The adiposity of CRLM before targeted therapy initiation may serve as a marker to distinguish KRAS mutation status：A retrospective study

The adiposity of CRLM before targeted therapy initiation may serve as a marker to distinguish KRAS mutation status：A retrospective study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The adiposity of CRLM before targeted therapy initiation may serve as a marker to distinguish KRAS mutation status：A retrospective study Renzhe Xiao, Ning Wang, Wei Xiao, Yulin Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4136576/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract OBJECTIVE This study aimed to explore the correlation between MRI-related parameters and the Kirsten rat sarcoma viral oncogene (KRAS) gene mutation status in colorectal cancer liver metastases (CRLM) prior to initiating targeted therapy. METHODS This retrospective study enrolled 118 patients diagnosed with colorectal cancer liver metastases, each presenting up to three lesions of the largest diameter, and all underwent testing for KRAS gene mutations. Patients were categorized into two groups: the KRAS gene mutant (+) group (53 patients, 119 lesions) and the wild-type (-) group (65 patients, 155 lesions). Normality of continuous variables was assessed using the Kolmogorov-Smirnov test, while the Mann-Whitney U test or T test was employed for comparing continuous variables, and the Chi-square test for categorical variables. Variables exhibiting significant differences (p < 0.05) were subjected to multivariate logistic regression to identify independent factors and construct predictive models. Model performance was assessed through the receiver operating characteristic curve (ROC), with the area under the curve (AUC) and its 95% confidence interval (CI) calculated. RESULTS The T2-SI mean was higher in the KRAS(+) group compared to the KRAS(-) group (0.91 ± 0.30 vs 0.87 ± 0.39, p < 0.001). Conversely, T2FS-SI mean was significantly lower in the KRAS(+) group compared to the KRAS(-) group (0.80 ± 0.27 vs 1.02 ± 0.65, p < 0.001). Additionally, the proportion of lesions with a well-defined boundary was notably higher in the KRAS(+) group compared to the KRAS(-) group (84.9% vs 65.2%, p = 0.011). ROC curve analysis demonstrated that the model (Y = 0.890 + 0.878 * Boundary + 5.955 * T2-SI mean − 5.667 * T2FS-SI mean ) yielded an AUC value of 0.745 (95% CI: 0.688–0.802) for predicting the mutational status of the KRAS gene. CONCLUSION In our study, higher T2-SI mean , lower T2FS-SI mean and clear tumor boundaries in CRLM prior to initiating targeted therapy were associated with KRAS mutations.This implies that the adiposity of CRLM before targeted therapy initiation may serve as a marker to distinguish KRAS mutation status. Colorectal cancer liver metastases Kirsten rat sarcoma viral oncogene MRI Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Colorectal cancer (CRC) ranks third globally in terms of incidence and mortality in 2023 [ 1 ] . Liver metastases are prevalent among 15%-25% of CRC patients at initial consultation [ 2 ] . Systemic chemotherapy serves as the cornerstone for treating unresectable CRLM, and with the development of molecularly targeted therapies, the treatment of CRLM has entered a new era. Molecular profiling, notably of the KRAS gene, is pivotal for treatment decision-making and prognostication [ 3 ] . KRAS mutations, predominantly occurring at codons 12, 13, and 61, afflict approximately 40% of CRC patients and are associated with worse prognosis, particularly in those developing distant metastases [ 4 – 6 ] . While cetuximab is commonly used in anti-EGFR therapy, its efficacy is nullified in CRC patients harboring mutations in KRAS exons 2 (codons 12 and 13), 3 (codons 59 and 61), and 4 (codons 117 and 146) [ 7 – 9 ] . The clinical significance of KRAS mutations in CRLM prognosis has surged, prompting the need for accurate risk stratification and treatment selection. Conventionally, invasive procedures like biopsy or surgery are required for KRAS mutation detection, employing methods such as direct sequencing, TheraScreen KRAS kit, StripAssay, SNaPshot, Cobas, Next-generation sequencing, Droplet digital PCR, and BEAMing [ 10 ] . However, these methods may lack precision due to intratumor heterogeneity, inconsistent KRAS status, and limited sampling, exacerbated by prior systemic chemotherapy, complicating pathologic evaluation. Hence, there's an imperative for developing a noninvasive approach to distinguish KRAS mutation status and guide adjuvant therapy selection [ 11 ] . 2. Patients and Methods 2.1. patients This retrospective study inclued 1,408 patients diagnosed with colorectal cancer who received targeted therapy between January 2018 and December 2022 at Hubei Provincial Cancer Hospital. Inclusion criteria were colorectal adenocarcinoma confirmed by pathological biopsy, liver MRI prior to initiating targeted therapy, and documented tumor KRAS mutation status in our cases. Exclusion criteria included: lack of liver MRI data prior to initiating targeted therapy (1077 cases), immunotherapy had been received prior to targeted therapy (13 cases), liver metastases had undergone surgery or interventional therapy prior to targeted therapy (62 cases), no liver metastases at the time of the initial diagnosis (98 cases), no KRAS test was performed (11 cases), and no measurable lesions (< 1cm in diameter) (29 cases) Fig. 1 . Each patient was instructed to choose up to three lesions with the largest diameter while avoiding blood vessels and bile ducts. Consequently, the study included 118 patients with colorectal liver metastases, encompassing 274 lesions. Patients were further stratified based on their KRAS gene status into two groups: the KRAS mutant group (n = 53) and the KRAS wild-type group (n = 65). 2.2. Magnetic Resonance Imaging Protocol All patients underwent liver MRI on two 3.0T MRI scanners (Verio /MAGNETOM Skyra, Siemens, Germany; uMR790, China United Imaging) and one 1.5T MRI scanner (Signa HDxt, GE Medical Systems, USA), with 8-channel body coil. A supine head advanced scanning position was adopted. MRI scanning sequences: Siemens Verio /MAGNETOM Skyra: ① transverse T2-HASTE sequence (TR 1400ms, TE 86ms, FOV 380×308, Thickness 6.5mm, Gap 1.3mm), ② transverse T2-HASTE fat suppression sequence (TR 1600ms, TE 95ms, FOV 380×296, Thickness 6.5mm, Gap 1.3mm), ③ transverse 3D-T1-VIBE sequence in portal vein phase (fat suppression sequence, delayed 50 seconds, TR 3.31ms, TE 1.30ms, FOV 380×261, Thickness 3.0mm); uMR790: ① transverse T2-FSE sequence (TR 8468ms, TE 107.36ms, FOV 380×300, Thickness 6.0mm, Gap 1.2mm), ② transverse T2-FSE fat suppression sequence (TR 3764ms, TE 113.96ms, FOV 380×300, Thickness 6.0mm, Gap 1.2mm), and ③ transverse T1-3D sequence in portal vein phase (fat suppression sequence, delayed 50 seconds, TR 4.78 ms, TE 2.24 ms, FOV 380×280, Thickness 3.0 mm); GE Signa HDxt: ① transverse T2-SSFSE sequence (TR 1800 ms, TE 80.16 ms, FOV 256×320, Thickness 7 mm, Gap 1.4 mm), ② transverse T2-SSFSE fat suppression sequence (TR 1800ms, TE 80.16ms, FOV 256×320, Thickness 7mm, Gap 1.4mm), ③ transverse LAVA sequence in portal vein phase (fat suppression sequence, delayed by 50 seconds, TR 3.82ms, TE 1.81ms, FOV 288×200, Thickness 5mm); contrast: gadopentetate-glucosamine injection(Gd- DTPA, Beijing Beilu Pharmaceutical Co., Ltd.) at a dose of 0.1 mmol/kg. Enhancement scanning time: axial scanning was initiated 10s after the completion of contrast injection. The scanned images were downloaded and stored in DICOM format in a dedicated computer. The specific parameters of each MRI scanning sequence are shown in Table 1 . Table 1 MRI scan sequence parameters Sequence TR(ms) TE(ms) Bandwidth Filp Angle Thickness(mm) Gap(mm) FOV Matrix Siemens Verio /MAGNETOM Skyra Transverse T2-HASTE 1400 86 700 135 6.5 1.3 380×308 320 Transverse T2-HASTE-FS 1600 95 710 160 6.5 1.3 380×296 256 Transverse 3D-T1-VIBE 3.31 1.3 445 9 3 / 380×261 320 uMR790 Transverse T2-FSE 8468 107.36 650 120 6 1.2 380×300 304 Transverse T2-FSE-FS 3764 113.96 500 120 6 1.2 380×300 272 Transverse 3D-T1 4.78 2.24 335 12 3 / 380×280 288 GE Signa HDxt Transverse T2-SSFSE 1800 80.16 122.07 90 7 1.4 320×256 320 Transverse T2-SSFSE-FS 1800 80.16 122.07 90 7 1.4 320×256 320 Transverse LAVA 3.82 1.81 244 15 5 / 288×200 288 2.3. Image analysis ROIs (Region of Interest) of liver metastases were outlined by two radiologists (Xiao and Wang, with 7 and 10 years of experience in diagnostic abdominal imaging, respectively) in consensus, and the KRAS mutation status and clinical information of all patients were kept confidential to them. All ROIs were outlined on 3D Slicer software (Version 5.3.0) [ 12 ] . Up to three lesions with the largest diameter were selected for each patient, avoiding blood vessels and bile ducts during outlining. 