Dataset Information

Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology.

ABSTRACT: Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating biophysical models to mpMRI data, assists in identifying macroscopic quantifiable tumor patterns of invasion and proliferation, potentially leading to improved (a) detection/segmentation of tumor subregions and (b) computer-aided diagnostic/prognostic/predictive modeling. This article presents a summary of (a) biophysical growth modeling and simulation,(b) inverse problems for model calibration, (c) these models' integration with imaging workflows, and (d) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.

SUBMITTER: Mang A

PROVIDER: S-EPMC7520881 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology.

Mang Andreas A Bakas Spyridon S Subramanian Shashank S Davatzikos Christos C Biros George G

Annual review of biomedical engineering 20200601

Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating ...[more]

PMID: 32501772

Similar Datasets

Project description:Brain tumors, characterized by the uncontrolled growth of abnormal cells, pose a significant threat to human health. Early detection is crucial for successful treatment and improved patient outcomes. Magnetic Resonance Imaging (MRI) is the primary diagnostic tool for brain tumors, providing detailed visualizations of the brain's intricate structures. However, the complexity and variability of tumor shapes and locations often challenge physicians in achieving accurate tumor segmentation on MRI images. Precise tumor segmentation is essential for effective treatment planning and prognosis. To address this challenge, we propose a novel hybrid deep learning technique, Convolutional Neural Network and ResNeXt101 (ConvNet-ResNeXt101), for automated tumor segmentation and classification. Our approach commences with data acquisition from the BRATS 2020 dataset, a benchmark collection of MRI images with corresponding tumor segmentations. Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. This involves extracting features based on tumor shape, position, shape, and surface characteristics. To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). AWO mimics the hunting behavior of humpback whales to iteratively search for the optimal feature subset. With the selected features, we perform image segmentation using the ConvNet-ResNeXt101 model. This deep learning architecture combines the strengths of ConvNet and ResNeXt101, a type of ConvNet with aggregated residual connections. Finally, we apply the same ConvNet-ResNeXt101 model for tumor classification, categorizing the segmented tumor into distinct types. Our experiments demonstrate the superior performance of our proposed ConvNet-ResNeXt101 model compared to existing approaches, achieving an accuracy of 99.27% for the tumor core class with a minimum learning elapsed time of 0.53 s.

Project description:Traditionally, the poor outcome for patients with malignant brain tumours led to therapeutic nihilism. In turn, this resulted in lack of interest in neurosurgical oncology subspecialisation, and less than ideal patient pathways. One problem of concern was the low rate of tumour resection. Between 1997 and 2006, 685 treated glioblastomas were identified. In the first four years only 40% of patients underwent tumour resection, rising to 55% in the last four years. Before revision of the pathway, the median length of hospital stay was 8 days, and 35% of patients received the results of their histology outside of a clinic setting. A pathway of care was established, in which all patients were discussed pre-operatively in an MDT meeting and then directed into a new surgical neuro-oncology clinic providing first point of contact. This limited the number of surgeons operating on adult glioma patients and aided recruitment into research studies. Now, three consultant neurosurgeons run this service, easily fulfilling IOG requirement to spend >50% of programmed activities in neuro-oncology. Nursing support has been critical to provide an integrated service. This model has allowed increased recruitment to clinical trials. The introduction of this service led to an increase in patients discussed pre-operatively in an MDT (66% rising to 87%; P=0.027), an increase in the rate of surgical resection (from 40% to 80%) and more patients being admitted electively (from 25% to 80%; P<0.001). There was a reduction in the median length of stay (8 days reduced to 4.5 days; P<0.001). For the cohort of GBM patients that went on to have chemoradiotherapy we improved median survival to 18 months, with 35% of patients alive at two years, comparable to international outcomes. Implementing a specialist neurosurgical oncology service begins with understanding the patient care pathway. Our patients have benefitted from the culture of subspecialisation and the excellent inter-disciplinary working relationships that have been developed.

Dataset Information

Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology.

Publications

Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets