OS were performed using Kaplan_Meier method. Cox proportional hazards model was used to estimate the HR and 95% CI. Proportional hazard assumption was evaluated graphically using log_minus log plots. Any result with P_value < 0.05 was considered to be statistically significant. Furthermore, Propensity Score Matching (PSM) was performed (and correlogram created) using R version 4.1.0 (the R foundation of statistical computing, vienna, Austria) to homogenize the groups and adjust possible bias and confounders. Variables that were used to calculate the PS index were age, sex, Ann arbor stage, histologic subtype, Albumin level, Hemoglobin level, WBC count, Monocytes count, neutrophils count, Platelets count, eosinophils count, ESR, LDH. PSM was carried out using the nearest neighbor 1:1 matching in MatchIt package. (Also, acorrelogram illustrating the correlation between variables was created under the corrplot pacckage.). The ability of three indicators to predict therapeutic response was evaluated through these properties:

Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), likelihood ratio (LR+,LR- ), Diagnostic Effectiveness, Relative Risk (RR).

Patent characteristics:

Patent characteristics are summarized in the table (2). There were 43 (51.2%) male and 41 (48.8%) female patients. The age of the patients ranged from 16 to 77 years (median, 36.74 years) and there were 38 patients aged equal to or greater than 45 years and 46 patients aged under 45 years. According to Ann arbor staging, 5 (5.9%) patients had stage I, 36 (42.9%) had stage ii, 30 (35.7%) had stage iii and 13 (15.5%) had stage iv. A majority of patients had a nodular sclerosis, noted in 50 (59.5%), followed by 21 (25%) patients had mixed cellularity subtype, 3(3.6%) patients had lymphocyte-depleted subtype and 10 (11.9%) patients had lymphocyte-rich subtype. 51 (61%) of patients presented with B symptoms. According to IPS, 16 patients have score of 0, 13 patients have score of 1, score of 2 ,3 and 4was found in 12, 33 and 10 patients respectively. None of the patients had scores: 5, 6 or 7.

Table (2): Characteristics of HL patients

Entire patients		Early stages		Advanced stages		Patient characteristic
Percent	Number	Percent	Number	Percent	Number	Patient characteristic
51.2 %	43	22.6 %	19	28.6 %	24	male	gender
48.8 %	41	26.2 %	22	22.6 %	19	female	gender
45.2 %	38	17.9 %	15	27.4 %	23	≥ 45 years	age
54.8 %	46	30.9 %	26	23.8 %	20	< 45 years	age
48.8 %	41	27.4 %	23	21.4 %	18	≥ 40 g/L	Albumin
51.2 %	43	21.4 %	18	29.8 %	25	< 40 g/L	Albumin
65.5 %	55	28.6 %	24	36.9 %	31	≥ 10.5 g/dL	Hemoglobin
34.5 %	29	20.2 %	17	14.3 %	12	< 10.5 g/dL	Hemoglobin
16.7 %	14	9.5 %	8	7.1 %	6	≥15000 cell/mcL	WBC
83.3 %	70	39.4 %	33	44 %	37	<15000 cell/mcL	WBC
98.8 %	83	48.8 %	41	50 %	42	≥600 cell/mcL	Absolute Lymphocyte Count
1.2 %	1	0 %	0	1.2 %	1	<600 cell/mcL	Absolute Lymphocyte Count

Propensity score Matching (PSM)

In order to homogenize the groups, PSM was performed by following parameter: age, sex, Ann arbor stage, histologic subtype, Albumin level, Hemoglobin level, WBC count, Monocytes count, neutrophils count, Platelets count, eosinophils count, ESR, LDH. After PSM, the new patient cohorts consisted of 17 pairs for ALC_Early stage group, 19 pairs for ALC_Advanced stage group, 16 pairs for LMR_Early stage group, 17 pairs for LMR_Advanced stage group, 18 pairs for IPS_Early stage group and 18 pairs for IPS_Advanced group as table 3 shows.

