Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_3916_Библиотеки_им_академика_М_И_Перельмана
.pdf
Decision-Support System for Diagnosis of Abdominal Aortic Aneurysm
177
Table 2. Az values (mean standard deviation) calculated for training and testing sets
using the 10-fold cross validation
Classifier
A
z
mean training
A
z
mean testing
MLP
GLCM
0.963 0.003
0.940 0.006
GLRLM
0.868 0.005
0.821 0.009
GLDM
0.945 0.005
0.915 0.008
SVM
GLCM
0.970 0.004
0.953 0.005
GLRLM
0.882 0.007
0.851 0.008
GLDM
0.951 0.005
0.926 0.006
The obtained values show again that the biggest area under the ROC curve (A
z
) for the
testing sets, and consequently the best classification performance is obtained by the GLCM
features applied to a support vector machine classifier (0.953 0.005) followed by the GLCM
feature set applied to a neural network (0.940 0.006). Following these two alternatives are
A
z
values for the GLDM support vector machine classifier (0.926 0.006) and the GLCM
multilayer perceptron neural network (0.915 0.008). Finally, we can find the A
z
values for
the GLRLM support vector machine classifier (0.851 0.008) and the GLRLM multilayer
perceptron neural network (0.821 0.009).
Although the GLCM method scores the highest AUC value for both classifiers, the
GLDM method follows them very closely. The GLRLM method performance is the worst of
three but it can still be considered as indicative. These ROC curves confirm the previous
results and reinforce the hypothesis of using texture analysis as discriminative information.
According to classification results for the three texture methods and the two type of
classifiers, we could affirm that texture analysis might offer complementary information to
support radiologist on classifying aneurysm evolution after EVAR.
CONCLUSION
In the present study we have analysed the discriminative capacity of textures features for
classifying the evolution experimented by aorta aneurysm treated by EVAR. For this purpose,
texture parameters have been extracted by three different methods, GLCM, GRLM, and
GLDM. The texture features set obtained by each method was separately fed to two type of
classifiers, namely a support vector machine and an artificial neural network classifier. The
classification performance of the three texture analysis methods and the different classifiers
were evaluated by percent of correctly classified cases and area under the ROC curve values
estimated by the 10-fold cross validation.
The results obtained by each texture analysis method on both type of classifiers permit to
assert that the two main aortic thrombus aneurysm evolutions, namely favourable or
unfavourable, correspond to different textures parameters. Consequently, we can conclude
that texture analysis could be utilized by physicians as complementary information to classify
the post-operative evolution in patients who underwent EVAR treatment. The results can be
consider as promising, taking into account the limited number of patients. A bigger patient
dataset would be needed in order to generalise the findings to different clinical situations. The
https://t.me/med1917
G. García, A. Tapia and M. De Blas
178
study can also be regarded as a first step to more specific studies, particularly for the
unfavourable-endotension cases. In these cases a better knowledge of the specific reasons that
provokes the leakage from prosthesis to the aneurysmatic sac could be precious at the time to
decide the treatment to follow.
As a final conclusion, it can be considered that the decision support system proposed
might be used as a second opinion tool for assisting physicians in characterization of
aneurysm evolution after EVAR treatment. However, further investigations would be needed
to assess the potential of the system as a clinical tool, considering the limited number of
patients.
REFERENCES
[1] Thompson, MM. Controlling the expansion of abdominal Aneurysm. Br. J. Surg, 2003,
90, 897-898.
[2] VanDamme, Sakalihasan, Limet: Factors promoting rupture of abdominal aortic
aneurysms. Acta Chir Belg, 2005, 105(1), 1-11.
[3] William Stavropoulos, S; Sridhar, R. Charagundla: Imaging Techniques for Detection
and Management of Endoleaks after Endovascular Aortic Aneurysm Repair,
Radiology, June, 2007, 243, 641-655.
[4] García, G; Maiora, J; Tapia, A; De Blas, M. Evaluation of texture for classification of
abdominal aortic aneurysm after endovascular repair. Journal of Medical Imaging.,
2012, 25(3), 369-376.
[5] Bashir, Mustafa, R; Ferral, Hector, Jacobs, Chad, McCarthy, Walter, Goldin, Marshall
Endoleaks After Endovascular Abdominal Aortic Aneurysm Repair: Management
Strategies According to CT Findings Am. J. Roentgenol., 2009, 192, W178-186.
