Differentiation of parotid gland tumors using intravoxel incoherent motion magnetic resonance imaging integrated with susceptibility-weighted imaging

Differentiation of parotid gland tumors using intravoxel incoherent motion magnetic resonance imaging integrated with susceptibility-weighted imaging | 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 Differentiation of parotid gland tumors using intravoxel incoherent motion magnetic resonance imaging integrated with susceptibility-weighted imaging Ningning Di, Wenna Cheng, Huacheng Chen, Junfang Fang, Chang Xu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6364778/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Purpose To evaluate the value of combining susceptibility-weighted imaging (SWI) with intravoxel incoherent motion (IVIM) magnetic resonance (MR) imaging in the characterisation of parotid gland tumors. Materials and Methods Seventy parotid gland tumors of 66 patients who had undergone SWI and IVIM were retrospectively reviewed. The true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and fraction of perfusion (f) values calculated from IVIM imaging and the intra-tumoural susceptibility signal (ITSS) obtained from SWI were assessed and compared using independent Student’s t , Mann–Whitney U , Chi-square, or Fisher’s exact tests. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic performance for the classification of parotid gland tumors. Results The D values of malignant parotid gland tumors (MT) were significantly lower than those of benign parotid gland tumors (BT) ( P <0.001). The ITSS of MT was significantly higher than that of BT ( P= 0.009). Subgroup analyses revealed that the D values of MT and Warthin tumors (WT) were significantly lower than those of pleomorphic adenoma (PA) ( P =0.001 and P =0.014, respectively) and basal cell adenoma (BCA) ( P =0.002 and P =0.005, respectively); however, the f values of MT and WT were significantly higher than those of BCA ( P =0.028 and P =0.04, respectively). The ITSS of MT was significantly higher than those of WT and PA ( P =0.038 and P =0.011, respectively). Integration with ITSS enhanced the diagnostic performance of D in differentiating MT from BT (area under the curve [AUC]0.858 vs 0.828) and PA (AUC 0.893 vs 0.852). Compared with the application of a single parameter (D or f), the application of the combination model enhanced the diagnostic performance for differentiating MT from BCA (AUC 0.950 vs 0.911 vs 0.790). The D* values exhibited no significant difference in terms of discriminating between the parotid gland tumors. Conclusions IVIM plays a critical role in discriminating MT from benign parotid tumors and characterizing the subgroups of benign parotid tumors. SWI offers additional diagnostic differentiation information for IVIM and servers a supplementary imaging marker for the characterization of parotid gland tumors. Differential diagnosis Parotid gland tumor Intravoxel incoherent motion Susceptibility-weighted imaging Figures Figure 1 Figure 2 Introduction Parotid gland tumors, the most common type of salivary gland tumour, account for 3–4% of all head and neck tumors. Pleomorphic adenoma (PA), a benign type of parotid gland, accounts for approximately 75% of cases, followed by Warthin’s tumour (WT) and basal cell adenoma (BCA). Malignant neoplasms, which seldom originate from the parotid gland, account for ≤ 3% of all cases of head and neck cancer[ 1 ]. Various treatment strategies have been proposed for different histopathological types of parotid gland tumors. Radical surgery and extracapsular dissection are recommended for the management of PA owing to the relatively high frequency of local recurrence and potential malignant transformation[ 2 ]. Conservative follow-up or partial parotidectomy is sufficient for the management of WT or BCA, given the low risk of malignancy[ 3 , 4 ]. Parotidectomy with potential removal of the facial nerve followed by adjuvant radiotherapy is recommended for the treatment of