Management of Elderly Patients With Gliomas

Pérez-Larraya, Jaime Gállego; Delattre, Jean‐Yves

doi:10.1634/theoncologist.2014-0170

Cited by 40 publications

(22 citation statements)

References 97 publications

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“…A possible reason may be due to the different ethnic populations they investigated. Another important reason may relate to the different age groups, as multiple lines of evidence have demonstrated that the incidence of glioma consistently rises among elderly people with the current progressive aging of the population [42,43]. These reports seem to indicate that common NER gene polymorphisms predispose to cancer possibly in an age-specific manner.…”

Section: Discussionmentioning

confidence: 99%

A Comprehensive Analysis of Influence ERCC Polymorphisms Confer on the Development of Brain Tumors

Geng

et al. 2015

Mol Neurobiol

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Within DNA repair genes, there lie a number of single nucleotide polymorphisms that may impair protein function and attenuate DNA repair capability, resulting in genomic instability and individual predisposition to malignancies. The purpose of this study was to assess the previously reported inconsistent association of polymorphisms in ERCC1 (rs11615, rs3212986), ERCC2 (rs13181, rs1799793, rs238406), and ERCC5 (rs17655) with the development of brain tumors. In the present work, we carried out a comprehensive meta-analysis of results from all published data (5 data sets for rs11615, 7 for rs3212986, 11 for rs13181, 5 for rs1799793, 3 for rs238406, and 4 for rs17655) to evaluate risk of brain tumors contributed by the polymorphisms being investigated. Either the analytic method described by Mantel and Haenszel or that proposed by DerSimonian and Laird was properly used to summarize the risk estimates (OR and 95% CI). Data analyses were done with Stata version 12.0. Meta-analyses were performed for all polymorphisms, and only rs3212986 in the ERCC1 gene showed a significant association with glioma incidence. In the homozygote comparison, we found 1.26-fold elevated risk of glioma in relation to presence of the AA genotype (OR = 1.26, 95% CI = 1.05-1.52, P OR = 0.013, P heterogeneity = 0.849, I(2) = 0.0%). We also noted that individuals with the rs3212986-AA as compared to those with rs3212986-CC/CA had a 28% higher risk to develop glioma (OR = 1.28, 95% CI = 1.06-1.53, P OR = 0.008, Pheterogeneity = 0.808, I(2) = 0.0%). No major effects were observed for Caucasians or Asians in subgroup analysis by ethnicity. ERCC1 rs3212986 is a common single nucleotide polymorphism and may contribute toward individual susceptibility for glioma. Further research in this filed is required to verify the association obtained based on a relatively small number.

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Section: Discussionmentioning

confidence: 99%

A Comprehensive Analysis of Influence ERCC Polymorphisms Confer on the Development of Brain Tumors

Geng

et al. 2015

Mol Neurobiol

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“…2 Surely, other causes for these differences are related to comorbid diseases, to intolerance to medications, and to a low physiologic reserve. [20][21][22][23][24][25][26][27][28][29] When analyzing the current evidence in the treatment of GB in the elderly, we can conclude that surgery has a key role in the treatment, increasing the quality of life (QOL) and the survival rate, and that a maximal and function preserving surgery is feasible and safe, not adding many risks. The factors which predict worse postoperative results are a low KS score, chronic pulmonary disease, large tumors (> 4 cm), and previous neurological deficits.…”

Section: Discussionmentioning

confidence: 99%

“…The factors which predict worse postoperative results are a low KS score, chronic pulmonary disease, large tumors (> 4 cm), and previous neurological deficits. [20][21][22][23][24][25][26][27][28][29] Subsequently, RT is also important after surgery, with a positive influence in survival and in symptom control. [10][11][12][13][14][15][16][17][18][19] Hypofractionated radiotherapy is the preferred option to treat elderly patients, warranting the same results with higher adhesion and comfort.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, patients with a poor prognosis should be included in less aggressive protocols. [20][21][22][23][24][25][26][27][28][29] We have conducted a survival analysis in a sample of elderly patients with GB. We have evaluated the impact of age, gender, Karnofsky scale (KS) score, tumor location and occurrence of perioperative complications.…”

