Integrating Nanotechnology in Neurosurgery, Neuroradiology, and Neuro-Oncology Practice—The Clinicians’ Perspective
Bibliographic record
Abstract
Body scanners and operating microscopes have become standard of care for diagnostic imaging and operative planning in neurosurgical oncology.Recently, the preclinical development of novel nanoscale materials for use in enhancing imaging and visualization of brain tumors in vivo have led to translational platforms that offer clinicians the potential for improving surgical outcomes and tailoring personalized treatment regimens.In this piece written by three physician-scientists with over 30 years of combined expertise in neurosurgery, neuroradiology, neuro-oncology, and CNS nanotherapeutics, we provide our collective opinion regarding the emerging uses of nanotechnology in our respective subspecialties. NANOTECHNOLOGY IN NEURORADIOLOGYCommon neuroimaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) provide anatomical details of the brain and spine.Both CT and MRI scans provide ~25-100 μm resolution of neural structures (Kaviarasi et al., 2019).CT uses X-rays while MRI uses radiowaves and magnetic fields for image acquisition and iodine-or gadolinium (Gd)based contrast dye agents, respectively, for further enhancement and delineation of lesions such as higher grade tumors, vascular lesions, or traumatic brain injuries that cause leakiness of the blood-brain barrier (BBB) (Martina et al., 2005;Bauer et al., 2014).Tracer-based imaging modalities such as positron emission spectroscopy (PET) and single-photon emission computerized tomography (SPECT) have limits of resolution between 2 and 10 mm (Moses, 2011;Bailey and Willowson, 2013) and rely on costly, injectable radioactive tracers to detect diseased cells.Nanotechnology for neuroimaging largely remains in early stage preclinical development.Functionalized nanoparticles containing iron oxide or gold or quantum dots have been tested in mouse models of stroke and brain tumors, demonstrating enhanced visualization of tumor foci, thrombi, or infarcted brain tissues (reviewed in Kaviarasi et al., 2019) (Kaviarasi et al., 2019).In particular, high resolution magnetic particle imaging (MPI) utilizing superparamagnetic iron oxide nanoparticles to acquire quantitative three-dimensional, in vivo real-time imaging shows promise in the fields of vascular, tumor, and cell labeling and tracking (reviewed in Wu et al., 2019) (Wu et al., 2019).Furthermore, the use of artificial intelligence and machine learning to deconvolute neural networks with brain mapping will further compliment the uses of nanotechnology in molecular neuroimaging (Lui et al., 2020;Yao et al., 2020).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".