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Gastric cancer (GC) is the fifth most common cancer worldwide and the leading cause of cancer-related mortality [
As an emerging functional imaging technique, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can provide pharmacokinetic models that enable the quantification of contrast agent exchange between the intravascular and interstitial spaces and the assessment of the functional features of a target tissue [
Histogram analysis not only calculates the average value of histogram parameters for the whole tumor but also publishes each voxel of the region of interest (ROI) to the histogram [
The Institutional Review Board of Shaoxing People’s Hospital approved this retrospective study and waived the requirement for written informed consent to review the medical records and images of patients, because of the retrospective nature of this study.
The imaging data of 80 patients with AGC admitted to Shaoxing People’s Hospital between February 2017 and May 2021 were enrolled in this study and analyzed retrospectively. The inclusion criteria were as follows: (1) GC confirmed by gastroscopic biopsy or postoperative pathology; (2) no absolute contraindication of magnetic resonance imaging (MRI); and (3) no anti-tumor treatment before DCE-MRI examination. In contrast, the exclusion criteria were as follows: (1) severe motion artifact contaminations in the DCE-MRI results; (2) a maximum tumor diameter of <1 cm; and (3) surgical or puncture taboos, such as coagulation dysfunction.
MRI studies were conducted using a 3.0T MRI scanner (Verio, Siemens, Germany) with a standard, 12-channel phased-array body coil. The preparation before scanning was as follows: (1) all patients were required to fast for 6–8 h before DCE-MRI to empty the gastrointestinal tract; (2) patients had to drink 800–1,000 ml of water to distend the stomach before MRI; and (3) anisodamine (Minsheng, Hangzhou, China) was intramuscularly administered to prevent gastrointestinal motility.
During the examination, the patient was in the supine position, where the scanning range included the whole stomach. All patients had to undergo a routine plain scan [T1-weighted image (T1WI), T2WI], followed by the DCE-MRI scan. DCE-MRI adopts free-breathing and is performed using a three-dimensional, radial volumetric interpolated, breath-hold examination technique. Multi-angle cross-sectional T1WI in the axial plane scan was initially performed with the following parameters: repetition time/echo time: 3.25 ms/1.17 ms, field of view: 350 × 284 mm, matrix: 288 × 164, layer thickness: 5 mm, scan at different flip angles (5°, 10°, and 15°) for a period of 6.5 s each, totaling time: 19.5 s. Thereafter, multi-phase dynamic enhanced scanning was performed with the following parameters: flip angle: 10°, number of phases scanned: 35, totaling time: 227.5 s; the remaining parameters were the same as above. Subsequently, a gadolinium contrast agent (Omniscan, GE Healthcare, China) was injected through the median elbow vein using the high-pressure injector in phase 3. The injection dose and speed were 0.1 mmol/kg and 3.5 ml/s, respectively. Finally, 20 ml of saline was injected at the same flow rate for flushing.
The original DCE data was transferred to the Omni Kinetics (GE Healthcare, China) software. First, in order to correct the ROI displacement caused by patients' breathing and other involuntary movements, the DCE images were pre-processed with three-dimensional non-rigid registration. Second, multi-flip angles of 5°, 10°, and 15° and corrected dynamic enhancement sequence scans were processed by the Omni Kinetics software. Further, the arterial input function was performed, and abdominal aorta was selected as the input artery. Third, application of the Tofts model obtained four quantitative parameter maps (Ktrans, Kep, Ve, and Vp) [
Different imaging modalities and histogram of quantitative perfusion parameters of a 40-year-old male with advanced gastric cancer.