malignant parotid gland tumors (MT). Chemotherapy or radiation therapy remains the standard of care for lymphoma[ 5 , 6 ]. Thus, the proper diagnosis of parotid gland tumors is crucial for selecting an appropriate treatment strategy in clinical settings. Fine-needle aspiration (FNA) biopsy is an effective and minimally invasive method for defining the pathology of parotid gland tumors; however, insufficient samples from the heterogeneous tumour area may result in misdiagnosis[ 7 ]. FNA biopsy can facilitate tumour dissemination and metastasis, which may lead to facial nerve palsy in some patients. Thus, non-invasive and accurate pre-operative differentiation of parotid gland tumors is a crucial part of treatment planning. Multiple advanced magnetic resonance (MR)-derived parameters that reflect tumour diffusion or perfusion information have been developed in recent years to evaluate the diagnostic efficiency of parotid gland tumors. Diffusion-weighted imaging (DWI), dynamic susceptibility contrast-enhanced (DSC), and dynamic contrast-enhanced (DCE) imaging have been frequently applied in clinical practice[ 8 , 9 ]. However, microcapillary perfusion may affect the apparent diffusion coefficient (ADC) derived from DWI, thereby limiting its differentiation potential [ 10 ]. DCE and DSC are gadolinium-based contrast agent (GBCA)-enhanced MRI perfusion techniques. An association between GBCA and nephrogenic systemic fibrosis has been reported in previous studies[ 11 ]. GBCA-dependent examinations have confirmed long-term gadolinium retention in the central nervous system[ 12 ]; this has limited the clinical application of DSC and DCE to some extent. Intravoxel incoherent motion (IVIM) MR was developed to quantitatively analyse the molecular diffusion of water and the microcirculation of blood in the capillary network. The pure diffusion coefficient (D) is related to water mobility and can be used to evaluate pure incoherent motion without disturbing blood perfusion. The pseudo-diffusion coefficient (D*) and perfusion fraction (f) facilitate the assessment of the microperfusion pattern while avoiding the disadvantages of Gd contrast[ 13 ]. Thus, IVIM is a promising method for evaluating the diffusion and perfusion patterns in tumors that can reflect the cellular structures and microvessels[ 14 , 15 ]. Few studies have confirmed the efficiency of IVIM in differentiating parotid gland tumors[ 16 – 21 ]. Moreover, most previous studies mainly focused on the differential diagnosis of MT, PA, and WT. Thus, studies that aimed to discriminate BCA from other parotid gland tumors using IVIM are scarce. Susceptibility-weighted imaging (SWI), a sensitive MRI technique used to detect the susceptibility differences in tissues, can noninvasively characterise haemorrhages, veins, and calcification structures. Consequently, SWI has been widely used in clinical practice[ 22 , 23 ]. The intratumoural susceptibility signal intensity (ITSS), a semi-quantitative parameter derived from SWI, has been defined as a hypo-signal with a linear or dot-like structure in the maximum intensity projection (MIP) image of SWI. Given its ability to provide information on tumour vascularisation and haemorrhage[ 24 ], ITSS plays an important role in tumour grading, differentiation, phenotyping, and post-treatment assessment and follow-up [ 25 , 26 ]. Only three studies have applied SWI to the assessment of parotid gland tumors to date[ 27 – 29 ]. Two of these studies focused on differentiating malignant from benign tumors, while one study classified malignant tumors separately from WT and PA, but did not consider BCA as an independent subgroup. To the best of our knowledge, no previous study has combined IVIM and SWI for diagnosing parotid gland tumors. Therefore, the present study aimed to evaluate the added value of combining SWI with IVIM in characterizing parotid gland tumors, particularly in terms of differentiating BCA from other parotid gland tumors. Materials and Methods Patients Sixty-six