Malignant glial tumours: prognostic value of quantitative microscopy

Scarpelli, Marina; Montironi, R; Collan, Yrjö; Sisti, S.; Carletti, Valerio; P, Criante; Pisani, Elizabeth; Papo, I; Mariuzzi, Gian Mario

doi:10.1055/s-2008-1054022

Cited by 3 publications

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“…A distinct overlap of morphometric data between different tumour types and tumour grades was found when investigating heterogeneous sets of gliomas [3,7,8,28,36,37]. Of note, glioblastomas show a large variation of morphometric data explaining the overlap with data from other brain tumours [8,21,33,38,39]. Better discrimination has been achieved in those studies that have performed pairwise morphometric comparisons between different tumour grades.…”

Section: Morphometry Of Tumour Cell Nucleimentioning

confidence: 99%

“…[19,46,47] Nuclear texture • Calculation of parameters from the grey values of the digitized nuclear image: -Parameters describing grey value distribution: mean, standard deviation, curtosis ( Figure 2), -Parameters describing the so-called 'co-occurrence matrix' and 'run-length matrix' of the grey values (entropy, contrast, etc.). [21,25,28,29,33,37,38,39] Nucleoli • Determination of parameters in toluidine blue stained smears or in silver-impregnated histologic slides for investigation of nucleolar organizer regions (' AgNORs'). [59,60,62] Important parameters: numerical density, size (area), distance from nuclear membrane.…”

Section: Introductionmentioning

confidence: 99%

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Histomorphometry of brain tumours

Nafe¹,

Schlote

2004

Neuropathology Appl Neurobio

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In this review, the results of previous histomorphometric studies of brain tumours are summarized and discussed with respect to their potential value for diagnostic purposes and for tumour research. In the majority of these studies, human gliomas were investigated. In a few studies, human meningiomas and other human or experimental tumour types were investigated. A computerized image analysis system was used for the morphometric analyses in most studies. The three main histologic structures examined were tumour cell nuclei, nucleolar organizer regions and tumour vessels. The current state of knowledge provides evidence that a diagnostic benefit could be provided by histomorphometric investigations of brain tumours, especially for grading of gliomas and with respect to independent prognostic information. Additional studies are necessary to delineate the spectrum of histomorphometric parameters and the investigation of their prognostic significance for cases with the same tumour type and tumour grade. Together with many recently published observations in this field, this review shows that histomorphometry is an important approach towards the investigation of brain tumour biology.

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Section: Morphometry Of Tumour Cell Nucleimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Histomorphometry of brain tumours

Nafe¹,

Schlote

2004

Neuropathology Appl Neurobio

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“…Moreover, these grading systems do not include clinical variables such as patient's age and site of tumor [14], which have been implicated in determining the prognosis to a significant extent [9,10,15]. To objectivise the grading systems, semi-automated and automated computerized image analysis techniques for morphometric evaluation of various histopathological features have been applied in brain tumors by some workers [16][17][18][19][20][21][22][23][24]. Recently, a semi-objectivised computer-aided malignancy classifier software named TESTAST 268 has been proposed for astrocytomas grade i to 4 and mixed gliomas [12,13].…”

Section: Introductionmentioning

confidence: 99%

A grading study of gliomas using computer aided malignancy classification and histologic morphometry

et al. 1996

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Forty three cases of astrocytic tumors and mixed gliomas were studied with the aim of evaluating the reproducibility of the Kernohan grading system vis a vis (a) grading using computer-aided malignancy classifier TESTAST 268 and (b) grading by quantitative morphometric evaluation of the various histological parameters of TESTAST 268. These patients were then followed up for variable periods with a maximum of forty months. High inter and intra-observer variability were observed in the Kernohan grading system. TESTAST 268 was found to be simpler, rapid and more reproducible. However, one drawback observed of this system was that it did not completely eliminate inter-observer variability because there was still some subjectivity in assignment of the categorical values against the histological features. Morphometric evaluation of the semi-quantitative assignment values of the 4 histological variables in the TESTAST 268 classifier using Zeiss Morphomat-30 revealed a statistically significant difference between the clusters of the measured quantitative values. A repeat grading using TESTAST 268 and categorical assignment values of histological features derived from the absolute values obtained by morphometry resulted in complete elimination of inter-observer variability. Thus, this study highlights the importance of objectivisation using TESTAST 268 and histologic morphometry in the grading of gliomas. However, since this is a preliminary study on a small number of cases, no cut off values of these measurements have been proposed.

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Morphology of Tumor Cell Nuclei Is Significantly Related with Survival Time of Patients with Glioblastomas

Nafe

Franz²,

Schlote

et al. 2005

Clinical Cancer Research

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Purpose: To investigate whether histomorphology of tumor cell nuclei has a significant and independent relation to survival time of patients with glioblastomas.Experimental Design: Seventy-two tumors from 72 patients were investigated by means of digital image analysis. Proliferating and nonproliferating nuclei were separately measured and parameters of nuclear size, shape, texture, and spatial relationships (topometric parameters) were detected. Survival analysis was done regarding morphometric data together with the patients' age, the amount of resection (total or subtotal), and the classification of the tumor as a ''primary'' (de novo) or ''secondary'' glioblastoma.Results: The overall relation of all morphometric data to the time of survival was highly significant (Cox analysis, P < 0.0001). Apart from the extent of surgical resection, parameters of nuclear shape and topometric variables, such as the distance between two nuclei lying nearest to each other, showed an independent and significant relation to survival time. The patients' age had also a significant but comparably slight relation to survival time.Conclusions: The morphology of tumor cell nuclei, as represented by morphometric data, shows a significant relation to survival time of patients with glioblastomas. This relation is statistically independent from the amount of surgical resection, from the patients' age and from the classification of the glioblastoma as being primary or secondary. The results support the view that histomorphometry of tumor cell nuclei is a valuable prognostic marker for patients with glioblastomas. We believe that such a marker ought to be incorporated into the formation of individual therapeutic decisions.

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Malignant glial tumours: prognostic value of quantitative microscopy

Cited by 3 publications

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Histomorphometry of brain tumours

Histomorphometry of brain tumours

A grading study of gliomas using computer aided malignancy classification and histologic morphometry

Morphology of Tumor Cell Nuclei Is Significantly Related with Survival Time of Patients with Glioblastomas

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