Establishment of age group classification for risk stratification in glioma patients

Abstract Objective: Age is associated with the prognosis of glioma patients, but there is no uniform standard of age-group classification to evaluate the prognosis of glioma patients. In this study, we aimed to establish an age group classification for risk stratification in glioma patients. Methods: A total of 1502 patients diagnosed with gliomas at Nanfang Hospital between 2000 and 2018 were enrolled. The WHO grade of glioma was used as a dependent variable to evaluate the effect of age on risk stratification. The evaluation model was established by logistic regression, and the Akaike information criterion (AIC) value of the model was used to determine the optimal cutoff points for age-classification. The differences in gender, WHO grade, pathological subtype, tumor cell differentiation direction, tumor size, tumor location, and molecular markers between different age groups were analyzed. The molecular markers included GFAP, EMA, MGMT, p53, NeuN, Oligo2, EGFR, VEGF, IDH1, Ki-67, 1p/19q, PR, CD3, H3K27M, and TS. Results: The proportion of men with glioma was higher than that of women with glioma (58.3% vs 41.7%). Analysis of age showed that appropriate classifications of age group were 0-14 years old (pediatric group), 15-47 years old (youth group), 48-63 years old (middle-aged group), and ≥64 years old (elderly group).The proportions of glioblastoma and large tumor size (4-6 cm) increased with age (p = 0.000, p = 0.018, respectively ). Analysis of the pathological molecular markers across the four age groups showed that the proportion of patients with larger than 10% area of Ki-67 expression or positive PR expression increased with age (p = 0.000, p = 0.017, respectively). Conclusion: Age was effective evaluating the risk of glioblastoma in glioma patients. Appropriate classifications of age group for risk stratification were 0-14 years old (pediatric group), 15-47 years old (young group), 48-63 years old (middle age group) and ≥ 64 years old (elderly group). There was significant heterogeneity in WHO grade, tumor size, tumor location and some molecular markers among the four age groups.

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Radar Recorded Child Vital Sign Public Dataset and Deep Learning-Based Age Group Classification Framework for Vehicular Application

Sensors ◽

10.3390/s21072412 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2412

Author(s):

Sungwon Yoo ◽

Shahzad Ahmed ◽

Sun Kang ◽

Duhyun Hwang ◽

Jungjun Lee ◽

...

Keyword(s):

Vital Sign ◽

Continuous Wave ◽

Age Groups ◽

Vital Signs ◽

Group Classification ◽

Age Group ◽

Smart Vehicles ◽

Fmcw Radar ◽

Wide Range ◽

Age Group Classification

The ongoing intense development of short-range radar systems and their improved capability of measuring small movements make these systems reliable solutions for the extraction of human vital signs in a contactless fashion. The continuous contactless monitoring of vital signs can be considered in a wide range of applications, such as remote healthcare solutions and context-aware smart sensor development. Currently, the provision of radar-recorded datasets of human vital signs is still an open issue. In this paper, we present a new frequency-modulated continuous wave (FMCW) radar-recorded vital sign dataset for 50 children aged less than 13 years. A clinically approved vital sign monitoring sensor was also deployed as a reference, and data from both sensors were time-synchronized. With the presented dataset, a new child age-group classification system based on GoogLeNet is proposed to develop a child safety sensor for smart vehicles. The radar-recorded vital signs of children are divided into several age groups, and the GoogLeNet framework is trained to predict the age of unknown human test subjects.

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Establishment of age group classification for risk stratification in glioma patients

BMC Neurology ◽

10.1186/s12883-020-01888-w ◽

2020 ◽

Vol 20 (1) ◽

Cited By ~ 1

Author(s):

Zhiying Lin ◽

Runwei Yang ◽

Kaishu Li ◽

Guozhong Yi ◽

Zhiyong Li ◽

...

