Skip to content
#diffusion models Open access

Quantitative, longitudinal measurements of response to immunotherapy for glioblastoma using multiparametric MRI in a long-term survivor treated with autologous tumor lysate–loaded dendritic cell vaccine: an illustrative case

Oct 2026 · Journal of Neurosurgery Case Lessons
Glioma Diagnosis and Treatment

Abstract

BACKGROUND Neurosurgeons are in the forefront of developing clinical trials that harness the immune system to combat glioblastoma. Autologous tumor lysate–loaded dendritic cell vaccine (DCVax-L) is a novel immunotherapy for glioblastoma. An unmet need is to develop a sensitive, specific, feasible, clinically relevant, neuroimaging biomarker that accurately reflects response to immunotherapy. OBSERVATIONS This illustrative case involves a 63-year-old female who survived for 64 months following near-total resection of a glioblastoma and enrollment in a clinical trial (NCT00045968) using DCVax-L. A multivariate logistic regression model was used to assess treatment response longitudinally at 11 time points measuring predictive probability (PP) of tumor progression based on diffusion tensor imaging– and perfusion-weighted imaging–derived parameters within the contrast-enhancing region. Lesions were considered true progression (TP) if the PP was ≥ 50% and pseudoprogression (PsP; predominant treatment effects) if the PP was < 50 %. These findings were subsequently validated by histopathological analysis during resection for tumor recurrence. LESSONS These findings support the use of multiparametric MRI to monitor the effects of immunotherapy in patients with glioblastoma. Such multiparametric prediction models using noninvasive, advanced neuroimaging may be helpful to differentiate TP from PsP in longitudinal assessment of treatment response. https://thejns.org/doi/10.3171/CASE26655

View source

Similar papers

#computer vision Open access Jun 2016

Software Development in Startup Companies: The Greenfield Startup Model

The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.

Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al. · 178 citations · ⚡14
#computer vision Open access Oct 2016

Software Startups - A Research Agenda

Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.

M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al. · 157 citations · ⚡17
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Conference Sep 2010

Exploring the Sources of Waste in Kanban Software Development Projects

The application of agile software methods and more recently the integration of Lean practices contribute to the trend of continuous improvement in the software industry. One such area warranting proper empirical evidence is a project’s operational efficiency when using the Kanban method. This short paper takes a new an...

Marko Ikonen, Petri Kettunen, Nilay V. Oza et al. · 67 citations · ⚡9

Related blog posts

MIT News · Artificial Intelligence Oct 15, 2026

AI model decodes the language cells use to communicate

A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.

Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.