## 1 Introduction  
Survival Prediction of Glioma Patients from Integrated Radiology and Pathology Images Using Machine Learning  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON],  
[PERSON],  
also through delivering integrated analysis, that can contribute to a collaborative attempt between different departments for administration of patients with gliomas. survival prediction; radiology images (rad); digital pathology images (path); magnetic resonance imaging (MRI); whole slide images (WSI); region of interest (ROI); glioblastoma (GBM); low grade glioma (LGG); high grade glioma (HGG); grey matter; machine learning algorithms; support vector regression (SVR); ensemble regression analysis  
## 1 Introduction  
Glioma, comprising around 50% of all intracranial primary tumors, is the most common brain tumor. This condition can induce a variety of neurological symptoms, primarily reflecting the function of the tumor site in the brain. This can result in motor dysfunction, cognitive problems, and change in personality. It is important to note that Motor dysfunction relates to schizophrenia, with a preceding clinical symptom in recent longitudinal investigations. The increased occurrence of involuntary motions that are evident from childhood and reflect subcortical brain disorders is of special interest. Cure is now uncommon, and any treatment aims for minimizing the symptoms delay the progression of disease and maximise the length of time of survival with the highest possible quality of life. The fact that glioma exhibits a wide range of clinical behavior suggests that our present concept of high-grade glioma as a stand-alone disease entity is oversimplified. In addition to host characteristics, such as age and gender, biological variables, including MGMT promoter methylation status, 1p/19q deletion, and IDH1 gene mutation status, were demonstrated to explain some of the observed variance. This also can be highlighted by the fact that molecular subtyping is used to separate. Gliomas have a dismal prognosis, with recurrence or progression being unavoidable. Its progression patterns can have a wide range of phenotypes, which could alter the treatment approach and clinical result. The inability of current therapeutic choices (chemotherapy, radiation, and surgical resection) to prevent disease development (known as relapse/recurrence) is responsible for bad prognosis. Gliomas infiltrate the underlying brain parenchyma and go far beyond the radiological or surgical brain tumor barrier on a microscopic level. There exist differences in the regional patterns of progression in gliomas, as well as differences in 'overall survival' or 'progression-free-survival' (PFS) on a clinical level. Such a diverse landscape of these tumors suggests the need to develop imaging-based markers that are prognostic of patient outcome at the initial presentation of the disease to assist in building patient-centric treatment plans [1; 2]. Several old analyses show the usefulness of MRI features obtained from different compartments of the gliomas in survival prediction in gliomas [3; 4; 5]. [PERSON] et al. [6]. extracted intensity, volumetric, and shape features from various subregions of gliomas and applied traditional machine learning models for prediction of OS. [PERSON] et al. [7] also used radiomic features extracted from several MR images to predict disease prognosis, thus providing useful knowledge for personalized care. This work uses FLAIR and TICE data to extract intensity-based length, first-order textural radiomic, shape-based and characteristics. The region of interest (ROI) is then decomposed using a stationary wavelet transform (WT) with low-pass (LP) and high-pass (HP) filtering. Furthermore, radiomic characteristics are recovered from these deconstructed pictures, assisting in the gathering of directional information. The proposed approach was evaluated using brain tumor segmentation (BraTS) challenge training, validation, and test datasets, providing 0.695, 0.571, and 0.558 on training, validation, and test datasets, respectively.