All GC specimens obtained via surgery or gastroscopy were embedded in paraffin and then cut into 2‐μm‐thick slices. First, antigen retrieval was performed after dewaxing and dehydrating the sections, followed by inhibiting endogenous peroxidase activity using 3% H2O2 solution at 37 °C for 10 min (H36021594, Nanchang Baiyun Pharmaceutical Co., Ltd., Nanchang, China). Second, the histological slices were stained using the rabbit anti-human VEGF (GT217002, Gene Tech, Shanghai, China) or EGFR (GT209302, Gene Tech, Shanghai, China) and stored in the special refrigerator at 4 °C overnight. Third, all slices were stained with secondary antibody (K5007, Dako, Beijing, China) and then incubated at 37 °C for 30 min. Fourth, DBA staining, rinsing, counterstaining, dehydration, transparency, and mounting were sequentially carried out. Lastly, two senior pathologists independently analyzed all slides and explained the staining results using a microscope, and the staining was explained according to the staining intensity and the percentage of positive cells in the tumor. The scoring method of staining intensity was as follows: zero point: no coloration, one point: light brown, two points: brown, and three points: dark brown. The scoring method of positive tumor cell percentage was as follows: zero points: no positive tumor cell, one point: percentage of positive tumor cells is <10%, two points: percentage of positive tumor cells is 10–50%, three points: percentage of positive tumor cells is 50–80%, and four points: percentage of positive tumor cells is >80%. Finally, the above two scores were multiplied to obtain the immunoreactive score (IRS) [
Tumor tissue samples were examined by two experienced pathologists who were blinded to patients’ information and samples were classified according to Lauren classification as follows [
The normality assumption of the above data was assessed using the Kolmogorov–Smirnov test. Continuous variables were presented as mean and standard deviation, and the Mann–Whitney U test and independent sample t-test were used when appropriate. Categorical variables were presented as frequency (%), and the Fisher’s exact test or chi-squared test were used when appropriate. Spearman correlation analysis was used to identify the relationship between VEGF, EGFR, and DCE-MRI perfusion histogram parameters. Statistical analysis and figure creation for the present study was performed using the R software (version 40.2, R Foundation for Statistical Computing, Vienna, Austria), and
The demographic characteristics of the two groups of patients with different Lauren classifications of AGC are summarized in
Clinical characteristics of patients with advanced gastric cancers.
Characteristics | Intestinal type (n = 45) | Diffuse type (n = 35) | F value |
|
---|---|---|---|---|
Gender | 2.075 | 0.150 | ||
Male | 36 (80.0%) | 23 (65.7%) | ||
Female | 9 (20.0%) | 12 (34.3%) | ||
Age (years, x ± s) | 69.53 ± 9.40 | 65.57 ± 11.63 | 2.040 | 0.096 |
Age range | 54–88 | 49–85 | ||
Location | 0.047 | 0.997 | ||
Cardia | 8 (17.8%%) | 6 (17.1%) | ||
Body | 28 (62.2%) | 22 (62.9%) | ||
Antrum | 6 (13.3%) | 5 (12.3%) | ||
Whole | 3 (6.7%) | 2 (5.7%) | ||
BMI (Kg/m2) | 22.41 ± 2.98 | 21.33 ± 5.89 | -0.517 | 0.607 |
Differentiation level | 28.821 | <0.001 | ||
High | 10 (22.2%) | 1 (2.9%) | ||
Moderate | 26 (57.8%) | 6 (17.1%) | ||
Poor | 9 (20.0%) | 28 (80.0%) | ||
CEA (ng/ml) | 1.512 | 0.219 | ||
Normal | 29 (64.4%) | 27 (77.1%) | ||
Elevated | 16 (35.6%) | 8 (22.9%) | ||
CA199 (µ/ml) | 0.369 | 0.544 | ||
Normal | 36 (80.0%) | 26 (74.3%) | ||
Elevated | 9 (20.0%) | 9 (25.7%) |
Of the aforementioned DCE-MRI perfusion histogram parameters, only Ve (Uniformity) was positively correlated with the differentiation levels (r = 0.273,
The correlation scatterplot between various differentiation levels and Ve (Uniformity) in advanced gastric cancer (AGC).
VEGF and EGFR expressions were identified by IHC (
Immunohistochemistry (IHC) images.
Heatmaps of the correlation between VEGF and EGFR expression.
Differences in vascular endothelial growth factor (VEGF) and epidermal growth factor receptor (EGFR) between the two types of advanced gastric cancer (AGC). The expression of VEGF in diffuse-type AGC was higher than that in intestinal-type AGC (
For VEGF, ROC curve analysis revealed that Quantile 90 of Ktrans (0.498), Meanvalue of Kep (0.448) and Quantile 50 of Ve (0.696) provided the perfect combination of sensitivity (0.721, 0.907, 0.539), specificity (0.919, 0.486, 0.838), positive predictive value [PPV] (0.912, 0.672, 0.700) and negative predictive value [NPV] (0.739, 0.818, 0.700) for distinguishing high VEGF expression from low expression in AGC (
ROC curve analysis of histogram parameters for differentiating VEGF high expression from low expression in AGC.