2.4. Quantitative analysis ROIs were outlined layer by layer along the tumor boundry on T2WI, T2WI-FS, and enhanced scanning portal vein phase (PVP) images, respectively, trying to encompass the whole lesion as much as possible; ROIs were placed at a total of three levels above and below the splenic hilum, with care taken to avoid splenic blood vessels; and the volume of interest (VOI) for the lesion and spleen were automatically generated by the computer. The spleen was chosen as the reference tissue in order to avoid excessive differences in signal intensity(SI) of the same tissue due to different parameters of different devices and the possibility that the signal intensity of the liver parenchyma may be altered by the side effects of systemic chemotherapy. The minimum, mean, and maximum signal intensity within the VOI of the tumor were calculated by 3D Slicer and compared with the mean signal intensity of the spleen VOI to quantify the signal intensity of the tumor. As shown in Fig. 2 . All signal intensity follow the following calculations: SI (min/mean/max) = \(\frac{{VOI}_{tumor}-{SI}_{（min/mean/max）}}{{VOI}_{spleen}-{SI}_{（mean）}}\) 2.5. Qualitative analysis The maximum diameter of the tumor, area, volume, whether the circumferential enhancement is obvious or not, whether the boundry is clear or not, whether the morphology is regular or not are all determined on the images of the PVP images of the enhancement scan. Obvious tumor circumferential enhancement was defined as: more than 75% of the tumor boundary in axial position had significantly higher signal intensity than the surrounding liver parenchyma; clear tumor boundary was defined as: more than 75% of the tumor boundry in axial position had a clear demarcation with the surrounding liver parenchyma; and regular tumor morphology was defined as: the tumor boundary was smooth, with no obvious protrusions or depressions. As shown in Fig. 3 a、3b. Clinical parameters that may be used to predict KRAS gene status in metastases were retrospectively analyzed, including gender, age, number of metastases (≤ 5 or > 5), location of metastasis occurrence (left or right lobe of the liver), and carcinoembryonic antigen (CEA) and carbohydrate antigen 19 − 9 (CA19-9) prior to targeted therapy. Tumor markers were classified into two groups based on serum levels: within or above the normal range (normal range: CEA ≤ 5 ng/ml, CA19-9 ≤ 40ng/ml). 2.6. Histopathological analysis KRAS status was determined by analyzing specimens of colorectal cancer primary foci obtained by colonoscopy. Codons 12 and 13 of KRAS exon 2, codons 59 and 61 of exon 3, and codons 117 and 146 of exon 4 were amplified by polymerase chain reaction (PCR) and analyzed for KRAS mutations. 2.7. Statistical analysis Statistical analyses were performed by IBM SPSS Statistics 26.0 statistical software (Armonk, NY: IBM Corp). Normality was tested using the Kolmogorov-Smirnov test. Inter-observer consistency was evaluated using the intragroup correlation coefficient (ICC) for continuous variables and the Kappa consistency test for categorical variables.The ICC/Kappa values were interpreted as follows: <0.2 indicates a poor degree of consistency; 0.2 to 0.4 indicates a fair degree of consistency; 0.4 to 0.6 indicates a moderate degree of consistency; 0.6 to 0.8 indicates a is strong; 0.8 to 1.0 indicates a strong degree of consistency. Continuous variables are expressed as mean ± standard deviation, and categorical variables are expressed as frequencies and percentages. The Mann-Whitney U test/t test and Pearson chi-square test was used to compare the continuous and categorical variables between the KRAS (+) and KRAS (-) groups, and P < 0.05 was considered statistically different. Variables with significant differences were included in multivariate logistic regression for screening independent factors and constructing model. The predictive performance of the model was evaluated using the receiver operating characteristic curve (ROC); the area under the ROC curve (AUC) and its 95% confidence interval (CI) were calculated. 3. Result 3.1. Univariate analysis of clinically and imaging-related variables between different KRAS states KRAS mutations were identified in 53 (45%) patients (30 males, 23 females, with average age 56 years, 29–78 years), whereas 65 (55%) patients (47 males, 18 females, with average age 57 years, 27–77 years) were considered KRAS wild-type. The median time between the last MR scan before targeted therapy and the first targeted therapy was 9 days. There were no significant differences between KRAS mutant and wild-type patients in terms of clinical factors such as sex (p = 0.075), age (p = 0.696), CEA prior to initiating targeted therapy (p = 0.084), and CA19-9 prior to initiating targeted therapy (p = 0.849). As for imaging-related variables, the percentage of tumors with clear boundary was significantly higher in the KRAS(+) group than in the KRAS(-) group (84.9% vs 65.2% p < 0.01), and the T2-SI mean and ΔT2-SI mean (T2-SI mean /T2FS-SI mean ) were higher in the KRAS(+) group than in the KRAS(-) group (0.91 ± 0.30 vs 0.87 ± 0.39 p = 0.014, 1.16 ± 0.26 vs 0.98 ± 0.47 p < 0.001, respectively), while PVP-SI min and T2FS-SI mean were lower in the KRAS(+) group than in the KRAS(-) group (0.25 ± 0.14 vs 0.30 ± 0.15 p = 0.008, 0.80 ± 0.27 vs 1.02 ± 0.27 p = 0.012, respectively). Specific results are shown in Table 2 . Table 2 Clinical and imaging characteristics of the KRAS (+) and KRAS (-) Variables KRAS(+) KRAS(-) P-value Sex Male 30(56.6%) 47(72.3%) 0.075 Female 23(43.4%) 18(27.7%) Number of lesions ≤ 5 27(50.9%) 41(63.1) 0.185 > 5 26(49.1%) 24(36.9%) Liver metastasis location Left 42(35.3%) 49(31.6%) 0.521 Right 77(64.7%) 106(68.4%) Circumferential enhancement Y 78(65.5%) 94(60.6%) 0.405 N 41(34.5%) 61(39.4%) Boundary Y 101(84.9%) 101(65.2%) 5 40(75.5%) 57(87.7%) CA19-9(ng/ml) ≤ 40 18(34.0%) 21(32.3%) 0.849 >40 35(66.0%) 44(67.7%) Age(year) 56 ± 11 57 ± 12 0.696 Largest diameter of the target lesion(cm) 3.45 ± 2.08 3.65 ± 2.18 0.445 Area(cm 2 ) 9.96 ± 14.92 10.41 ± 12.86 0.792 Volume(cm 3 ) 40.33 ± 144.74 39.49 ± 80.73 0.401 T2-SI min 0.54 ± 0.18 0.54 ± 0.21 0.578 T2-SI mean 0.91 ± 0.30 0.87 ± 0.39 0.014* T2-SI max 1.42 ± 0.69 1.35 ± 0.78 0.104 PVP-SI min 0.25 ± 0.14 0.30 ± 0.15 0.008* PVP-SI mean 0.63 ± 0.17 0.61 ± 0.16 0.329 PVP-SI max 1.10 ± 0.29 1.01 ± 0.28 0.416 T2FS-SI min 0.45 ± 0.15 0.49 ± 0.24 0.771 T2FS-SI mean 0.80 ± 0.27 1.02 ± 0.27 0.012* T2FS-SI max 1.33 ± 0.69 1.28 ± 0.89 0.059 ΔT2-SI min (T2-SI min /T2FS-SI min ) 1.29 ± 0.69 1.19 ± 0.40 0.316 ΔT2-SI mean (T2-SI mean /T2FS-SI mean ) 1.16 ± 0.26 0.98 ± 0.47 <0.001* ΔT2-SI max (T2-SI max /T2FS-SI max ) 1.13 ± 0.38 1.21 ± 0.49 0.495 3.2. Multivariate Logistic Regression Analysis The correlates associated with different KRAS statuses (tumor boundary, T2-SI mean , PVP-SI min , T2FS-SI mean , ΔT2-SI mean ) in the univariate analysis were included in the multivariate logistic regression analysis, and the results are shown in Table 3 . Tumor boundary(OR 2.406 95%CI 1.223–4.733 p = 0.011)、T2-SI mean ༈OR 385.699 95%CI 20.312-7323.908 p < 0.001), and T2FS-SI mean (OR 0.003 95%CI 0-0.058 p < 0.001) were the independent correlates to differentiate between different KRAS statuses. Table 3 Multivariate Logistic Regression Variables B SE Wald X2 P-value OR 95 CI T2-SI mean 5.955 1.502 15.72 <0.001* 385.699 20.312-7323.908 PVP-SI min -1.019 1.002 1.035 0.309 0.361 0.051–2.571 T2FS-SI mean -5.667 1.44 15.479 <0.001* 0.003 0-0.058 ΔT2-SI mean -1.692 0.981 2.976 0.085 0.184 0.027–1.259 Boundary 0.878 0.345 6.466 0.011* 2.406 1.223–4.733 3.3. Multivariate logistic regression model for receiver operating curve (ROC) analysis The model Y = 0.890 + 0.878*boundary + 5.955*T2-SI mean -5.667*T2FS-SI mean predicted an AUC of 0.745 (95% CI 0.688–0.802) for KRAS(+) (Fig. 4 ). The model Y predicted KRAS(+) with a cutoff value of 2.325. When the metastases were divided into responder and non-responder groups based on the Y cutoff value, there was a significant excess of KRAS(+) lesions in the responder group (Y ≥ 2.325) over those in the non-responder group (Y < 2.325) (71% vs 27.6% p < 0.001) Table 4 . Table 4 Group of lesions based on model Y cut-off values Y ≥ 2.325 Y<2.325 P-value KRAS(+) 71(71%) 48(27.6%) P<0.001 KRAS(-) 29(29%) 126(72.4%) 3.3. Inter-observer consistency The continuous variables T2-SI min , PVP-SI mean , PVP-SI max , ΔT2-SI mean and ΔT2-SI max showed strong consistency (ICC/Kappa values of 0.664–0.788), and the rest of the categorical and continuous variables showed very strong consistency (ICC/Kappa values all > 0.8), as shown in Table 5 . Table 5 Results of inter-observer variable consistency test Type of variables Variables ICC/Kappa value 95%CI Categorical variables Number of lesions 0.913 0.839–0.988 Liver metastasis location 0.959 0.923–0.995 Circumferential enhancement 0.816 0.746–0.886 Boundary 0.873 0.808–0.937 Morphology 0.953 0.916–0.990 Continuous variables Largest diameter of the target lesion 0.887 0.858–0.910 Area 0.951 0.938–0.961 Volume 0.963 0.953–0.971 T2-SI min 0.774 0.721–0.818 T2-SI mean 0.802 0.756–0.841 T2-SI max 0.869 0.836–0.895 PVP-SI min 0.867 0.834–0.893 PVP-SI mean 0.664 0.592–0.726 PVP-SI max 0.680 0.610–0.739 T2FS-SI min 0.814 0.770–0.851 T2FS-SI mean 0.881 0.851–0.905 T2FS-SI max 0.891 0.864–0.913 ΔT2-SI min (T2-SI min /T2-SIFS min ) 0.835 0.795–0.868 ΔT2-SI mean (T2-SI mean /T2FS-SI mean ) 0.776 0.724–0.819 ΔT2-SI max (T2-SI max /T2FS-SI max ) 0.788 0.738–0.829 4. Disscussion The tumor microenvironment is pivotal in tumor evolution. Adipose tissue in the human body serves as an energy reservoir and is involved in cell signaling, immune response regulation, and internal environment homeostasis maintenance. It intricately modulates cell growth, proliferation, differentiation, and apoptosis within the CRC tumor microenvironment [ 13 ] . Thus, variations in KRAS mutation statuses in colorectal cancers may lead to differences in adipose tissue composition. To date, no study has examined disparities in adipose tissues within CRLM lesions across different KRAS mutation statuses. Hence, we investigated the relationship between observable MRI features and KRAS mutations, utilizing these findings to assess MRI's potential as an indicator of gene mutation