Factors influencing timely diagnosis in neurolymphomatosis.


Journal

Journal of neuro-oncology
ISSN: 1573-7373
Titre abrégé: J Neurooncol
Pays: United States
ID NLM: 8309335

Informations de publication

Date de publication:
08 Aug 2024
Historique:
received: 31 05 2024
accepted: 01 08 2024
medline: 8 8 2024
pubmed: 8 8 2024
entrez: 8 8 2024
Statut: aheadofprint

Résumé

Neurolymphomatosis refers to infiltration of the peripheral nervous system (PNS) by non-Hodgkin lymphoma (NHL). Diagnostic intervals in neurolymphomatosis and factors delaying diagnosis have not been evaluated. We therefore aimed to analyze diagnostic intervals in a large cohort. The quality control database at Yale Cancer Center, Section of Neuro-Oncology, was searched for neurolymphomatosis cases diagnosed between 2001 and 2021. Univariate analyses were performed to identify parameters influencing diagnostic intervals. We identified 22 neurolymphomatosis cases including 7 with primary and 15 with secondary disease, which occurred a median (range: 4-144) of 16 months after initial NHL diagnosis. Patients typically presented with painful polyneuropathy (73%), that was asymmetrical and rapidly progressive. Diagnosis was based on PNS biopsy (50%) or integration of neuroimaging findings (50%) with NHL history and diagnostic cerebrospinal fluid examinations. Median interval from symptom onset to diagnosis was 3 months (range: 1-12). Secondary neurolymphomatosis compared to primary disease (median 2 vs. 6 months, p = 0.02), and cases with rapidly-progressive asymmetrical neuropathy as opposed to other presentations (median 2 vs. 6 months; p < 0.001) were diagnosed earlier. Upfront conventional CT compared to other modalities (median 2 vs. 5 months p = 0.04) and nerve root localization as opposed to other disease sites (median 1.5 vs. 4 months; p = 0.04) delayed diagnosis. NL type and localization, neuropathy course and distribution, and imaging modality selected for initial evaluation influence diagnostic intervals in neurolymphomatosis. Knowledge of this rare entity is critical for early suspicion, and diagnosis.

Sections du résumé

BACKGROUND BACKGROUND
Neurolymphomatosis refers to infiltration of the peripheral nervous system (PNS) by non-Hodgkin lymphoma (NHL). Diagnostic intervals in neurolymphomatosis and factors delaying diagnosis have not been evaluated. We therefore aimed to analyze diagnostic intervals in a large cohort.
METHODS METHODS
The quality control database at Yale Cancer Center, Section of Neuro-Oncology, was searched for neurolymphomatosis cases diagnosed between 2001 and 2021. Univariate analyses were performed to identify parameters influencing diagnostic intervals.
RESULTS RESULTS
We identified 22 neurolymphomatosis cases including 7 with primary and 15 with secondary disease, which occurred a median (range: 4-144) of 16 months after initial NHL diagnosis. Patients typically presented with painful polyneuropathy (73%), that was asymmetrical and rapidly progressive. Diagnosis was based on PNS biopsy (50%) or integration of neuroimaging findings (50%) with NHL history and diagnostic cerebrospinal fluid examinations. Median interval from symptom onset to diagnosis was 3 months (range: 1-12). Secondary neurolymphomatosis compared to primary disease (median 2 vs. 6 months, p = 0.02), and cases with rapidly-progressive asymmetrical neuropathy as opposed to other presentations (median 2 vs. 6 months; p < 0.001) were diagnosed earlier. Upfront conventional CT compared to other modalities (median 2 vs. 5 months p = 0.04) and nerve root localization as opposed to other disease sites (median 1.5 vs. 4 months; p = 0.04) delayed diagnosis.
CONCLUSIONS CONCLUSIONS
NL type and localization, neuropathy course and distribution, and imaging modality selected for initial evaluation influence diagnostic intervals in neurolymphomatosis. Knowledge of this rare entity is critical for early suspicion, and diagnosis.

Identifiants

pubmed: 39115616
doi: 10.1007/s11060-024-04792-2
pii: 10.1007/s11060-024-04792-2
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

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Auteurs

Sofia Doubrovinskaia (S)

Department of Neurology, Yale School of Medicine, New Haven, USA.
Department of Neurology, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany.

Antonia Egert (A)

Department of Neurology, Yale School of Medicine, New Haven, USA.

Philipp Karschnia (P)

Department of Neurology, Yale School of Medicine, New Haven, USA.
Department of Neurosurgery, Ludwig Maximilian University (LMU), Munich, Germany.

Georg T Scheffler (GT)

Department of Neurology, Yale School of Medicine, New Haven, USA.
Department of Neurosurgery, Ludwig Maximilian University (LMU), Munich, Germany.

Benjamin-Leon Traub (BL)

Department of Neurology, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany.
Clinical Cooperation Unit Neuro-Oncology, German Consortium for Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.

Daniela Galluzzo (D)

Department of Neurology, Yale School of Medicine, New Haven, USA.

Anita Huttner (A)

Department of Pathology, Yale School of Medicine, New Haven, USA.

Robert K Fulbright (RK)

Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, USA.

Joachim M Baehring (JM)

Department of Neurology, Yale School of Medicine, New Haven, USA. joachim.baehring@yale.edu.
Department of Neurosurgery, Yale School of Medicine, New Haven, USA. joachim.baehring@yale.edu.

Leon D Kaulen (LD)

Department of Neurology, Yale School of Medicine, New Haven, USA. leon.kaulen@med.uni-heidelberg.de.
Department of Neurology, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany. leon.kaulen@med.uni-heidelberg.de.
Clinical Cooperation Unit Neuro-Oncology, German Consortium for Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany. leon.kaulen@med.uni-heidelberg.de.

Classifications MeSH