patients diagnosed with parotid neoplasms between December 2018 and December 2024 were enrolled in the present study. The inclusion criteria were as follows: (a) age >18 years, (b) SWI and IVIM were performed pre-operatively, (c) a definite pathological diagnosis was confirmed by surgery or biopsy, (d) no treatment commenced before MRI studies, and (e) image quality sufficient for analysis. The final study cohort comprised 70 pathologically confirmed focal tumors of the parotid gland of 66 patients. The lesions were divided into the MT (n=10) and BT (n=60) groups, with the BT group being further divided into the PA (n=27), WT (n=17), basal cell adenoma (BCA; n=10), and other (n=6) groups. This study was conducted according to the tenets of the Declaration of Helsinki. Each patient signed informed consent. Ethics approval was obtained from the ethical committee of Binzhou Medical University Hospital. MRI examinations All MRI examinations were conducted using a 3.0 T MRI scanner (Discovery MR750, GE Healthcare) with an 8-channel head array coil. All 66 patients underwent water T2 imaging, SWI, and IVIM-MRI. The acquisition parameters of the water T2 IDeal image were as follows: repetition time (TR)/echo time (TE), 4129/85 ms; matrix, 256×256; field-of-view (FOV), 240 mm 2 ; number of slices, 24; slice thickness, 4 mm without gap; scan time, 2’41’’. SWI was performed using a three-dimensional full-flow compensated gradient-echo sequence with the following parameters: TR/TE, 41/22.5 ms; matrix, 256×256; FOV, 240 mm 2 ; number of slices, 36; slice thickness, 2 mm without gap; and scan time, 2’41’’. IVIM was performed using a single-shot echo-planar imaging diffusion sequence in the axial plane, with 19 b-values (b = 0, 30, 50, 100, 150, 200, 300, 500, 800, 1000, 1300, 1500, 1700, 2000, 2500, 3000, 3500, 4000, and 4500 s/mm 2 ) in three diffusion directions. The detailed parameters were as follows: TR/TE, 4500/111.5 ms; matrix, 128×128; FOV, 240 mm 2 ; number of slices, 16; slice thickness, 4 mm without gap; and scan time, 8’15’’. Image processing A GE AW4.6 workstation (General Electric Healthcare) was used to perform post-processing after obtaining the IVIM data. The MADC program of the Functool 9.4.05 software was used to process and automatically generate D, D*, and f. Based on the bi-exponential signal evaluation model, Le Bihan et al[13] evaluated the relationship between signal attenuation and b values using the: S b ∕S 0 = (1－f) ∙ e (−bD) ＋f ∙ e (−bD*) where S b and S 0 represent the signal attenuation when the b values are given a value and 0 s/mm 2 , respectively. f denotes the perfusion fraction, representing the fraction of microcirculation over the entire incoherent signal. D and D* denote the true molecular diffusion and perfusion-related pseudo-diffusion coefficients, respectively. The IVIM parameters were obtained by implementing two steps. The first step involved calculating D using a reduced set of high b-values of >200 s/mm 2 with a mono-exponential fitting. The contribution of D* to the signal can be neglected for b ≥200 s/mm 2 . The second step involved the extraction of D as a fixed parameter; f and D* were calculated using full bi-exponential curve fitting with nonlinear regression. Region-of-interest (ROI) analysis was conducted to measure the IVIM parameters. The placement of each ROI was determined based on the consensus between two senior radiologists with 12 and 8 years of experience who were blinded to the pathological results. The D, D*, and f values for each tumour were determined by placing two or three circular ROIs (ROI≥4 mm 2 ) on the D, D*, and f maps within the solid areas of the tumour solid. The regions with relatively low intensities in the D maps ( Fig . 1. 1-4B2 ) that exhibited high intensity in the f ( Fig . 1. 1-4C2 ) and D* maps were targeted ( Fig . 1. 