status in CRLM patients. This study is the first to explore the association between fat content of metastases in CRLM patients and KRAS mutation status. The study findings indicated that T2-SI mean , T2FS-SI mean , and metastasis boundary served as independent risk factors for KRAS(+). Multivariate logistic regression analysis revealed a positive association between higher T2-SI mean , lower T2FS-SI mean , and sharper tumor boundary with KRAS mutation. Generally, fluid and fat exhibited high signal intensity in T2WI. The T2-SI mean was higher in the KRAS(+) group compared to the KRAS(-) group, suggesting a higher presence of fluid and fat in metastatic tumors with KRAS(+). Conversely, the T2WI-FS sequence selectively inhibited fat signals, resulting in high signal intensity reflecting the fluid component only. The T2FS-SI mean of the KRAS(+) group was significantly lower than that of the KRAS(-) group, indicating lower fluid content within lesions of the KRAS(+) group. KRAS(+) lesions exhibited higher T2-SI mean and lower T2FS-SI mean , suggesting higher fat components within them. Tumor occurrence and progression result from the dynamic interplay between the tumor and its surrounding environment. The local biological environment, comprising various cells, factors, and physicochemical factors, constitutes the tumor microenvironment [ 14 ] . The tumor microenvironment plays a pivotal role in tumor evolution, encompassing components like the tumor-associated inflammatory microenvironment, hypoxic conditions, and intestinal flora, all critical in CRC tumor microenvironment. Several studies have highlighted the role of cancer-associated adipocytes (CAA) in the tumor microenvironment, promoting tumor progression, metastasis, invasion, and drug resistance [ 15 – 20 ] . Moreover, excessive intake of exogenous fat leading to obesity also contributes to tumor progression. KRAS mutations are recognized as key drivers in the development and progression of pancreatic ductal adenocarcinoma (PDAC). However, mutant KRAS alone is insufficient to drive PADC progression. Environmental risk factors such as obesity and pancreatitis synergistically associate with mutant KRAS, inducing oncogenic KRAS hyperactivation and promoting PDAC development [ 21 ] . Thus, the biological characteristics observed in CRLM patients with different KRAS mutation statuses may also be linked to lesion adiposity. KRAS mutations activate the G protein signaling pathway, promoting tumor cell proliferation while reducing apoptosis [ 22 , 23 ] . Some studies have explored the feasibility of 18 F-FDG PET/CT imaging to detect KRAS mutations in terms of metabolism [ 24 , 25 ] . Kawada et al. [ 25 ] in a study of 35 patients with a total of 55 liver metastases found that Mean SUV max was significantly higher in the KRAS(+) than in the KRAS(-) group (8.3 ± 4.1 vs. 5.7 ± 2.4 p = 0.03) and the AUC value of the ROC curve predicting KRAS mutations was 0.70. Mao et al. [ 24 ] Imaging analysis of a total of 87 liver metastases in 49 patients using dual time point 18 F-FDG PET/CT revealed that SUV early , SUV delayed , and ΔSUV max were significantly lower in the KRAS(-) group than in the KRAS(+) (7.8 ± 3.3 vs 10.7 ± 6.0 p = 0.024, 10.0 ± 4.2 vs 15.5 ± 10.1 p = 0.001 and 2.2 ± 2.0 vs 4.8 ± 4.7 p = 0.001, respectively), with AUCs of 0.694, 0.760, and 0.757. In addition to glucose metabolism, fatty acid metabolism is one of the most important aspects affecting the development of tumors, and Propionibacterium intestinalis in the human body secretes a type of short-chain fatty acid called acetate. Currently there are studies [ 26 ] which believe that acetate is an important alternative energy source for cancer cells, and acetate can also stimulate tumor growth and metastasis in an acetyl-CoA synthase 2 (ASCC2) and hypoxia inducible factor 2 (HIF-2)-dependent manner [ 27 ] . Zhang et al. [ 28 ] performed metabolic analysis of CRC patients with lymph node metastasis by 1 H-MRS and found that acetate content was significantly increased in tumor tissues of metastatic patients, suggesting that acetate plays a key role in CRC development and metastasis. Tumors with robust fatty acid metabolism tend to have a poorer prognosis, although there are no consistent results on the relationship between KRAS mutations and colorectal cancer survival, most studies have shown that KRAS mutations have a poorer prognosis [ 29 , 30 ] , which may explain why KRAS(+) contains more adipose tissue. However, in the present study we did not investigate the relationship between KRAS mutations and prognosis. In addition Mosconi et al. [ 31 ] used 1 H-MRS to examine adipose tissue around lesions on postoperative CRC specimens and found that monounsaturated fatty acid (MUFA) was significantly increased in adipose tissue close to the lesion in patients with higher TNM staging, whereas MUFA far from the lesion did not show this manifestation, which may explain why KRAS ( +) is more often shown as well-defined on portal images. Our study still has some limitations, the first point is that this study is a retrospective study and has a small sample size. The second point is that we only quantitatively analyzed the differences in fat content of CRLM liver metastases with different KRAS statuses and did not qualitatively analyze the differences in different fatty acids. The third point is that our ROIs were based on manual outlining, which may lead to biased results, which is a limitation of all ROI-based studies, and to overcome this limitation, we used whole-tumor volumetric analysis to outline ROIs at all levels of the tumor. The fourth point is the lack of histological confirmation of the KRAS mutation status of liver metastases; histopathological confirmation of the diagnosis of liver metastases is impractical and usually unnecessary, and one study in 305 patients showed a high correlation of KRAS mutation status between liver metastases and the primary tumor (96.4%) [ 32 ] . 5. Conclusion Higher T2-SI mean , lower T2FS-SI mean , and clear tumor boundary in CRLM prior to initiating targeted therapy were associated with KRAS mutations. This implies that the adiposity of CRLM before targeted therapy initiation may serve as a marker to distinguish KRAS mutation status to guide clinicians' therapeutic decisions and predict prognosis. Larger studies are needed to explore the relationship between KRAS mutations and adiposity composition. Abbreviations KRAS:kirsten rat sarcoma viral oncogene CRLM:colorectal cancer liver metastase ROC:receiver operating characteristic AUC:area under the curve CI:confidence interval CRC:colorectal cancer EGFR:epidermal growth factor receptor ROI:region of interest PVP:portal vein phase VOI:volume of interest CEA:carcinoembryonic antigen CA19-9:carbohydrate antigen 19-9 PCR:polymerase chain reaction ICC:intragroup correlation cofficient SI:signal intensity CAA:cancer-associated adipocytes PDAC:pancreatic ductal adenocarcinoma ASCC2:acetyl-CoA synthase 2 HIF-2:hypoxia inducible factor 2 MUFA:monounsaturated fatty acid Declarations Ethics approval and consent to participate Written informed consent was obtained from all patients recruited in this study. All methods were carried out in accordance with Declaration of Helsinki and Good Clinical Practice (GCP) guidelines.Institutional Review Board of Hubei Cancer Hospital have approved the study protocol. Consent for publication Written form of consent for publication have been obtained from all of the patients whom involved in this study. Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no conflict of interest. Funding This study has received funding from Hubei Provincial Science and Technology Innovation Special Projects for 2021 (2021ACA013) Author contributions RX, data analysis and manuscript preparation; NW and WX, data collection and data analysis; YL, concept, funding, study conduct and review of final manuscript. Acknowledgements Not applicable. References Siegel RL, Wagle NS, Cercek A, et al. Colorectal cancer statistics, 2023[J]. 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Relationship between KRAS mutation and diffusion weighted imaging in colorectal liver metastases; Preliminary study[J]. Eur J Radiol. 2020;125:108895. 3D Slicer. software (Version 5.3.0)(open source software, from the official website, https://www.slicer.org/ ). Booth A, Magnuson A, Fouts J, et al. Adipose tissue, obesity and adipokines: role in cancer promotion[J]. Horm Mol Biol Clin Investig. 2015;21(1):57–74. Arneth B, Kaunas. Lithuania). 2019;56(1):15. Afrin S, El Sabah M, Manzoor A, et al. Adipocyte coculture induces a pro-inflammatory, fibrotic, angiogenic, and proliferative microenvironment in uterine leiomyoma cells[J]. Biochim Et Biophys Acta Mol Basis Disease. 2023;1869(1):166564. Wang S, Su X, Xu M, et al. Exosomes secreted by mesenchymal stromal/stem cell-derived adipocytes promote breast cancer cell growth via activation of Hippo signaling pathway[J ]. Stem Cell Res Ther. 2019;10(1):117. Au Yeung CL, Co N-N, Tsuruga T, et al. Exosomal transfer of stroma-derived miR21 confers paclitaxel resistance in ovarian cancer cells through targeting APAF1[J]. Nat Commun. 2016;7:11150. Wu Q, Li B, Li Z, et al. Cancer-associated adipocytes: key players in breast cancer progression[J]. J Hematol Oncol. 2019;12(1):95. Liu Q, Dong H-T, Zhao T, et al. Cancer-associated adipocytes release FUCA2 to promote aggressiveness in TNBC[J]. Endocrine-related Cancer. 