Jing Zhang1, Yang Song1, Yu-dong Zhang2, Xu Yan3, Yefeng Yao1, and Guang Yang1
1Shanghai Key Laboratory of Magnetic Resonance, Department of Physics, East China Normal University, shanghai, China, 2Department of Radiology, The First Affiliated Hospital with Nanjing Medical University,, Nanjing, China, 3MR Scientific Marketing, Siemens Healthcare, shanghai, China
Synopsis
Texture features
plays an important role in radiomics. To make the texture features rotation-invariant,
pyradiomics computes the texture features along all directions and use their
mean values. In this study, we demonstrated that maximum and minimum values of
these features along different directions, which is also rotation-invariant,
may provide added value to radiomics studies. We trained models using mean,
maximum and minimum values of texture features along different directions to
classify clinically significant (CS) prostate cancer (PCa) and non-CS PCa on PROSTATEx
dataset. We found that using extremum instead of mean texture
features improved the performance of model.
INTRODUCTION
Radiomics is a high-throughput method to extract
features from medical images and associate them with clinical diagnosis and
prognosis1. Radiomics have been used in cancer classification and
prognosis with great success and texture features have been proved to be useful
for the classification of many lesions2. Pyradiomics , an open-source toolbox for radiomics feature extraction, extracts texture
features from 13 different directions and exports their mean values3.
This spatial averaging makes the orientation dependent texture features
rotation-invariant, thus more stable. However, if a texture feature is only
prominent along certain direction, averaging will make the feature less
conspicuous. Since the maximum and minimum values of the texture are also
rotation-invariant and will not obscure features prominent along certain
directions, we compared models built with mean, maximum and minimum values of texture
features in this study.
METHODS
Data: PROSTAETx data set (https://doi.org/10.7937/K9TCIA.2017.MURS5CL)
was used in this study. PROSTATEx was a multi-parametric Magnetic Resonance
Imaging (mp-MRI) dataset on prostate cancer acquired with Siemens 3T scanners, Modalities
used in this study included T2W(TSE,0.5×0.5×3.6mm3),DWI(SSEP,2×2×3.6mm3,b=800s/mm2)and ADC map. 185 cases included 252
cancer lesions (CS/NCS=68/184) whose boundaries were marked by professional
radiologists. We randomly selected 177 patients (CS/NCS= 48/129) as the
training set and 75 patients (CS/NCS=20/55) as the independent test set for
model evaluation.
Texture Feature: We extracted gray level co-occurrence matrix (GLCM) and gray level
run length matrix (GLRLM) features with 13 directions from all sequence images by
pyradiomics on python3.6 (Figure 1). We calculated the mean, minimum and maximum
values as texture features to build the respective models. To compare the
effectiveness of mean and extremum values, we built models sole from mean
values, maximum values and minimum values respectively and compared their
performances.
Model building and
evaluation: The workflow is illustrated in Figure
2. We applied recursive feature elimination (RFE) for feature selection,
logistic regression (LR) for classification, and 5-fold cross-validation in the
machine learning pipeline. The models were evaluated on the test dataset using receiver operating characteristic (ROC) curve and area under
ROC curve (AUC).RESULTS
AUC values of all
models built with mean and extremum values are listed in Table 1. We found
that for GLCM, the optimal classification model was built from mean features in
DWI, with AUC of 0.744, and for GLRLM, the optimal model comes from maximum
features in ADC, with AUC of 0.774. DISCUSSION
Many radiomics studies depend on pyradiomics
toolbox for feature extraction. So mean values of the orientation dependent
texture are mostly used. Using mean values makes these features rotation
invariant so they no longer depend on the direction of the lesion or the
patient being scanned. However, our experiments showed models built from mean
values did not always perform best. At least, in our simplified experiments on
PROSTATEx dataset, extremum values produced better models than mean values.
Thus, extremum values may be included in the radiomics studies. It is
understandable the using mean values is more powerful if the texture feature is
distributed equally in all directions. It the texture feature is only prominent
in one or a few directions, the use maximum values will be a better choice. For
example, for 2D scan in MRI, the inter-slice resolution was lower than the
intra-slice resolution, using extremum values of 3D texture features may be
better than using mean values. The maximum value, minimum value and mean value
all have their own unique characteristics, so we could compare those values of
texture feature carefully for radiomics future studies.CONCLUSION
By comparing the
models built solely from mean, maximum and minimum values of texture features along
different directions, we found at least in certain cases, extremum values can
be used instead of mean values for texture feature extraction. To get the
optimal radiomics model for a specific problem, we need to wisely compare their
performances and choose wisely. Acknowledgements
This project is supported by National Natural Science Foundation of China (61731009, 81771816).References
- Lambin, Philippe; Rios-Velazquez, Emmanuel; Leijenaar,
Ralph; Carvalho, Sara; van Stiphout, Ruud G. P. M.; Granton, Patrick; Zegers,
Catharina M. L.; Gillies, Robert; Boellard, Ronald (2012-03-01). "Radiomics:
Extracting more information from medical images using advanced feature
analysis". European Journal of Cancer.
- G. Litjens,
O. Debats, J. Barentsz, N. Karssemeijer and H. Huisman. "Computer-aided
detection of prostate cancer in MRI", IEEE Transactions on Medical Imaging
2014;33:1083-1092
- Van Griethuysen, J. J. M., Fedorov, A.,
Parmar, C., Hosny, A., Aucoin, N., Narayan, V., Beets-Tan, R. G. H.,
Fillon-Robin, J. C., Pieper, S., Aerts, H. J. W. L. (2017). Computational
Radiomics System to Decode the Radiographic Phenotype. Cancer Research, 77(21),
e104–e107.