Machine learning applications in R for the differentiation of primary CNS tumors.
Authors: Khammad Vasilii Alexandrovich , Otero José Javier , Guerra Francisco Jose Garagorry , Izquierdo Yolanda Cabello , Becker Aline Paixao , Kharchenko Nataliy Vladimirovna , Zapirov Gadzhimurad Magomedovich 
Published: April 2020

Primary Central Nervous System (CNS) malignancies consist of various groups of tumors. Each sub‐class results in distinct clinical decision and related outcomes. According to current WHO classification of brain tumors, the integration of both histomorphology and molecular features is critical for diagnosis including tumor grade, and directly impacts treatment approaches and prognosis. However, in the United States and worldwide, scarce resources are available to perform all the required tests.

The aim of this work is to improve and standardize neuropathological diagnoses for brain cancer patients throughout the world, with a particular emphasis on resource‐poor diagnostic scenarios. To achieve this goal, we have developed a population model based on articles from the open resources of MEDLINE database and the latest version of WHO classification of CNS tumors. Our data frame is comprised of 50 primary brain tumor diagnoses. Data on clinical, histomorphology and molecular features were carefully sorted according to particularities of each individual CNS tumor type. To distinguish features with the highest value for accurate diagnosis prediction we used Boruta function (Figure
1) and Model Weights Extraction (Table
1). Both methods could not detect tentative features and showed that common clinical data (age and specific tumor location) have a high impact on diagnosis prediction. In our work we implemented modern machine learning (ML) classification algorithms, such as randomForest (RF), Support Vector Machine (SVM) and Naive Bayes (NB) after splitting the data into training and validation set. We discovered that models trained to predict diagnosis on the basis of top 6 Boruta and Weight selected features showed the highest diagnostic accuracy on the validation set even in the absence of molecular data. The accuracy for all 3 algorithms with the mentioned features was as follows: RF (92%); SVM (79%); NB (52%). We also observed that ki67 plays a key role in differentiating CNS tumors as its presence significantly increased the accuracy for each trained model independently of ML algorithm. Importantly, despite the obvious value in further clinical decisions, the inclusion of all molecular data (BRAFV600E, FISH codeletion 1p19q, EGFR amplification, MGMT status and etc.) did little to contribute to distinguishing between different primary CNS malignancies in our study. In fact, the molecular data managed to slightly improve the accuracy of our prediction models (only ~ 5–6 %) compared to data on ki67 alone (~37%), suggesting that the proliferation markers should be considered as a more reliable tool for accurate staging and morphological evaluation of CNS tumors.

In conclusion, our results confirm that the implementation of open sources and computing platforms by pathologists in low resource environments in the US and abroad would allow to develop a reliable cost‐effective panel of markers and significantly improve the quality of neuropathological diagnoses.

The value of features according to Boruta

Figure 1

The value of features according to Weight Extraction Model

##

names

attribute_importance

## 32

site

1.790710364

## 18

ki67

1.123777357

## 1

Age

0.887462753

## 34

solid.with.infiltration

0.553816679

## 13

GFAP

0.454128477

## 29

perivascular.pseudorosettes

0.442591199

## 17

infiltrative.diffuse

0.436200109

## 14

high.cellularity

0.398945890

## 25

Necrosis

0.330965699

## 26

nodular

0.318905215

## 10

Endothelial.Proliferation

0.295229054

## 19

low.cellularity

0.279927747

## 5

chicken.wire

0.278482297

## 7

desmoplasia

0.260748678

## 8

dysplastic.neurons

0.246787762

## 28

perivascular.lymphocytes

0.244935349

## 11

eosinophil.granular.bodies

0.239584778

## 30

pseudopapiliar.papillary

0.228171358

## 33

Solid.discohesive

0.217511802

## 20

medium.cellularity

0.216013780

## 6

columns.cords

0.212469602

## 15

idh1.2.mutation

0.201122902

## 22

microcystic

0.199572761

## 35

storiform

0.189573543

## 24

myxoid.areas

0.186702316

## 16

idh1.R132H

0.170479121

## 4

calcifications

0.163422951

## 36

FISH_codeletion_1p19q

0.158014816

## 37

LOH_codeletion_1p19q

0.150564459

## 27

p53

0.149535733

## 21

MGMT

0.128631095

## 3

BRAF.V600E.mutation

0.103997736

## 2

ATRX.loss

0.100807078

## 31

rosenthal.fibers

0.089142945

## 9

EGFR.amplification

0.058714262

## 12

Gender

0.022632343

## 23

multilayered.rosettes

0.008671977