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“Innumerable” lesion burden on brain MRI ‒ a diagnostic approach

  • Pasquale F. Finelli ORCID logo EMAIL logo
Published/Copyright: May 23, 2025

Abstract

Objectives

The MR imaging lesion burden described as “innumerable” is rare, and can present a diagnostic challenge. Brain MR imaging with this descriptive term has not been systematically studied. We determine risk factors and MR imaging sequences helpful in a diagnostic algorithm for innumerable brain MR lesions.

Methods

Twelve thousand four hundred ninety-five brain MR imaging studies done at our institution from July 1, 2013 to June 30, 2016 were surveyed for the term “innumerable”. Inclusion criteria included 50 or more parenchymal lesions. Patients were classified into active and chronic groups, based on MR characteristics and clinical features.

Results

One hundred and twenty three reports contained the term “innumerable”. Thirty-one met inclusion criteria and 19 showed active, and 12 chronic brain process. The active group included 9 metastasis, 6 infarction, 2 microbleeds, and one each, foreign body granulomatous reaction and fungal abscesses. The MR feature accompanied or heralded onset of illness in eight patients. Malignancy was a risk factor in nine patients with metastasis and in 2 with infarct from cancer-associated hypercoagulation. Other risk factors included immunosuppression, endocarditis, long bone fracture and aortic dissection.

Conclusions

MR defined active innumerable brain lesions occurred in 0.25 % of studies. Fifteen of 19 in the active group were due to metastasis or infarction, defined by T1-weighted gadolinium enhancement and restricted-diffusion respectively. A diagnostic algorithm based on MR imaging features and risk factors can guide critical decision for brain biopsy.


Corresponding author: Pasquale F. Finelli, MD, Department of Neurology, Hartford Hospital and University of Connecticut School of Medicine, Hartford, CT, USA; and Hartford Hospital, 80 Seymour Street, Hartford, CT, 06102-5037, USA. E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The author states no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-02-14
Accepted: 2025-03-21
Published Online: 2025-05-23

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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