Aims and objectives
Colorectal cancer yields significant morbidity and mortality and it is often associated to metastatic diseases,
commonly localized in liver parenchyma.
As a general tendency,
half of all patients with colorectal cancer will develop liver metastases [1].
Malignant changes of tissues are known to cause vascular reshaping and neoangiogenic processes occurring to ensure the transport of nutrients and oxygen and favour tumour growth [2].
Therefore,
the ability to early detect regional alterations in organs’ hemodynamics is one of the major challenges of standard medical imaging techniques,...
Methods and materials
Patients and CTp protocol
This study involved 46 patients enrolled in one Centre of the PIXEL project,
one of the widest European study aiming at identifying perfusion parameters (with a special focus on HPI) able to predict the development of liver metastases at colorectal cancer diagnosis.
Six patients developed liver metastases within the subsequent three-years.
All patients underwent an axial liver CTp study during which they were asked to breath slowly in order to reduce motion artefacts.
Contemporaneously with the beginning of CTp acquisitions,
an...
Results
4 out of 276 couples of features have been selected because they showed the best performance in discriminating the group of patients (i.e.,
the highest number) who will develop liver metastases (hereafter,
mets). Moreover,
all the couples selected refer to one feature,
the skewn-HPI (range [-0.5÷0.6]),
as shown in Fig.
7 that,
when coupled with mean values of BF [118÷285]ml/min/100g (Fig.
1(a)),
BV [63÷66]ml/min/100g (Fig.
1(b)),
MTT [16÷27]s (Fig.
1(c)),
TTP [30÷53]s (Fig.
1(d)) allows detecting four (ID-39,
44,
53,
87) out of six patients...
Conclusion
This work employed 6 perfusion parameters and 4 statistical descriptors to compute 24 features,
to achieve 276 different couples analysed in order to select features permitting a linear separation into the bidimensional features space.
This feature selection analysis allowed selecting HPI as a promising feature to be applied in future steps of classification since its showed high performance in discriminating patients who will develop liver metastases,
with maximum specificity.
Results emphasized the potentiality of HPI in characterizing early vascular changes of livers,
which further developed...
Personal information
Margherita Mottola,
MS
- Department of Electrical,
Electronic and Information Engineering "Guglielmo
Marconi" (DEI),
University of Bologna,
Bologna,
Italy
- Advanced Research Center on Electronic Systems for Information and Communication Technologies "Ercole De Castro" (ARCES),
University of Bologna,
Bologna,
Italy
[email protected]
Prof.
Alessandro Bevilacqua,
MS,
PhD
- Department of Computer Science and Engineering (DISI),
University of Bologna,
Bologna,
Italy
- Advanced Research Center on Electronic Systems for Information and Communication Technologies "Ercole De Castro" (ARCES),
University of Bologna,
Bologna,
Italy
[email protected]
Prof.
Valérie Vilgrain,
MD...
References
Van Clamp L.,
Deak P.,
Haspeslagh M.,
Coenegrachts K.,
A prospective clinical study using a dynamic contrast-enhanced CT-protocol for detection of colorectal liver metastases, Eur.
J.
Radiol.
107,
143-148 (2018)
Tylcz J.-B.,
El Alaoui-Lasmaili K.,
Djermoune E.-H.,
Thomas N.,
Faivre B.,
Bastogne T.,
Data-driven modeling and characterization of antiangiogenic molecule effects on tumoral vascular density,
Biomed.
Signal Process.
Control,
20,
52–60 July (2015)
Coolens C.,
Mohseni H.,
Dhodi S.,
Ma S.,
Keller H.,
Jaffray D.A.,
Quantification accuracy for dynamic contrast enhanced (DCE) CT imaging: phantom...