2022;29(3):139–49. Zhou S, Wang R, Xiao H. Adipocytes induce the resistance of ovarian cancer to carboplatin through ANGPTL4[J]. Oncol Rep. 2020;44(3):927–38. Luo Y, Li X, Ma J, et al. Pancreatic Tumorigenesis: Oncogenic KRAS and the Vulnerability of the Pancreas to Obesity[J]. Cancers. 2021;13(4):778. Ward RL, Todd AV, Santiago F, et al. Activation of the K-ras oncogene in colorectal neoplasms is associated with decreased apoptosis[J]. Cancer. 1997;79(6):1106–13. Kobayashi M, Watanabe H, Ajioka Y, et al. Effect of K-ras mutation on morphogenesis of colorectal adenomas and early cancers: relationship to distribution of proliferating cells[J]. Hum Pathol. 1996;27(10):1042–9. Mao W, Zhou J, Zhang H, et al. Relationship between KRAS mutations and dual time point 18F-FDG PET/CT imaging in colorectal liver metastases[J]. Abdom Radiol (New York). 2019;44(6):2059–66. Kawada K, Toda K, Nakamoto Y, et al. Relationship Between 18F-FDG PET/CT Scans and KRAS Mutations in Metastatic Colorectal Cancer[J]. J Nuclear Medicine: official Publication Soc Nuclear Med. 2015;56(9):1322–7. Comerford SA, Huang Z, Du X, et al. Acetate dependence of tumors[J]. Cell. 2014;159(7):1591–602. Rodríguez-Enríquez S, Robledo-Cadena DX, Gallardo-Pérez JC, et al. Acetate Promotes a Differential Energy Metabolic Response in Human HCT 116 and COLO 205 Colon Cancer Cells Impacting Cancer Cell Growth and Invasiveness [J]. Front Oncol. 2021;11:697408. Zhang H, Qiao L, Li X, et al. Tissue metabolic profiling of lymph node metastasis of colorectal cancer assessed by 1H NMR[J]. Oncol Rep. 2016;36(6):3436–48. Kadowaki S, Kakuta M, Takahashi S, et al. Prognostic value of KRAS and BRAF mutations in curatively resected colorectal cancer[J]. World J Gastroenterol. 2015;21(4):1275–83. Andreyev HJ, Norman AR, Cunningham D, et al. Kirsten ras mutations in patients with colorectal cancer: the RASCAL II study[J]. Br J Cancer. 2001;85(5):692–6. Mosconi E, Minicozzi A, Marzola P et al. (1) H-MR spectroscopy characterization of the adipose tissue associated with colorectal tumor[J]. Journal of magnetic resonance imaging: JMRI, 2014, 39(2): 469–474. Knijn N, Mekenkamp LJM, Klomp M, et al. KRAS mutation analysis: a comparison between primary tumours and matched liver metastases in 305 colorectal cancer patients[J]. Br J Cancer. 2011;104(6):1020–6. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4136576","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283804323,"identity":"f24a0c24-4f1e-46ca-9073-cea14d4bd0fb","order_by":0,"name":"Renzhe Xiao","email":"","orcid":"","institution":"Hubei Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Renzhe","middleName":"","lastName":"Xiao","suffix":""},{"id":283804325,"identity":"c5bc0c53-2cd8-44b8-beb0-aa09372af6db","order_by":1,"name":"Ning Wang","email":"","orcid":"","institution":"Hubei Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Wang","suffix":""},{"id":283804328,"identity":"4a5704e9-6f05-4f18-b78b-d90800bc5e42","order_by":2,"name":"Wei Xiao","email":"","orcid":"","institution":"Hubei Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Xiao","suffix":""},{"id":283804330,"identity":"23fd2e46-1a76-4523-8009-c7e10ac094e4","order_by":3,"name":"Yulin Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYBACPmYGBmaGChs5Nvb2A8RpYQNrOZNmzMdzJoFILUDMzNh2KHGehIMBkVrYecykGdgOpLdJMCQw/KjYRozDQFp47uS2STceYOw5c5tYLRLPcttkDiQAXUi0FoPD6WwSCQakaEk4nECKFrZia4YDaYZtwEA+SJRf+PkPb7z985+NvHx7+8EHPyqI0AIELBIw1gGi1AMB8wdiVY6CUTAKRsEIBQCDADGLYbpOtQAAAABJRU5ErkJggg==","orcid":"","institution":"Hubei Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yulin","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-03-20 11:07:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4136576/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4136576/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53673650,"identity":"b95d70f6-dd66-4952-8ef9-c7bc53ddcd18","added_by":"auto","created_at":"2024-03-28 18:24:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":130255,"visible":true,"origin":"","legend":"\u003cp\u003ecase screening flowchart\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4136576/v1/bda90c61c9687676ec472fa1.png"},{"id":53673652,"identity":"eaa63789-d71b-412a-8bfc-5bea44bd495b","added_by":"auto","created_at":"2024-03-28 18:24:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":235824,"visible":true,"origin":"","legend":"\u003cp\u003eAfter manually outlining ROIs layer by layer on axial portal phase enhancement images, the computer automatically generated metastases and spleen VOIs.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4136576/v1/7732303b0abe4c27f02a7bb7.png"},{"id":53673649,"identity":"abb6928a-fa26-498e-a8f8-a67782e6a067","added_by":"auto","created_at":"2024-03-28 18:24:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":188735,"visible":true,"origin":"","legend":"\u003cp\u003e3a. Circumferential enhancement is obvious, clear boundry, and regular morphology Fig. 3b. Circumferential enhancement is not obvious, unclear boundry, and irregular morphology\u003c/p\u003e","description":"","filename":"Figure3aand3b.png","url":"https://assets-eu.researchsquare.com/files/rs-4136576/v1/9c6701a0195d73bfc60fda8e.png"},{"id":53675027,"identity":"451e355d-ab72-401e-a043-7ea4206d68d9","added_by":"auto","created_at":"2024-03-28 18:32:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30098,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate logistic regression model Y predicting KRAS (+) in subjects with work characteristic curves (ROC)\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4136576/v1/583a57fb1bbc175d3d463081.png"},{"id":75232431,"identity":"b39780c2-ba26-4953-aa71-b02107b9454a","added_by":"auto","created_at":"2025-02-01 14:01:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1870084,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4136576/v1/5cc27132-dfb2-4b88-b9f3-9cef4987b803.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The adiposity of CRLM before targeted therapy initiation may serve as a marker to distinguish KRAS mutation status：A retrospective study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eColorectal cancer (CRC) ranks third globally in terms of incidence and mortality in 2023\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Liver metastases are prevalent among 15%-25% of CRC patients at initial consultation\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Systemic chemotherapy serves as the cornerstone for treating unresectable CRLM, and with the development of molecularly targeted therapies, the treatment of CRLM has entered a new era. Molecular profiling, notably of the KRAS gene, is pivotal for treatment decision-making and prognostication\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. KRAS mutations, predominantly occurring at codons 12, 13, and 61, afflict approximately 40% of CRC patients and are associated with worse prognosis, particularly in those developing distant metastases\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. While cetuximab is commonly used in anti-EGFR therapy, its efficacy is nullified in CRC patients harboring mutations in KRAS exons 2 (codons 12 and 13), 3 (codons 59 and 61), and 4 (codons 117 and 146)\u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The clinical significance of KRAS mutations in CRLM prognosis has surged, prompting the need for accurate risk stratification and treatment selection. Conventionally, invasive procedures like biopsy or surgery are required for KRAS mutation detection, employing methods such as direct sequencing, TheraScreen KRAS kit, StripAssay, SNaPshot, Cobas, Next-generation sequencing, Droplet digital PCR, and BEAMing\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. However, these methods may lack precision due to intratumor heterogeneity, inconsistent KRAS status, and limited sampling, exacerbated by prior systemic chemotherapy, complicating pathologic evaluation. Hence, there's an imperative for developing a noninvasive approach to distinguish KRAS mutation status and guide adjuvant therapy selection\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e .\u003c/p\u003e"},{"header":"2. Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. patients\u003c/h2\u003e \u003cp\u003eThis retrospective study inclued 1,408 patients diagnosed with colorectal cancer who received targeted therapy between January 2018 and December 2022 at Hubei Provincial Cancer Hospital. Inclusion criteria were colorectal adenocarcinoma confirmed by pathological biopsy, liver MRI prior to initiating targeted therapy, and documented tumor KRAS mutation status in our cases. Exclusion criteria included: lack of liver MRI data prior to initiating targeted therapy (1077 cases), immunotherapy had been received prior to targeted therapy (13 cases), liver metastases had undergone surgery or interventional therapy prior to targeted therapy (62 cases), no liver metastases at the time of the initial diagnosis (98 cases), no KRAS test was performed (11 cases), and no measurable lesions (\u0026lt;\u0026thinsp;1cm in diameter) (29 cases) Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Each patient was instructed to choose up to three lesions with the largest diameter while avoiding blood vessels and bile ducts. Consequently, the study included 118 patients with colorectal liver metastases, encompassing 274 lesions. Patients were further stratified based on their KRAS gene status into two groups: the KRAS mutant group (n\u0026thinsp;=\u0026thinsp;53) and the KRAS wild-type group (n\u0026thinsp;=\u0026thinsp;65).