Keyword(s):

Risk Stratification ◽

Group Classification ◽

Age Group ◽

Age Group Classification

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Age Group Classification of Facial Images Using Rank Based Edge Texture Unit (RETU) and Fuzzy Texture

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1952176 ◽

2019 ◽

pp. 605-611

Author(s):

Jammula Nimitha ◽

Kuraganti Nukeswari ◽

Kuraganti Sudha ◽

Kurapati Sumithra ◽

Rama Devi Gunnam

Keyword(s):

Age Groups ◽

Group Classification ◽

Facial Features ◽

Age Group ◽

Human Beings ◽

Age Group Classification ◽

Facial Images

We as human beings can estimate the age of a person based on his facial features but there are situations where there is a need for the computers to determine the age of a person based on the picture or photograph. Here comes the situation to teach a machine to determine the age group of a person with his picture. This is applicable in the fields like determining the age of a criminal with his picture or determining the age of a patient when he has undergone an accident and many other fields. To address this problem the paper proposed a technique of finding the age with Rank Based Edge Texture Unit (RETU). The uniqueness of this method is that it divides the age group into 7 classes i.e. the age groups are 1-10, 11-20, 21-30,31-0,41-50,51-60,>60 . With this method, the results cope up to 97.16% and to slightly increase the efficiency the present paper proposes to add Fuzzy Texton features.

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Age Group Classification using Convolutional Neural Network (CNN)

Journal of Physics Conference Series ◽

10.1088/1742-6596/2084/1/012028 ◽

2021 ◽

Vol 2084 (1) ◽

pp. 012028

Author(s):

Muhammad Firdaus Mustapha ◽

Nur Maisarah Mohamad ◽

Ghazali Osman ◽

Siti Haslini Ab Hamid

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Age Groups ◽

Group Classification ◽

Classification Model ◽

Age Group ◽

Health Security ◽

The Face ◽

Age Group Classification ◽

Two Age Groups

Abstract Age group classification is a complex task that is used to classify facial images or videos into predetermined age categories. It is an important task due to its numerous applications such as health, security, authentication system, recruitment, and also in intelligent social robots. Convolutional Neural Network (CNN) has recently shown excellent performance in analysing human face images and videos. This paper proposed an age group classification task using CNN that trained and tested with an All-Age Face (AAF) dataset. FaceNet deep learning model that uses CNN was applied in this study to compute a 128-d embedding that quantifies the face of the age group. The experiment included two age groups: Adolescence and Mature Adulthood. The proposed age group classification model achieved 84.90% accuracy for the training images and 85.12% accuracy for the test images. The experimental results showed that CNN is capable of achieving competitive classification accuracy throughout two age groups in the AAF dataset with unbalanced data distribution.

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Prognostic Significance of Preoperative Neutrophil-to-Lymphocyte Ratio in Patients With Meningiomas

Frontiers in Oncology ◽

10.3389/fonc.2020.592470 ◽

2020 ◽

Vol 10 ◽

Author(s):

Yuki Kuranari ◽

Ryota Tamura ◽

Noboru Tsuda ◽

Kenzo Kosugi ◽

Yukina Morimoto ◽

...

Keyword(s):

Prognostic Significance ◽

Laboratory Data ◽

Progression Free Survival ◽

Cost Effective ◽

Tumor Location ◽

Neutrophil To Lymphocyte Ratio ◽

Ki 67 ◽

Who Grade ◽

Lymphocyte Ratio ◽

Subtotal Removal

BackgroundMeningiomas are the most common benign intracranial tumors. However, even WHO grade I meningiomas occasionally show local tumor recurrence. Prognostic factors for meningiomas have not been fully established. Neutrophil-to-lymphocyte ratio (NLR) has been reported as a prognostic factor for several solid tumors. The prognostic value of NLR in meningiomas has been analyzed in few studies.Materials and MethodsThis retrospective study included 160 patients who underwent surgery for meningiomas between October 2010 and September 2017. We analyzed the associations between patients’ clinical data (sex, age, primary/recurrent, WHO grade, extent of removal, tumor location, peritumoral brain edema, and preoperative laboratory data) and clinical outcomes, including recurrence and progression-free survival (PFS).ResultsForty-four meningiomas recurred within the follow-up period of 3.8 years. WHO grade II, III, subtotal removal, history of recurrence, Ki-67 labeling index ≥3.0, and preoperative NLR value ≥2.6 were significantly associated with shorter PFS (P < 0.001, < 0.001, 0.002, < 0.001, and 0.015, respectively). Furthermore, NLR ≥ 2.6 was also significantly associated with shorter PFS in a subgroup analysis of WHO grade I meningiomas (P = 0.003). In univariate and multivariate analyses, NLR ≥2.6 remained as a significant predictive factor for shorter PFS in patients with meningioma (P = 0.014).ConclusionsNLR may be a cost-effective and novel preoperatively usable biomarker in patients with meningiomas.

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