Muge Karaman1, Lei Tang2, Ziyu Li3, Yu Sun4, Jia Fu Ji3, and Xiaohong Joe Zhou1,5
1Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States, 2Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China, 3Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing, China, 4Department of Pathology, Peking University Cancer Hospital and Institute, Beijing, China, 5Departments of Radiology, Neurosurgery, and Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
Synopsis
Gastric cancer (GC) is the
second most common cause of cancer-related mortality globally. Histological assessment
of GC has been based on Lauren classification which categorizes the tumor according
to its morphological features. In respond to the need for probing biological
tissue complexity, a growing number of diffusion-weighted imaging studies have focused
on revealing tissue microstructures by measuring non-Gaussian diffusion
behaviors. One of these techniques is the fractional order calculus (FROC)
model. In this study, we have used the FROC model to investigate non-invasive
imaging-based assessment of GC, providing an alternative to
histopathology-based Lauren classification.
Introduction
As the second leading
cause of cancer-associated mortality1, gastric cancer (GC) is one of
the most common types of cancer worldwide. Lauren classification is commonly employed
to divide GC into intestinal type (IT), diffuse type (DT), and mixed type (MT; combination
of IT and DT), each with distinct molecular and clinical characteristics2.
The sensitivity of GC to different treatment strategies, such as neoadjuvant
chemotherapy and pre-/post-operative chemoradiotherapy, varies according to Lauren
classification3,4. Thus, Lauren classification is important for
tailoring individualized therapy as well as following treatment response. Lauren
classification involves invasive biopsy procedures that are susceptible to
sampling errors. With its sensitivity to
tissue structures in vivo, diffusion-weighted
imaging (DWI) may provide information similar to Lauren classification
noninvasively while avoiding sampling errors. In this study, we employ a
non-Gaussian DWI technique with a fractional order calculus (FROC) model5-10
to investigate the possible correlations between the FROC model parameters and Lauren
classification in a group of GC patients. Methods
Patients: The study enrolled 28 patients with confirmed GC
who underwent chemotherapy followed by gastrectomy. Surgical specimens were
obtained for histopathological analysis to determine Lauren classification,
resulting in 7 patients with DT, 12 with IT, and 9 with MT. For the analysis,
the DT and MT were combined as mixed-and-diffuse type (MDT) and compared with
the IT. Image Acquisition: All patients were scanned on a 1.5T Siemens Aera
scanner. The MRI protocol included T1-weighted (VIBE), T2-weighted (turbo spin
echo with respiratory-trigger), and DWI with 11 b-values (0 to 2000 s/mm2) with the following parameters: TR/TE=4400/62ms, slice thickness=5mm,
FOV=28.5cm×38cm, and reconstruction matrix=128×96. Trace-weighted images were
obtained to minimize the effect of diffusion anisotropy. DWI Analysis:
The multi-b-value diffusion images
were analyzed with the FROC model5-7,$$S/S_0=\exp\left[-D\mu^{2(\beta-1)}\left(\gamma G_d\delta\right)^{2\beta}\left(Δ-\frac{2\beta-1}{2\beta+1}\right)\delta\right], (1) $$ which yields a set of
diffusion parameters: an anomalous diffusion coefficient D (in μm2/ms), a spatial parameter μ (in μm), and a spatial fractional order measure β (dimensionless) that has been linked
to intra-voxel tissue heterogeneity8-10. In Eq. (1), Gd is gradient amplitude, δ (51ms)
is pulse width, and Δ (100.6ms) is lobe separation. A least squares algorithm
was used for nonlinear fitting to obtain the three model parameters. For
comparison, a conventional mono-exponential model was also used to compute ADC
by using images from two b-values of 50 and 800 s/mm2. Statistical
Analysis: For each patient, the regions-of-interest (ROIs) were drawn on b=0 images guided by other anatomic images.
