Roberta Fusco1 and Antonella Petrillo1
1National Cancer Institute of Naples Pascale Foundation, Naples, Italy
Synopsis
Aim of this
study is to determine the diagnostic performance of MR imaging for the
assessment of tumor response after Short Course Radiotherapy (SCR) in patients with LARC using Standardized Index of Shape (SIS)
obtained by DCE-MRI and using ADC, DKI and IVIM derived parameters obtained by
DW-MRI.
We demostrated that SIS is a hopeful DCE-MRI angiogenic biomarker to
assess preoperative treatment response after SCR with delayed surgery and it permits
to discriminate pCR allowing to direct surgery for tailored and conservative
treatment.
Introduction
Short
Course Radiotherapy (SCR) is known to be a valuable therapeutic option in
patients with locally advanced rectal cancer (LARC). A recent meta-analysis [4] reported that SCR with immediate
surgery is as effective as long CRT with deferred surgery in terms of overall
and disease-free survival rates, local and distant control, and toxicity. Also,
SCR with Delayed Surgery (SCRDS) (after 4~8 weeks), optional therapy described
for patients with locally advanced tumours who are not fit for CRT, leads to
similar results in terms of negative margin resection percentage and
satisfactory results about the downstaging and pathological response rate
compared to traditional preoperative CRT [5-13].
The use of
new imaging modalities to make individual assessments of therapy response could
be of great clinical value to adjust subsequent strategies tailored for each
patient. Such strategies range from a tailored surgical approach, to administering
an adjuvant regimen, or even to a wait and see policy without surgery for
patients with high surgical risks [14-16].
Dynamic contrast-enhanced
MRI (DCE-MRI) has demonstrated promising to detect residual tumor after
pre-surgery CRT [17-21]. Previous studies have been investigated functional
parameters derived by DCE-MRI data in rectal cancer [18-21] such as the
Standardized Index of Shape (SIS) proposed by Petrillo et al [18] as a simple
semi-quantitative parameter capable to differentiate pathological significant
and complete response after CRT in LARC. Moreover, in various oncology fields,
researchers have recommended the use of diffusion-weighted imaging (DW-MRI) to
assess treatment response [22-29]. DW-MRI provides functional information on
the tissues microstructure by means of the evaluation of water proton mobility
differences [22-23]. By quantifying these differences by means of the
individual apparent diffusion coefficient (ADC) it’s possible to quantify
biological tumor changes and to monitor treatment response [24-25]. Moreover,
using a by-exponential model to analyse DWI-MRI, information both on diffusion
and perfusion tissue proprieties derived from Intravoxel Incoherent motion
method (IVIM) can be obtained: the pure tissue coefficient (Dt) that describe
water macroscopic motion in the cellular interstitial space, the
pseudo-diffusion coefficient (Dp) that describe blood microscopic motion in the
vessels and the perfusion fraction (fp) that describe the proportion of two
different motions [26-29]. Moreover, the conventional DWI model is based on the
assumption that water diffusion within a voxel has a single component and
follows a Gaussian behavior that water molecules diffuse without any
restriction. However, due to the presence of microstructures (i.e., two tissue
types or components within one voxel, and organelles and cell membranes),
random motion or diffusion of thermally agitated water molecules within
biologic tissues exhibits a non-Gaussian phenomena. Jensen and co-workers in
2005 proposed a non-Gaussian diffusion model called as diffusion kurtosis
imaging [30]. This model calculates the kurtosis coefficient (K) that signifies
the deviation of tissue diffusion from a Gaussian model, and the diffusion
coefficient (D) with the correction of non-Gaussian bias. DKI performed better
than conventional ADC in tumor detecting and grading.
Aim of this
study is to determine the diagnostic performance of MR imaging for the
assessment of tumor response after SCRDS in patients with LARC using SIS by DCE-MRI and using ADC, DKI and IVIM derived parameters by
DW-MRI.
Methods
35 patients
with LARC underwent MR scan before and after SCR followed by delayed surgery, retrospectively,
were enrolled. SIS, ADC, K, D, Dt, Dp, fp were extracted by MRI for each
patient before and after SCR. Tumor regression grade (TRG) were estimated.
The parameters of conventional DWI (ADC), of IVIM (fp, Dt,
Dp) and of DKI (MK and MD) were obtained from the multi-b DWI data with all
measured b values using the prototype post-processing software Body Diffusion
Toolbox (Siemens Healthcare GmbH, Erlangen, Germany).
Receiver
operating characteristic curve (ROC), linear classification were performed using the
Statistic Toolbox of Matlab R2007a (The Math-Works
Inc., Natick, MA).
Results
Sixteen
patients were classified as responders (TRG≤2) and 19 as non-responders. Seven
patients had TRG1 (pathological complete response pCR). The best parameter to
discriminate responders by non-responders was SIS (sensitivity 94%, specificity
84%, accuracy 89%, cut-off value=-7.8%). For DWI derived parameters the best results
to discriminate responders versus non-responders were obtained with Dp (sensitivity
75%, specificity 74%, accuracy 74%, cut-off value= 25.59%). SIS obtained the best diagnostic performance
also to discriminate pCR (sensitivity 86%, specificity 89%, accuracy 89%, cut-off
value=68.2%).
For DWI derived parameters the best results to detect complete
pathological responders were obtained with MK (sensitivity 100%, specificity 100%,
accuracy 783%, cut-off value= 2.19%). These
results were comparable with those of SIS.Discussion and Conclusion
SIS is a hopeful DCE-MRI
angiogenic biomarker to assess preoperative treatment response after SCR with
delayed surgery and it permits to discriminate pCR allowing to direct surgery
for tailored and conservative treatment.
Acknowledgements
Acknowledgement at Robert Grimm (Robert.grimm@siemens.com)
and Berthold Kiefer (Berthold.kiefer@siemens.com)
for development of the MR Body Diffusion Toolbox, a post-processing software to
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