Dataset Description:

A retrospective cohort consisting of 59 MRI studies (Male = 30, Female = 29 ) was acquired from The University

Hospitals between 2003 – 2016.

The tumors were pathologically classified (MB = 22, EP = 12, Gliomas = 25) based on 2016 WHO Classification

of Tumors of CNS. [10]

The training set consisted of 30 patients (MB = 11, EP = 6, Gliomas = 13) and the independent validation set

comprised of 29 patients (MB = 11, EP = 6, Gliomas = 12)

• Wilcoxon rank sum test implemented to determine the top 10 discriminable features within training set. (p < 0.05)

• Random forest (RF) classifier model with a randomized 3-fold cross-validation was developed on this dataset.

• Using this model, an independent test set was validated via area under receiver operating characteristic (AUC).

52 radiomic features

capturing lesion

heterogeneity (CoLlAGe)

were extracted from the

entire region of interest

3Tesla MRI sequences :

• Gd-T1w,

• T2w,

• FLAIR

We presented a novel radiomics descriptor – CoLlAGe, based approach to identify non-invasive surrogate

markers of Posterior Fossa tumor types on routine T2w - MRI protocol.

CoLIAGe captures disorder in local arrangement of pixel-wise gradient orientations on radiologic imaging.

Medulloblastoma (aggressive brain tumor) reported higher CoLlAGe entropy values than Ependymomas and

Gliomas on MRI for pediatric brain tumor cases.

Our presented radiomic approach may allow for a noninvasive understanding of the various posterior fossa

tumor types as manifested on T2w images.

Future directions:

To validate our analysis on a multi-institutional independent dataset.

Background:

Medulloblastoma (MB), Ependymomas (EP), and Gliomas are common posterior fossa (PF) tumors, which

account for ~47% of all pediatric brain tumors. [1]

PF tumor types have different treatment regimens. MB are the most aggressive and require gross total

resection (GTR), radiation and chemotherapy whereas Gliomas are just monitored after GTR. [2]

Currently, histological findings are used to confirm the type of PF tumor via stereotactic biopsy. [3]

There is a clinical need to identify novel structural, and functional imaging based markers that can predict PF

tumor type in a non invasive preoperative setting – that can help with prognosis and treatment planning.

Previous Work:

Radiomics: The conversion of quantitative medical images into high-dimensional data that can be mined to

uncover the underlying pathophysiology. [4]

Radiomic features like Haralick capture 1st and 2nd order statistical patterns, while steerable filters like Gabor

capture dominant orientations that solicit a textural response.

Previous studies have used 2D and 3D based radiomic features to distinguish the PF types. But did not have

an independent validation set. [5,6]

Recently, we developed a new computerized descriptor, CoLlAGe, that distinguishes subtly different

pathologies in adult brain tumors. [7]

CoLlAGe captures co-occurrence of localized dominant orientations at pixel level, and brings together classical

Haralick and steerable filters by capturing statistics and frequency of local gradient orientations.

[1] Zakrzewski et al., “Posterior fossa tumours in children and adolescents. A clinicopathological study of 216 cases”, Folia Neuropathol 2003.

[2] Massimino et al., “Long-term results of combined pre-radiation chemotherapy and age-tailored radiotherapy doses for childhood medulloblastoma”, J

Neurooncol 2012.

[3] Chen et al., “Stereotactic biopsy for brainstem lesion: Comparison of approaches and reports of 10 cases”, J Chinese Medical Association 2011

[4] Gillies et al., “Radiomics: Images Are More than Pictures, They Are Data”, Radiology 2016.

[5] Orphanidou-Vlachoua et al., “Texture analysis of MR images and use of probabilistic neural network to discriminate posterior fossa tumors in children”,

NMR in Biomedicine 2014.

[6] Fetit et al., “3-D textural features of conventional MRI improve diagnostic classification of childhood brain tumors”, NMR in Biomedicine 2015.

[7] Prasanna et al., “Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor”, Sci Rep. 2016.

[8] Avants et al., “Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain.

Med Image Anal. 2008.

[9] Richards, J.E et al., A database of age-appropriate average MRI templates. Neuroimage, 2015

[10] Louis et al., “The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary”, Acta Neuropathol 2016

Objective: Identify a set of radiomics (computer extracted) features on routine MRI sequences that can

distinguish the most common posterior fossa tumors - Medulloblastoma, Ependymomas, & Gliomas in children.

INTRODUCTION RESULTS

MATERIALS and METHODS

CONCLUSIONSREFERENCES

MRI textural features can differentiate pediatric posterior fossa tumors

Niha Beig1, Ramon Correa1, Rajat Thawani1, Prateek Prasanna1, Chaitra Badve2, Deborah Gold2, Anant Madabhushi1, Peter deBlank3, Pallavi Tiwari1

(1) Case Western Reserve University, Cleveland, OH(2) University Hospitals, Cleveland, OH

(3) Cincinnati Children's Hospital Medical Center, Cincinnati, OH

Lesion annotated by an

expert into four regions:

• Tumor necrosis

• Enhancing tumor

• Non enhancing tumor

• Edema (not shown)

T2w, and FLAIR were co-

registered to Gd-T1w MRI

using BSpline registration

and normalized using

pediatric atlases by age

[8,9]

Data acquisition and Feature extraction

Feature selection and classification

Hypothesis: We hypothesize that radiomic descriptor – CoLlAGe, can identify subtle

microarchitectural differences between MB, EP and Gliomas on routine pediatric MRI scans.

p = 5.75 x 10-4

Medulloblastoma Ependymoma

FIGURE 1 : Boxplot of CoLIAGe Sum Variance

FIGURE 2 : Scatterplot of the independent validation set in the feature space of CoLIAGe

Entropy and Sum Variance

FIGURE 3 : A – CoLIAGe sum variance for GliomaB – CoLlAGe sum variance for Ependymoma

C – CoLlAGe sum variance for Medulloblastoma

C

A

Result and Take-aways:

On the independent set, we found that sum variance and entropy of CoLlAGe (p< 0.002) on T2w protocol could

reliably distinguish MB from ( EP + Gliomas ) with an AUC of 0.88 (Accuracy = 0.82, Sensitivity = 0.63 and

Specificity = 0.94)

Sum variance of CoLlAGe possibly accounts for greater variation of scattered atypia and local accumulation of

mitotic processes as observed on histopathology. [7]

Higher entropy of CoLlAGe is indicative of more chaotic arrangement in areas of high viable cell population. [7]

Our results suggest that entropy of CoLlAGe from the entire tumor potentially captures high cellularity of MB

which enables its discrimination from ( EP + Gliomas ).

B

*

Glioma