Glioma Prognosis: Segmentation of the Tumor and Survival Prediction Using Shape, Geometric and Clinical Information

Islam, Mobarakol; Jose, V. Jeya Maria; Ren, Hongliang

doi:10.1007/978-3-030-11726-9_13

Cited by 29 publications

(23 citation statements)

References 19 publications

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“…RF is also used for OS pre- diction using volumetric as well as age feature of the patient. Authors in [154], [60], [65], [37] attempts the endto-end segmentation approach. Table 10 compares the segmentation results of endto-end methods.…”

Section: End To End Methods For Tumor Segmentation and Os Predictionmentioning

confidence: 99%

A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

Agravat¹,

Raval²

2021

Preprint

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<div>Glioma is the most deadly brain tumor with high mortality. Treatment planning by human experts depends on the proper diagnosis of physical symptoms along with Magnetic Resonance(MR) image analysis. Highly variability of a brain tumor in terms of size, shape, location, and a high volume of MR images makes the analysis time-consuming. Automatic segmentation methods achieve a reduction in time with excellent reproducible results.</div><div>The article aims to survey the advancement of automated methods for Glioma brain tumor segmentation. It is also essential to make an objective evaluation of various models based on the benchmark. Therefore, the 2012 - 2019 BraTS challenges database evaluates state-of-the-art methods. The complexity of tasks under the challenge has grown from segmentation (Task1) to overall survival prediction (Task 2) to uncertainty prediction for classification (Task 3). The paper covers the complete gamut of brain tumor segmentation using handcrafted features to deep neural network models for Task 1. The aim is to showcase a complete change of trends in automated brain tumor models. The paper also covers end to end joint models involving brain tumor segmentation and overall survival prediction. All the methods are probed, and parameters that affect performance are tabulated and analyzed.</div>

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Section: End To End Methods For Tumor Segmentation and Os Predictionmentioning

confidence: 99%

A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

Agravat¹,

Raval²

2021

Preprint

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“…Texture based features such as entropy, energy, correlation, dissimilarity are extracted from each subregion [15]. The features extracted from radiographic medical images are known as radiomics.…”

Section: ) Feature Extractionmentioning

confidence: 99%

“…Content may change prior to final publication. [44] x x 61% [46] x 61% [52] x x 61% [14] x x 89%(CV) [53] x x 67%(CV) [54] x x 73%(CV) [56] x x 89%(CV) [15] x 46.8% [39] x 6 months, 1, 2, and 3 years after the diagnosis. They have developed 4 separate models that returns the most probable outcomes after 4 time-periods.…”

Section: ) Support Vector Machinementioning

confidence: 99%

“…Neural Network is utilized in glioma survival prediction to predict the survival time in days directly. Islam et al [15] have proposed an artificial neural network to predict overall survival in glioma patients with radiomics. In this work, the input layer consists of the radiomic features such as geometrical features, volume features, texture features extracted from the tumor region, and the output layer is a single node directly predicting the overall survival in days.…”

Section: ) Artificial Neural Networkmentioning

confidence: 99%

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Glioma Survival Analysis Empowered With Data Engineering—A Survey

et al. 2021

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Survival analysis is a critical task in glioma patient management due to the inter and intra tumor heterogeneity. In clinical practice, clinicians estimate the survival with their experience, which can be biased and optimistic. Over the past decades, diverse survival analysis approaches were proposed incorporating distinct data such as imaging and genetic information. The remarkable advancements in imaging and high throughput omics and sequencing technologies have enabled the acquisition of this information of glioma patients efficiently, providing novel insights for survival estimation in the present day. Besides, in the past years, machine learning techniques and deep learning have emerged into the field of survival analysis of glioma patients trading off the traditional statistical analysis-based survival analysis approaches. In this survey paper, we explore the prognostic parameters acquired, utilizing diagnostic imaging techniques and genomic platforms for survival or risk estimation of glioma patients. Further, we review the techniques, learning and statistical analysis algorithms, along with their benefits and limitations used for prognosis prediction. Consequently, we highlight the challenges of the existing state-of-the-art survival prediction studies and propose future directions in the field of research.

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“…Classifier(s) Accuracy% [13] Ensemble of random forest and multi layer perceptron 52.6 [14] Linear Discriminant 46 [19] Linear SVM GTR set 63 [20] Neural network and random forest 38 [21] Artificial neural network 54.5 [22] Multi layer perceptron 50.8 [25] XGBoost 65 [26] Ensemble of random forest and regression network 47.5…”

Section: Refmentioning

confidence: 99%

Prediction of Overall Survival of Brain Tumor Patients

Agravat

Raval

2019

TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)

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Automated brain tumor segmentation plays an important role in the diagnosis and prognosis of the patient. In addition, features from the tumorous brain help in predicting patients' overall survival. The main focus of this paper is to segment tumor from BRATS 2018 benchmark dataset and use age, shape and volumetric features to predict overall survival of patients. The random forest classifier achieves overall survival accuracy of 59% on the test dataset and 67% on the dataset with resection status as gross total resection. The proposed approach uses fewer features but achieves better accuracy than state-ofthe-art methods.

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Glioma Prognosis: Segmentation of the Tumor and Survival Prediction Using Shape, Geometric and Clinical Information

Cited by 29 publications

References 19 publications

A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

Glioma Survival Analysis Empowered With Data Engineering—A Survey

Prediction of Overall Survival of Brain Tumor Patients

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