Abstract Purpose/Objective(s) Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (RILD) - pneumonitis and fibrosis. For these features to be clinically useful, they need to be relatively invariant (robust) to tumor size and not correlated with normal lung volume. Materials/Methods The free-breathing CTs of 14 lung SBRT patients were studied. Different sizes of GTVs were simulated with spheres (diameters 10 to 60 mm) placed in the lung contralateral to the tumor. Twenty-seven texture features (9 from intensity histogram, 8 from the gray-level co-occurrence matrix [GLCM], and 10 from the gray-level run-length matrix [GLRM]) were extracted from [lung – GTV]. The Bland-Altman method was applied to measure the normalized range of agreement (nRoA) of each texture feature when GTV size varied. A feature was considered as robust when its nRoA was less than that of [lung – GTV] volume (8.8%) and regarded as not correlated when their absolute correlation coefficient was lower than 0.70. Results Eighteen texture features were identified as robust. All intensity histogram features were robust except sum and kurtosis. All GLCM features were robust except energy and Haralick's Correlation. Five GLRM features (two run emphasis and three high gray-level emphasis) were robust while the other five (two nonuniformity and three low gray-level emphasis) were nonrobust. Particularly, all three low gray-level emphasis features had extremely large nRoAs (∼30%), indicating huge variations when GTV size changed. None of the robust features was correlated with the normal lung [lung – GTV] volume, suggesting that they can provide additional information. Three nonrobust features (sum and two nonuniformity features) were highly correlated with the normal lung volume. None feature showed statistically significant differences (P < 0.05) with respect to GTV location (upper vs. lower lobe). Conclusion We identified 18 robust lung CT texture features which were invariant to varying tumor volumes. Particularly the three GLRM high gray-level emphasis features can characterize the radiologic manifestations of pulmonary abnormalities. Hence these features can be further examined for the prediction of the RILD.

1. Robust Normal Lung CT Texture Features for the Prediction
of Radiation-Induced Lung Disease
Medical Physics and Radiology, Memorial Sloan Kettering Cancer Center
Wookjin Choi, PhD, Sadegh Riyahi, PhD, Chia-Ju Liu, MD, Wei Lu, PhD
INTRODUCTION RESULTS & DISCUSSION
•Normal lung CT texture features have been used
for the prediction of radiation-induced lung
disease (RILD) - pneumonitis and fibrosis.
•For these features to be clinically useful
–Robust to tumor volume variation
–Not correlated (non-redundant) with the tumor
volume
•Identified 16 robust lung CT texture features
– Relatively invariant to tumor size variations
– Not correlated with the tumor volume
•Particularly, HGRE, SRHGE, and LRHGE can characterize
the radiologic manifestations (increased lung
attenuation) of pulmonary abnormalities.
•Hence these features can be further examined for the
prediction of the RILD.
•Free-breathing CT scans of 14 lung cancer patients
•Different sizes of gross tumor volumes (GTVs) were
simulated with spheres and placed in the lung
contralateral to the tumor
– Diameters: 10 to 60 mm
•27 texture features from the peri-tumoral region
(uniform 30mm expansion around the GTV in the lung)
– 9 Intensity histogram features
– 8 Gray-level co-occurrence matrix (GLCM) features
– 10 Gray-level run-length matrix (GLRM) features
•The Bland-Altman analysis was applied to measure the
normalized range of agreement (nRoA).
nRoA
=
95% Limit upper bound − 95% Limit lower bound
Mean 𝐹 value
× 100%
=
𝜇 𝐹bias
+ 1.96 ∙ 𝜎 𝐹bias
− 𝜇 𝐹bias
− 1.96 ∙ 𝜎 𝐹bias
𝜇 𝐹
× 100%,
with
𝜇 𝐹 =
1
nGTVs 𝑖
nGTVs
𝐹𝑖 ,
– 𝐹bias_𝑖 = 𝐹ref − 𝐹𝑖 : the difference of 𝐹ref (𝐹 in the peri-tumoral
region of the [ref] tumor) and 𝐹𝑖 (𝐹 calculated in the peri-tumoral
region of the ith GTV)
– 𝜇 𝐹bias
and 𝜎 𝐹bias
: the mean and the standard deviation of 𝐹bias_𝑖
over all GTVs
– nGTVs and 𝜇 𝐹: the total number of GTVs and the mean 𝐹 value
across all patients
•Robust features
– nRoA < threshold (100%)
– Absolute correlation coefficient < 0.70
•16 robust normal lung CT texture features
– All intensity histogram features were robust except sum and
kurtosis.
