Correlation Between Ki-67 Index, World Health Organization Grade and Patient Survival in Glial Tumors With Astrocytic Differentiation

Stoyanov, George S; Dzhenkov, Deyan; Kitanova, Martina; Donev, Ivan; Ghenev, Peter

doi:10.7759/cureus.1396

Cited by 16 publications

(12 citation statements)

References 21 publications

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“…The precise morphological features extracted from the most aggressive regions help to investigate intratumoral histological heterogeneity for precise histopathology grading (Walker et al, 2001). The primary grading results still support the conclusion that the only sure way to determine the histopathological WHO grade remains the pathohistological evaluation of the H&E stained tumor sample (Stoyanov et al, 2017).…”

Section: Discussionmentioning

confidence: 73%

Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations

Wang

Yao

et al. 2019

Front. Neurosci.

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Gliomas are the most common primary malignant brain tumors in adults. Accurate grading is crucial as therapeutic strategies are often disparate for different grades and may influence patient prognosis. This study aims to provide an automated glioma grading platform on the basis of machine learning models. In this paper, we investigate contributions of multi-parameters from multimodal data including imaging parameters or features from the Whole Slide images (WSI) and the proliferation marker Ki-67 for automated brain tumor grading. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. On the basis of machine learning models, our platform classifies gliomas into grades II, III, and IV. Furthermore, we quantitatively interpret and reveal the important parameters contributing to grading with the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. The performance of our grading model was evaluated with cross-validation, which randomly divided the patients into non-overlapping training and testing sets and repeatedly validated the model on the different testing sets. The primary results indicated that this modular platform approach achieved the highest grading accuracy of 0.90 ± 0.04 with support vector machine (SVM) algorithm, with grading accuracies of 0.91 ± 0.08, 0.90 ± 0.08, and 0.90 ± 0.07 for grade II, III, and IV gliomas, respectively.

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Section: Discussionmentioning

confidence: 73%

Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations

Wang

Yao

et al. 2019

Front. Neurosci.

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“…Çalışmamız da mitoz sayısı ve Ki-67 indeksi arasında olumlu, orta derecede korelasyon bulduk. Bu bulgumuz literatür ile uyumludur (4,5). Palizatlaşan hücre nekrozu GBM'nin patolojik karakteristik özelliklerinden birisidir.…”

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“…Ki-67 pozitif hücrelerinin oranı Ki-67 proliferasyon indeksini belirler. Literatürde, Ki-67 fraksiyonunun yüksek olduğu glial tümörlerin daha agresif seyrettiğine dair birçok çalışma mevcuttur (5,6). Diğer yandan, iskemik strok veya subaraknoid kanama gibi intrakraniyal patolojilerde platelet volüm indeksi (PVİ) ve prognoz ile ilgili yayınlarda artış görülmektedir.…”

Section: Introductionunclassified

Comparasion Between Mitotic Activity, Ki-67 Index and Platelet Volume Index as a Prognostic Marker in Gliablastomas

Dündar¹

2019

atfm

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Objectives: An elevated platelet count is considered an independent prediction of survey in glioblastoma and various other intracranial entities. Activity of the tumor microcirculation resulting in platelet activation and release of several cytokines and factors from activated platelets has been suggested to play an active role in neuro-inflammation. Ki-67 index and mitotic activity are markers which could be showed proliferation in the glial tumor. High Ki-67 expression and mitosis rate of high grade glial tumors have been more aggressive clinical progression, poor prognosis, more vascular invasion than low. Recently, Mean Platelet Volume (MPV) to platelet count (PLT) ratio has become a trend indicator to anticipate the outcome of a patient suffering from intracranial pathologies. The study aims to determine first time whether the comparison of the Volume Index (PVI) as an immune response marker with the Ki-67 and mitotic activity could be used as a prognostic factor in gliablastoma (WHO Grade IV) Materials and Methods: In the retrospective study, we studied 65 patients diagnosed with glioblastoma who had inclusion and exclusion criteria. The correlation between PVI, Ki-67 and mitotic activity was examined all cases after pathologically diagnosed, by the routine processes of the pathological preparates. PVI was calculated from pre-operative first complete blood count (CBC) and was defined as MPV value (fL)x100/PLT (per 1000). The Kolmogorov-Smirnov test and Spearman's correlation coefficient was used in the analysis of the interrelationship. Results: The correlation between the PVI, Ki-67 and mitotic index was found to be leak statistically correlation.

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“…The Ki-67 proliferation index increases in malignant cells (15). Previous studies have shown the correlation between Ki-67 and brain tumor malignancy class is useful in the evaluation of clinical course and patient outcome (16,17).…”

Section: Introductionmentioning

confidence: 99%

Apparent diffusion coefficient values and non-homogeneity of diffusion in brain tumors in diffusion-weighted MRI

et al. 2019

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Background The values that have been received from apparent diffusion coefficient (ADC) maps of diffusion-weighted magnetic resonance imaging (DW-MRI) might play a vital role in evaluating tumors and their grading scale. Purpose To investigate the predictive role of this heterogeneity in brain tumor pathologies and its correlation with Ki-67. Material and Methods A total of 124 patients with brain tumors underwent brain MRI with gadolinium injection. ADC and standard deviation of each lesion have been obtained from manual localization of the region of interest on the ADC map. A receiver operating characteristic analysis was conducted to determine the minimum cut-off values of the mean ADC and mean standard deviation of ADC maps having the highest sensitivity and specificity to differentiate high-grade and low-grade tumors. Results Mean ADC values in the region of interest were significantly lower for malignant tumors (grade IV and metastasis) than grade I brain tumors, while a higher mean standard deviation was observed. In a more detailed comparison of tumor groups, the mean standard deviation of the ADC for glioblastoma multiform was significantly higher than meningioma grade I ( P < 0.001) and metastasis was significantly higher than grade III and IV astrocytic tumors ( P = 0.004). The analysis of Ki-67 proliferation index and mean ADC values in gliomas showed a significant inverse correlation between the parameters (r = –0.0429, P < 0.001) and direct correlation between Ki-67 and mean standard deviation of the ADC (r = 0.551, P < 0.001). As an index for the ADC to differentiate high-grade and low-grade tumors, the cut-off values of 1.40*10−3 mm2/s for mean ADC and 45*10−3 mm2/s for mean standard deviation have the highest combination of sensitivity, specificity, and area under the curve. Conclusion The mean value and standard deviation of the ADC could be considered for differentiating between low-grade and high-grade brain tumors, as two available non-invasive methods.

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Correlation Between Ki-67 Index, World Health Organization Grade and Patient Survival in Glial Tumors With Astrocytic Differentiation

Cited by 16 publications

References 21 publications

Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations

Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations

Comparasion Between Mitotic Activity, Ki-67 Index and Platelet Volume Index as a Prognostic Marker in Gliablastomas

Apparent diffusion coefficient values and non-homogeneity of diffusion in brain tumors in diffusion-weighted MRI

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