Non-invasive prediction of KRAS mutation in rectal cancer using hybrid intravoxel incoherent motion and diffusion kurtosis model

doi:10.21203/rs.3.rs-5328808/v1

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Research Article

Non-invasive prediction of KRAS mutation in rectal cancer using hybrid intravoxel incoherent motion and diffusion kurtosis model

https://doi.org/10.21203/rs.3.rs-5328808/v1

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Purpose: This study seeks to evaluate the efficacy of the hybrid intravoxel incoherent motion and diffusion kurtosis imaging (IVIM-DKI) model in predicting Sarcoma Viral Oncogene Homologue (KRAS) mutation status in rectal cancer patients.

Materials and Methods: Rectal cancer patients received hybrid IVIM-DKI MRI, surgery, and KRAS mutation status was assessed. The parameters derived from the hybrid IVIM-DKI model, including the apparent diffusion coefficient (ADC), true diffusion coefficient (D), diffusion kurtosis (K), perfusion fraction (f), and pseudo-diffusion coefficient (D*), were compared between the KRAS mutation group and wild-type group. The diagnostic performance was evaluated using the receiver operating characteristic (ROC) curve. The hybrid IVIM-DKI parameters and their association with clinicopathological features were also explored.

Results: In this prospective study, 73 patients (mean age, 66 ± 11 years) of 50 men and 23 women were included. Significant differences were observed between the KRAS mutation and wild-type groups for ADC, D, and K values (p < 0.05). The K value derived from the IVIM-DKI model demonstrated the highest area under the ROC curve (AUC = 0.779) in characterizing KRAS mutation status, with a sensitivity of 88.1% and specificity of 70.3%. The ADC value also showed satisfactory diagnostic performance (AUC = 0.702). Specific IVIM-DKI parameters, such as f and K, were associated with various clinicopathological features, suggesting their potential as imaging biomarkers.

Conclusion: The hybrid IVIM-DKI model, especially the K value, shows promise as a non-invasive tool for predicting KRAS mutation status in rectal cancer patients, potentially improving personalized treatment strategies.

KRAS mutation

Rectal cancer

Hybrid Intravoxel Incoherent Motion and Diffusion Kurtosis model

Non-invasive prediction

Colorectal cancer is a common malignant tumor, ranking third in both males and females and causes a significant public health concern [1]. Kirsten Rat Sarcoma Viral Oncogene Homologue (KRAS) mutation is one of the key factors in its pathogenesis. KRAS mutation can promote the growth of tumor cells, resist chemotherapy drugs, and increase the risk of recurrence [2, 3]. So the detection of KRAS mutation is of great significance. However, the current gold standard techniques for KRAS mutation detection, such as PCR-based methods and Sanger sequencing, often require invasive tissue biopsies [4].

Functional magnetic resonance imaging technology has been used to quantitatively evaluate tumor characteristics. It can noninvasively evaluate the tissue microenvironment and thus help in cancer characterization. The apparent diffusion coefficient (ADC) value extracted from diffusion weighted imaging (DWI), measures the restricted Brownian motion of water molecules. ADC has already been shown to have a high discrimination ability in the detection of rectal cancer [5]. Recent studies indicate that, in clinical evaluations of rectal cancer tumors, the bi-exponential model intravoxel incoherent motion (IVIM) and non-Gaussian model diffusion kurtosis imaging (DKI) have higher sensitivity and specificity than the mono-exponential model DWI [6, 7]. IVIM distinguishes between water molecular diffusion and microcirculation or perfusion by separating random molecular diffusion from the pseudo-random motion of blood flow in the capillaries [8]. Although standard diffusion MRI assumes that water diffusion in tissues follows a Gaussian distribution, DKI recognizes that this diffusion is often non-Gaussian in biological tissues due to structural heterogeneities. In the context of tumors, DKI can assess the heterogeneity of the tumor microstructure [9].

Because the IVIM and DKI models address different facets of diffusion, a hybrid IVIM-DKI model could provide a more precise characterization of diffusion in perfused biological tissues. Lu et al. integrated IVIM and DKI into a hybrid model, estimating diffusion and perfusion parameters from IVIM and kurtosis (K) parameters from DKI in 2012 [10]. Compared with the application of DWI, IVIM, or DKI models alone, this method enhances the model's ability to capture complex details of tumor microstructure [11]. KRAS mutations in tumors affect tumor proliferation and survival, and are closely related to the characteristics of the tumor microenvironment [12]. As advanced diffusion models, the hybrid IVIM and DKI models have great potential as non-invasive alternatives for KRAS mutation detection. It can comprehensively characterize the tumor microenvironment by quantifying tumor perfusion, diffusion, and microstructural complexity.