Kinetic parameter | Histogram metrics | Cut-off | Sensitivity | Specificity | Accuracy | PPV | NPV | AUC |
|
---|---|---|---|---|---|---|---|---|---|
Ktrans | Meanvalue | 0.549 | 0.697 | 0.973 | 0.825 | 0.968 | 0.735 | 0.880 | <0.001 |
Quantile 10 | 0.566 | 0.535 | 0.946 | 0.725 | 0.920 | 0.636 | 0.746 | <0.001 | |
Quantile 25 | 0.559 | 0.605 | 0.919 | 0.75 | 0.897 | 0.667 | 0.819 | <0.001 | |
Quantile 50 | 0.593 | 0.628 | 0.973 | 0.788 | 0.964 | 0.692 | 0.859 | <0.001 | |
Quantile 75 | 0.536 | 0.698 | 0.946 | 0.813 | 0.938 | 0.729 | 0.876 | <0.001 | |
Quantile 90 | 0.498 | 0.721 | 0.919 | 0.813 | 0.912 | 0.739 | 0.895 | <0.001 | |
Kep | Meanvalue | 0.448 | 0.907 | 0.486 | 0.713 | 0.672 | 0.818 | 0.695 | 0.003 |
Quantile 10 | 0.528 | 0.535 | 0.730 | 0.625 | 0.697 | 0.574 | 0.654 | 0.018 | |
Quantile 25 | 0.484 | 0.837 | 0.459 | 0.663 | 0.643 | 0.708 | 0.671 | 0.009 | |
Quantile 50 | 0.470 | 0.837 | 0.514 | 0.688 | 0.667 | 0.731 | 0.688 | 0.003 | |
Quantile 75 | 0.452 | 0.930 | 0.459 | 0.713 | 0.667 | 0.85 | 0.685 | 0.005 | |
Quantile 90 | 0.427 | 0.953 | 0.405 | 0.700 | 0.651 | 0.882 | 0.691 | 0.003 | |
Ve | Quantile 10 | 0.489 | 0.558 | 0.784 | 0.663 | 0.750 | 0.604 | 0.692 | 0.003 |
Quantile 25 | 0.557 | 0.465 | 0.838 | 0.638 | 0.769 | 0.574 | 0.687 | 0.004 | |
Quantile 50 | 0.696 | 0.539 | 0.838 | 0.675 | 0.793 | 0.608 | 0.700 | 0.003 | |
Quantile 75 | 0.540 | 0.465 | 0.869 | 0.650 | 0.800 | 0.582 | 0.673 | 0.008 |
Graphs showing the ROC curves of DCE-MRI perfusion histogram parameters for differentiating high and low
ROC curve analysis of histogram parameters for differentiating EGFR high expression from low expression in AGC.
Kinetic parameter | Histogram metrics | Cut-off | Sensitivity | Specificity | Accuracy | PPV | NPV | AUC |
|
---|---|---|---|---|---|---|---|---|---|
Ktrans | Skewness | 0.579 | 0.864 | 0.639 | 0.763 | 0.745 | 0.793 | 0.715 | 0.001 |
Kurtosis | 0.590 | 0.886 | 0.583 | 0.750 | 0.722 | 0.808 | 0.706 | 0.002 | |
Energy | 0.596 | 0.795 | 0.583 | 0.700 | 0.700 | 0.700 | 0.700 | 0.002 | |
Entropy | 0.581 | 0.750 | 0.583 | 0.675 | 0.688 | 0.656 | 0.686 | 0.004 | |
Kep | Meanvalue | 0.522 | 0.773 | 0.639 | 0.713 | 0.723 | 0.697 | 0.682 | 0.005 |
Skewness | 0.602 | 0.818 | 0.722 | 0.775 | 0.783 | 0.767 | 0.777 | <0.001 | |
Kurtosis | 0.624 | 0.840 | 0.694 | 0.775 | 0.771 | 0.781 | 0.778 | <0.001 | |
Energy | 0.629 | 0.864 | 0.750 | 0.813 | 0.809 | 0.818 | 0.836 | <0.001 | |
Entropy | 0.554 | 0.886 | 0.750 | 0.825 | 0.813 | 0.844 | 0.834 | <0.001 | |
Quantile 10 | 0.514 | 0.795 | 0.528 | 0.675 | 0.673 | 0.679 | 0.654 | 0.018 | |
Quantile 25 | 0.527 | 0.773 | 0.611 | 0.700 | 0.708 | 0.688 | 0.696 | 0.003 | |
Quantile 50 | 0.526 | 0.773 | 0.611 | 0.700 | 0.708 | 0.688 | 0.694 | 0.003 | |
Quantile 75 | 0.511 | 0.773 | 0.611 | 0.700 | 0.708 | 0.688 | 0.696 | 0.003 | |
Quantile 90 | 0.513 | 0.750 | 0.611 | 0.688 | 0.702 | 0.667 | 0.667 | 0.001 | |
Vp | Uniformity | 0.533 | 0.795 | 0.556 | 0.688 | 0.686 | 0.690 | 0.648 | 0.023 |
Energy | 0.507 | 0.795 | 0.556 | 0.688 | 0.686 | 0.690 | 0.650 | 0.021 | |
Entropy | 0.578 | 0.750 | 0.611 | 0.688 | 0.702 | 0.667 | 0.660 | 0.014 |
Several perfusion histogram parameters that are significantly correlated to the expression of VEGF and EGFR are summarized in
Association between dynamic contrast-enhanced magnetic resonance imaging perfusion histogram parameters and vascular endothelial growth factor expression in different Lauren classifications of advanced gastric cancer.
