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                     PREDICTION OF GLIOBLASTOMA  
SURVIVAL USING TECHNIQUES 
BASED ON  PRE-OPERATIVE BRAIN 
MRI IMAGING  PHD RESEARCH 
ADn IARcaEdeCmiTc pIrOeseNntSat ioFn Oby R 2022
Dr. Nancy Agnes, Head, Technical Operations, 
Phdassistance  Group www.phdassistance.com
Email: [email protected]
Glioblastoma multiforme (GBM) is a grade IV brain tumour with a short survival rate. 
To  execute precision surgery followed by chemotherapy treatment, physicians and 
oncologists  urgently require automated tools in clinics for brain tumour segmentation 
(BTS) and survival  prediction (SP) of GBM patients.
This blog will look at new approaches for automating the SP process created utilizing  
automated learning and radiomics. Pre-operative raw magnetic resonance is the best  
research topic imaging (MRI) images, and clinical data from GBM patients are used in  
automated algorithms. The general procedure for SP is extracted from all SP techniques  
submitted for the multimodal brain tumour segmentation (BraTS) competition.
INTRODUCTIONS
A brain tumour is an uncontrolled proliferation of  
abnormal cells in the brain. According to a study done in  
the United States, brain tumours associated with the  
central nervous system were detected in  23 persons 
out  of every 100,000 people diagnosed each year 
(CNS).
Tumours are classified as benign (noncancerous) or  
malignant (cancerous) in the medical world  based  on  
their aggressiveness and malignancy, as seen in  
dissertation topics Fig. 1. Primary brain tumours  arise  
from the same brain tissue or  adjacent  underlying  
tissues, and primary tumours can be benign or 
malignant  in nature.
Malignant tumours that begin elsewhere and quickly  
spread to brain regions are secondary or metastatic  
tumours.
Fig. 1 Brain tumours are classified according to their aggressiveness 
and origin.
Gliomas are the most lethal and severe  
malignant tumours that arise from the brain's 
 glial cells. Gliomas account for more than 
60%  of adult brain tumours.
Gliomas include astrocytomas, 
eGpBeMn,dymomasm, edulloblastomas, an
oligodendrogliomas. Gliomas d  
ainrteo four classes by the classifie
WOrograldnizatio (WHO) based on their d  
n aggressiveness,   infiltrHaetiaolnt,h  
malignancy and other histology-based
,  
recurrence, 
 features.
KEY OBJECTIVES OF THE 
OVERALL  SURVIVAL USING PRE-
OOPveEralRl suArvTivaIl V(OES) prediction offers both benefits and problems because of the  
abundance of complex and high-dimensional data dissertations in proposal writing  
services.
There is a need to research SP literature based on pre-operative MRI images and 
clinical  data research proposal for PhD.
This intends to give  readers an overview of  the most recent approaches for  
predicting  the survival time of G B M  patients. The focus is solely on the BraTS 2020 
dataset because  it contains the greatest number of cases
To investigate the difficulties in using pre-  
operative MRI scans and  clinical  data to 
 do automated OS prediction.
Using the BraTS dataset gives a general  
workflow of OS prediction algorithms for  
GBM patients.
To better understand the assessment  
measures used to compare the  
performance of automated OS prediction 
 systems.
To supply readers and young researchers 
 with useful information  regarding  BTS  
and OS prediction  using  pre-operative  
MRI images.
Fig-2 Research directions for BTS and OS prediction in the future
MAGNETIC RESONANCE IMAGE ANALYSIS  
FOR BRAIN TUMOUR TREATMENT 
PLANNING
Structural MRI is frequently employed in brain tumour research due to its non-invasiveness and  higher 
soft-tissue resolution. Due to imaging artefacts and problems associated with various  tumour sub-
regions,M au sl tinimgloed satlr uMctRuIr a(lm MMRRII )is  aindsdusffi toc ieonutr  tuon sdeeprastraanted ianlgl  toufm doivuer rssueb g-rleiogmioan ss.ub-regions. A majority 
of  the tumour is defined by the TC, which is usually excised. Compared to T1-weighted MRI and 
 healthy White Matter (WM) regions in T1ce, areas of T1ce  hyperintensity  represent  the  
Enhancing tumour.
Because T1ce includes both the TC and the ED, the appearance of necrosis (NCR) and non-  
enhancing tumour (ET) is often less pronounced than in T1. The WT depicts the whole 
malignant  brain area, commonly represented by a circle.
NEED FOR AUTOMATED 
TCEomCputHer-aNideIdQ glUiomEa sSegmentation is critical for overcoming the challenge of  
radiologists doing manual tumour markings.
According to experts, categorical estimations for SP range from 23 to 78 %  accurate. 
At  the same time, there are specific challenges, such as picture capture technique  
variability and the lack of a reliable prognostic model.
Biological distinct sub-regions within the tumour such as NCR, NET, ET, and Edema 
(ED)  coexist, which mMRI scans can reveal. Tumour subregions are still difficult  to  
distinguish since they come in various forms and appearances in a PhD literature  
review.
CHALLENGES IN MAGNETIC 
RESONANCE  IMAGE ANALYSIS
The computer-assisted analysis allows a human specialist to spot the tumour in less time 
while  still preserving the data. Sufficient data and appropriate working processes are 
required for  computerized analysis.
The low signal-to-noise ratio (SNR) and abnormalities in raw MRI pictures are caused by  
radiofrequency emissions created by the thermal mobility of ions in the patient's body and 
the  coils and electronic circuits in the MRI scanner.
Remaining to signal-dependent data biases, image contrasts are diminished due to random  
fluctuations. Non-uniformity in the intensity of MRI signals is referred to as  MRI  non-  
uniformity.
GENERIC WORKFLOW FOR 
BRAIN  TUMOUR 
SEGMENTATION AND
SMUanyR enVd-to-end techniques for BTS and SP have been presented in the  literature. AIllV of AtheLse  tePchnRiquEes DemIphCasTize IthOeirN superiority and utility  
above the others somehow.
The BraTS competition is held every year to encourage academics to  
demonstrate their automated BTS and OS prediction algorithms.
PREPROCESSIN
GData operation algorithms are deep 
convolutional  neural networks (DCNNs). These 
algorithms need  a large amount of data to 
arrive at relevant  findings in the dissertation 
literature review.  Because such large datasets 
are rarely accessible,  preprocessing and data 
augmentation  are  needed.
Min-max normalization 
 z-score normalization  
Bias field correction
Denoising
Volume cropping 
 Intensity 
clipping
Spherical coordinate 
transformation  Neuromorphic 
map generation
POST-
PSevReraOl pCost-EproScesSsinIg NstrGategies have been  
suggested for reducing false  positives  and  
enhancing segmentation outcomes. Traditional  
post-processing approaches , such as threshold- or  
region-growing methods, use manually determined  
points to focus on isolated areas or pixels.
Connected component 
analysis  Conditional random 
field
Morphological operations  
Relabeling the output 
label
Fig-2 Research directions for BTS and OS prediction in the 
future
CONCLUSION
The low accuracies found in literature 
 prompted us to examine the   
approaches and assessment smeevterriacls taou itdoemnatitfeyd  
research gaps and other findings linked to GBM  
patients' survival prognosis  so that future  
accuracies might be improved.
Finally, the report identifies the most interesting  
future research avenues for  improving  
automated SP approaches '  performance and  
therapeutic usefulness.
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