1-4D2 ). Necrotic, cystic, calcified, and haemorrhagic areas, as well as the edges, were avoided to minimise the partial volume effect during ROI placement. The parameters were defined using the average D, D*, and f values of the targeted ROIs. ITSS was introduced and defined as a low-signal intensity with a fine linear or dot-like structure observed in the tumour on SWI-MIP images in the semi-quantitative SWI analysis[24]. Regions of necrosis and calcification within the tumors were excluded with reference to T2 IDeal and SWI phase images. The degree of ITSS was divided into four groups as described in a previous study[30]: Grade 0, no ITSS; Grade 1, 1–5 dot-like or fine linear ITSSs; Grade 2, 6–10 dot-like or fine linear ITSS; and Grade 3, ≥11 dot-like or fine linear ITSS within the tumour. All SWI images were evaluated by two radiologists. Disagreements between the two radiologists were resolved by reaching a consensus through discussion with a senior radiologist (with 12 years of experience). Statistical analysis All statistical analyses were conducted using SPSS (version 27.0; IBM Corp., Armonk, NY, USA) and MedCalc (version 20.0; MedCalc software). Statistical significance was set at P <0.05. Based on their normality, an independent sample t -test or the Mann–Whitney U test was used to assess the differences between the malignant and benign groups or subgroups of benign tumors in terms of patient age, D, D*, and f values. The Chi-square test or Fisher’s exact test was used to assess the differences in sex distribution and ITSS grade among patients with parotid tumors. A combined model of IVIM parameters and ITSS was established through logistic regression analysis. The diagnostic performance of D, f, D*, ITSS, and the combined model was assessed using receiver operating characteristic (ROC) curve analyses. Results Seventy parotid gland tumour lesions (66 patients) were divided into two groups, the malignant and benign groups. The malignant group comprised 10 lesions (10 patients, seven men and three women; average age 49.40±11.52 years, adenoid cystic carcinoma [n=3], mucoepidermoid carcinomas [n=3], acinic cell carcinomas [n=2], squamous cell carcinoma [n=1], and melanoma metastasis [n=1]). The benign group comprised 60 lesions (57 patients, 35 men and 22 women; average age 51.91±10.63 years, Pas [n=27], WTs [n=17], BCAs [n=10], myoepitheliomas [n=2], schwannoma [n=1], neuroinoma [n=1], eosinophilic adenoma [n=1], and lymphoepithelial cyst [n=1]). No significant differences were observed between the benign and malignant groups in terms of age ( P =0.735) or sex distribution ( P =0.490). Comparison and diagnostic performance of D, D*, f, and ITSS in differentiating MT from BT The D value of MT was significantly lower than that of BT; however, the ITSS grade of MT was significantly higher than that of BT ( P <0.001, P =0.009). No significant differences were observed between the groups in terms of the D* or f values ( P =0.681 and P =0.718, respectively) ( Table 1 ). The area under the curve (AUC), sensitivity, and specificity were 0.828, 70%, and 83.33%, respectively, when the D ≤0.161×10 -3 mm 2 /s was set as the cut-off value in the ROC analyses. Optimal diagnostic performance was achieved when the ITSS grade was set as ≥2 (AUC=0.793, sensitivity=70%, and specificity=73.33%). The application of the combined model of D and ITSS enhanced the diagnostic performance: AUC, 0.858; sensitivity, 70%; and specificity, 90% ( Table 3 and Fig . 2A ). Comparison and diagnostic performance of D, D*, f, and ITSS among MT, WT, PA, and BCA ITSS achieved a higher value for differentiating MT than WT ( P <0.001). However, the IVIM parameters D, D*, and f exhibited invalid diagnostic performance ( P ≥0.05) ( Table 2 ). ROC analysis revealed that the use of ITSS ≥1 as the cut-off value yielded AUC, sensitivity, and specificity of 0.803, 100%, and 41.18%, respectively ( Table 3 ). Table 4 summarizes the detailed ITSS grades of each pathological tumour type. The D values of MT were significantly lower, but the ITSS of MT was higher than that of PA ( P =0.001 and P =0.011. respectively) ( Table 2 ). ROC analysis revealed that setting