A5102723166- Radiomics and Machine Learning in Medical Imaging
- Glioma Diagnosis and Treatment
- AI in cancer detection
- MRI in cancer diagnosis
- Advanced MRI Techniques and Applications
- Brain Tumor Detection and Classification
- Medical Imaging Techniques and Applications
- Dementia and Cognitive Impairment Research
- Medical Image Segmentation Techniques
- Image Retrieval and Classification Techniques
- Advanced Neural Network Applications
- Molecular Biology Techniques and Applications
- Ferroptosis and cancer prognosis
- Alzheimer's disease research and treatments
- Cancer, Hypoxia, and Metabolism
- Cancer Treatment and Pharmacology
- Artificial Intelligence in Healthcare
- Advanced Neuroimaging Techniques and Applications
- Cancer Genomics and Diagnostics
- Functional Brain Connectivity Studies
- Bioinformatics and Genomic Networks
- Advanced Radiotherapy Techniques
- stochastic dynamics and bifurcation
- Cardiac Imaging and Diagnostics
- Advanced Image Fusion Techniques
University of Pennsylvania
2016-2024
Mayo Clinic in Florida
2024
Eli Lilly (United States)
2020-2024
Emory University
2024
University of Iowa Hospitals and Clinics
2024
California University of Pennsylvania
2018-2019
Pakistan Institute of Engineering and Applied Sciences
2012-2018
University of Pennsylvania Health System
2018
University of Azad Jammu and Kashmir
2014-2015
National Hospital for Neurology and Neurosurgery
2013
The growth of multiparametric imaging protocols has paved the way for quantitative phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics spatiotemporal heterogeneity, can guide personalized planning. This underlined need efficient analytics to derive high-dimensional signatures diagnostic predictive value in this emerging era integrated precision diagnostics. paper presents phenomics toolkit (CaPTk), a new dynamically growing...
Abstract Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack consistent acquisition protocol, c) quality, d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute “University Pennsylvania Imaging, Genomics, Radiomics”...
The remarkable heterogeneity of glioblastoma, across patients and over time, is one the main challenges in precision diagnostics treatment planning. Non-invasive vivo characterization this using imaging could assist understanding disease subtypes, as well risk-stratification planning glioblastoma. current study leveraged advanced analytics radiomic approaches applied to multi-parametric MRI de novo glioblastoma (n = 208 discovery, n 53 replication), discovered three distinct reproducible...
Standard surgical resection of glioblastoma, mainly guided by the enhancement on postcontrast T1-weighted magnetic resonance imaging (MRI), disregards infiltrating tumor within peritumoral edema region (ED). Subsequent radiotherapy typically delivers uniform radiation to FLAIR-hyperintense regions, without attempting target areas likely be infiltrated more heavily. Noninvasive in vivo delineation infiltration and prediction early recurrence ED could assist targeted intensification local...
Prostate is a second leading causes of cancer deaths among men. Early detection can effectively reduce the rate mortality caused by cancer. Due to high and multiresolution MRIs from prostate require proper diagnostic systems tools. In past researchers devel oped Computer aided diagnosis (CAD) that help radiologist detect abnormalities. this research paper, we have employed novel Machine learning techniques such as Bayesian approach, Support vector machine (SVM) kernels: polynomial, radial...
Purpose: The epidermal growth factor receptor variant III (EGFRvIII) mutation has been considered a driver and therapeutic target in glioblastoma, the most common aggressive brain cancer. Currently, detecting EGFRvIII requires postoperative tissue analyses, which are ex vivo unable to capture tumor's spatial heterogeneity. Considering increasing evidence of imaging signatures capturing molecular characteristics cancer, this study aims detect primary glioblastoma noninvasively, using routine...
Background Imaging of glioblastoma patients after maximal safe resection and chemoradiation commonly demonstrates new enhancements that raise concerns about tumor progression. However, in 30% to 50% patients, these primarily represent the effects treatment, or pseudo‐progression (PsP). We hypothesize quantitative machine learning analysis clinically acquired multiparametric magnetic resonance imaging (mpMRI) can identify subvisual characteristics provide robust, noninvasive signatures...
Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It also key requirement multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods have obtained state-of-the-art results recent years, primarily targeted brain extraction without considering pathologically-affected...
Abstract The use of biomarkers for early detection Alzheimer's disease (AD) improves the accuracy imaging‐based prediction AD and its prodromal stage that is mild cognitive impairment (MCI). Brain parcellation‐based computer‐aided methods detecting MCI segregate brain in different anatomical regions their features to predict MCI. parcellation generally carried out based on existing atlas templates, which vary boundaries number regions. This works considers dividing atlases combining...
This Breast Cancer in women is the most frequency diagnosed and second leading cause of cancer deaths. Due to complex nature microcalcification masses, radiologist fail properly diagnose breast cancer. In past researchers developed Computer aided diagnosis (CAD) systems that help detect abnormalities an efficient manner. this research, we have employed robust Machine learning classification techniques such as Support vector machine (SVM) kernels Decision Tree distinguish mammograms from...
Purpose: Glioblastoma, the most common and aggressive adult brain tumor, is considered noncurative at diagnosis, with 14 to 16 months median survival following treatment. There increasing evidence that noninvasive integrative analysis of radiomic features can predict overall progression-free survival, using advanced multiparametric magnetic resonance imaging (Adv-mpMRI). If successfully applicable, such markers considerably influence patient management. However, patients prior initiation...
To construct a multi-institutional radiomic model that supports upfront prediction of progression-free survival (PFS) and recurrence pattern (RP) in patients diagnosed with glioblastoma multiforme (GBM) at the time initial diagnosis.We retrospectively identified data for newly GBM from two institutions (institution 1, n = 65; institution 2, 15) who underwent gross total resection followed by standard adjuvant chemoradiation therapy, pathologically confirmed recurrence, sufficient follow-up...
Gene expression deviates from its normal composition in case a patient has cancer. This variation can be used as an effective tool to find In this study, we propose novel gene expressions based colon classification scheme (GECC) that exploits the variations for classifying samples into and malignant classes. Novelty of GECC is two complementary ways. First, cater overwhelmingly larger size data sets, various feature extraction strategies, like, chi-square, F-Score, principal component...
Recent advancements in image translation for enhancing mixed-exposure images have demonstrated the transformative potential of deep learning algorithms. However, addressing extreme exposure variations remains a significant challenge due to inherent complexity and contrast inconsistencies across regions. Current methods often struggle adapt effectively these variations, resulting suboptimal performance. In this work, we propose HipyrNet, novel approach that integrates HyperNetwork within...
Which children with fetal ventriculomegaly, or enlargement of the cerebral ventricles in utero, will develop hydrocephalus requiring treatment after birth is unclear.To determine whether extraction multiple imaging features from magnetic resonance (MRI) and integration using machine learning techniques can predict which patients require postnatal cerebrospinal fluid (CSF) diversion birth.This retrospective case-control study used an institutional database 253 ventriculomegaly January 1,...