Classification | Kinetic parameter | Histogram metrics | R value |
|
---|---|---|---|---|
Intestinal type | Ktrans | Meanvalue | 0.829 | <0.001 |
Quantile 50 | 0.788 | <0.001 | ||
Quantile 75 | 0.834 | <0.001 | ||
Quantile 90 | 0.854 | <0.001 | ||
Kep | Entropy | 0.325 | 0.029 | |
Ve | Entropy | 0.361 | 0.015 | |
Quantile 10 | 0.513 | <0.001 | ||
Quantile 25 | 0.527 | <0.001 | ||
Quantile 50 | 0.513 | <0.001 | ||
Quantile 75 | 0.456 | 0.002 | ||
Quantile 90 | 0.349 | 0.019 | ||
Vp | Skewness | 0.469 | <0.001 | |
Kurtosis | 0.310 | 0.038 | ||
Energy | 0.522 | <0.001 | ||
Diffuse type | Ktrans | Meanvalue | 0.635 | <0.001 |
Quantile 25 | 0.472 | 0.004 | ||
Quantile 50 | 0.604 | <0.001 | ||
Quantile 75 | 0.597 | <0.001 | ||
Quantile 90 | 0.599 | <0.001 | ||
Kep | Skewness | -0.422 | 0.012 | |
Vp | Quantile 10 | 0.430 | 0.010 | |
Quantile 25 | 0.420 | 0.012 |
Association between dynamic contrast-enhanced magnetic resonance imaging perfusion histogram parameters and epidermal growth factor receptor expression in different Lauren classifications of advanced gastric cancer.
Classification | Kinetic parameter | Histogram metrics | R value |
|
---|---|---|---|---|
Intestinal type | Ktrans | Meanvalue | 0.315 | 0.035 |
Entropy | 0.391 | 0.008 | ||
Quantile 25 | 0.326 | 0.029 | ||
Quantile 50 | 0.336 | 0.024 | ||
Kep | Meanvalue | 0.392 | 0.008 | |
Uniformity | 0.351 | 0.018 | ||
Entropy | 0.627 | <0.001 | ||
Quantile 50 | 0.442 | 0.002 | ||
Quantile 75 | 0.458 | 0.002 | ||
Vp | Skewness | 0.408 | 0.005 | |
Energy | 0.379 | 0.010 | ||
Diffuse type | Ktrans | Entropy | 0.458 | 0.006 |
Quantile 50 | 0.341 | 0.045 | ||
Kep | Meanvalue | 0.344 | 0.043 | |
Uniformity | 0.466 | 0.005 | ||
Entropy | 0.656 | <0.001 | ||
Quantile 25 | 0.415 | 0.013 | ||
Quantile 50 | 0.406 | 0.016 |
The correlation scatterplot between vascular endothelial growth factor (VEGF), epidermal growth factor receptor (EGFR), and dynamic contrast-enhanced magnetic resonance imaging perfusion histogram parameters of advanced gastric cancer (AGC) in different Lauren classifications.