In this study, we intend to apply a hybrid IVIM-DKI model to predict the effectiveness of KRAS mutation status in patients with rectal cancer. Through this study, we intend to establish a non-invasive imaging biomarker of KRAS mutation status to guide clinical treatment decisions and evaluate patient outcomes.

Patients

This prospective study was approved by our hospital’s Institutional Review Board, and all participants had written informed consent. The inclusion and exclusion criteria are outlined in Fig. 1. This study included a cohort of 73 patients.

MRI Acquisition

MRI images were obtained using a 3.0T MRI scanner (MAGNETOM Skyra, Siemens Healthcare) equipped with an 18-channel body coil. The hybrid IVIM-DKI model sequence employed the echo planar imaging (EPI) technique with the following parameters: repetition time (TR) of 5700 ms, echo time (TE) of 76 ms, flip angle (FA) of 90°, field of view (FOV) of 200 mm × 200 mm, slice thickness of 3 mm, bandwidth of 1724 Hz/pixel, and b values of 0, 20, 40, 60, 80, 150, 400, 600, 800, 1000, 1500, 2000, and 2500 mm²/s. Diffusion was acquired in three directions, with a total scan time of 572 seconds. Routine MRI sequences included axial high-resolution T2-weighted imaging (TR/TE of 5300 ms/93 ms, FA of 150°, FOV of 250 mm × 250 mm, slice thickness of 2 mm, bandwidth of 248 Hz/pixel) with a scan time of 138 seconds. Additionally, coronal T2-weighted imaging, sagittal T2-weighted imaging, and axial T1-weighted imaging were performed as part of the protocol.

Histopathology

Pathological features were evaluated by specialized gastrointestinal pathologists using surgically resected specimens. The staging of rectal cancer follows the American Joint Committee on Cancer Tumor Node Metastasis Staging System [13]. Other histological features of rectal cancer were evaluated, including tumor differentiation degree (classified as well differentiated, moderate, or poor), lymphovascular invasion (LVI), and nerve infiltration (NI). Pathologists perform routine processing on the excised tissue blocks and select tumor areas for KRAS gene testing. Genomic DNA was extracted from tumor tissue sections fixed in formalin and embedded in paraffin using the QIAamp DNA FFPE tissue kit (Qiagen, Chatsworth, CA). The detection of KRAS using the mutation amplification refractory mutation system method is achieved through polymerase chain reaction (PCR) [14].

Quantitative hybrid IVIM-DKI model Analysis

Two radiologists with more than a decade of experience in abdominal diagnosis profiled each tumor using a polygonal area of interest (ROI) on b0 images from the DWI. Accurate delineation was ensured by referencing T1WI and T2WI images. High b-value imaging data were particularly used to identify the solid component of the tumor and align it with the ROI. The sections with the deepest tumor infiltration were selected for delineation and necrotic cystic lesion areas were excluded. The tumor ROI is then replicated on images corresponding to each b value.

The ADC is obtained by fitting all b values to the following equation [1]:

\(\:\frac{{S}_{\left(b\right)}}{{S}_{0}}=\text{exp}\left(-b\times\:ADC\right)\) [1]

Where S_(b) is the average of the signal strength at 13 b values and \(\:{S}_{0}\) is the signal strength observed measured in the absence of a diffusion gradient.

Parameter estimation in the hybrid IVIM-DKI analysis is performed using nonlinear least squares (NLLS) optimization and parallel computation along with a custom MATLAB toolbox (version 9.1, MathWorks, Natick, MA, USA). The analysis utilized all 13 b values from 0 to 2500 s/mm², combining a reconstruction based on the iterated total variational (TV) penalty function with a hybrid IVIM-DKI model.

The parameters of the function are calculated using the signal strength derived from all 13 b values, including true diffusion coefficient (D), and kurtosis (K), perfusion fraction (f), pseudo-diffusion coefficient (D*). The calculation process is as follows [15]:

\(\:\frac{{S}_{\left(b\right)}}{{S}_{0}}=f\times\:\text{exp}\left(-b\times\:{D}^{*}\right)+(1-f)\times\:\text{e}\text{x}\text{p}(-b\times\:D+\frac{1}{6}\times\:{b}^{2}\times\:{D}^{2}\times\:K)\) [2]

In Equation [2], S_(b) is the signal intensity at a given b value, S₀ denotes the signal intensity at b = 0, and b is the b factor. The Levenberg-Marquardt algorithm was employed to perform the least squares fit of the signal intensities corresponding to the b values. All parameters were derived from the average signal intensity within the ROI. To enhance the fitting accuracy of the bi-exponential model and prevent overfitting, the following procedure was implemented: Initially, the data for b > 200s/mm² were used to determine the parameters D and K. Subsequently, keeping D and K fixed, curve fitting was conducted for f and D∗ across all b values using Equation [2].

