MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI.

Omneya Attallah

doi:10.3390/diagnostics11020359

Abstract

Medulloblastoma (MB) is a dangerous malignant pediatric brain tumor that could lead to death. It is considered the most common pediatric cancerous brain tumor. Precise and timely diagnosis of pediatric MB and its four subtypes (defined by the World Health Organization (WHO)) is essential to decide the appropriate follow-up plan and suitable treatments to prevent its progression and reduce mortality rates. Histopathology is the gold standard modality for the diagnosis of MB and its subtypes, but manual diagnosis via a pathologist is very complicated, needs excessive time, and is subjective to the pathologists’ expertise and skills, which may lead to variability in the diagnosis or misdiagnosis. The main purpose of the paper is to propose a time-efficient and reliable computer-aided diagnosis (CADx), namely MB-AI-His, for the automatic diagnosis of pediatric MB and its subtypes from histopathological images. The main challenge in this work is the lack of datasets available for the diagnosis of pediatric MB and its four subtypes and the limited related work. Related studies are based on either textural analysis or deep learning (DL) feature extraction methods. These studies used individual features to perform the classification task. However, MB-AI-His combines the benefits of DL techniques and textural analysis feature extraction methods through a cascaded manner. First, it uses three DL convolutional neural networks (CNNs), including DenseNet-201, MobileNet, and ResNet-50 CNNs to extract spatial DL features. Next, it extracts time-frequency features from the spatial DL features based on the discrete wavelet transform (DWT), which is a textural analysis method. Finally, MB-AI-His fuses the three spatial-time-frequency features generated from the three CNNs and DWT using the discrete cosine transform (DCT) and principal component analysis (PCA) to produce a time-efficient CADx system. MB-AI-His merges the privileges of different CNN architectures. MB-AI-His has a binary classification level for classifying among normal and abnormal MB images, and a multi-classification level to classify among the four subtypes of MB. The results of MB-AI-His show that it is accurate and reliable for both the binary and multi-class classification levels. It is also a time-efficient system as both the PCA and DCT methods have efficiently reduced the training execution time. The performance of MB-AI-His is compared with related CADx systems, and the comparison verified the powerfulness of MB-AI-His and its outperforming results. Therefore, it can support pathologists in the accurate and reliable diagnosis of MB and its subtypes from histopathological images. It can also reduce the time and cost of the diagnosis procedure which will correspondingly lead to lower death rates.

Highlights

Brain tumors are very common pediatric solid tumors accounting for around 25% of all types of pediatric cancers [1]
In the case of the multi-class classification level, for the spatial-time-frequency deep learning (DL) features (1074 features) extracted from the ResNet 50 convolutional neural networks (CNNs), the linear discriminant analysis (LDA) classifier achieved the highest accuracy of 96.66%, which is higher than the 95.74% obtained with the same classifier trained with only spatial DL features of higher dimension extracted from the ResNet-50 CNN
For the spatial-time-frequency DL features (1010) extracted from the DensNet-201 CNN, the LDA classifier attained a peak accuracy of 98.46%, which is better than the 97.16% obtained with the same classifier when trained with spatial features only which have a higher dimension of (1920 features)

Summary

Introduction

Brain tumors are very common pediatric solid tumors accounting for around 25% of all types of pediatric cancers [1]. MB is the main reason for cancer-related illness and death among children [5,6]. It develops inside the cerebellum on the posterior part of the brain and rapidly grows. Because MB is a pediatric brain tumor, there is a necessity to attain a serious examination, so as not to result in an over or under treatment, which in both cases leads to an excessive death rate [7]. The accurate diagnosis of pediatric MB and its subtypes is essential to reduce mortality rates and select the appropriate treatment plan that prevents its progression

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