D ≤0.230×10 -3 mm 2 /s as the cut-off value yielded AUC, sensitivity, and specificity of 0.852, 80%, and 81.48%, respectively. Setting ITSS ≥2 as the cut-off value yielded AUC, sensitivity, and specificity of 0.819, 70%, and 81.48%, respectively. Notably, the application of the combined model enhanced the diagnostic performance, with an AUC of 0.893 ( Table 3 and Fig. 2B ). The D values of MT were significantly lower than those of BCA; however, the f value of MT was higher than that of BCA ( P =0.002 and P =0.028, respectively) (Table 2 ). ROC analysis demonstrated good diagnostic performance with AUC, sensitivity, and specificity of 0.911, 70%, and 100%, respectively, when D ≤0.161×10 -3 mm 2 /s was set as the cut-off value. The AUC, sensitivity, and specificity were 0.790, 90%, and 70%, respectively, when f >0.876 was set as the cut-off value. The application of the combined model enhanced the diagnostic performances, with an AUC of 0.950 ( Table 3 and Fig . 2C ). The D values of WT were significantly lower than those of BCA; however, the f value of WT was higher than that of BCA ( P =0.005 and P =0.04, respectively) ( Table 2 ). ROC analysis revealed good diagnostic efficiency when D ≤0.505×10 -3 mm 2 /s was set as the cut-off value with AUC, sensitivity, and specificity of 0.829, 94.12%, and 60%, respectively. The AUC, sensitivity, and specificity were 0.741, 82.35%, and 70% when f >0.786 was set as the cut-off value. Notably, the application of the combined D and f value enhanced the diagnostic efficiency, with an AUC of 0.824 ( Table 3 and Fig . 2D ). The D values of WT were significant compared with those of PA ( P =0.014) ( Table 2 ). ROC analyses revealed that AUC, sensitivity, and specificity were 0.721, 64.71%, and 81.48%, respectively, setting D ≤0.248×10 -3 mm 2 /s was set as the cut-off value ( Table 3 ). Fig . 1 shows representative images of adenoid cystic carcinoma, WT, PA, and BCA. Table 1 Comparison of IVIM- and SWI- derived parameters between malignant and benign groups Parameters D D* f ITSS MT 0.183±0.084 0.135±0.063 0.92±0.047 2.10±0.876 BT 0.439±0.361 0.132±0.084 0.883±0.103 0.98±0.983 P value ＜0.001* 0.681 0.718 0.009* Data are reported as mean±standard deviation. The unit of D and D* is×10 -3 mm 2 /s. D true diffusion coefficient, D * pseudo-diffusion coefficient, f fraction of perfusion, ITSS intratumoral susceptibility signal intensity, MT malignant tumor, BT benign tumor, WT Warthin tumor, * the statistically significant P values. Table 2 Comparison of IVIM- and SWI- derived parameters among MT, WT, PA and BCA Parameters MT WT PA BCA P value MT vs WT MT vs PA MT vs BCA WT vs PA WT vs BCA PA vs BCA D 0.183±0.084 0.257±0.132 0.520±0.481 0.466±0.192 0.097 0.001* 0.002* 0.014* 0.005* 0.602 D* 0.135±0.063 0.136±0.072 0.146±0.101 0.092±0.065 0.955 0.959 0.147 0.838 0.117 0.180 f 0.92±0.047 0.890±0.145 0.903±0.081 0.847±0.079 0.269 0.891 0.028* 0.173 0.04* 0.070 ITSS 2.10±0.876 0.94±0.966 0.93±0.874 1.20±1.229 0.038* 0.011* 0.210 0.560 0.796 0.338 Data are reported as mean±standard deviation. The unit of D and D* is×10 -3 mm 2 /s. D true diffusion coefficient, D * pseudo-diffusion coefficient, f fraction of perfusion, ITSS intratumoral susceptibility signal intensity, MT malignant tumor, WT Warthin tumor, PA pleomorphic adenoma, BCA basal cell adenoma, * the statistically significant P values Table 3 Diagnostic performance of IVIM- and SWI-derived parameters in discriminating parotid gland tumors Parameters Cut-off value Area under curve Sensitivity（%） Specificity（%） P value Discriminating MT from BT D ≦0.161 0.828 70 83.33 ＜0.001 ITSS ≧2 0.793 70 73.33 ＜0.001 D+ITSS — 0.858 70 90 ＜0.001 Discriminating MT from PA D ≦0.230 0.852 80 81.48 ＜0.001 ITSS ≧2 0.819 70 81.48 ＜0.001 D+ITSS — 0.893 70 96.30 ＜0.001 Discriminating MT from WT ITSS ≧1 0.803 100 41.18 ＜0.001 Discriminating MT from BCA D ≦0.161 0.911 70 100 ＜0.001 f >0.876 0.790 90 70 0.0106 D+f — 0.950 90 90 ＜0.001 Discriminating WT from PA D ≦0.248 0.721 64.71 81.48 0.0058 