Table (3): Distribution of patients before and after PSM

			Before PSM	After PSM
Early stages	ALC	High ALC arm	24	17
	ALC	Low ALC arm	17	17
	LMR	High LMR arm	25	16
	LMR	Low LMR arm	16	16
	IPS	High IPS arm	23	18
	IPS	Low IPS arm	18	18
Advanced stages	ALC	High ALC arm	19	19
	ALC	Low ALC arm	24	19
	LMR	High LMR arm	17	17
	LMR	Low LMR arm	26	17
	IPS	High IPS arm	18	18
	IPS	Low IPS arm	25	18

Response to treatment:

In entire patients, We found that patients with an LMR less than 2.9 had a worse treatment response than patients with LMR ≥ 2.9 (P value = .0495) (table 4) and LMR had Sensitivity of 75%, Specificity of 54.4%, Negative Predictive Value (NPV) of 90.5%, Diagnostic Effectiveness (DE) of 59.5%, Relative Risk (RR) of 3. In advanced disease, patients with an IPS < 3 had a better treatment response compared to patients with an IPS ≥ 3 (P value = .031), where IPS had Sensitivity of 81.8%, Specificity of 62%, NPV of 90%, DE of 67.5%, and RR of 4.5 (table 5).

Table (4): Distribution of patients according to treatment response among the subgroups

		Response to treatment
		Without PSM			With PSM
		Bad response	Good response	P value	Bad prognosis	Good prognosis	P value
Entire patients
ALC	<1500	10	31	.173	10	31	.404
ALC	≥ 1500	6	37	.173	6	35	.404
LMR	<2.9	12	30	.0495	12	30	.0495
LMR	≥ 2.9	4	38	.0495	4	38	.0495
IPS	≥ 3	12	31	.0507	11	30	.084
IPS	<3	4	37	.0507	4	37	.084
Early/Advanced stages
ALC	<1500	4/6	15/16	.164/1.	4/6	15/15	.340/1.
ALC	≥ 1500	1/5	21/16	.164/1.	1/5	18/16	.340/1.
LMR	<2.9	4/8	12/18	.067/.480	4/5	12/12	.333/.688
LMR	≥ 2.9	1/3	24/14	.067/.480	1/3	15/14	.333/.688
IPS	≥ 3	3/9	17/14	.663/.039	3/9	17/11	1./.031
IPS	<3	2/2	19/18	.663/.039	2/2	18/18	1./.031

Table (5): Comparing the efficiency of the three indicators

	ALC			LMR			IPS
	Entire	Early	Advanced	Entire	Early	Advanced	Entire	Early	Advanced
	This results after PSM
Sensitivity	62.5%	80%	54.5%	75%	80%	62.5%	73.3%	60%	81.8%
Specificity	53%	54.5%	51.6%	54.4%	55.6%	53.8%	55.2%	51.4%	62%
PPV	28.6%	21%	30.8%	28.6%	25%	29.4%	26.8%	15%	45%
NPV	90.5%	94.7%	76.2%	90.5%	93.75%	82.3%	90.2%	90%	90%
LR+	1.33	1.76	1.13	1.7	1.8	1.35	1.64	1.24	2.16
LR-	0.71	0.37	0.88	0.45	0.36	0.70	0.48	0.78	0.29
Diagnostic Effectiveness	54.9%	57.9%	52.4%	59.5%	59.4%	55.9%	58.5%	52.5%	67.5%
Relative Risk	1.67	4.0	1.2	3.0	4.0	1.67	2.75	1.5	4.5

Progression Free Survival (PFS) and Overall Survival (OS):

There was a difference in progression-free survival between the high and low LMR group when studying the early disease group and the whole sample (HR=5.46 CI=1.040_ 28.715 ,P value=.045) (HR=3.83 CI=1.013_ 14.48 P value=.048) Respectively. By studying the entire cohort, we found a difference in overall survival between the two groups: LMR < 2.9 and patients with LMR ≥ 2.9 (HR=9.37 CI=1.027- 85.557, P value=.047).

