ACCURATE DIAGNOSIS OF BRAIN TUMORS USING ARTIFICIAL INTELLIGENCE

The classification of brain tumors – and
thus the choice of optimal treatment options – can become more accurate
and precise through the use of artificial intelligence in combination
with physiological imaging. This is the result of an extensive study
conducted by the Karl Landsteiner University for Health Sciences (KL
Krems). Multiclass machine learning methods were used to analyze and
classify brain tumors using physiological data from magnetic resonance
imaging. The results were then compared with classifications made by
human experts. Artificial intelligence was found to be superior in the
areas of accuracy, precision and misclassification, among others, while
professionals performed better in sensitivity and specificity.

Brain tumors can be easily detected by magnetic resonance imaging (MRI),
but their exact classification is difficult in this way. Yet that’s
precisely what’s crucial for choosing the best possible treatment
options. Now, an international team led by KL Krems has used data from
modern MRI methods as the basis for machine learning (ML) protocols and
assessed the use of artificial intelligence to classify brain tumors.
They found that in certain areas, classification using artificial
intelligence can be superior to that performed by trained professionals.

MORE MRI. MORE DATA.
The team led by Prof. Andreas Stadlbauer, a scientist at the Central
Institute for Medical Radiology Diagnostics at St. Pölten University
Hospital, used both advanced and physiological MRI data for the study.
Both methods provide enhanced insight into the structure and metabolism
of a brain tumor and have allowed better classification for some time.
But the price to pay for such a differentiated picture is enormous
amounts of data that need to be expertly assessed. «We have now analyzed
whether and how an artificial intelligence using ML can be enabled to
support trained professionals in this Herculean task,» explains Prof.
Stadlbauer. «And the results are very promising. When it comes to
accuracy, precision and avoiding misclassification, an AI can classify
brain tumors well using MRI data.»

To achieve their impressive result, the team trained nine well-known
Multiclass ML algorithms with MRI data from 167 previous patients who
had one of the five most common brain tumors and had accurate
classification using histology. A total of 135 so-called classifiers
were generated in a complex protocol. These are mathematical functions
that assign the material to be examined to specific categories. «In
contrast to previous studies, we also took into account data from
physiological MRIs,» explains Prof. Stadlbauer. «This included details
on the vascular architecture of the tumors and their formation of new
vessels, as well as the supply of oxygen to the tumor tissue.»

RADIOPHYSIONOMICS
The team named the combination of data from different MRI methods with
multiclass ML «radiophysiomics.» It’s a term that’s likely to catch on
quickly, as the potential of this approach became apparent in the second
part of the project, the testing phase. In this, the now-trained
multiclass ML algorithms were fed with corresponding MRI data from 20
current brain tumor patients and the results of the classifications thus
obtained were compared with those of two certified radiologists.
Thereby, the two best ML algorithms (referred to as «adaptive boosting»
and «random forest»), outperformed the human assessment results in the
areas of accuracy and precision. Also, these ML algorithms resulted in
less misclassification than by the professionals (5 versus 6). On the
other hand, when it came to the sensitivity and specificity of the
assessment, the human assessments proved to be more accurate than the AI
tested.

«This also makes it clear,» says Prof. Stadlbauer, «that the ML approach
should not be a substitute for classification by qualified personnel,
but rather a supplement to it. In addition, the time and effort required
for this approach is currently still very high. But it offers a
possibility whose potential should be further pursued for everyday
clinical use.» Overall, this study again demonstrates the focus of
research at KL Krems on fundamental findings with real clinical added
value.

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