The investigation and management of follicular lymphoma

McNamara, Christopher; Montoto, Silvia; Eyre, Toby A.; Ardeshna, Kirit M.; Combes, Burton; Illidge, Tim; Linton, Kim; Rule, Simon; Townsend, William; Wong, Wai Lup; McKay, Pam

doi:10.1111/bjh.16872

Cited by 17 publications

(18 citation statements)

References 122 publications

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“…Regarding the choice of sensitivity threshold (0.01%), it was made extrapolating data from other lymphoproliferative disorders and other clinical settings, but it may not be the most clinically relevant in staging and in FL. (McNamara et al, 2020;Rutherford et al, 2017). Furthermore, the few studies that have used techniques with greater sensitivity than IHC have found increasingly good survival in patients with BMI and ever smaller prognostic impacts, if any (Bachy et al, 2018;Mozas et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Flow cytometry to detect bone marrow involvement by follicular lymphoma

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Marcé

Jurado

et al. 2022

Cytometry Part B Clinical

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Background High‐quality data on bone marrow involvement (BMI) assessed by flow cytometry (FC) in follicular lymphoma (FL) is lacking. Aims We set up a prospective protocol with a 10‐color tube and acquisition of 500.000 leukocytes on a Nav flow cytometer for evaluation of BMI in FL by FC. Materials and methods FC was compared with a combination of histopathology and IGH gene rearrangement, which were considered the gold standard. We also compared BMI by FC with PET. Results Fifty‐two patients were included (median 67 years, 54% female). BMI by FC was seen in 35 (67%), with a median involvement of 1.2% (interquartile range: 0.3%–7%) of leukocytes. Comparison with the gold standard revealed two false negatives and two false positives (potentially true involvement undetected by the gold standard). BMI by PET was seen in 14/46 (30%). Immunophenotype of FL in the bone marrow was highly heterogeneous. The most common phenotypic abnormality was dim expression of CD19 (>0.5 log loss in 30% of patients). CD10 was negative in 13 (37%) and incompletely positive (overlap with the negative population) in a further 8 (28%) while entirely positive only in 14 (48%). Other abnormalities (loss of CD20, gain or loss of CD79b, expression of CD43, and substantial loss of CD45) were rare. Computational analysis by means of FlowSOM confirmed the heterogeneous phenotype, with FL from different patients clustering in unrelated metaclusters. Conclusion BMI by FL was frequent and immunophenotype was heterogeneous. However, this protocol enabled detection of FL in bone marrow in the vast majority of patients with bone marrow involvement by the gold standard.

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Section: Discussionmentioning

confidence: 99%

Flow cytometry to detect bone marrow involvement by follicular lymphoma

Aren

Marcé

Jurado

et al. 2022

Cytometry Part B Clinical

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“…Introduction Follicular lymphoma (FL) represents the most common indolent B cell non-Hodgkin lymphoma with a relapsing and remitting natural history which typically spans many years. Although uptake of this approach is somewhat variable across the globe (1), high dose chemotherapy and autologous stem cell transplantation (autoSCT) has for a number of decades been considered a treatment option for young, fit patients (usually < 70 years old) and is most often now reserved for those with relapsed or refractory (R/R) FL (2). Recent evidence has helped further define the efficacy of this approach, particularly in high risk patients; defined by the length of the first remission being < 24 months (progression of disease (POD) 24) (3,4).…”

Section: Main Mainscript Words 3716mentioning

confidence: 99%

“…1 Wilcoxon rank-sum test, 2 Fisher's exact test, 3 Logistic regression, 4 Does not apply to patients in CR1 or with refractory disease. Abbreviations: m: months, y: year, B: carmustine, E: etoposide, A: cytarabine, M: melphalan, C: cyclophosphamide, L: lomustin e, POD24: progression of disease within 24 months, PET: positron emission tomography, SCT: stem cell transplantation, FLIPI: follicular lymphoma international prognostic index, CR: complete response, PR: partial response, SD: stable disease, PD: progressive disease, ULN: upper limit of normal, HGT: high grade transformation, LDH: lactate dehydrogenase…”

