1. ‘TexRAD’ CT TEXTURE ANALYSIS:
A NOVEL QUANTITATIVE IMAGING BIOMARKER IN ONCOLOGY

2. Quantitative Imaging Biomarkers
Quantitative imaging is the extraction of quantifiable features from medical images for the assessment of normal or the severity, degree of change, or status of a disease, injury, or chronic condition relative to normal.

3. Quantitative CT-based Biomarkers in Oncology
Tumor Size
Tumor Attenuation
Tumor Perfusion
Tumor Heterogeneity

4. Size Criteria in Cancer Imaging
T & N Staging
Response Evaluation
Before
Therapy
After
Therapy
>20% increase = Progressive Disease
>30% decrease = Response
Years since CT

5. How repeatable is RECIST?
Mean diameter
4.1 cm
(range: cm)
2 readers
Single reader
Progressive Disease (>20%)
29.75%
( %)
9.5%
( %)
Response (>30%)
13.75%
( %) 3%
(0 - 5%)
Erasmus JJ et al. J Clin Oncol 2003;21:2574

6. Imatinib Therapy: Choi, H. et al. J Clin Oncol; 25:1753-1759 2007
CT Attenuation & Tumor Response: Gastro-Intestinal Stromal Tumors (GIST)
Size Criteria
Size & Attenuation
Imatinib Therapy: Choi, H. et al. J Clin Oncol; 25:

7. Perfusion CT & survival in esophageal squamous cell carcinoma
Perfusion <50 ml/min/100g
predicts poor survival
Responder: Perf 102 ml/min/100g
Non-responder: Perf 32 ml/min/100g
Hayano Y et al. Oncol Rep 2007;18:901-8

8. Vascular Heterogeneity
NORMAL
TUMOR
Vascular ‘hot spots’ (low resistance)
Flow voids → Hypoxia
Long pathways (high resistance)

9. Biological Impact of Tumor Heterogeneity
Adverse tumor microenvironment
Hypoxic voids and acidic milieu
Increased invasion and metastasis
Impaired delivery of chemotherapeutic agents
Increased cellular resistance to chemotherapy & radiotherapy
Inhibition of immune responses

10. Quantifying Tumor Heterogeneity with CT
Assess distribution of gray-levels with Texture Analysis
Visual assessments of heterogeneity tend to reflect radiologists’ impression of image quality
CT texture reported to reflect tumor vascularization (modelling studies)

11. CTTA Aims to Highlight Biological Heterogeneity
Image filtration (Laplacian of Gaussian)
‘Tune’ filter to appropriate scale (filter value)
Fine texture = noise
Medium/coarse texture: biologically important
Ganeshan B et al Academic Radiology, 2007; 14(12):

12. Quantifying Heterogeneity
Statistical measures
Uniformity – uniform distribution of gray-levels , probabilistic value varying between 0 - non-uniform and 1 - uniform
Entropy – measures irregularity or complexity, indicated by a higher value
Skogen K et al In Cancer Imaging, ICIS 2011, Copenhagen, Denmark

13. Quantifying Heterogeneity
Histogram analysis
Proportion/mean of all/positive pixels - location of distribution
Standard deviation – scale of the distribution
Skogen K et al In Cancer Imaging, ICIS 2011, Copenhagen, Denmark

14. Quantifying Heterogeneity
Histogram analysis
Skewness – symmetry of the distribution may reflect structures
Kurtosis – pointiness of the distribution may reflect vessels
Skogen K et al In Cancer Imaging, ICIS 2011, Copenhagen, Denmark

15. Quantifying Heterogeneity
Histogram analysis vs Statistical measures
Histogram measures may have the advantage of reflecting structures in a region
They compensate for the standard deviation and have the potential to be less sensitive to image noise and region size
Skogen K et al In Cancer Imaging, ICIS 2011, Copenhagen, Denmark

16. Tumor Heterogeneity as an Imaging Correlate for Hypoxia in NSCLC
pimonidazole
Ganeshan B et al Radiological Society of North America (RSNA) 2010