Currently, the commonly used classification systems for AGC outside Japan include the World Health Organization (WHO) classification 2019 and Lauren classification. The WHO classification, which classifies gastric adenocarcinoma into many subtypes, such as tubular, parietal cell, micropapillary, mucinous, poorly cohesive, signet ring cell, hepatoid, mucoepidermoid, medullary carcinoma with lymphoid stroma, and Paneth cell type, has been criticized for its complexity [
DCE-MRI, a promising functional imaging technique in oncology, allows for quantitative assessment of functional aspects of tumor microcirculation, such as tumor blood flow, vessel permeability, and vascular and interstitial volumes [
This study found that DCE-MRI perfusion histogram parameters had good diagnostic performance in identifying high and low VEGF expressions. ROC curve analysis revealed that Quantile 90 of Ktrans (0.498), Meanvalue of Kep (0.448), and Quantile 50 of Ve (0.696) provided the perfect combination of sensitivity (0.721, 0.907, 0.539), specificity (0.919, 0.486, 0.838), PPV (0.912, 0.672, 0.700), and NPV (0.739, 0.818, 0.700) for distinguishing high and low VEGF expressions in AGC (
EGFR is a type of membrane tyrosine kinase receptor that is overexpressed in 30–60% of GCs, and it initiates an intracellular signal pathway that promotes cancer cell proliferation, cell migration, and angiogenesis [
According to these results, there was a specific association between the histogram parameters derived from Ve and Vp and VEGF and EGFR expressions. Ve represents the volume of EES per unit volume of tissue, and Vp predominantly reflects the percentage of contrast agents in blood [
Overall, VEGF and EGFR play essential roles in tumor growth, invasion, metastasis, and angiogenesis. Preoperatively assessing the expression of VEGF and EGFR has an essential influence on the identification of high-risk patients and the choice of treatment strategy. For example, if high-expression (VEGF/EGFR) was indicated for a given patient, appropriate targeted therapies could be applied. This study preliminarily demonstrated the potential of DCE-MRI in diagnosing the expressions of VEGF and EGFR and estimating their difference in different Lauren classifications of AGC. In the future, we plan to include more patients (based on existing studies) and explore the predictive models based on DCE-MRI to predict VEGF and EGFR expression in AGC.
Several limitations of this study should be kept in mind when interpreting these data. First, this was a retrospective study conducted by a single agency. Second, the number of patients enrolled in this study was relatively small. Last, the spatial distribution of perfusion parameters was uneven; thus, it was difficult for samples to accurately match the corresponding ROI. Evidently, prospective and large-scale multi-center studies are required to overcome these limitations and confirm the preliminary results in the future.
This study showed that a certain correlation exists between the quantitative perfusion histogram parameters derived from DCE-MRI and VEGF and EGFR expressions in AGC. DCE-MRI quantitative perfusion histogram parameters can serve as imaging biomarkers to noninvasively reflect VEGF and EGFR expressions and estimate their difference in different Lauren classifications of AGC, which provides a reference for early diagnosis and individual treatment of AGC patients.
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.
The studies involving human participants were reviewed and approved by Ethics Committee of Shaoxing People’s Hospital; Shaoxing People’s Hospital, Shaoxing, China. The ethics committee waived the requirement of written informed consent for participation. Written informed consent was not obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
ZXL and ZZ designed the study and helped to revise the manuscript. ZHL was involved in study design, analyzed and interpreted the patient regarding advanced gastric cancer, performed some image processing, and was the main writer of the manuscript. CW, HM, DW, and FT collected the clinical data. CW, YY, and FL performed immunohistochemistry. All authors read and approved the final manuscript.
This work was supported by Public Welfare Technology Application Research Project of Zhejiang Province (Grant number: LGF19H220002), Medical and Health Science and Technology Plan Project of Zhejiang Province (Grant Number: 2021KY1140), Medical and Health Science and Technology Platform Project of Zhejiang Province (Grant Number: 2018ZD047), Medical and Health Science and Technology Platform Project of Zhejiang Province (Grant Number: 2021KY1150), Medical and Health Science and Technology Plan Project of Zhejiang Province (Grant Number: 2020KY977), and Key Laboratory of Functional Molecular Imaging of Tumor (Shaoxing People’s Hospital, Shaoxing, Zhejiang, China). The funding departments had no role in the collection, analysis, or interpretation of the data, or in the decision to submit the manuscript for publication.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We sincerely appreciate the financial and technical support from Key Laboratory of Functional Molecular Imaging of Tumor (Shaoxing People’s Hospital, Shaoxing, Zhejiang, China) for this project.