Statistical Analysis

Statistical analysis was performed using IBM SPSS Statistics version 26.0. Initially, the data were tested for normality. As the data did not follow a normal distribution, non-parametric tests were subsequently applied. The Mann-Whitney U test compared continuous variables, including IVIM-DKI model parameters and ADC values, between the KRAS mutation and wild-type groups. Receiver operating characteristic (ROC) curve analysis evaluated the discriminatory power of parameters showing significant differences between these groups. The ROC analysis provided the area under the curve (AUC), sensitivity, specificity, and optimal cut-off values for these significant parameters. Additionally, the Mann-Whitney U test and Kruskal-Wallis test examined the relationship between IVIM-DKI model parameters, ADC values, and clinicopathological features in rectal cancer patients.

Patient characteristics

All participants were screened and recruited, subsequently undergoing MRI and total mesorectal excision. The study included a total of 73 patients. Table 1 provides a summary of the detailed demographic and clinical profiles. The average age of the cohort was 66 ± 11 years. Of the participants, 36 were classified into the KRAS mutation group, while 37 were categorized as the wild-type group. Table 2 displays the hybrid IVIM-DKI model parameters for both groups, including mean, minimum, maximum, median, as well as the 25th and 75th percentile values.

Table 1

Demographic and Clinical Features of Patients.
Variables	Patients
Number (n)	73
Age, years (mean ± SD)	66 ± 11 (37–86)
Sex, male, n (%)	50 (68.5%)
T stages
T2, n (%)	13 (17.8%)
T3, n (%)	54 (74.0%)
T4, n (%)	6 (8.2%)
N stages
N0, n (%)	27 (37.0%)
N1, n (%)	23 (31.5%)
N2, n (%)	23 (31.5%)
Tumor grade, n (%)
High differented, n (%)	8 (11.0%)
Medium differented, n (%)	44 (60.3%)
Low differented, n (%)	21 (28.8%)
Lymph-vascular
Negative	38 (52.1%)
Positive	35 (47.9%)
Neural invasion
Negative	40 (54.8%)
Positive	33 (45.2%)
Mutations
Wild Type, n (%)	37 (50.7%)
KRAS Mutation, n (%)	36 (49.3%)

Table 2

ADC values and respective parameters for IVIM-DKI model in KRAS mutation group and wild type groups
IVIM-DKI model-derived parameters	ADC		D		K		f		D*
IVIM-DKI model-derived parameters	KRAS Mutation	Wild Type	KRAS Mutation	Wild Type	KRAS Mutation	Wild Type	KRAS Mutation	Wild Type	KRAS Mutation	Wild Type
Mean	0.5840	0.6270	0.0012	0.0014	0.9351	0.8221	0.0916	0.1173	0.0668	0.0729
Minimum	0.5180	0.5330	0.0010	0.0007	0.8318	0.5232	0.0294	0.0236	0.0005	0.0003
Maximum	0.6820	0.8250	0.0016	0.0022	1.0732	1.0775	0.3300	0.2910	0.2872	0.2585
25th percentile	0.5440	0.5815	0.0011	0.0012	0.8797	0.7247	0.0531	0.0523	0.0010	0.0011
50th percentile	0.5900	0.6210	0.0012	0.0013	0.9145	0.8306	0.0656	0.1015	0.0220	0.0189
75th percentile	0.6068	0.6525	0.0014	0.0017	0.9996	0.9002	0.1289	0.1606	0.1516	0.1776
z	-2.974		-2.394		-4.105		-1.137		-0.154
p	0.003		0.017		0.000		0.256		0.877
ADC, apparent diffusion coefficient; D, true diffusion coefficient; K, diffusion kurtosis; f, perfusion fraction; D*, pseudo-diffusion coefficient.

Comparison of hybrid IVIM-DKI parameters between KRAS mutant and wild-type groups

Statistically significant differences in ADC, D, and K values were found between the KRAS mutation and wild-type groups (z = -2.974, p = 0.003; z =-2.394, p = 0.017; z = -4.105, p = 0.000; respectively), as detailed in Table 2 and illustrated in Fig. 2. However, no significant differences in ADC, D, and K values were found between the KRAS mutation and wild-type groups the f and D* values between the groups (z = -1.137, p = 0.256; z = -0.154, p = 0.877; respectively).