Discriminating WT from BCA D ≦0.505 0.829 94.12 60 ＜0.001 f >0.876 0.741 82.35 70 0.0173 D+f — 0.824 100 60 ＜0.001 Table 4 Comparison of ITSS grade among each pathological tumor type Tumor pathology ITSS grade 0 1 2 3 MT 0 3 3 4 BT 23 21 10 6 WT 7 5 4 1 PA 9 13 3 2 BCA 4 2 2 2 ITSS intratumoral susceptibility signal intensity, MT malignant tumor, BT benign tumor, WT Warthin tumor, PA pleomorphic adenoma, BCA basal cell adenoma Discussion Proper pre-operative diagnosis of parotid gland tumors plays a critical role in personalised treatment planning. The present study confirmed that IVIM and SWI-derived parameters are promising non-invasive methods for differentiating malignant parotid gland tumors from benign tumors and subgroups. Furthermore, combining IVIM parameters with ITSS derived from SWI improved the diagnostic accuracy. To the best of our knowledge, this is the first study to combine IVIM and SWI to characterise parotid gland tumors and differentiate BCA from other parotid gland tumors. IVIM is a functional diffusion MRI technique that can quantify the diffusion and microvascular perfusion of tissues. The IVIM-derived D values reflect the pure diffusion of water molecules, excluding the influence of microcirculation to a large extend. Consistent with the findings of previous studies[ 17 , 18 , 21 ], the present study confirmed the diagnostic value of D in differentiating MT from BT. Subgroup analyses revealed that the D value of PA was significantly higher than than that of MT or WT ( P = 0.001, P = 0.014), indicating the lower cell density and higher pure diffusion in PA compared with that of MT or WT. Although the D values of MT were lower than those of WT (0.183 ± 0.084 mm 2 /s vs 0.257 ± 0.132 mm 2 /s), no significant differences were observed between them ( P > 0.05). This may be attributed to the lymphoid components in WT containing dense small lymphocytes, which greatly limit pure diffusion. Thus, WT may exhibit D values lower than those of MT (0.61×10 − 3 ±0.11 mm 2 /s vs 0.96×10 − 3 ±0.22 mm 2 /s) as demonstrated in the study conducted by Sumi et al[ 16 ]. IVIM-derived D* and f, which are non-intravenous contrast medium-dependent perfusion parameters, reflect information regarding microcirculation perfusion at the capillary level. These parameters have been identified as biomarkers for tumour angiogenesis and microvasculature[ 14 , 15 ]. Angiogenesis is an oblique feature of tumour growth that results in hyper-microvessels in MT; the capillary density of WT is higher than that of PA [ 31 ]. Therefore, D* and f values have been used to differentiate these tumors owing to their varied vascular statuses. However, their diagnostic efficiency varies. Sumi et al.[ 16 ]reported that the f values of WT were significantly higher than those of PA, consistent with the findings of the study conducted by Ma et al.[ 18 ], who demonstrated that the D* and f values of WT were significant higher than those of PA and MT, respectively. In contrast, Markit et al.[ 19 ] reported that the f values of WT were lower than those of PA. Zhang et al.[ 20 ]also found that D* increased significantly from PA to MT and WT; however, the f values exhibited no significant differences. However, D* and f values can cannot be used to differentiate among MT, WT, and PA[ 17 , 21 ]. The present study yielded a similar result: limited diagnostic efficiency of the D* and f values in differentiating between MT, BT, WT, and PA. These conflicting results may be attributed to the differences in the histopathological subtypes of parotid gland tumors included in these studies, the relatively low microvascular density in low-grade carcinoma or lymphoma in MT, and the high vascularisation in WT and solid-type PA, which led to a bias in statistical analysis[ 32 , 33 ]. Moreover, the different b-values used in different studies may have affected the diagnostic ability of IVIM[ 34 ]. Wide-high b-values (19 b values with an upper limit of 4500 s/mm 2 ) were applied in IVIM in the present study, which is different from other studies that used relatively narrow b-values (ranging from 10 to 14 b values with an upper limit of 800–2500 s/mm 2 ). A study conducted by Shen et al.