Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (radiation pneumonitis and radiation 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. http://scitation.aip.org/content/aapm/journal/medphys/43/6/10.1118/1.4955803

1. Identification of Robust Normal Lung CT Texture Features
for the Prediction of Radiation-Induced Lung Disease
Wookjin Choi, Sadegh Riyahi, Wei Lu
Medical Physics, Memorial Sloan Kettering Cancer Center
Radiation Oncology, University of Maryland School of Medicine
Purpose
• Radiomics features for normal tissue in lung CT
can help predict radiation induced lung disease
during radiotherapy
– Radiation pneumonitis and Radiation fibrosis
• Clinically useful features
– Relatively invariant (robust) to tumor size as well as
not correlated with normal lung volume
Results
Conclusion
Supported in part by NIH R01CA172638.
*Contact: Wei Lu, Ph.D., luw@mskcc.org
(a) (b)
Figure 3. (a) The feature variations with respect to tumor size (the diameter of the
GTV) in robust and unrobust features; (b) A small normal lung in axial CT that caused
by atelectasis (red arrow); right lung – cancerous, left lung – normal, and green circular
hole – 60-mm diameter GTV.
Figure 2. (a) Distributions of feature variations for each feature, the red line (5%) is the robustness threshold; (b) Correlations between each texture feature and the
volume of the simulated normal lung without GTV
• We identified 11 robust normal lung CT texture
features.
• The robust features can be further examined for
the prediction of radiation-induced lung disease.
• Interestingly, low grey-level run features
identified normal lung diseases.
• Only 11 features were robust.
– All first-order intensity-histogram features (min,
max, mean, and median), two of the GLCM and four
of the GLRM features were robust.
• Correlation with normal lung volume
– All robust features were not correlated
– Three unrobust features showed high correlation
• Larger GTV resulted greater feature variation
• There was no dependence on GTV location
• Excessive variations were observed
– Two low grey-level run features
– Identified local lung diseases (atelectasis)Figure 1. A flow chart for the normal lung CT texture features’ robustness analysis
Different sizes of GTVs were simulated at the normal lung (contralateral to
tumor).
Simulation for
Normal lung without GTV
20, 30, 40, 50 and 60 mm diameter sphere
at upper or lower lobe
GTV(hole) Generation
Reference
Lung CT
features
Reference
Lung CT
features
Simulated
Lung CT
features
Robustness Analysis
Results
Lung CT
Lung
Contour
Texture Feature Extraction
𝑭𝒓𝒆𝒇 𝑭𝒔𝒊𝒎
𝑫 =
𝑭 𝒓𝒆𝒇 − 𝑭 𝒔𝒊𝒎
𝑭 𝒓𝒆𝒇
∙ 𝟏𝟎𝟎%
• Feature robustness evaluation
– Relative difference
– Pearson correlation
Method
• The free-breathing CTs of 14 lung SBRT patients
were studied.
• 27 texture features were extracted from
simulated normal lung volume
–
– 9 intensity histogram based features
– 8 grey-level co-occurrence matrix (GLCM) features
– 10 grey-level run-length matrix (GLRM) features
𝑉𝑠𝑖𝑚 = 𝑉𝑙𝑢𝑛𝑔 − 𝑉𝐺𝑇𝑉