			18 months PFS		18 months OS
			HR (95% CI)	P value	HR (95% CI)	P value
Early stages	ALC (<1500 vs ≥1500)	Patient cohort (41)	0.948 (0.212-4.25)	.944	0.641 (0.058-7.095)	.717
	ALC (<1500 vs ≥1500)	Matched subset (34)	0.966 (0.195-4.789)	.966	1.00 (0.063-15.988)	1.
	LMR (<2.9 vs ≥ 2.9)	Patient cohort (41)	5.211 (1.11-29. 187)	.063	4.49 (0.386-52.22)	.23
	LMR (<2.9 vs ≥ 2.9)	Matched subset (32)	5.465 (1.04-28.715)	.045	67.504 (0.001-5870121.812)	.468
	IPS (≥3 vs <3)	Patient cohort (41)	3.23 (0.623-16.76)	.162	2.416 (0.218-26.821)	.473
	IPS (≥3 vs <3)	Matched subset (36)	3.959 (0.746-21.024)	.106	4.606 (0.413-51.309)	.214
Advanced stages	ALC (<1500 vs ≥1500)	Patient cohort (43)	1.138 (0.188-6.893)	.888	2.162 (0.222- 21.01)	.506
	ALC (<1500 vs ≥1500)	Matched subset (38)	1.161 (0.191-7.043)	.871	2.191 (0.225- 21.329)	.499
	LMR (<2.9 vs ≥ 2.9)	Patient cohort (43)	5.047 (0.476-53.549)	.179	119.972 (0.032- 1443830.686)	.253
	LMR (<2.9 vs ≥ 2.9)	Matched subset (34)	3.76 (0.387- 36.51)	.253	92.518 (0.007- 1217340.366)	.350
	IPS (≥3 vs <3)	Patient cohort (43)	4.34 (0.481-39.126)	.191	76.02 (0.023- 247994.874)	.294
	IPS (≥3 vs <3)	Matched subset (36)	4.564 (0.508-41.01)	.175	78.338 (0.024- 257266.155)	.291
Entire patients	ALC (<1500 vs ≥1500)	Patient cohort (84)	1.124 (0.362-3.486)	.840	1.512 (0.338- 6.763)	.588
	ALC (<1500 vs ≥1500)	Matched subset (82)	1.07 (0.345-3.321)	.907	1.452 (0.324- 6.496)	.626
	LMR (<2.9 vs ≥ 2.9)	Patient cohort (84)	4.07 (1.057-15.68)	.041	9.374 (1.027- 85.557)	.047
	LMR (<2.9 vs ≥ 2.9)	Matched subset (84)	4.07 (1.057-15.68)	.041	9.374 (1.027- 85.557)	.047
	IPS (≥3 vs <3)	Patient cohort (84)	3.208 (0.863-11.922)	.082	5.827 (0.701- 48.47)	.103
	IPS (≥3 vs <3)	Matched subset (82)	3.35 (0.902-12.443)	.071	6.094 (0.732- 50.707)	.095

According to this study, we did not find any patient with IPS greater than 4 and this does not agree with many Western researchers such as Hasenclever (17), Moccia (19), Axdorph (20) and Diefenbach (24) who found a relatively high proportion of patients with elevated IPS values (greater than 4). This disagreement can be explained by the fact that only three factors of IPS are relatively prevalent among our sample (age > 45 years, gender: male, Alb 4 g/dL), while other factors (Hb 10.5 g/dL, WBC ≥ 15000 cell/ mcL, ALC < 600 cell/ mcL or < 8%, stage IV) are outliers and difficult to find except in exacerbated disease. We found that the best cut-off point for the LMR was 2.9, which is consistent with what was found by Koh (29). However, our result is inconsistent with what was found by Romano (30), Jia (31), Simon (32), Porrata (33) and Jakovitch (22) who found that (2,2.5,2.11,1.1,2), respectively, represent the best cutoff point.‏

It was also found that ALC = 1500 cell/ mcL represents the best cut-off point between patients with good and bad prognosis, which is consistent with what was found by Hancock [12]. However, our result is inconsistent with what was found by Koh [13], Sethi [14], Hasenclever [7], who found that (1100, 1000, 600) cell/ mcL respectively, represent the best cutoff point for ALC. ‏The difference in threshold values between studies can be explained by the difference in Hodgkin's manifestations and in the differential white blood cell count according to the different races.

LMR is a prognostic indicator for predicting response to treatment in entire classical Hodgkin patients and this is in agreement with (Romano 2018) [15], who found that low LMR is associated with PET2 + which means failure to respond to treatment. In our study we found LMR had a high NPV and that means it can be used to exclude the possibility of non-response to treatment, the benefit of which is reflected in the possibility of using a non-escalating treatment regimen, which reduces the toxic effects of the treatment.