[ 15 ]revealed that IVIM-derived D* values showed better efficiency in evaluating glioma angiogenesis and perfusion status when using a wide-high b value (22 b values with an upper limit of 5000 s/mm 2 ) scheme compared with that observed with a narrow-low b value (15 b values with an upper limit of 1500 s/mm 2 ) pattern. Thus, the high b-values applied herein provide further insights into the IVIM-derived perfusion parameters of f and D* in differentiating parotid gland tumors. In addition, to comparisons of MT, WT, and PA, the present study included BCA as a separate subgroup and emphasised comparisons with other tumors. The D values of BCA were significantly higher than those of MT or WT ( P = 0.002 and P = 0.005, respectively), implying a higher cell density in BCA. Furthermore, the f value could help distinguish BCA from MT and WT. The f value of BCA was lower than those of MT and WT owing to the more microvascular nature of parotid gland carcinomas and WT. PA have lower microvessel counts[ 31 ], whereas BCA have abundant small capillaries and venues[ 35 ]. This discrepancy in the vasculature may have contributed to the higher f values in the BCA[ 21 ]. However, the present study gained contrary result that BCA showed lower f values than PA (0.847 ± 0.079 vs 0.903 ± 0.081), although there was no significant difference(P = 0.070). In a pathological analysis, Monestier et al.[ 32 ]found that the hypercellular subtype of PA was more vascular than the hypocellular subtype. Lee et al.[ 36 ] found that the typical solid type of BCA usually has numerous endothelial-lined vascular channels with prominent small capillaries and venules; however, the trabecular and tubular types often feature a markedly sparse vasculature in the myxoid area. Therefore, the variation in vascularity among the PA and BCA subtypes might explain the differences in the f values between the two groups in our study. MT exhibited a significantly higher ITSS, an SWI-derived semi-quantitative parameter, compared with that of BT ( P = 0.009) in the present study, which is consistent with the findings of previous studies[ 27 , 29 ]. This may be attributed to MT exhibiting greater tumour angiogenesis characterised by the presence of immature and tortuous vascular structure that are more susceptible to bleeding. Therefore, MT exhibited a higher ITSS compared with those of the WT and PA groups, with the subgroup analysis indicating significant difference ( P = 0.038 and P = 0.011, respectively). Jiang et al. reported that MT exhibited ITSS higher than that of PA ( P = 0.024) [ 28 ]. However, they found no significant difference in the ITSS between MT and BT or WT mice ( P > 0.05), which contradicts our results. This discrepancy might be attributed to the lymphoma portion of MT in the study cohort. No lymphoma was included in the MT group in the present study. However, lymphoma accounted for 36.4% (4/11) of cases of MT in the study by Jiang et al.[ 28 ]. Neovascularization is not a prominent feature of lymphoma; this may explain the rarely visible ITSS in lymphoma[ 26 ]. Mungai et al.[ 37 ]reported that the MR perfusion pharmacokinetic parameters K trans , Ve, and Vp of parotid gland lymphomas were lower than those of other malignant tumors and even lower than those of WT and benign parotid tumors. This finding further indicates the lower microvasculature in gland lymphomas. Thus, the lymphoma portion may have affected the diagnostic efficiency of the ITSS. To the best of our knowledge, this study is the first to combine IVIM and SWI parameters to differentiate between parotid gland tumors. The diagnostic efficiency of D for differentiating MT from BT (AUC, 0.828 vs 0.858) and PA (AUC, 0.852 vs 0.893) was enhanced when combined with ITSS; however, the difference was not significant. Thus, SWI can add diagnostic