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Magnetic Resonance Imaging Protocol\u003c/h2\u003e \u003cp\u003eAll patients underwent liver MRI on two 3.0T MRI scanners (Verio /MAGNETOM Skyra, Siemens, Germany; uMR790, China United Imaging) and one 1.5T MRI scanner (Signa HDxt, GE Medical Systems, USA), with 8-channel body coil. A supine head advanced scanning position was adopted. MRI scanning sequences: Siemens Verio /MAGNETOM Skyra: ① transverse T2-HASTE sequence (TR 1400ms, TE 86ms, FOV 380\u0026times;308, Thickness 6.5mm, Gap 1.3mm), ② transverse T2-HASTE fat suppression sequence (TR 1600ms, TE 95ms, FOV 380\u0026times;296, Thickness 6.5mm, Gap 1.3mm), ③ transverse 3D-T1-VIBE sequence in portal vein phase (fat suppression sequence, delayed 50 seconds, TR 3.31ms, TE 1.30ms, FOV 380\u0026times;261, Thickness 3.0mm); uMR790: ① transverse T2-FSE sequence (TR 8468ms, TE 107.36ms, FOV 380\u0026times;300, Thickness 6.0mm, Gap 1.2mm), ② transverse T2-FSE fat suppression sequence (TR 3764ms, TE 113.96ms, FOV 380\u0026times;300, Thickness 6.0mm, Gap 1.2mm), and ③ transverse T1-3D sequence in portal vein phase (fat suppression sequence, delayed 50 seconds, TR 4.78 ms, TE 2.24 ms, FOV 380\u0026times;280, Thickness 3.0 mm); GE Signa HDxt: ① transverse T2-SSFSE sequence (TR 1800 ms, TE 80.16 ms, FOV 256\u0026times;320, Thickness 7 mm, Gap 1.4 mm), ② transverse T2-SSFSE fat suppression sequence (TR 1800ms, TE 80.16ms, FOV 256\u0026times;320, Thickness 7mm, Gap 1.4mm), ③ transverse LAVA sequence in portal vein phase (fat suppression sequence, delayed by 50 seconds, TR 3.82ms, TE 1.81ms, FOV 288\u0026times;200, Thickness 5mm); contrast: gadopentetate-glucosamine injection(Gd- DTPA, Beijing Beilu Pharmaceutical Co., Ltd.) at a dose of 0.1 mmol/kg. Enhancement scanning time: axial scanning was initiated 10s after the completion of contrast injection. The scanned images were downloaded and stored in DICOM format in a dedicated computer. The specific parameters of each MRI scanning sequence are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e MRI scan sequence parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTR(ms)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTE(ms)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBandwidth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFilp Angle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThickness(mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGap(mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMatrix\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSiemens Verio /MAGNETOM Skyra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransverse T2-HASTE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e380\u0026times;308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransverse T2-HASTE-FS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e380\u0026times;296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransverse 3D-T1-VIBE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e380\u0026times;261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003euMR790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransverse T2-FSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e380\u0026times;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransverse T2-FSE-FS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e380\u0026times;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransverse 3D-T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e380\u0026times;280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGE Signa HDxt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransverse T2-SSFSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e320\u0026times;256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransverse T2-SSFSE-FS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e320\u0026times;256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransverse LAVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e288\u0026times;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Image analysis\u003c/h2\u003e \u003cp\u003eROIs (Region of Interest) of liver metastases were outlined by two radiologists (Xiao and Wang, with 7 and 10 years of experience in diagnostic abdominal imaging, respectively) in consensus, and the KRAS mutation status and clinical information of all patients were kept confidential to them. All ROIs were outlined on 3D Slicer software (Version 5.3.0)\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Up to three lesions with the largest diameter were selected for each patient, avoiding blood vessels and bile ducts during outlining.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Quantitative analysis\u003c/h2\u003e \u003cp\u003eROIs were outlined layer by layer along the tumor boundry on T2WI, T2WI-FS, and enhanced scanning portal vein phase (PVP) images, respectively, trying to encompass the whole lesion as much as possible; ROIs were placed at a total of three levels above and below the splenic hilum, with care taken to avoid splenic blood vessels; and the volume of interest (VOI) for the lesion and spleen were automatically generated by the computer. The spleen was chosen as the reference tissue in order to avoid excessive differences in signal intensity(SI) of the same tissue due to different parameters of different devices and the possibility that the signal intensity of the liver parenchyma may be altered by the side effects of systemic chemotherapy. The minimum, mean, and maximum signal intensity within the VOI of the tumor were calculated by 3D Slicer and compared with the mean signal intensity of the spleen VOI to quantify the signal intensity of the tumor. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. All signal intensity follow the following calculations:\u003c/p\u003e \u003cp\u003eSI\u003csub\u003e(min/mean/max)\u003c/sub\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{VOI}_{tumor}-{SI}_{（min/mean/max）}}{{VOI}_{spleen}-{SI}_{（mean）}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Qualitative analysis\u003c/h2\u003e \u003cp\u003eThe maximum diameter of the tumor, area, volume, whether the circumferential enhancement is obvious or not, whether the boundry is clear or not, whether the morphology is regular or not are all determined on the images of the PVP images of the enhancement scan. Obvious tumor circumferential enhancement was defined as: more than 75% of the tumor boundary in axial position had significantly higher signal intensity than the surrounding liver parenchyma; clear tumor boundary was defined as: more than 75% of the tumor boundry in axial position had a clear demarcation with the surrounding liver parenchyma; and regular tumor morphology was defined as: the tumor boundary was smooth, with no obvious protrusions or depressions. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea、3b.\u003c/p\u003e \u003cp\u003eClinical parameters that may be used to predict KRAS gene status in metastases were retrospectively analyzed, including gender, age, number of metastases (\u0026le;\u0026thinsp;5 or \u0026gt;\u0026thinsp;5), location of metastasis occurrence (left or right lobe of the liver), and carcinoembryonic antigen (CEA) and carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9 (CA19-9) prior to targeted therapy. Tumor markers were classified into two groups based on serum levels: within or above the normal range (normal range: CEA\u0026thinsp;\u0026le;\u0026thinsp;5 ng/ml, CA19-9\u0026thinsp;\u0026le;\u0026thinsp;40ng/ml).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Histopathological analysis\u003c/h2\u003e \u003cp\u003eKRAS status was determined by analyzing specimens of colorectal cancer primary foci obtained by colonoscopy. Codons 12 and 13 of KRAS exon 2, codons 59 and 61 of exon 3, and codons 117 and 146 of exon 4 were amplified by polymerase chain reaction (PCR) and analyzed for KRAS mutations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed by IBM SPSS Statistics 26.0 statistical software (Armonk, NY: IBM Corp). Normality was tested using the Kolmogorov-Smirnov test. Inter-observer consistency was evaluated using the intragroup correlation coefficient (ICC) for continuous variables and the Kappa consistency test for categorical variables.The ICC/Kappa values were interpreted as follows: \u0026lt;0.2 indicates a poor degree of consistency; 0.2 to 0.4 indicates a fair degree of consistency; 0.4 to 0.6 indicates a moderate degree of consistency; 0.6 to 0.8 indicates a is strong; 0.8 to 1.0 indicates a strong degree of consistency. Continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical variables are expressed as frequencies and percentages. The Mann-Whitney U test/t test and Pearson chi-square test was used to compare the continuous and categorical variables between the KRAS (+) and KRAS (-) groups, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically different. Variables with significant differences were included in multivariate logistic regression for screening independent factors and constructing model. The predictive performance of the model was evaluated using the receiver operating characteristic curve (ROC); the area under the ROC curve (AUC) and its 95% confidence interval (CI) were calculated.