IPS appeared as a prognostic indicator to predict response to treatment in advanced stage Classical Hodgkin patients and this partially agree with (Moccia 2012) [6], who found that its prognostic significance is limited to advanced HL, however he did not study its role in predicting a response to treatment. The previous findings could be explained by the fact that both IPS and LMR include factors such as lymphocyte counts that counter tumor and synergize with therapy to achieve a therapeutic response.

LMR was a prognostic indicator to predict 18-month PFS in both entire patients and early stage patients, LMR also had prognostic significance for 18-month OS prediction in entire patients, This agree with researchers: (Koh 2013) [16], who found LMR as a prognostic factor for predicting OS in patients under 60 years of age, (Romano 2018) [15] who found LMR was not a prognostic predictor in advanced disease patients but effective in the early stage of disease, (Gao 2021) [17] who found that LMR was a predictive indicator of PFS and OS, (lee 2019) [18] who found that low LMR is associated with low PFS and OS, (Jia 2019) [19] who found that LMR was an independent prognostic index for PFS and OS, and finally we agrees with (simon 2015) [20] who studied both LMR and PET2 (after the second cycle of ABVD) and found that they are two independent prognostic indicators for predicting PFS and OS and the combination of LMR and PET2 together gives further prognosis stratification of patients, but the previews result does not agree with (Alessia 2020) [21] who found LMR no longer had any predictive value in the modern era. IPS did not show prognostic significance in predicting both OS and PFS in any of our study groups and this is in agreement with (Alessia 2020) [21] who also found that IPS lose its value in the modern era, but we disagree with (Wang 2016) [9] and (Moccia 2012) [6] who found that IPS still valid as a prognostic factor for OS and PFS but its ability to separate patients between good and bad prognosis has been reduced compared to before. ALC did not show any predictive value that might be because it consists of a single factor whose values can easily change depending on several variables such as the time of sampling during the day, the presence of the patient's distress and taking certain drugs, all of which make this indicator not stable.

Compared to modern prognostic indices such as PET, classic prognostic indices are of particular interest because they determine prognosis at diagnosis. While PET has an important role in determining the therapeutic response in the middle of treatment [22], and thus contributes to adapting the remainder of the treatment according to the initial response of each patient. Here, its limitations appear that it has not been used to determine the initial prognosis for each patient [23] and therefore does not contribute to making the initial therapeutic decision. Determining the prognosis at the time of diagnosis is more important than in the middle of the treatment, as it will depend on the selection of the most appropriate treatment protocol for the patient that achieves recovery with minimal exposure to treatment side effects.

A classical prognostic indicator that is effective in all stages of HL is still elusive, but this study confirms that LMR has efficacy in predicting therapeutic response, 18-month PFS and 18-month OS in the state of the entire cohort. IPS can be used as an effective predictor of therapeutic response in advanced disease.

We recommend investigating ways to increase the efficiency of classical indices, such as investigating the efficacy of combining these markers with other prognostic factors in Hodgkin lymphoma, in order to obtain effective markers in all stages of Hodgkin's lymphoma.

Ethics approval and consent to participate:

Ethical Approval was obtained from the Ethics Committee and the Board of Scientific Research at the Faculty of Pharmacy at Tishreen University (Number 2374). Also, all participants in this study provided a signed, informed consent.

Consent for publication:

The patients have given their consent for their clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.

Availability of data and materials:

The dataset used during the study are available from the corresponding author upon request.

Competing interests:

The authors declare that they have no competing interests.

Funding: Not applicable

Authors' contributions: Hasan Khalil contributed to the study design, literature search, clinical studies, data collection, analysis and manuscript writing. Firas Hussein and Suzan Samrah contributed to the study design, analysis, interpretation and manuscript review. All authors read and approved the final manuscript.

