Meningiomas: Objective assessment of proliferative indices by immunohistochemistry and automated counting method

Chavali, Pooja; Uppin, Megha S; Uppin, Shantveer G; Challa, Sundaram

doi:10.4103/0028-3886.217934

Cited by 3 publications

(1 citation statement)

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“…This protein has been shown to be effective at determining the CPI in meningiomas (25) and has been routinely used in clinical practice (13,26) because it is related to the mitotic index and histopathological grade in meningiomas (26,27). Higher Ki67 expression in recurrent meningiomas than in primary patients has been previously described, and analysis of this marker is an important method for predicting early recurrence in meningiomas (27)(28)(29). In breast cancer, Ki67 is well established as a marker of prognosis and the responses to endocrinological treatment and chemotherapy (26).…”

Section: Introductionmentioning

confidence: 99%

Recurrence/Regrowth in Grade I Meningioma: How to Predict?

Carvalho

Silva-Martins

Magalhães³

et al. 2020

Front. Oncol.

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The HLA-G and HLA-E molecules, Ki67, progesterone (PR), estrogen (ER) and androgen receptors (AR), p53, COX-2, and HER2 were studied to assess whether the biological behavior of grade I meningiomas is related to their expression. Tissue samples from 96 patients with grade I intracranial meningiomas were analyzed by immunohistochemistry on tissue microarray blocks (TMA) using antibodies specific for HLA-G, HLA-E, Ki67, PR, ER, AR, p53, COX-2, and HER2. Meningiomas were classified as small (≤2 cm, 1.0%), medium (>2 and ≤4 cm, 32.3%), and large (>4 cm, 66.7%). Tumor size was not related to recurrence/regrowth (p = 0.486), but was significantly correlated with peritumoral edema (p = 0.031) and intratumoral calcifications (p = 0.018). Recurrent meningiomas were observed in 14.6% of cases. Immunostaining for each marker was: HLA-G 100%; HLA-E 95.6%; PR 62%; ER 2.1%; AR 6.5%; p53 92.6%; COX-2 100%; HER2 0%; Ki67, mean 2.61 ± 2.29%, median 2.1%. Primary and recurrent meningiomas showed no significant relation with HLA-E and hormone receptors (p > 0.05), except for Ki67, where a higher median was observed in recurrent tumors than in primary (p = 0.014). The larger the tumor, the more severe the peritumoral edema, and the greater the presence of calcifications. Ki67 appears to be a good biomarker of recurrence/regrowth in grade I meningiomas.

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Section: Introductionmentioning

confidence: 99%

Recurrence/Regrowth in Grade I Meningioma: How to Predict?

Carvalho

Silva-Martins

Magalhães³

et al. 2020

Front. Oncol.

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Radiomics and machine learning for predicting the consistency of benign tumors of the central nervous system: A systematic review

Koechli¹,

Zwahlen²,

Schucht³

et al. 2023

European Journal of Radiology

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Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

Windisch

Koechli

Rogers

et al. 2022

Cancers

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Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE database was searched for the use of ML in patients with any benign tumor of the CNS, and the records were screened according to PRISMA guidelines. Results: Eleven retrospective studies focusing on meningioma (n = 4), vestibular schwannoma (n = 4), pituitary adenoma (n = 2) and spinal schwannoma (n = 1) were included. The majority of studies attempted segmentation. Links to repositories containing code were provided in two manuscripts, and no manuscripts shared imaging data. Only one study used an external test set, which raises the question as to whether some of the good performances that have been reported were caused by overfitting and may not generalize to data from other institutions. Conclusion: Using ML for detecting and segmenting benign brain tumors is still in its infancy. Stronger adherence to ML best practices could facilitate easier comparisons between studies and contribute to the development of models that are more likely to one day be used in clinical practice.

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Meningiomas: Objective assessment of proliferative indices by immunohistochemistry and automated counting method

Abstract: Both the techniques used in the study had advantage over, as well as, correlated well with the existing techniques and hence, can be applied to routine use.

Cited by 3 publications

References 0 publications

Recurrence/Regrowth in Grade I Meningioma: How to Predict?

Recurrence/Regrowth in Grade I Meningioma: How to Predict?

Radiomics and machine learning for predicting the consistency of benign tumors of the central nervous system: A systematic review

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

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