Diagnostic performance of hybrid IVIM-DKI parameters

The AUC values for ADC, D, K, f, and D* were calculated as 0.702, 0.663, 0.779, 0.577, and 0.511, respectively, as presented in Table 3 and illustrated in Fig. 3. Among these, the K value derived from the IVIM-DKI model demonstrated the highest AUC and accuracy in identifying KRAS mutation status in rectal cancer patients. The ADC value also exhibited satisfactory diagnostic capability. In contrast, the other IVIM-DKI parameters, including D, f, and D*, showed lower AUCs for predicting KRAS mutation status.

Table 3

ROC analysis results for hybrid IVIM-DKI model in KRAS mutation group and wild type group.
	AUC	Sensitivity	Specificity	95% CI
ADC	0.702	80.6%	56.8%	0.583 to 0.821
D	0.663	94.4%	37.8%	0.537 to 0.789
K	0.779	88.1%	70.3%	0.668 to 0.891
f	0.577	75.0%	56.8%	0.442 to 0.713
D*	0.511	94.4%	16.2%	0.376 to 0.645
AUC, area under curve; ADC, apparent diffusion coefficient; D, true diffusion coefficient; K, diffusion kurtosis; f, perfusion fraction; D*, pseudo-diffusion coefficient.

IVIM-DKI Parameter Associations with Clinicopathological Features

The f value derived from the hybrid IVIM-DKI model showed significant differences across T-stage groups (h = 6.961, p = 0.031) and lymph-vascular invasion groups (z = -2.970, p = 0.003). The K value also demonstrated significant differences between N-stage groups (h = 9.917, p = 0.007) and differentiation groups (h = 14.314, p = 0.001). ADC value showed significant differences in the differentiation groups (h = 7.667, p = 0.022). In contrast, the D and D* parameters did not show significant differences in any of the clinicopathological features examined. These findings suggest that specific hybrid IVIM-DKI parameters, such as f and K, may serve as potential imaging biomarkers for rectal cancer characterization.

The results of this study indicate that the hybrid IVIM-DKI model parameters have important value as imaging biomarkers for predicting KRAS mutations in rectal cancer features. The K value extracted by the hybrid IVIM-DKI model shows the highest diagnostic ability in predicting KRAS mutation status. This indicates that the K value can serve as a valuable imaging biomarker for identifying KRAS mutant rectal cancer patients, providing a new approach for clinical patients to choose appropriate treatment strategies.

KRAS mutations are associated with increased cell proliferation, angiogenesis, and metastasis in various cancers, including colorectal cancer [16, 17]. The K value reflecting diffusion kurtosis extracted by the hybrid IVIM-DKI model is related to the complex microstructure changes of tumors, such as cell density, cell number, and tissue heterogeneity [18]. Thus, the association between K values and KRAS mutation status is biologically plausible. Changes in tumor tissue microstructure lead to changes in diffusion properties, making the hybrid IVIM-DKI model, especially the K value, a sensitive biomarker to distinguish KRAS mutants from wild-type rectal cancers. Previous studies of tumor volume histograms have shown that DKI indicators, especially K_75th, are associated with KRAS mutations in colorectal adenocarcinoma [19].

D values and ADC values also showed satisfactory diagnostic power in predicting KRAS mutation status in patients with rectal cancer, in addition to K values. The ADC and D values primarily reflect diffusion parameters, not perfusion [20]. Since the diffusion coefficient is primarily affected by the ratio of tissue to extracellular space, D and ADC are generally regarded as being negatively correlated with cell viability and positively correlated with tissue cystic and necrosis changes. [21, 22]. They indicate the extent of water diffusion in tissues and are widely studied as biomarkers for tumor characterization and treatment response assessment [23, 24]. Our findings support the utility of the ADC value in identifying KRAS mutant rectal cancer, likely due to the increased cellularity and reduced extracellular space associated with KRAS mutations. In a DWI study using an 8 b values, the maximum ADC, average ADC, and D values in the KRAS mutant group of rectal cancer were significantly lower than the wild-type group [25]. Compared with ADC, the quantitative parameters drived from the hybrid IVIM-DKI model, especially the K value, have a high diagnostic ability in predicting KRAS gene mutations. This indicates that this advanced DWI method has the ability to provide more comprehensive and detailed tumor phenotype information.

