To identify the effective quantitative image features (radiomics features) for prediction of response, survival, recurrence and metastasis of hepatocellular carcinoma (HCC) in radiotherapy.

1. To identify the effective quantitative image
features (radiomics features) for prediction of
response, survival, recurrence and metastasis of
hepatocellular carcinoma (HCC) in radiotherapy.
Materials/Methods
• Multiphase contrast enhanced liver CT images
were acquired in 16 patients with HCC on pre and
post radiation therapy (RT).
• Arterial phase CT images were selected to
analyze.
• Response evaluated by RECIST criteria, survival,
local recurrence (LR), distant metastasis (DM)
and liver metastasis (LM) were examined.
• A radiation oncologist manually delineated the
tumor and normal liver in Figure 1.
• Quantitative image features were extracted to
characterize the intensity distribution (n=8),
spatial patterns (texture, n=36), and shape (n=16)
of the tumor and liver, respectively.
• Differences between pre and post image features
were calculated (n=120).
• A total of 360 features were extracted and then
analyzed by unpaired student’s t-test to rank the
effectiveness of features for the prediction of
response.
Results
Quantitative Image Feature Analysis of Multiphase
Liver CT for Hepatocellular Carcinoma (HCC) in
Radiation Therapy
Wookjin Choi, Min Kyu Kang, Jiahui Wang, Wei Lu
Dept. of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA
Dept. of Radiation Oncology, Yeungnam University College of Medicine, Daegu, South Korea
Purpose
Conclusion
Quantitative image features were identified to be
potential predictors of the five endpoints of HCC in
RT.
Texture and shape features were significant.
• The most effective features were selected for
prediction of each outcome in Figure 2.
• Intensity distribution features were not significant
(p>0.09).
• Tumor response (Resp)
− Changes in tumor shape
− More favorable response with larger decrease in
Second Major Axes Length (Diff.T.L.SF09, p=
0.002)
− SD of Grey Level Nonuniformity, (Diff.L.A.TF29,
p=0.01)
• Survival (Sur)
− Tumor shape change is dominant.
− Patients survive with larger decrease in
Eccentricity (Diff.T.L.SF13, p=0.0002)
− Mean of Inertia (Post.L.A.TF05, p=.0.003)
• Local recurrence (LR)
− Liver texture on pre and post CT images
− Standard Deviation (SD) of High Grey Level
Run Emphasis (HGRE) (Pre.L.A.TF32,
p=0.005) was linearly correlated to LR
− High risk of LR with higher SD of Entropy
(Post.L.A.TF10, p=0.005)
• Distant metastasis (DM)
− Tumor texture on pre CT image
− High risk of DM with lower SD of Entropy
(Pre.T.A.TF10, p=0.01)
− Centroid Y (Diff.L.L.SF03, p=0.02)
• Liver metastasis (LM)
− Liver and tumor texture on pre CT image
− Linearly related to DM
− Lower value of Mean of Cluster Shade
(Pre.L.A.TF06, p=0.004) could be LM
− SD of Entropy (Pre.T.A.TF10, p=0.006)
Future Works
• Cell (1cmx1cmx1cm cube) based heterogeneity
analysis with local feature histogram
• Dynamic enhancement analysis on multiphase
(Non contrast, Arterial, Portal, and Venous) CT
• More than 30 patients
Figure 1. Tumor (red) and normal liver (blue) contours on
arterial phase CE CT, axial (left) and sagittal (right) plane.
Supported in part by NIH R01CA172638.
*Contact: Wei Lu, Ph.D., wlu@umm.edu
Pre
Post
Figure 2. Correlation between the selected features (x axis)
and the responses (y axis). The selected features were
standard Deviation (SD) of GLN, Centroid Y, Second Major
Axes Length, Eccentricity, Mean of Inertia, SD of Entropy
(post liver), Mean of Cluster Shade, SD of HGRE and SD of
Entropy (pre tumor).