[6] Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and
Future Potential. Kunio Doi. Comput Med Imaging Graph., 2007, 31(4-5), 198211.
[7] Morton, MJ, Whaley, DH; Brandt, KR; Amrami, KK. Screening mammograms:
interpretation with computer-aided detection-prospective evaluation. Radiology, 2006,
239, 375383.
[8] Boniha, L; Kobayashi, E; Castellano, G; Coelho, G; Tinois, E; Cendes, F; et al. Texture
analysis of hippocampal sclerosis. Epilepsia, 2003, 44, 154650.
[9] Arimura, H; Li, Q; Korogi, Y; Hirai, T; Abe, H; Yamashita, Y; Katsuragawa, S; Ikeda,
R; Doi, K. Automated computerized scheme for detection of unruptured intracranial
aneurysms in threedimensional MRA. Acad Radiol, 2004, 11, 10931104.
[10] Jianguo Zhang, Tieniu Tan, Brief review of invariant texture analysis methods, Pattern
Recognition, Volume 35, Issue 3, March 2002, Pages 735-747, ISSN 0031-3203.
[11] Haralick, R; Shanmugam, K; Dinstein, I. Textural features for image classification.
IEEE Transactions on Systems, Man, and Cybernetics SMC-3 (1973), 610621.
[12] Graphic.
Image Processing, vol. 4, pp. 172179, 1975.
[13] of run lengths for
texture Pattern Recognit. Lett., vol. 11, pp. 415420. June 1990.
https://t.me/med1917
Decision-Support System for Diagnosis of Abdominal Aortic Aneurysm
179
[14] measures for
terrain classification IEEE Trans. Syst., Man, Cybern., vol. SMC-6, pp. 269285,
Apr. 1976.
[15] Conners, RW; Harlow, CA. A theoretical comparison of texture algorithms. IEEE
Trans Pattern Anal, 1980, 2, 204-22.
[16] Mir, AH; Hanmandlu, M; Tandon, SN; "Texture analysis of CT images," Engineering
in Medicine and Biology Magazine, IEEE , vol.14, no.6, pp.781-786, Nov/Dec 1995.
[17] Chen, EL; Chung, PC; Chen, CL; Tsa, HM
system for CT liver image classificationBiomed. Eng., vol. 45, no. 6, pp.
783-794, 1998.
[18] Gibbs, P; Turnbull, LW. Textural analysis of contrast-enhanced MR images of the
breast. Magn Reson Med, 2003, 50, 9298.
[19] Nikita, A; Nikita, KS; Mougiakakou, SG; Valavanis, IK. In: Evaluation of texture
features in hepatic tissue characterization from non-enhanced CT images. Engineering
in medicine and biology society, 2007. EMBS 2007, 29th annual international
conference of the IEEE; 2007. p. 3741-4.
[20] Chen, DR; Chang, RF; Huang, YL. Computeraided diagnosis applied to US of solid
breast nodules by using neural networks. Radiology, 1999, 213, 407412.
[21] Kato, H; Kanematsu, M; Zhang, X; Saio, M; Kondo, H; Goshima, S; et al. Computer-
aided diagnosis of hepatic fibrosis: Preliminary evaluation of MRI texture analysis
using the finite difference method and an artificial neural network. Am J Roentgenol.,
2007, 189(1), 117-22.
[22] O'neill, MC; Song, L. Neural network analysis of lymphoma microarray data: Prognosis
and diagnosis near-perfect. BMC Bioinformatics., 2003, 4(1), 13.
[23] Maglogiannis, I; Zafiropoulos, E; Anagnostopoulos, I. An intelligent system for
automated breast cancer diagnosis and prognosis using SVM based classifiers. Appl
Intell., 2009, 30(1), 24-36.
[24] Sethian, J. Level Set Methods and Fast Marching Methods. Cambridge University
Press. 1996.
[25] Ibanez, L; Schroeder, W; The ITK Software Guide. Kitware, Inc. ISBN 1-930934-10-6,
http://www.itk.org/ItkSoftwareGuide.pdf. 2003.
[26] Caselles, V; Kimmel, R; Sapiro, G. Geodesic active contours. International Journal on
Computer Vision, 1997, 22 (1), 6197.