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Univariate analysis of clinically and imaging-related variables between different KRAS states\u003c/h2\u003e \u003cp\u003eKRAS mutations were identified in 53 (45%) patients (30 males, 23 females, with average age 56 years, 29\u0026ndash;78 years), whereas 65 (55%) patients (47 males, 18 females, with average age 57 years, 27\u0026ndash;77 years) were considered KRAS wild-type. The median time between the last MR scan before targeted therapy and the first targeted therapy was 9 days. There were no significant differences between KRAS mutant and wild-type patients in terms of clinical factors such as sex (p\u0026thinsp;=\u0026thinsp;0.075), age (p\u0026thinsp;=\u0026thinsp;0.696), CEA prior to initiating targeted therapy (p\u0026thinsp;=\u0026thinsp;0.084), and CA19-9 prior to initiating targeted therapy (p\u0026thinsp;=\u0026thinsp;0.849). As for imaging-related variables, the percentage of tumors with clear boundary was significantly higher in the KRAS(+) group than in the KRAS(-) group (84.9% vs 65.2% p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and the T2-SI\u003csub\u003emean\u003c/sub\u003e and ΔT2-SI\u003csub\u003emean\u003c/sub\u003e (T2-SI\u003csub\u003emean\u003c/sub\u003e /T2FS-SI\u003csub\u003emean\u003c/sub\u003e ) were higher in the KRAS(+) group than in the KRAS(-) group (0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30 vs 0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39 p\u0026thinsp;=\u0026thinsp;0.014, 1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26 vs 0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47 p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively), while PVP-SI\u003csub\u003emin\u003c/sub\u003e and T2FS-SI\u003csub\u003emean\u003c/sub\u003e were lower in the KRAS(+) group than in the KRAS(-) group (0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 vs 0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 p\u0026thinsp;=\u0026thinsp;0.008, 0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27 vs 1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27 p\u0026thinsp;=\u0026thinsp;0.012, respectively). Specific results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Clinical and imaging characteristics of the KRAS (+) and KRAS (-)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKRAS(+)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKRAS(-)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(56.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47(72.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(43.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(27.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of lesions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(50.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41(63.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(49.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24(36.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver metastasis location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42(35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49(31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77(64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106(68.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCircumferential enhancement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78(65.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94(60.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61(39.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoundary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101(84.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101(65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54(34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorphology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74(62.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100(64.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(37.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55(35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(24.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(12.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(75.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57(87.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA19-9(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(34.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(66.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44(67.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLargest diameter of the\u003c/p\u003e \u003cp\u003etarget lesion(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea(cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.96\u0026thinsp;\u0026plusmn;\u0026thinsp;14.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.41\u0026thinsp;\u0026plusmn;\u0026thinsp;12.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume(cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.33\u0026thinsp;\u0026plusmn;\u0026thinsp;144.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.49\u0026thinsp;\u0026plusmn;\u0026thinsp;80.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2-SI\u003csub\u003emin\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2-SI\u003csub\u003emean\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.014*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2-SI\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVP-SI\u003csub\u003emin\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVP-SI\u003csub\u003emean\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVP-SI\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2FS-SI\u003csub\u003emin\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2FS-SI\u003csub\u003emean\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.012*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2FS-SI\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔT2-SI\u003csub\u003emin\u003c/sub\u003e(T2-SI\u003csub\u003emin\u003c/sub\u003e/T2FS-SI\u003csub\u003emin\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔT2-SI\u003csub\u003emean\u003c/sub\u003e(T2-SI\u003csub\u003emean\u003c/sub\u003e/T2FS-SI\u003csub\u003emean\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔT2-SI\u003csub\u003emax\u003c/sub\u003e(T2-SI\u003csub\u003emax\u003c/sub\u003e/T2FS-SI\u003csub\u003emax\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Multivariate Logistic Regression Analysis\u003c/h2\u003e \u003cp\u003eThe correlates associated with different KRAS statuses (tumor boundary, T2-SI\u003csub\u003emean\u003c/sub\u003e, PVP-SI\u003csub\u003emin\u003c/sub\u003e, T2FS-SI\u003csub\u003emean\u003c/sub\u003e, ΔT2-SI\u003csub\u003emean\u003c/sub\u003e) in the univariate analysis were included in the multivariate logistic regression analysis, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Tumor boundary(OR 2.406 95%CI 1.223\u0026ndash;4.733 p\u0026thinsp;=\u0026thinsp;0.011)、T2-SI\u003csub\u003emean\u003c/sub\u003e༈OR 385.699 95%CI 20.312-7323.908 p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and T2FS-SI\u003csub\u003emean\u003c/sub\u003e (OR 0.003 95%CI 0-0.058 p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were the independent correlates to differentiate between different KRAS statuses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Multivariate Logistic Regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald X2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95 CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2-SI\u003csub\u003emean\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.955\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.502\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.72\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e385.699\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.312-7323.908\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVP-SI\u003csub\u003emin\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.051\u0026ndash;2.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT2FS-SI\u003c/b\u003e\u003csub\u003e\u003cb\u003emean\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-5.667\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e15.479\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0-0.058\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔT2-SI\u003csub\u003emean\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.027\u0026ndash;1.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBoundary\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.878\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.345\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6.466\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.011*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.406\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.223\u0026ndash;4.733\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Multivariate logistic regression model for receiver operating curve (ROC) analysis\u003c/h2\u003e \u003cp\u003eThe model Y\u0026thinsp;=\u0026thinsp;0.890\u0026thinsp;+\u0026thinsp;0.878*boundary\u0026thinsp;+\u0026thinsp;5.955*T2-SI\u003csub\u003emean\u003c/sub\u003e-5.667*T2FS-SI\u003csub\u003emean\u003c/sub\u003e predicted an AUC of 0.745 (95% CI 0.688\u0026ndash;0.802) for KRAS(+) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The model Y predicted KRAS(+) with a cutoff value of 2.325. When the metastases were divided into responder and non-responder groups based on the Y cutoff value, there was a significant excess of KRAS(+) lesions in the responder group (Y\u0026thinsp;\u0026ge;\u0026thinsp;2.325) over those in the non-responder group (Y\u0026thinsp;\u0026lt;\u0026thinsp;2.325) (71% vs 27.6% p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Group of lesions based on model Y cut-off values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u0026thinsp;\u0026ge;\u0026thinsp;2.325\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u0026lt;2.325\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKRAS(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71(71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48(27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKRAS(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126(72.