Although K values showed the highest diagnostic power, perfusion-related parameters, such as D* and f, did not show significant differences between the KRAS mutant and wild-type groups. This suggests that changes in diffusion characteristics may be more closely related to KRAS mutation status than the perfusion-related characteristics of tumors. This may be due to complex interactions between perfusion and diffusion processes in the tumor microenvironment, which vary by tumor type and molecular subtype.

In this study, we also investigated the relationship between quantitative parameters extracted from the hybrid IVIM-DKI model and clinicopathological features in cancer patients. It was found that the perfusion-related parameter f was significantly different between the T stage groups. This indicates that f reflects information related to blood vessels and angiogenesis in rectal cancer, which is related to tumor invasion and progression [26]. Previous studies have also supported that f reflects microvascular volume fraction, and in studies of esophageal squamous cell cancer, elevated f is associated with more advanced T stage [27].

Our study also found significant differences in K values between groups with N stage of rectal cancer. The K value increased with the increase of N stage of rectal cancer, which may be due to the increased tissue complexity and heterogeneity associated with lymph node metastasis. Earlier research has also highlighted the role of the K value in distinguishing between metastatic and non-metastatic lymph nodes in endometrial cancer [28]. A study employing a hybrid IVIM-DKI model for head and neck squamous cell carcinomas demonstrated that K values could predict the development of distant metastasis [29].

This study found significant differences in ADC and K values between tumor differentiation groups. Poorly differentiated tumors have lower ADC values and higher K values. This may be due to increased cell density in poorly differentiated tumors, limited diffusion of water molecules, and greater tissue heterogeneity. These findings are consistent with previous studies on the use of ADC and K values in evaluating colorectal cancer tumor differentiation [30].

This study also found that f values derived from the hybrid IVIM-DKI model differed significantly between the LVI groups. In tumors with LVI, a reduction in f may contribute to cancer invasion and metastasis. However, this contradicts previous findings in gastric cancer [31]. This may be due to biological and pathological differences between cancers, as well as changes in imaging protocols, patient populations, and analysis techniques.

There are several limitations that should be considered in this study. First, the sample size was relatively small and the study was conducted at one institution. Larger, multicenter studies are needed in the future to validate these findings and assess the generalizability of the results. Secondly, a standardized hybrid IVIM-DKI model should be established, including the use of b values and some other imaging parameters, to validate the results of this study. Third, we do not assess repeatability when performing calculations using advanced diffusion models. The variability of the scan-rescan changes the diagnostic ability of the diffusion parameters, so this problem needs to be further analyzed.

In conclusion, the results of this study demonstrate the potential of the hybrid IVIEM-DKI model in predicting KRAS mutation status in patients with rectal cancer. The ability to noninvasively characterize underlying tumor biology using advanced diffusion-weighted imaging techniques has important implications for personalized management and targeted treatment strategies.

IVIM Intravoxel incoherent motion

DKI Diffusion kurtosis imaging

ADC Apparent diffusion coefficient

D True diffusion coefficient

K Diffusion kurtosis

f Perfusion fraction

D* Pseudo-diffusion coefficient

ROC Receiver operating characteristic

KRAS Sarcoma viral oncogene homologue

EPI Echo planar imaging

LVI Lymphovascular invasion

NI Neural invasion

PCR Polymerase chain reaction

ROI Region of interest

Author Contribution

Jie Yuan: Writing – original draft, Methodology, Investigation, Formal analysis. Ziyuan Wang: Project administration, Methodology, Formal analysis, Conceptualization, Funding acquisition. Wenli Tan: Methodology, Formal analysis, Data curation, Conceptualization. Yun Zhang: Resources, Data curation. Huamei Yan: Data curation, Software. Mengxiao Liu: Software. Hangjun Gong: Supervision, Resources, Project administration, Conceptualization. Songhua Zhan: Review & editing, Supervision, Resources, Project administration, Conceptualization.

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No competing interests reported.

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24 Oct, 2024

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Non-invasive prediction of KRAS mutation in rectal cancer using hybrid intravoxel incoherent motion and diffusion kurtosis model

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Patients

MRI Acquisition

Histopathology

Quantitative hybrid IVIM-DKI model Analysis

Statistical Analysis

Results

Patient characteristics

Comparison of hybrid IVIM-DKI parameters between KRAS mutant and wild-type groups

Diagnostic performance of hybrid IVIM-DKI parameters

IVIM-DKI Parameter Associations with Clinicopathological Features

Discussion

Abbreviations

Declarations

Author Contribution

References

Additional Declarations

Status:

Version 1