[27] Haykin, S. Neural networks: A comprehensive foundation, Prentice-Hall, 1999.
[28] Weiss, SM; Kulikowski, CA. Computer systems that learn: Classification and
prediction methods from statistics, neural nets, machine learning, and expert systems.
San Mateo, CA: Morgan Kaufmann. 1991.
[29] Cortes and Vapnik, C. Cortes and V. Vapnik, Support-vector networks, Machine
Learning, 1995, 20 (2), pp. 273297.
[30] XinZhou; Wu, XY; Mao, KZ; Tuck, DP. "Fast Gene Selection for Microarray Data
Using SVM-Based Evaluation Criterion," Bioinformatics and Biomedicine, 2008.
BIBM '08. IEEE International Conference on , vol., no., 2008, pp.386-389, 3-5 Nov.
[31] Ali, A; Khan, U; Tufail, A; Minkoo Kim; "Analyzing Potential of SVM Based
Classifiers for Intelligent and Less Invasive Breast Cancer Prognosis," Computer
Engineering and Applications (ICCEA), 2010 Second International Conference on ,
vol.2, no., pp.313-319, 19-21 March 2010.
https://t.me/med1917
https://t.me/med1917
In: Aortic Aneurysms ISBN: 978-1-62618-458-9
Editors: Daniel Fischhof and Franz Hatig © 2013 Nova Science Publishers, Inc.
Chapter 9
CLINICAL, HEMODYNAMICAL
AND MECHANICAL ASPECTS OF AORTIC ANEURISMS
AND ENDOVASCULAR REPAIR
Moshe Brand
1
, Idit Avrahami
1,
, Asaph Nardi
1
, Daniel Silverberg
2
and Moshe Halak
2
1
Ariel Biomechanics Center, Ariel University, Israel
2
Department of Vascular Surgery, the Chaim Sheba Medical Center,
Tel Hashomer, Israel
PROGNOSIS
Aortic aneurysm (AA) is a pathologic entity defined by a localized dilatation in the
thoracic (TAA), abdominal (AAA) or thoracoabdominal (TAAA) aorta. Aneurysms rupture is
an unexpected and usually fatal event, which usually leads to death due to exsanguination.
Another significant complication of AA is thromboembolism due to poor hemodynamics,
which is characterized by disturbed flow and circulation zones.
The first section of the chapter discusses the flow regimes of healthy and aneurysmatic
aorta from hemodynamic point of view using computational fluid dynamics (CFD) methods.
For both aortic arch and abdominal aorta, the aneurysm provokes a highly disturbed flow and
large recirculation regions in comparison to the cases without aneurysms.
The disturbed flow observed in the aneurysm sacs, especially during diastole, imply poor
particle washout from the aneurysm sac and regions of stagnation in the center of the
circulation zones. In addition, poor hemodynamics towards the iliac arteries might impair
perfusion.
iditavrahami@gmail.com
https://t.me/med1917
Moshe Brand, Idit Avrahami, Asaph Nardi et al.
182
MANAGEMENT
The second section of the chapters presents the mechanical aspects of fenestrated
endografts (f-EVAR), which are an important advance in the treatment of AA. These stent
grafts are modifications of existing endografts with side branches providing critical end-organ
blood supply to the abdominal viscera (kidneys, bowel and liver). The mechanical
hemodynamic forces and moments applied on the components of the f-EVAR by the pulsatile
flow might cause detachment or fracture of the components. Component separation is a
dreadful complication that might lead to treatment failure and death. In the second section an
analysis is presented demonstrating the stress developed in juxta-renal f-EVAR stent graft,
using a numerical simulation of a post-grafted endograft assembly. The analysis results
suggest that specific care should be given to the branching area, to avoid endograft
detachment, fracture or twists and to prevent interference of mal-positioned endografts to the
renal flow.
AORTIC ANEURYSMS – PROGNOSIS
Aortic aneurysm (AA) is a pathologic entity defined by a localized dilatation. The precise
amount of dilatation necessary for a definition of "aneurysm" is of some debate, but it is
generally held to require an increase of at least 50% compared to a "normal" baseline. Each
segment of the aorta is potentially susceptible for aneurysmal formation. The natural history,
pathogenesis, clinical complexity and management policy differs significantly between
aneurysms of different segments of the aorta. As such, the discussion is subdivided to
ascending / descending thoracic (a/d TAA); abdominal (AAA); and thoracoabdominal
(TAAA) aortic aneurysms.
