Identification of Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induced Lung Disease Wookjin Choi, Sadegh Riyahi, Wei LuMedical Physics, Memorial Sloan Kettering Cancer CenterRadiation 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 analysisDifferent sizes of GTVs were simulated at the normal lung (contralateral to tumor).

Simulation forNormal lung without GTV

20, 30, 40, 50 and 60 mm diameter sphereat 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

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

𝑉 𝑠𝑖𝑚=𝑉 𝑙𝑢𝑛𝑔−𝑉 𝐺𝑇𝑉