Acknowledgements: Not applicable

Shankland, K.R., J.O. Armitage, and B.W. Hancock, Non-hodgkin lymphoma. The Lancet, 2012. 380(9844): p. 848–857.
Wahed, A. and A. Dasgupta, Hematology and Coagulation. 2019.
Jaffe, E.S., et al., Hematopathology e-book. 2010: Elsevier Health Sciences.
Silberstein, L.E. and J. Anastasi, Hematology: Basic Principles and Practice E-Book. 2017: Elsevier Health Sciences.
Cuccaro, A., et al., Prognostic factors in hodgkin lymphoma. Mediterranean journal of hematology and infectious diseases, 2014. 6(1).
Moccia, A.A., et al., International Prognostic Score in advanced-stage Hodgkin's lymphoma: altered utility in the modern era. 2012. 30(27): p. 3383–3388.
Hasenclever, D., et al., A prognostic score for advanced Hodgkin's disease. 1998. 339(21): p. 1506–1514.
Stefaniuk, P., et al., The Neutrophil to lymphocyte and lymphocyte to monocyte ratios as new prognostic factors in hematological malignancies–a narrative review. 2020. 12: p. 2961.
Wang, Q., et al., Decreased prognostic value of international prognostic score in Chinese advanced Hodgkin lymphoma patients treated in the contemporary era. 2016. 129(23): p. 2780–2785.
Jakovic, L.R., et al., Prognostic value of lymphocyte/monocyte ratio in advanced Hodgkin lymphoma: correlation with International Prognostic Score and tumor associated macrophages. 2016. 57(8): p. 1839–1847.
Kulkarni, N.M., et al., Imaging for oncologic response assessment in lymphoma. 2017. 208(1): p. 18–31.
Hancock, B., I. Dunsmore, and H.J.S.J.o.H. Swan, Lymphopenia: a bad prognostic factor in Hodgkin's disease. 1982. 29(3): p. 193–199.
Koh, Y.W., et al., The ratio of the absolute lymphocyte count to the absolute monocyte count is associated with prognosis in Hodgkin's lymphoma: correlation with tumor-associated macrophages. 2012. 17(6): p. 871–880.
Sethi, T.K., et al., Interim Absolute Lymphocyte Count (ALC) As a Predictor of Survival in Hodgkin Lymphoma. 2015. 126(23): p. 2640.
Romano, A., et al., Prognostic meaning of neutrophil to lymphocyte ratio (NLR) and lymphocyte to monocyte ration (LMR) in newly diagnosed Hodgkin lymphoma patients treated upfront with a PET-2 based strategy. 2018. 97(6): p. 1009–1018.
Koh, Y.W., et al., The absolute lymphocyte to monocyte ratio is associated with poor prognosis in classical Hodgkin lymphoma patients younger than 60 years of age. 2015. 33(3): p. 133–140.
Gao, F., et al., Prognostic value of peripheral blood lymphocyte/monocyte ratio in lymphoma. 2021. 12(12): p. 3407.
Lee, S.F., T.Y. Ng, and D.J.B.c. Spika, Prognostic value of lymphocyte-monocyte ratio at diagnosis in Hodgkin lymphoma: a meta-analysis. 2019. 19(1): p. 1–12.
Jia, T., et al., The prognostic significance of peripheral lymphocyte/monocyte ratio and PET-2 evaluation in adult Hodgkin's lymphoma. 2019. 40(5): p. 372–377.
Simon, Z., et al., Combined prognostic value of absolute lymphocyte/monocyte ratio in peripheral blood and interim PET/CT results in Hodgkin lymphoma. 2016. 103(1): p. 63–69.
Bari, A., et al., The classic prognostic factors in advanced Hodgkin’s lymphoma patients are losing their meaning at the time of Pet-guided treatments. 2020. 99(2): p. 277–282.
Frood, R., et al., Baseline PET/CT imaging parameters for prediction of treatment outcome in Hodgkin and diffuse large B cell lymphoma: a systematic review. 2021. 48(10): p. 3198–3220.
Paydas, S., et al., Easier and more explanatory indices by integrating leukocyte lymphocyte ratio (LLR) and prognostic nutritional index (PNI) to IPS systems in cases with classical Hodgkin lymphoma. 2021. 107: p. 106586.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Comparison of ALC, LMR and IPS in predicting Prognosis in Hodgkin lymphoma

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction:

Subjects And Methods:

Results:

Discussion:

Conclusions:

Declarations:

References:

Additional Declarations

Status:

Version 1