17. Tumor Heterogeneity is an independent predictor of survival in NSCLC – Study 1
Kaplan-Meier survival curves for NSCLC patients with lung lesions separated by (A) CT Texture analysis (B) SUV from PET and (C) PET stage. Survival curves were significantly different for CT texture analysis (p<0.002), PET stage (p<0.005) but not for SUV.
Ganeshan B et al European Radiology 2011; [In press]

18. Tumor Heterogeneity is an independent predictor of survival in NSCLC – Study 2
Tumor Characteristic
Hazard Ratio
95% Confidence Limits
p-value
Heterogeneity (CTTA)
4.70
1.91 – 11.60
0.001
Clinical Stage
7.38
2.36 – 23.04
Permeability
4.09
1.65 – 10.13
0.002
No significant interaction was observed between the above significant predictors and treatment on overall patient survival
Courtesy Prof Ashley Groves, INM, University College London, UK

19. Tumor Heterogeneity correlates with glucose uptake in esophageal cancer
Ganeshan B et al Clinical Radiology 2011; [Epub ahead of print]

20. Tumor Heterogeneity is an independent predictor of survival in esophageal cancer
Kaplan-Meier survival curves for oesophageal patients separated by (A) CT Texture analysis, (B) stage and (C) SUV from PET. Survival curves were significantly different for CT texture analysis (p=0.0006), stage (p=0.023) and SUV (p=0.0032).
Ganeshan B et al Clinical Radiology 2011; [Epub ahead of print]

21. Tumor Heterogeneity & Treatment Response: Renal Cancer
DISEASE-FREE SURVIVAL
PRE
Size & Attenuation
POST
Heterogeneity
Goh V et al Radiology 2011; Aug 3 [Epub]

22. Hepatic Heterogeneity and Survival in Colorectal Cancer
Fine
Medium
Coarse
Miles KA et al Radiology 2009;250:444-52

23. Economic Analysis of CTTA in Colorectal Cancer Surveillance
Full Surveillance
Cancer patients for surveillance
(Baseline Strategy)
Imaging Biomarker
(Comparator Strategy)
Reduced Surveillance
High Risk
Low Risk M
Progression
Free
Undetected Recurrence
Detected
Recurrence
Death p1 p2
p4TM
p4IM p3 p5
3 monthly cycle
Markov Model
Strategy
High Risk
Low Risk
CEA CT
Full Surveillance
3 monthly
Yearly
Reduced A
6 monthly
Reduced B
18 monthly

24. Impact of CTTA-based Surveillance Strategies
Reductions in frequency of CEA ± CT in low risk
Minimal increase in mortality at 5 years
74.2% versus 73.9%
Minimal reduction in average life expectancy
10-11 days
Cost savings:
£1248 per patient
Cost-effectiveness
Net Monetary Benefit: £427 per patient
(based on a willingness to pay £30,000 per life year)
Miles KA et al European Society of Radiology 2011, Vienna, Austria

25. CT Texture Analysis (CTTA) in Cancer Care
Performance Status
Serum
Markers
Symptoms
Serum
Markers
Disease Severity
Risk of Recurrence
Pathological Markers
Treatment
Surveillance
Staging
TexRAD
RECIST
TexRAD

26. Evolution of TexRAD
Initial Research & Development (R&D) was undertaken by researchers – engineers and clinicians at Brighton & Sussex Medical School, University of Sussex
Commercialisation of TexRAD software is being undertaken by TexRAD Ltd –
Come and visit us for a software demonstration and discussion
at our Booth LL-QRR3002 in the Quantitative Imaging Reading
Room

27. Publications Patent 14 Peer-review publications
B. Ganeshan, K.A. Miles, R.C.D. Young, C.R. Chatwin. Method, apparatus and computer program for analysing medical image data. Patent applied for (International Publication Number WO 2008 / ).
14 Peer-review publications
24 Conference proceedings