An understanding of the management of patients with an aortic aneurysm requires
knowledge of the natural history of these disorders. Rupture of AA is the dreadful
consequence of this pathological entity. The majority of aneurysms never rupture, but when
they do, sudden death from exsanguination is usual unless surgery is performed immediately.
Acute AA rupture is one of the most dramatic emergencies in medicine, particularly because
it often masquerades as another problem. In the United States, ruptured AAA is estimated to
cause 4 to 5 percent of sudden deaths and is the thirteenth most common cause of death
(Approximately 15,000 deaths annually in the United States are attributed to AAA).
ANEURYSM HEMODYNAMICS
Another significant complication arises from AA hemodynamics which leads to
formation of thrombus emboli. The hemodynamics in AA is characterized by disturbed flow
and circulation zones. These flow characteristics are strongly related with blood platelet
activation and aggregation and thus with thromboembolism.
The flow in the healthy and aneurysmatic aorta is demonstrated herein using numerical
simulations. The simulations used computational fluid dynamics (CFD) methods to delineate
the time-dependent flow dynamics in the aortic arch and the abdominal aorta with and
https://t.me/med1917
Clinical, Hemodynamical and Mechanical Aspects
183
without aneurysm. The geometric models of the arch and of the abdominal aorta with and
without an aneurysm are shown in Figure 1 and Figure 2, respectively.
The flow and pressure fields in the lumen were calculated by numerically solving the
equations governing momentum and continuity in the fluid domain:
(1)
where p is static pressure, t is time, V is velocity vector,
and
are density and the dynamic
viscosity of blood, and g is vector of gravity. Blood was assumed homogenous,
incompressible (with density
= 1 gr/mL), and Newtonian (with viscosity
= 3.5 cP). The
flow was assumed laminar. Gravity of g=9.81 m/s
2
was employed. The boundary conditions
were set according to typical physiological conditions with pressure of 110/75
mmHg, heart
rate of 75 BPM, and average flowrate of 480 mL/min directed to each renal artery and
1920 mL/min directed to each iliac artery.
In the aortic arch case, time-dependent pressure was imposed at the outlet, time-
dependent inlet flow rates was imposed at the aortic root inlet, and time-dependent outlet flow
rates were imposed at the upper vessels, as described in Figure 1. In the abdominal aorta case,
time-dependent pressure was imposed at the inlet as described in Figure 2b and time-
dependent outlet flow rates were imposed at each outlet as shown in Figure 2c. The
commercial software ADINA (ADINA RandD Inc., MA) was used to solve the set of fluid
and structure equations using the finite-element scheme. The numerical meshes for the fluid
domains of the pre- and post-grafted aneurysm models consisted of about 800,000 tetrahedral
elements each. For each case, four cardiac cycles were calculated (0 sec<t<3.2 sec) with total
800 time steps per cycle. The results of the third cycle were fully periodic.
Figure 1. The geometric models of healthy (a) and aneurysmatic (b) aortic arch; (c) imposed inlet aortic
flow and outlet aortic pressure as a function of time.
gV
V
V
2
0
p
Dt
D
https://t.me/med1917
Moshe Brand, Idit Avrahami, Asaph Nardi et al.
184
Figure 2. (a) The geometric models of healthy and aneurysmatic abdominal aorta; (b) imposed inlet
aortic pressure as a function of time; (c) imposed outlet iliac and renal flow rates as a function of time.
Figure 3. Results for flow field as calculated for the healthy (top) and aneurysmatic (bottom) aortic
arch: (a) pressure distribution in the models during peak systole (t=1.9 sec) (in dyn/cm
2
), (b) velocity
vectors plot (in cm/s) during diastole (t=2.2 sec), and (c) traces of particles released in the ascending
aorta during end diastole (t=2.4 sec).
https://t.me/med1917
Clinical, Hemodynamical and Mechanical Aspects
185
Figure 4. Results for flow field as calculated for the healthy (top) and aneurysmatic (bottom) abdominal
aorta: (a) pressure distribution in the models during peak systole (t=1.9 sec) (in dyn/cm
2
), (b) velocity
vectors plot (in cm/s) during diastole (t=2.2 sec), and (c) traces of particles released in the ascending
aorta during end diastole (t=2.4 sec).
