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 LuDept. of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USADept. of Radiation Oncology, Yeungnam University College of Medicine, Daegu, South Korea

Purpose

ConclusionQuantitative 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).