Mapping Tumor Interfaces with Brain Networks: A Tractography-Based Surgical Planning Tool
Poster No:
1970
Submission Type:
Abstract Submission
Authors:
Hampali Shamanth1, Aryan Tiwary2, Bhagyasree Kanuparthi3, Radha Kumari1, Rimjhim Agrawal1
Institutions:
1BrainsightAI, Bengaluru, Karnataka, 2BrainSightAI, Bengaluru, Karnataka, 3BrainsightAI, Bengaluru, MT
First Author:
Co-Author(s):
Introduction:
Preserving critical neurological function during brain tumor resection is a central challenge in neurosurgery. Traditional structural imaging provides spatial information about tumor boundaries but offers limited insight into the functional networks potentially at risk. Resting-state functional MRI (rs-fMRI) and diffusion-weighted imaging (DWI)-based tractography can identify key functional pathways. By integrating functional connectivity with probabilistic tractography, surgeons can better understand how tumor margins interface with critical networks, potentially reducing the risk of postoperative deficits (Shalan et al., 2021).
Methods:
Five patients (3 males, 2 females; mean age: 48 ± 9 years) with intracranial tumors located in proximity to cortex underwent preoperative MRI at 3T. Imaging protocols included high-resolution T1-weighted scans (1 mm isotropic), rs-fMRI (TR=2000 ms, TE=30 ms), and DWI (b-values of 1000 s/mm², 30 diffusion directions). Tumor segmentation was performed on T1-weighted images using semi-automated tools. Functional networks were identified from rs-fMRI data using independent component analysis (McKeown et al., 2003), focusing on sensorimotor, language, and default mode networks. Each functional network's cortical nodes served as seed regions for probabilistic fiber tractography, generating connectivity distributions that revealed pathways associated with these networks. Resulting streamlines were color-coded by network and overlaid with tumor margins, enabling a detailed visualization of tumor-network interfaces. Surgical risk zones were defined as regions within a 10 mm boundary of tumor infiltration that intersected high-probability network pathways.
Results:
In all five patients, integrated functional and structural connectivity maps delineated pathways of critical functional networks interacting with tumor borders. Sensorimotor pathways (red) (see figures) were often displaced or compressed adjacent to the tumor, while language-related tracts (green) and default mode connections (yellow) were variably affected depending on tumor location. These maps identified regions where resection carried heightened risk to primary sensorimotor or language functions, guiding intraoperative decision-making. Across cases, the approach provided surgeons with intuitive, color-coded visual guides distinguishing high-risk from lower-risk resection zones.
Conclusions:
This integrated mapping strategy merges functional connectivity with tractography to elucidate tumor interfaces with eloquent brain networks. By highlighting critical pathways at risk, it informs neurosurgical planning and may improve functional outcomes. Although this study is limited by a small sample size and requires validation in larger cohorts, it demonstrates the potential for these advanced imaging techniques to enhance surgical safety and preserve key neurological functions.
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
White Matter Anatomy, Fiber Pathways and Connectivity 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Diffusion MRI
Multi-Modal Imaging 1
Keywords:
Data analysis
FUNCTIONAL MRI
Language
Motor
Neoplastic Disease
Segmentation
Somatosensory
STRUCTURAL MRI
Tractography
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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Provide references using APA citation style.
Shalan, M. E., Soliman, A. Y., Nassar, I. A., & Alarabawy, R. A. (2021). Surgical planning in patients with brain glioma using diffusion tensor MR imaging and tractography. The Egyptian Journal of Radiology and Nuclear Medicine, 52(1), 1–10. https://doi.org/10.1186/s43055-021-00490-5