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Inter-observer consistency\u003c/h2\u003e \u003cp\u003eThe continuous variables T2-SI\u003csub\u003emin\u003c/sub\u003e, PVP-SI\u003csub\u003emean\u003c/sub\u003e, PVP-SI\u003csub\u003emax\u003c/sub\u003e, ΔT2-SI\u003csub\u003emean\u003c/sub\u003e and ΔT2-SI\u003csub\u003emax\u003c/sub\u003e showed strong consistency (ICC/Kappa values of 0.664\u0026ndash;0.788), and the rest of the categorical and continuous variables showed very strong consistency (ICC/Kappa values all \u0026gt;\u0026thinsp;0.8), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Results of inter-observer variable consistency test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICC/Kappa value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCategorical variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of lesions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.839\u0026ndash;0.988\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiver metastasis location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.923\u0026ndash;0.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCircumferential enhancement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.746\u0026ndash;0.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoundary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.808\u0026ndash;0.937\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMorphology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.916\u0026ndash;0.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003eContinuous variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLargest diameter of the target lesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.858\u0026ndash;0.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.938\u0026ndash;0.961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVolume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.953\u0026ndash;0.971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2-SI\u003csub\u003emin\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.721\u0026ndash;0.818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2-SI\u003csub\u003emean\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.756\u0026ndash;0.841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2-SI\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.836\u0026ndash;0.895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePVP-SI\u003csub\u003emin\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.834\u0026ndash;0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePVP-SI\u003csub\u003emean\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.592\u0026ndash;0.726\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePVP-SI\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.610\u0026ndash;0.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2FS-SI\u003csub\u003emin\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.770\u0026ndash;0.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2FS-SI\u003csub\u003emean\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.851\u0026ndash;0.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2FS-SI\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.864\u0026ndash;0.913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔT2-SI\u003csub\u003emin\u003c/sub\u003e(T2-SI\u003csub\u003emin\u003c/sub\u003e/T2-SIFS\u003csub\u003emin\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.795\u0026ndash;0.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔT2-SI\u003csub\u003emean\u003c/sub\u003e(T2-SI\u003csub\u003emean\u003c/sub\u003e/T2FS-SI\u003csub\u003emean\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.724\u0026ndash;0.819\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔT2-SI\u003csub\u003emax\u003c/sub\u003e(T2-SI\u003csub\u003emax\u003c/sub\u003e/T2FS-SI\u003csub\u003emax\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.738\u0026ndash;0.829\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Disscussion","content":"\u003cp\u003eThe tumor microenvironment is pivotal in tumor evolution. Adipose tissue in the human body serves as an energy reservoir and is involved in cell signaling, immune response regulation, and internal environment homeostasis maintenance. It intricately modulates cell growth, proliferation, differentiation, and apoptosis within the CRC tumor microenvironment\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Thus, variations in KRAS mutation statuses in colorectal cancers may lead to differences in adipose tissue composition. To date, no study has examined disparities in adipose tissues within CRLM lesions across different KRAS mutation statuses. Hence, we investigated the relationship between observable MRI features and KRAS mutations, utilizing these findings to assess MRI's potential as an indicator of gene mutation status in CRLM patients.\u003c/p\u003e \u003cp\u003eThis study is the first to explore the association between fat content of metastases in CRLM patients and KRAS mutation status. The study findings indicated that T2-SI\u003csub\u003emean\u003c/sub\u003e, T2FS-SI\u003csub\u003emean\u003c/sub\u003e, and metastasis boundary served as independent risk factors for KRAS(+). Multivariate logistic regression analysis revealed a positive association between higher T2-SI\u003csub\u003emean\u003c/sub\u003e, lower T2FS-SI\u003csub\u003emean\u003c/sub\u003e, and sharper tumor boundary with KRAS mutation. Generally, fluid and fat exhibited high signal intensity in T2WI. The T2-SI\u003csub\u003emean\u003c/sub\u003e was higher in the KRAS(+) group compared to the KRAS(-) group, suggesting a higher presence of fluid and fat in metastatic tumors with KRAS(+). Conversely, the T2WI-FS sequence selectively inhibited fat signals, resulting in high signal intensity reflecting the fluid component only. The T2FS-SI\u003csub\u003emean\u003c/sub\u003e of the KRAS(+) group was significantly lower than that of the KRAS(-) group, indicating lower fluid content within lesions of the KRAS(+) group. KRAS(+) lesions exhibited higher T2-SI\u003csub\u003emean\u003c/sub\u003e and lower T2FS-SI\u003csub\u003emean\u003c/sub\u003e, suggesting higher fat components within them.\u003c/p\u003e \u003cp\u003eTumor occurrence and progression result from the dynamic interplay between the tumor and its surrounding environment. The local biological environment, comprising various cells, factors, and physicochemical factors, constitutes the tumor microenvironment\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The tumor microenvironment plays a pivotal role in tumor evolution, encompassing components like the tumor-associated inflammatory microenvironment, hypoxic conditions, and intestinal flora, all critical in CRC tumor microenvironment. Several studies have highlighted the role of cancer-associated adipocytes (CAA) in the tumor microenvironment, promoting tumor progression, metastasis, invasion, and drug resistance\u003csup\u003e[\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Moreover, excessive intake of exogenous fat leading to obesity also contributes to tumor progression. KRAS mutations are recognized as key drivers in the development and progression of pancreatic ductal adenocarcinoma (PDAC). However, mutant KRAS alone is insufficient to drive PADC progression. Environmental risk factors such as obesity and pancreatitis synergistically associate with mutant KRAS, inducing oncogenic KRAS hyperactivation and promoting PDAC development\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Thus, the biological characteristics observed in CRLM patients with different KRAS mutation statuses may also be linked to lesion adiposity.\u003c/p\u003e \u003cp\u003eKRAS mutations activate the G protein signaling pathway, promoting tumor cell proliferation while reducing apoptosis\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Some studies have explored the feasibility of \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT imaging to detect KRAS mutations in terms of metabolism\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Kawada et al.\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e in a study of 35 patients with a total of 55 liver metastases found that Mean SUV\u003csub\u003emax\u003c/sub\u003e was significantly higher in the KRAS(+) than in the KRAS(-) group (8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 vs. 5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 p\u0026thinsp;=\u0026thinsp;0.03) and the AUC value of the ROC curve predicting KRAS mutations was 0.70. Mao et al.\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e Imaging analysis of a total of 87 liver metastases in 49 patients using dual time point\u003csup\u003e18\u003c/sup\u003e F-FDG PET/CT revealed that SUV\u003csub\u003eearly\u003c/sub\u003e, SUV\u003csub\u003edelayed\u003c/sub\u003e, and ΔSUV\u003csub\u003emax\u003c/sub\u003e were significantly lower in the KRAS(-) group than in the KRAS(+) (7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3 vs 10.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0 p\u0026thinsp;=\u0026thinsp;0.024, 10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2 vs 15.