Publications

BibTex format

@article{Patel:2021:neuonc/noab195.001,
author = {Patel, M and Zhan, J and Natarajan, K and Flintham, R and Davies, N and Sanghera, P and Grist, J and Duddalwar, V and Peet, A and Sawlani, V},
doi = {neuonc/noab195.001},
journal = {Neuro-Oncology},
pages = {iv1--iv1},
title = {Artificial intelligence for early prediction of treatment response in glioblastoma},
url = {http://dx.doi.org/10.1093/neuonc/noab195.001},
volume = {23},
year = {2021}
}

Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:title>Abstract</jats:title>               <jats:sec>                  <jats:title>Aims</jats:title>                  <jats:p>Treatment response assessment in glioblastoma is challenging. Patients routinely undergo conventional magnetic resonance imaging (MRI), but it has a low diagnostic accuracy for distinguishing between true progression (tPD) and pseudoprogression (psPD) in the early post-chemoradiotherapy time period due to similar imaging appearances. The aim of this study was to use artificial intelligence (AI) on imaging data, clinical characteristics and molecular information within machine learning models, to distinguish between and predict early tPD from psPD in patients with glioblastoma.</jats:p>               </jats:sec>               <jats:sec>                  <jats:title>Method</jats:title>                  <jats:p>The study involved retrospective analysis of patients with newly-diagnosed glioblastoma over a 3.5 year period (n=340), undergoing surgery and standard chemoradiotherapy treatment, with an increase in contrast-enhancing disease on the baseline MRI study 4-6 weeks post-chemoradiotherapy. Studies had contrast-enhanced T1-weighted imaging (CE-T1WI), T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences, acquired at 1.5 Tesla with 6-months follow-up to determine the reference standard outcome. 76 patients (mean age 55 years, range 18-76 years, 39% female, 46 tPD, 30 psPD) were included. Machine learning models utilised information from clinical characteristics (age, gender, resection extent, performance status), O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and 307 quantitative imaging features; extracted from baseline study CE-T1WI/ADC and T2WI sequences using semi-automatically segmented enhancing disease and perilesional oedema masks respectively. Feature selection was performed within bootstrapped cross-validate
AU  - Patel,M
AU  - Zhan,J
AU  - Natarajan,K
AU  - Flintham,R
AU  - Davies,N
AU  - Sanghera,P
AU  - Grist,J
AU  - Duddalwar,V
AU  - Peet,A
AU  - Sawlani,V
DO  - neuonc/noab195.001
EP  - 1
PY  - 2021///
SN  - 1522-8517
SP  - 1
TI  - Artificial intelligence for early prediction of treatment response in glioblastoma
T2  - Neuro-Oncology
UR  - http://dx.doi.org/10.1093/neuonc/noab195.001
VL  - 23
ER  -

Download

DrMatthewWilliams

Contact

Location

Summary

Citation

BibTex format

RIS format (EndNote, RefMan)