A web-based and user friendly planning tool to predict
outcome for percutaneous microwave ablation of liver
tumors including validation of the simulation model.
Poster No.:
C-0241
Congress:
ECR 2016
Type:
Scientific Exhibit
Authors:
C. Schnedl , P. I. Kalmar , P. Weir , M. Kolesnik , T. van
1
1
4
2
3
5
1
Oostenbrugge , M. Moche , H. A. Deutschmann , R. H.
1
1 1
2
3
Portugaller , P. Stiegler ; Graz/AT, Dublin/IE, St. Augustin/DE,
4
5
Nijmegen/NL, Leipzig/DE
Keywords:
Liver, Interventional non-vascular, CT, Ablation procedures,
Computer Applications-Virtual imaging, Image registration, Image
verification
DOI:
10.1594/ecr2016/C-0241
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Aims and objectives
Hepatocellular carcinoma and liver metastases are one of the most common causes
of cancer in the world with increasing incidence. Besides curative resection being
possible in 20% of patients radiofrequency ablation (RFA), cryoablation, transarterial
chemoembolisation (TACE) and microwave ablation (MW) are possible interventional
techniques to treat these malignancies. Except TACE, each of these types of treatment
uses needles that are inserted into the tumor tissue, which destroy the tumor through
heating, cooling or the application of an electric field.
Microwave ablation utilizes dielectric hysteresis to produce heat, which occurs when polar
molecules are forced to continuously realign with an oscillating electromagnetic field.
Currently, there is no common software environment for researchers and interventional
radiologists to use to plan and to predict the results from different types of minimally
invasive cancer treatment.
Within the framework of the GO-SMART-project (European FP7 ICT-Project, co-funded
by the European Commission, grant agreement no. 600641) the aim of our study was
to develop and validate a web-based planning tool to predict outcome for percutaneous
image-guided microwave ablation for liver tumors.
Methods and materials
A web-based environment including a viewer and image segmentation and registration
tools was developed (GO-SMART cloud based environment available at http://smartmict.eu). A simulation model was incorporated. Pseudo-anonymized scans of microwave
procedures were used to validate the performance of the developed simulation model.
First, the pre-interventional CT was uploaded and the liver and tumor were semiautomatically segmented. In the next step, intra-operative scans were uploaded and
registered to the pre-operative images. This generated needle coordinates likewise to
the original situation.
After placing virtual needles using these coordinates, equipment parameters and
treatment protocols were set. A simulation was executed which estimated the microwave
lesion. The pysical (real) lesion was segmented using a 1-month post-microwave followup scan and quantitively compared to the predicted lesion.
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Outcome measures of this comparison were the average absolute error (#) between the
segmented (S) and simulated (#) lesion and the volumetric ratio called target overlap (#s).
Underestimation of the simulated lesion is shown by #s < 1.0. Overestimation, where
S<#, is indicated by large # and (#s) # 1.
Images for this section:
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Fig. 2: Go-Smart workflow of web based environment.
© Medical University Graz - Graz/AT
Fig. 1: GoSmart software environment
© Medical University Graz - Graz/AT
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Results
Thirty five cases were successfully tested and up to now five were used for validation.
Mean (range) for #s and # (in mm) were 0.9414 ±0.2542 and 2.9119 ± 1.8772,
respectively.
Images for this section:
Fig. 3: Microwave simulation and validation of an ablated liver lesion
© Medical University Graz - Graz/AT
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Conclusion
We present a planning tool to predict outcome for percutaneous microwave ablation of
liver tumors. Based on first validation results, further refinement of the simulation model
is needed.
Personal information
References
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