For group analysis, the test parameters for each individual patient were
computed as the mean value from the selected ROI for β and μ, and from the lower
10% of the D or ADC histogram to improve
robustness against contamination from the body fluid whose water diffusion is drastically
faster. A Mann-Whitney U test was used for the group comparison, followed by a
receiver operating characteristic (ROC) analysis to assess the performance of the
FROC model with its multiple parameters for GC classification in comparison
with ADC.Results
Figure 1a shows ADC,
D,
β,
and
μ maps from a representative
patient in each of the DT, MT, and IT groups. The DT and MT lesions showed
lower mean ADC,
D, and
β, and higher
μ than the IT lesions (Fig. 1b). The differences in the diffusion parameters
were substantiated in the group analysis, as illustrated by the box-and-whisker
plots (Fig. 2a) and descriptive statistics (Fig. 2b) where statistical
significance (
p-values<0.05) was achieved.
Figure 3 summarizes the ROC results of using different combinations of the FROC
parameters as well as ADC for imaging-based classification of MDT and IT. The (
D,
β,
μ) combination showed the best overall
performance, yielding the highest accuracy (0.862) and area-under-the-curve
(AUC, 0.911) while providing a high sensitivity (0.882) and specificity (0.833).
Overall, the FROC parameters outperformed ADC.
Discussion and Conclusion
Our results showed that the
FROC model, which offers additional parameters that are sensitive to tissue microstructures
(β and μ), performed better than ADC for imaging-based assessment of Lauren
classification in GC. Using an automated “filter” based on the lower 10% of diffusion
parameters within an ROI, our approach not only provides robustness against
contamination from body fluid, but also is consistent with the histopathology
practice where worse-case scenario in typically used to make a diagnosis11.
The novel approach and encouraging results may lead to a non-invasive
imaging-based method for obtaining the similar information to Lauren
classification for characterizing GC. With further validation, the non-invasive
approach described herein is expected to enable repeated evaluations of GC at various
time points throughout the disease progression and regression.Acknowledgements
This work was supported in part by NIH 1S10RR028898
and grants from National Natural Science Foundation of China No. 81171308 and No.
81401389. MK and LT contributed equally to this work.References
- Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA Cancer J Clin. 2011;61:6990.
- Lauren P. The two histological main types of gastric carcinoma: Diffuse and so-called intestinal-type carcinoma. An attempt at a histo-clinical classification. Acta Pathol Microbiol Scand. 1965;64:3149.
- Ma J, Shen H, Kapesa L, et al. Lauren classification and individualized chemotherapy in gastric cancer. Oncology Letters. 2016;11(5):2959-2964.
- Jackson C, Mochlinski K, Cunningham D. Therapeutic options in gastric cancer: neoadjuvant chemotherapy vs postoperative chemoradiotherapy. Oncology (Williston Park) 2007;21:1084–1087.
- Magin RL, Abdullah O, Baleanu D, et al. Anomalous diffusion expressed through fractional order differential operators in the Bloch-Torrey equation. J Magn Reson Im. 2008;190:255–270.
- Zhou XJ, Gao Q, Abdullah O, Magin RL. Studies of anomalous diffusion in the human brain using fractional order calculus. Magn Reson Med. 2010;63:562–569.
- Ingo C, Magin RL, Colon-Perez L, et al. On random walks and entropy in diffusion-weighted magnetic resonance imaging studies of neural tissue. Magn Reson Med. 2014;71(2):617–27.
- Sui Y, He W, Damen FW, et al. Differentiation of low- and high- grade pediatric brain tumors with high b-value diffusion weighted MR imaging and a fractional order calculus model. Radiology. 2015;277(2):489–496.
- Tang L, Sui Y, Zhong Z, et al. Non-Gaussian diffusion imaging with a fractional order calculus model to predict response of gastrointestinal stromal tumor to second-line sunitinib therapy. Magn Reson Med. 2017.
- Sui Y, Xiong Y, Xie KL, et al. Differentiation of low- and high-grade gliomas using high b-value diffusion imaging with a non-Gaussian diffusion model. Am J Neuroradiol. 2016; 37(9):1643-1649.
- Just N. Improving tumour heterogeneity MRI assessment with histograms. British Journal of Cancer. 2014;111(12):2205-2213.