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1 p\u0026thinsp;=\u0026thinsp;0.001 and 2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 vs 4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 p\u0026thinsp;=\u0026thinsp;0.001, respectively), with AUCs of 0.694, 0.760, and 0.757. In addition to glucose metabolism, fatty acid metabolism is one of the most important aspects affecting the development of tumors, and Propionibacterium intestinalis in the human body secretes a type of short-chain fatty acid called acetate. Currently there are studies\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e which believe that acetate is an important alternative energy source for cancer cells, and acetate can also stimulate tumor growth and metastasis in an acetyl-CoA synthase 2 (ASCC2) and hypoxia inducible factor 2 (HIF-2)-dependent manner\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Zhang et al.\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e performed metabolic analysis of CRC patients with lymph node metastasis by \u003csup\u003e1\u003c/sup\u003eH-MRS and found that acetate content was significantly increased in tumor tissues of metastatic patients, suggesting that acetate plays a key role in CRC development and metastasis. Tumors with robust fatty acid metabolism tend to have a poorer prognosis, although there are no consistent results on the relationship between KRAS mutations and colorectal cancer survival, most studies have shown that KRAS mutations have a poorer prognosis\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, which may explain why KRAS(+) contains more adipose tissue. However, in the present study we did not investigate the relationship between KRAS mutations and prognosis. In addition Mosconi et al.\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e used \u003csup\u003e1\u003c/sup\u003eH-MRS to examine adipose tissue around lesions on postoperative CRC specimens and found that monounsaturated fatty acid (MUFA) was significantly increased in adipose tissue close to the lesion in patients with higher TNM staging, whereas MUFA far from the lesion did not show this manifestation, which may explain why KRAS ( +) is more often shown as well-defined on portal images.\u003c/p\u003e \u003cp\u003eOur study still has some limitations, the first point is that this study is a retrospective study and has a small sample size. The second point is that we only quantitatively analyzed the differences in fat content of CRLM liver metastases with different KRAS statuses and did not qualitatively analyze the differences in different fatty acids. The third point is that our ROIs were based on manual outlining, which may lead to biased results, which is a limitation of all ROI-based studies, and to overcome this limitation, we used whole-tumor volumetric analysis to outline ROIs at all levels of the tumor. The fourth point is the lack of histological confirmation of the KRAS mutation status of liver metastases; histopathological confirmation of the diagnosis of liver metastases is impractical and usually unnecessary, and one study in 305 patients showed a high correlation of KRAS mutation status between liver metastases and the primary tumor (96.4%)\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e .\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eHigher T2-SI\u003csub\u003emean\u003c/sub\u003e, lower T2FS-SI\u003csub\u003emean\u003c/sub\u003e, and clear tumor boundary in CRLM prior to initiating targeted therapy were associated with KRAS mutations. This implies that the adiposity of CRLM before targeted therapy initiation may serve as a marker to distinguish KRAS mutation status to guide clinicians' therapeutic decisions and predict prognosis. Larger studies are needed to explore the relationship between KRAS mutations and adiposity composition.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eKRAS:kirsten rat sarcoma viral oncogene\u003c/p\u003e\n\u003cp\u003eCRLM:colorectal cancer liver metastase\u003c/p\u003e\n\u003cp\u003eROC:receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eAUC:area under the curve\u003c/p\u003e\n\u003cp\u003eCI:confidence interval\u003c/p\u003e\n\u003cp\u003eCRC:colorectal cancer\u003c/p\u003e\n\u003cp\u003eEGFR:epidermal growth factor receptor\u003c/p\u003e\n\u003cp\u003eROI:region of interest\u003c/p\u003e\n\u003cp\u003ePVP:portal vein phase\u003c/p\u003e\n\u003cp\u003eVOI:volume of interest\u003c/p\u003e\n\u003cp\u003eCEA:carcinoembryonic antigen\u003c/p\u003e\n\u003cp\u003eCA19-9:carbohydrate antigen 19-9\u003c/p\u003e\n\u003cp\u003ePCR:polymerase chain reaction\u003c/p\u003e\n\u003cp\u003eICC:intragroup correlation cofficient\u003c/p\u003e\n\u003cp\u003eSI:signal intensity\u003c/p\u003e\n\u003cp\u003eCAA:cancer-associated adipocytes\u003c/p\u003e\n\u003cp\u003ePDAC:pancreatic ductal adenocarcinoma\u003c/p\u003e\n\u003cp\u003eASCC2:acetyl-CoA synthase 2\u003c/p\u003e\n\u003cp\u003eHIF-2:hypoxia inducible factor 2\u003c/p\u003e\n\u003cp\u003eMUFA:monounsaturated fatty acid\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all patients recruited in this study. All methods were carried out in accordance with Declaration of Helsinki and Good Clinical Practice (GCP) guidelines.Institutional Review Board of Hubei Cancer Hospital have approved the study protocol.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten form of consent for publication have been obtained from all of the patients whom involved in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has received funding from Hubei Provincial Science and Technology Innovation Special Projects for 2021 (2021ACA013)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRX, data analysis and manuscript preparation; NW and WX, data collection and data analysis; YL, concept, funding, study conduct and review of final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Wagle NS, Cercek A, et al. 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Br J Cancer. 2011;104(6):1020\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Colorectal cancer liver metastases, Kirsten rat sarcoma viral oncogene, MRI","lastPublishedDoi":"10.21203/rs.3.rs-4136576/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4136576/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eOBJECTIVE\u003c/h2\u003e \u003cp\u003eThis study aimed to explore the correlation between MRI-related parameters and the Kirsten rat sarcoma viral oncogene (KRAS) gene mutation status in colorectal cancer liver metastases (CRLM) prior to initiating targeted therapy.\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e \u003cp\u003eThis retrospective study enrolled 118 patients diagnosed with colorectal cancer liver metastases, each presenting up to three lesions of the largest diameter, and all underwent testing for KRAS gene mutations. Patients were categorized into two groups: the KRAS gene mutant (+) group (53 patients, 119 lesions) and the wild-type (-) group (65 patients, 155 lesions). Normality of continuous variables was assessed using the Kolmogorov-Smirnov test, while the Mann-Whitney U test or T test was employed for comparing continuous variables, and the Chi-square test for categorical variables. Variables exhibiting significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were subjected to multivariate logistic regression to identify independent factors and construct predictive models. Model performance was assessed through the receiver operating characteristic curve (ROC), with the area under the curve (AUC) and its 95% confidence interval (CI) calculated.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eThe T2-SI\u003csub\u003emean\u003c/sub\u003e was higher in the KRAS(+) group compared to the KRAS(-) group (0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30 vs 0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, T2FS-SI\u003csub\u003emean\u003c/sub\u003e was significantly lower in the KRAS(+) group compared to the KRAS(-) group (0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27 vs 1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, the proportion of lesions with a well-defined boundary was notably higher in the KRAS(+) group compared to the KRAS(-) group (84.9% vs 65.2%, p\u0026thinsp;=\u0026thinsp;0.011). ROC curve analysis demonstrated that the model (Y\u0026thinsp;=\u0026thinsp;0.890\u0026thinsp;+\u0026thinsp;0.878 * Boundary\u0026thinsp;+\u0026thinsp;5.955 * T2-SI\u003csub\u003emean\u003c/sub\u003e \u0026minus;\u0026thinsp;5.667 * T2FS-SI\u003csub\u003emean\u003c/sub\u003e) yielded an AUC value of 0.745 (95% CI: 0.688\u0026ndash;0.802) for predicting the mutational status of the KRAS gene.\u003c/p\u003e\u003ch2\u003eCONCLUSION\u003c/h2\u003e \u003cp\u003eIn our study, higher T2-SI\u003csub\u003emean\u003c/sub\u003e, lower T2FS-SI\u003csub\u003emean\u003c/sub\u003e and clear tumor boundaries in CRLM prior to initiating targeted therapy were associated with KRAS mutations.This implies that the adiposity of CRLM before targeted therapy initiation may serve as a marker to distinguish KRAS mutation status.\u003c/p\u003e","manuscriptTitle":"The adiposity of CRLM before targeted therapy initiation may serve as a marker to distinguish KRAS mutation status：A retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-28 18:24:23","doi":"10.21203/rs.3.rs-4136576/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"55f89f81-3ee2-4645-b115-86e09905e98e","owner":[],"postedDate":"March 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-01T13:53:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-28 18:24:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4136576","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4136576","identity":"rs-4136576","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}