Section: Supplementary Tables and Figuresmentioning

confidence: 99%

Impact of positron emission tomography - computed tomography status on progression-free survival for relapsed follicular lymphoma patients undergoing autologous stem cell transplantation

et al. 2022

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The optimum management approach for patients with relapsed or refractory (R/R) follicular lymphoma (FL) remains uncertain. Autologous stem cell transplantation (autoSCT) is considered a standard option in suitable, younger patients with relapsed FL. AutoSCT is associated with very durable remissions in a minority but also significant, wellestablished toxicities. Although PET status pre-autoSCT is established in diffuse large B-cell lymphoma and Hodgkin lymphoma, no data exists in FL. We describe survival outcomes according to pre-autoSCT PET status according to Lugano criteria (complete metabolic remission (CMR) vs non-CMR) in 172 R/R FL patients within a national, multicentre, retrospective BSBMTCT registry study. Median number of prior lines pre-autoSCT was 3 (range 1-6). Median follow-up post-autoSCT was 27 months (range 3–70). Median progression-free survival (PFS) for all patients post-autoSCT was 28 months (interquartile range 23-36). There was no interaction between age at autoSCT, sex, number of months since last relapse, Karnofsky performance status or comorbidity index (HCT-CI) and achieving CMR pre-autoSCT. Superior progression-free survival was observed in 115 (67%) patients obtaining CMR versus 57 (33%) non-CMR patients (3-year PFS 50% vs. 22%, p=0.011) and by Deauville score pre-autoSCT (continuous variable 1-5, hazard ratio (HR) 1.32, p=0.049). PETstatus was independently associated with PFS (non-CMR HR 2.02, p=0.003), overall survival (non-CMR HR 3.08, p=0.010) and risk for relapse (non-CMR HR 1.64, p=0.046) post autoSCT by multivariable analysis. Our data suggests PET-status pre-autoSCT provides clear prognostic value and may help improve patient selection for autoSCT.

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“…The most common symptom of the FL type is the emergence of a mass or many accumulated masses. This is due to the accumulated lymphoma cells leading to the emergence of a tumor in the neck area or above the collar bones and can develop in the armpits and thighs [4]. The MCL type develops in the mantle that is part of the lymph nodes and affects B cells.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Histopathological Images to Distinguish Types of Malignant Lymphomas Using Hybrid Techniques Based on Fusion Features

et al. 2022

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Malignant lymphoma is one of the types of malignant tumors that can lead to death. The diagnostic method for identifying malignant lymphoma is a histopathological analysis of lymphoma tissue images. Because of the similar morphological characteristics of the lymphoma types, it is difficult for doctors and specialists to manually distinguish the types of lymphomas. Therefore, deep and automated learning techniques aim to solve this problem and help clinicians reconsider their diagnostic decisions. Because of the similarity of the morphological characteristics between lymphoma types, this study aimed to extract features using various algorithms and deep learning models and combine them together into feature vectors. Two datasets have been applied, each with two different systems for the reliable diagnosis of malignant lymphoma. The first system was a hybrid system between DenseNet-121 and ResNet-50 to extract deep features and reduce their dimensions by the principal component analysis (PCA) method, using the support vector machine (SVM) algorithm for classifying low-dimensional deep features. The second system was based on extracting the features using DenseNet-121 and ResNet-50 and combining them with the hand-crafted features extracted by gray level co-occurrence matrix (GLCM), fuzzy color histogram (FCH), discrete wavelet transform (DWT), and local binary pattern (LBP) algorithms and classifying them using a feed-forward neural network (FFNN) classifier. All systems achieved superior results in diagnosing the two datasets of malignant lymphomas. An FFNN classifier with features of ResNet-50 and hand-crafted features reached an accuracy of 99.5%, specificity of 100%, sensitivity of 99.33%, and AUC of 99.86% for the first dataset. In contrast, the same technique reached 100% for all measures to diagnose the second dataset.

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The investigation and management of follicular lymphoma

Cited by 17 publications

References 122 publications

Flow cytometry to detect bone marrow involvement by follicular lymphoma

Flow cytometry to detect bone marrow involvement by follicular lymphoma

Impact of positron emission tomography - computed tomography status on progression-free survival for relapsed follicular lymphoma patients undergoing autologous stem cell transplantation

Diagnosis of Histopathological Images to Distinguish Types of Malignant Lymphomas Using Hybrid Techniques Based on Fusion Features

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