Casali, M.

Ciammella, P.

Elisa, B.

Fama, A.

Feliciani, G.

Fioroni, F.

Iori, M.

Merli, F.

Podgornii, A.

Versari, A.

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ESTRO 35 2016                                                                                                                                                    S879 
________________________________________________________________________________ 
 
Results: Texture parameters were computed in the three 
perfusion maps and their 3D wavelet transforms, which 
resulted in 945 texture features defined for each of the two 
tumor sites. The discretization of images using set number of 
bins and setintervals gave the similar number of stable 
texture parameters (Table 1). 40 parameters were correlated 
with tumor volume. Potentially standardizable factors 
introduced more variability into texture features than non-
standardizable. The highest variability was observed for pixel 
size. It caused instability in about 80% of parameters for both 
HN and lung tumors. Ten parameters were found to bestable 
in both HN and lung for potentially non-standardizable 
factors after thecorrection for inter-parameters correlations:  
 
• BF: entropy, sum entropy, LHH low gray-level size 
emphasis 
• MTT: long size low gray-level emphasis 
• BV: difference entropy, coarseness, long size high 
gray-level emphasis, HLH information measure of 
correlation 2, LLL covariance, LLLaverage. 
 
 
 
Conclusion: The set of stable texture parameters in CTP was 
identified. Pixel size, image discretization and HU intervals 
have to be standardized to build a reliable prediction models 
based on CTP texture analysis. 
 
EP-1864  
A 18FDG-PET texture analysis study on early stage Hodgkin 
Lymphoma patient outcome prediction 
G. Feliciani
1IRCCS - Arcispedale Santa Maria Nuova, Medical Physics, 
Reggio Emilia, Italy 
1, A. Fama2, P. Ciammella3, F. Fioroni1, M. Casali4, 
B. Elisa2, A. Podgornii3, A. Versari4, F. Merli2, M. Iori1 
2IRCCS - Arcispedale Santa Maria Nuova, Hematology, Reggio 
Emilia, Italy 
3IRCCS - Arcispedale Santa Maria Nuova, Radiation Oncology, 
Reggio Emilia, Italy 
4IRCCS - Arcispedale Santa Maria Nuova, Nuclear Medicine, 
Reggio Emilia, Italy 
 
Purpose or Objective: The aim of the study was to employ 
texture analysis to predict early stage Hodgkin Lymphoma 
(HL) patients’ outcome after chemotherapy and to give a 
quantitative description of HL characteristics. Predicting an 
early cancer's response to chemotherapy could enhance 
clinical care management by enabling the personalization of 
treatment plans based on predicted outcome.  
 
Material and Methods: We reviewed medical records of 
patients with early stage HL diagnosed between January 2012 
and December 2014 treated with standard combined modality 
therapy. 24 pre-treatment PET scans of the patients, 
acquired with a GE discovery STE, were selected for the 
analysis. A local nuclear medicine physician, blinded for the 
clinical outcome and interim PET (iPET) results, reviewed all 
PET scans. Volume of Interests (VOIs) were segmented 
employing cubes of volume 27 cm3 and 64 cm3 and centering 
them on the highest metabolic active mediastinic area. 
Texture analysis (TA) was applied through CGITA open source 
software and TA features correspondence with iPET results 
was assessed. Furthermore, we segmented isolated 
lymphnodes with a 40% of SUVmax isocontour algorithm. Each 
lymphnode was analyzed with TA as a “stand-alone patient” 
in order to increase the number of observations. TA features 
correspondence with iPET outcome of each lymphnode was 
assessed. Kruskall Wallis non-parametric test was employed 
to select most predictive features. Features, which showed 
prognostic power (or patient stratification ability), were 
employed to build Receiver Operating Curve (ROC) in order to 
score their sensibility and specificity. 
 
Results: After iPET revision, 17 patiets were considered 
disease free after 2 cycles of ABVD whereas the remaining 7 
patients had a positive iPET. Results obtained employing the 
27 cm3 cubes showed that 4 features are able to predict iPET 
response with statistical significance (p<0.02) and a high 
efficiency up to 85% employing “uniformity feature”. Using 
64 cm3 cubes, we were able to isolate a feature named short 
zone emphasis, which has a discrimination sensibility of 100% 
with specificity 65% and indicates for ABVD resistant tumors 
the presence of short active zone in the surrounding of the 
mediastinic region highest metabolic active area. Lymphnode 
analysis showed that 5 out of 74 TA features could separate 
iPET responders and non-responders patients with statistical 
significance (p<0.01). In particular, “coarseness” feature has 
a discrimination efficiency of 73% (sensibility 77% and 
specificity 70%). Patients with lymphnodes that appear 
coarser have a higher probability of being positive at iPET. 
 
Conclusion: In this work we presented a method to predict 
early stage HL patient outcome and to quantitatively 
describe tumor morphology combining textural features. This 
method requires further validation in larger prospective 
study. 
 
EP-1865  
DCE-CT lung tumour and aorta enhancement: is it an 
appropriate input vessel for kinetic modelling? 
M. La Fontaine
1Netherlands Cancer Institute, Radiotherapy, Amsterdam, 
The Netherlands 
1, W. Van Elmpt2, M. Kwint1, J. Belderbos1, J.J. 
Sonke1 
2MAASTRO, Radiation Oncology, Maastricht, The Netherlands 
 
Purpose or Objective: Dynamic contrast-enhanced Computed 
Tomography (DCE-CT) is a quantitative imaging modality to 
characterize heterogeneity in tumour vascularization. 
Problems in DCE-CT kinetic modelling have been reported 
due to lung tumour enhancement arriving prior to aortal 
enhancement. Studies have attempted to correct the issue by 
shifting the enhancement curve in time, segmenting different 
input vessels and/or dual-input kinetic modelling. The 
purpose of this project was to develop a methodology for a 
detailed spatio-temporal analysis of the heterogeneous lung 
tumour enhancement for the purpose of applying different 
input vessels in kinetic modelling. 
 
Material and Methods: Nine patients with non-small cell lung 
cancer (NSCLC) received DCE-CT scans (Siemens Definition 
Flash) prior to radiation therapy using shuttle mode 
acquisition with a longitudinal FOV of 13 cm. The DCE-CT 
scans were first motion corrected (Siemens VPCT) and 
subsequently, the primary lung tumour was contoured by a 
radiation oncologist. Using an in-house model, tumour and 
aorta time attenuation curves were analysed by gamma-
variate function fitting. The arrival time of the contrast 
agent was estimated by a threshold of 1% of the maximum 
enhancement of the fit. To determine the percentage of 
tumour enhancement prior and after aortic enhancement, 
the arrival times of the gamma-variate fit of the tumour and 
aorta were compared. Tumour voxels were considered 
acausal if the arrival time was greater than 2 s before the 


EP-1864: A 18FDG-PET texture analysis study on early stage Hodgkin Lymphoma patient outcome prediction 

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EP-1864: A 18FDG-PET texture analysis study on early stage Hodgkin Lymphoma patient outcome prediction

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