Welcome to PsychiatryAI.com: [PubMed] - Psychiatry AI Latest

Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification

Evidence

NMR Biomed. 2024 Mar 17:e5129. doi: 10.1002/nbm.5129. Online ahead of print.

ABSTRACT

Proton magnetic resonance spectroscopy (1 H-MRS) is increasingly used for clinical brain tumour diagnosis, but suffers from limited spectral quality. This retrospective and comparative study aims at improving paediatric brain tumour classification by performing noise suppression on clinical 1 H-MRS. Eighty-three/forty-two children with either an ependymoma (ages 4.6 ± $$ pm $$ 5.3/9.3 ± $$ pm $$ 5.4), a medulloblastoma (ages 6.9 ± $$ pm $$ 3.5/6.5 ± $$ pm $$ 4.4), or a pilocytic astrocytoma (8.0 ± $$ pm $$ 3.6/6.3 ± $$ pm $$ 5.0), recruited from four centres across England, were scanned with 1.5T/3T short-echo-time point-resolved spectroscopy. The acquired raw 1 H-MRS was quantified by using Totally Automatic Robust Quantitation in NMR (TARQUIN), assessed by experienced spectroscopists, and processed with adaptive wavelet noise suppression (AWNS). Metabolite concentrations were extracted as features, selected based on multiclass receiver operating characteristics, and finally used for identifying brain tumour types with supervised machine learning. The minority class was oversampled through the synthetic minority oversampling technique for comparison purposes. Post-noise-suppression 1 H-MRS showed significantly elevated signal-to-noise ratios (P < .05, Wilcoxon signed-rank test), stable full width at half-maximum (P > .05, Wilcoxon signed-rank test), and significantly higher classification accuracy (P < .05, Wilcoxon signed-rank test). Specifically, the cross-validated overall and balanced classification accuracies can be improved from 81% to 88% overall and 76% to 86% balanced for the 1.5T cohort, whilst for the 3T cohort they can be improved from 62% to 76% overall and 46% to 56%, by applying Naïve Bayes on the oversampled 1 H-MRS. The study shows that fitting-based signal-to-noise ratios of clinical 1 H-MRS can be significantly improved by using AWNS with insignificantly altered line width, and the post-noise-suppression 1 H-MRS may have better diagnostic performance for paediatric brain tumours.

PMID:38494431 | DOI:10.1002/nbm.5129

Document this CPD Copy URL Button

Google

Google Keep Add to Google Keep

LinkedIn Share Share on Linkedin Share on Linkedin

Estimated reading time: 5 minute(s)

Latest: Psychiatryai.com #RAISR4D

Cool Evidence: Engaging Young People and Students in Real-World Evidence ☀️

Real-Time Evidence Search [Psychiatry]

AI Research [Andisearch.com]

Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification

Copy WordPress Title

🌐 90 Days

Evidence Blueprint

Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification

QR Code

☊ AI-Driven Related Evidence Nodes

(recent articles with at least 5 words in title)

More Evidence

Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification

🌐 365 Days

Floating Tab
close chatgpt icon
ChatGPT

Enter your request.