The resulted flow field as calculated for the fluid domains for healthy and aneurysmatic
aorta during the third calculated cycle (1.6 sec<t<2.4 sec) are shown in Figure 3 and Figure 4.
Figure 3a and Figure 4a show pressure distributions at peak systole (t=1.9 sec). Figure 3b and
Figure 4b show velocity vector plots during diastole (t=2.2 sec). Figure 3c and Figure 4c
show Lagrangian tracking pathlines of 300 particles released in the ascending aorta or aortic
root during end diastole (t=2.4 sec). In both cases the effect of aneurysm on blood flow is
clearly shown. For both aortic arch and abdominal aorta the aneurysm provokes a highly
disturbed flow and large recirculation regions (lower figures) in comparison to the cases
without aneurysms (upper figures). The disturbed flow observed in the aneurysm sacs,
especially during diastole, imply poor particle washout from the aneurysm sac and regions of
stagnation in the center of the circulation zones where activated platelets can accumulate
and aggregate to form thromboembolism. Moreover, the non-oriented flows in the aneurysms
https://t.me/med1917
Moshe Brand, Idit Avrahami, Asaph Nardi et al.
186
require flow particles to change their direction and velocity on their way downstream. For
example, in the case of abdominal aorta (Figure 4) this leads to losses as flow enters the iliac
arteries, and thus the pressure drop along the descending aorta during peak systole reaches
7.5 mmHg. In the healthy case without aneurysm, the flow field is significantly smoother and
particles are well directed towards the iliac arteries in a uniform flow profile, even at the last
stage of diastole. The pressure drop along the graft is less than 2.5 mmHg. This increase in
pressure drop in the aneurysmatic aorta may further intensify the risk for wall rupture. Poor
hemodynamics towards the iliac arteries might impair perfusion. This may explain the flow
reduction toward branched arteries in AAA patients [1, 2]. The aneurysm not only increases
pressure inside the aneurysm sac and deteriorates aortic flow, but also interferes with iliac
hemodynamics. These findings agree with previous studies that discussed aneurysm
hemodynamics [3-7].
MANAGEMENT
SURGERY VERSUS WATCHFUL WAITING Appropriate patient selection and
timing for aneurysm repair is based upon identifying individuals at the greatest risk of
aneurysm rupture. Once rupture occurs, emergency repair is indicated, but mortality is
extremely high due to rapid exsanguination. The established therapeutic approach to this
problem has been surgical replacement of the aneurysm with a prosthetic graft. Endovascular
aneurysm repair (EVAR) is an important advance in the treatment of AA which offers an
alternative to the conventional open surgery treatment. EVAR is performed by inserting
folded graft components through the lumen of a vessel, usually the common femoral artery.
Upon deployment, the endograft expands, contacting the aortic wall proximally and distally to
exclude the aortic aneurysm sac from aortic blood flow and pressure excess.
Recently, the development of fenestrated endografts (f-EVAR) has widened the
therapeutic options for complex AAAs. The development of fenestrated and branched
endografts has widened the therapeutic options for complex aortic aneurysms. These stent
grafts are modifications of existing endografts that have been studied for the treatment of
AAAs. Side branches are added to the main tubular component providing critical end-organ
blood supply to the abdominal viscera (kidneys, bowel and liver) during and after the
procedure. This modification allows covering of the visceral segment of the aorta with the
main body, thus providing a good seal zone proximally and distally to the aneurysm.
The flow to the visceral segment is uninterrupted during the procedure thus avoiding
ischemic time to the viscera. The current technology that stands as the basis of f-EVAR
mandates a sequential buildup of a three-dimensional geometrical intra-luminal structure. As
this structure has to be built from a remote location, it is done by using the telescoping
technique relying on a proper overlap between the different components. Contemporary
articles about the use of branched and fenestrated EVAR technology in clinical practice report
encouraging results with relatively low acute mortality rate and lack of short-term branch
vessel loss [8, 9]. The mechanical hemodynamic forces and moments applied on the
components are poorly investigated. Repetitive stresses activated upon the endograft by the
pulsatile flow might cause detachment or fracture of the components. Component separation
is a dreadful complication that might lead to treatment failure and death.
https://t.me/med1917
Соседние файлы в папке Библиотека им академика М.И. Перельмана