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Classification of paediatric brain tumours by diffusion weighted imaging and machine learning
Jan Novak1,2,3,4, Niloufar Zarinabad1,2, Heather Rose1,2
1Institute of Cancer and Genomic Sciences, School of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
Insights
Apparent diffusion coefficients (ADC) can accurately classify common pediatric posterior fossa brain tumors across multiple centers. Histogram analysis of ADC values provides high diagnostic accuracy for distinguishing between tumor types.
Area of Science:
- Neuroimaging
- Radiology
- Pediatric Oncology
Background:
- Posterior fossa brain tumors are a significant cause of morbidity in children.
- Accurate and early diagnosis is crucial for effective treatment planning.
- Distinguishing between different types of posterior fossa tumors can be challenging with conventional imaging.
Purpose of the Study:
- To evaluate the ability of apparent diffusion coefficients (ADC) to differentiate between common pediatric posterior fossa brain tumors.
- To assess the multicenter applicability of ADC histogram analysis for tumor classification.
- To determine the diagnostic accuracy of ADC metrics in classifying Medulloblastomas, Pilocytic Astrocytomas, and Ependymomas.
Main Methods:
- Diffusion-weighted imaging was performed on 124 pediatric patients across 12 centers using 18 scanners.
- Apparent diffusion coefficient (ADC) maps were generated, and histogram data was extracted from tumor regions of interest.
- Machine learning classifiers (Naïve Bayes, Random Forest) were trained using histogram metrics for classification, with accuracy assessed by tenfold cross-validation.
Main Results:
- Mean ADC values significantly differed between tumor types (ANOVA P < 0.001).
- A mean ADC cutoff of 0.984 × 10⁻³ mm²/s distinguished Ependymomas from Medulloblastomas with 80.8% sensitivity and 80.0% specificity.
- ADC histogram metrics achieved high classification accuracies: 85% with Naïve Bayes and 84% with Random Forest.
Conclusions:
- Apparent diffusion coefficient histogram analysis is a reliable method for classifying common pediatric posterior fossa brain tumors on a multicenter basis.
- ADC metrics offer a non-invasive approach to improve diagnostic accuracy and potentially guide treatment decisions.
- This technique holds promise for enhancing the diagnostic workflow in pediatric neuro-oncology.
Abstract:
To determine if apparent diffusion coefficients (ADC) can discriminate between posterior fossa brain tumours on a multicentre basis. A total of 124 paediatric patients with posterior fossa tumours (including 55 Medulloblastomas, 36 Pilocytic Astrocytomas and 26 Ependymomas) were scanned using diffusion weighted imaging across 12 different hospitals using a total of 18 different scanners. Apparent diffusion coefficient maps were produced and histogram data was extracted from tumour regions of interest. Total histograms and histogram metrics (mean, variance, skew, kurtosis and 10th, 20th and 50th quantiles) were used as data input for classifiers with accuracy determined by tenfold cross validation. Mean ADC values from the tumour regions of interest differed between tumour types, (ANOVA P < 0.001). A cut off value for mean ADC between Ependymomas and Medulloblastomas was found to be of 0.984 × 10-3 mm2 s-1 with sensitivity 80.8% and specificity 80.0%. Overall classification for the ADC histogram metrics were 85% using Naïve Bayes and 84% for Random Forest classifiers. The most commonly occurring posterior fossa paediatric brain tumours can be classified using Apparent Diffusion Coefficient histogram values to a high accuracy on a multicentre basis.
