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MR Imaging Perfusion and Diffusion analysis to assess preoperative Short Course Radiotherapy response in locally advanced rectal cancer: Standardized Index of Shape by DCE-MRI and Intravoxel Incoherent Motion derived parameters by DW-MRI
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 calculate IVIM and Kurtosis maps

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Figures

ROC analysis results

Diagnostic performance analysis results

Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)
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