– All GLCM features were robust except energy and Haralick's
Correlation.
– Five GLRM features (two run emphasis and three high gray-
level emphasis) were robust while the other five (two
nonuniformity and three low gray-level emphasis) were
unrobost.
•No feature showed statistically significant differences
(P<0.05) on GTV location (upper vs. lower lobe).
•Lung texture
– Low attenuation tissues: air within the airways and alveoli, a
large portion of the lung volume
– High attenuation tissues: vasculature and interstitium, a small
portion of the lung normal tissues
– Also, various pathologic states such as a tumor, pneumonia,
hemorrhage, edema, and fibrosis lead to increased lung
attenuation.
•When the GTV size increased, long low gray-level runs
were truncated into shorter runs.
•The high-attenuation tissues consist mainly of short
runs
– The distribution of these short high gray-level runs was not
significantly affected
•Limitations
1.A simulation study
– Examining the robustness of normal lung CT texture
features when simulated tumor volume changes
– The simulated spherical GTV were much simplified
compared to the shapes of real tumors
2.No prediction model was constructed, and no real RILD case
was studied
•Future work
– Build prediction models using the identified robust texture
features along with the conventional dose and clinical risk
factors in real RILD datasets
CONCLUSION
MATERIAL & METHODS
*Contact: Wei Lu, Ph.D., luw@mskcc.org, Supported in part by NIH R01CA172638.
nRoA (%) Correlation
Volume of peri-tumoral region 122.3 1
Robust Features
Intensity
Minimum 7.0 -0.32
Median 5.6 0.06
Mean 8.5 0.09
Variance 67.6 0.30
SD Standard deviation 35.6 0.33
Skewness 42.3 -0.36
GLCM
Energy 72.6 -0.22
Entropy 11.6 -0.31
Correlation 69.5 -0.17
IDM Inverse difference moment 32.3 -0.08
Inertia 83.6 0.03
GLRM
SRE Short run emphasis 1.5 0.25
LRE Long run emphasis 42.9 -0.63
HGRE High gray-level run emphasis 66.1 0.39
SRHGE Short run high gray-level emphasis 66.7 0.39
LRHGE Long run high gray-level emphasis 55.3 0.25
Mean of Robust Features 41.8
Unrobust Features
Intensity
Maximum 125.0 -0.07
Sum 119.9 -0.85
Kurtosis 120.1 -0.40
GLCM
CS Cluster shade 139.5 0.09
CP Cluster prominence 194.7 0.08
HC Haralick’s correlation 130.9 0.38
GLRM
GNU Gray-level nonuniformity 117.8 0.62
RNU Run-length nonuniformity 124.4 0.85
LGRE Low gray-level run emphasis 159.6 -0.24
SRLGE Short run low gray-level emphasis 155.0 -0.23
LRLGE Long run low gray-level emphasis 264.0 -0.32
Mean of unrobust features 150.1
Figure 2. The cumulative number of features with a given
nRoA. Blue diamonds: texture features, orange circle:
volume of the peri-tumoral region, and red line: a threshold
for robustness.
Table 1. Normal lung CT texture features: normalized
range of agreement (nRoA) and correlation with the
volume of peri-tumoral region
Figure 1. A flow chart for the robustness analysis of normal
lung CT texture features extracted from the peri-tumoral
region (red contour excluding the simulated blue GTV). The
mid-size (30mm) GTV was used as the reference tumor [ref].