Section: Introductionmentioning

confidence: 99%

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Survival Analysis in Elderly Patients with Glioblastomas: The Influence of Clinical Status, Tumor and Surgical Features

et al. 2018

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Introduction Glioblastomas are malignant neoplasms, notorious for their poor prognosis. We have conducted a survival analysis in a sample of elderly patients with glioblastomas. Methods The sample of the present study consisted of elderly patients consecutively admitted from January 2014 to January 2016 (24 months) at the Hospital do Servidor Público Estadual de São Paulo. We have evaluated the impact of age, Karnofsky scale (KS) score, tumor location, and occurrence of perioperative complications. Results A total of 42 patients were analyzed. Of these, 23 (54.7%) were men, and 19 (45.3%) were women. Patients > 60 years old, with low KS score, deep-seated tumors, and those with perioperative complications had worst outcomes. Discussion and conclusion Surgery, perioperative chemotherapy and radiotherapy add survival time and quality of life to these patients. In patients with low KS score, isolated radiotherapy and/or chemotherapy might be adequate. Decreasing perioperative complications is essential to adequately deliver adjuvant therapy in elderly patients.

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“…On the contrary, glioblastoma multiforme (GBM) is the most aggressive tumor type (WHO grade 4) with dismal prognoses despite advances in various aspects of its clinical management [4]. Since therapeutic strategies for the various grades are not identical [5], distinguishing the different grades of diffuse gliomas is a critical issue in clinical settings. Determining the tumor grade relies on different pathological features including mitotic activity, cytological atypia, neoangiogenesis, and tumor necrosis.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Glioma Grading Based on Deep Transfer Learning of MRI Radiomic Features

Chen

Weng

et al. 2019

Applied Sciences

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According to a classification of central nervous system tumors by the World Health Organization, diffuse gliomas are classified into grade 2, 3, and 4 gliomas in accordance with their aggressiveness. To quantitatively evaluate a tumor’s malignancy from brain magnetic resonance imaging, this study proposed a computer-aided diagnosis (CAD) system based on a deep convolutional neural network (DCNN). Gliomas from a multi-center database (The Cancer Imaging Archive) composed of a total of 30 grade 2, 43 grade 3, and 57 grade 4 gliomas were used for the training and evaluation of the proposed CAD. Using transfer learning to fine-tune AlexNet, a DCNN, its internal layers, and parameters trained from a million images were transferred to learn how to differentiate the acquired gliomas. Data augmentation was also implemented to increase possible spatial and geometric variations for a better training model. The transferred DCNN achieved an accuracy of 97.9% with a standard deviation of ±1% and an area under the receiver operation characteristics curve (Az) of 0.9991 ± 0, which were superior to handcrafted image features, the DCNN without pretrained features, which only achieved a mean accuracy of 61.42% with a standard deviation of ±7% and a mean Az of 0.8222 ± 0.07, and the DCNN without data augmentation, which was the worst with a mean accuracy of 59.85% with a standard deviation ±16% and a mean Az of 0.7896 ± 0.18. The DCNN with pretrained features and data augmentation can accurately and efficiently classify grade 2, 3, and 4 gliomas. The high accuracy is promising in providing diagnostic suggestions to radiologists in the clinic.

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Management of Elderly Patients With Gliomas

Cited by 40 publications

References 97 publications

A Comprehensive Analysis of Influence ERCC Polymorphisms Confer on the Development of Brain Tumors

A Comprehensive Analysis of Influence ERCC Polymorphisms Confer on the Development of Brain Tumors

Survival Analysis in Elderly Patients with Glioblastomas: The Influence of Clinical Status, Tumor and Surgical Features

Intelligent Glioma Grading Based on Deep Transfer Learning of MRI Radiomic Features

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