TITLE:
Technical Note: Identification of CT Texture Features Robust to Tumor Size Variations for Normal Lung Texture Analysis
AUTHORS:
Wookjin Choi, Sadegh Riyahi, Seth J. Kligerman, Chia-Ju Liu, James G. Mechalakos, Wei Lu
KEYWORDS:
Radiation-Induced Lung Disease, Normal Lung Texture, Radiomics, CT, Stereotactic Body Radiotherapy
JOURNAL NAME:
International Journal of Medical Physics, Clinical Engineering and Radiation Oncology,
Vol.7 No.3,
August
7,
2018
ABSTRACT: Normal lung CT texture
features have been used for the prediction of radiation-induced lung disease (RILD).
For these features to be clinically useful, they should be robust to tumor size
variations and not correlated with the normal lung volume of interest, i.e., the
volume of the peri-tumoral region (PTR). CT images of 14 lung cancer patients
were studied. Different sizes of gross tumor volumes (GTVs) were simulated and placed in the lung
contralateral to the tumor. 27 texture features [nine from intensity histogram,
eight from the gray-level co-occurrence matrix (GLCM) and ten from the
gray-level run-length matrix (GLRM)] were extracted from the PTR. The
Bland-Altman analysis was applied to measure the normalized range of agreement
(nRoA) for each feature when GTV size varied. A feature was considered as
robust when its nRoA was less than the threshold (100%). Sixteen texture
features were identified as robust. None of the robust features was correlated
with the volume of the PTR. No feature showed statistically significant
differences (P 0.05) on GTV locations. We identified 16 robust
normal lung CT texture features that can be further examined for the prediction
of RILD.