information to IVIM to discriminate parotid tumors. In addition to the small cohort size, the present study has some limitations. First, MT has several histopathologic subtypes and histologic grades. The small sample size for each subtype or grade limited further subgroup differentiation. Furthermore, the large discrepancy between the number of MT and BT may have led to a bias in the statistical analysis. Further studies with larger sample sizes must be conducted to clarify the value of IVIM and SWI in subtype comparisons and confirm the findings of the present study. Second, a validated diagnostic model with internal and external datasets was not used owing to the limited sample size. Further multicentre studies with sample sizes must be conducted to validate the findings of the present study. Third, images often exhibit distortion in the high b-value phase of IVIM with the SS-EPI sequence, which increases the bias for parameter measurements. The RS-EPI sequence may overcome this disadvantage and improve the image quality of IVIM. In conclusion, the present study confirmed that IVIM plays an important role in differentiating parotid gland tumors, although an overlap between MT and WT was observed. Notably, this overlap could be compensated for by SWI. The addition of SWI to IVIM enhanced the diagnostic efficiency in differentiating parotid gland tumors. Thus, SWI should be integrated with IVIM to make use of the supplementary diagnostic information. Abbreviations IVIM : intravoxel incoherent motion magnetic; D : true diffusion coefficient; D* : pseudo-diffusion coefficient; f : fraction of perfusion; SWI : susceptibility-weighted imaging; ITSS : intratumoral susceptibility signal intensity; MT : malignant tumor; BT : benign tumor; WT : Warthin tumor; PA: pleomorphic adenoma; BCA : basal cell adenoma; ROC : receiver operating characteristic; AUC : area under the curve; FNA : fine needle aspiration; DWI : diffusion weighted imaging; DSC : dynamic susceptibility contrast enhanced; DCE : dynamic contrast enhanced; ADC : apparent diffusion coefficient; GBCA : gadolinium based contrast agent, ROI : region of interest Declarations Wenna Cheng contributed equal work to the first author Ningning Di in this manuscript and was listed as the joint first author. Ethics approval and consent to participate This study was conducted according to the tenets of the Declaration of Helsinki. Informed consent was obtained from each patient. The project was approved by the ethical committee of Binzhou Medical University Hospital. Consent for publication Not Applicable Availability of data and materials The datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Funding This work was supported by Natural Science Foundation of Shandong Province (Grant No.ZR2019BH025) and Science and Technology Research Program for Colleges and Universities in Shandong Province (Grant No.J18KB115). Authors’ contributions NND designed the study, acquired funding, acquired data, and wrote the main manuscript text. WNC analyzed data and revised the article. CHC and FJF acquired and analyzed data. XYJ and CX conceived and supervised the study. All authors have read and agreed to the published version of the manuscript. Acknowledgments Not Applicable References Zbären P, Schüpbach J, Nuyens M, Stauffer E, Greiner R, Häusler R: Carcinoma of the parotid gland . The American Journal of Surgery 2003, 186 (1):57-62. Park YM, Kang MS, Kim DH, Koh YW, Kim S, Lim J, Choi EC: Surgical extent and role of adjuvant radiotherapy of surgically resectable, low-grade parotid cancer . ORAL ONCOL 2020, 107 :104780. Quer M, Vander Poorten V, Takes RP, Silver CE, Boedeker CC, de Bree R, Rinaldo A, Sanabria A, Shaha AR, Pujol A et al : Surgical options in benign parotid tumors : a proposal for classification . EUR ARCH OTO-RHINO-L 2017, 274 (11):3825-3836. Thielker J, Grosheva M, Ihrler S, Wittig A, Guntinas-Lichius O: Contemporary Management of Benign and Malignant Parotid tumors . FRONT SURG 2018, 5 : 39 Feinstein AJ, Ciarleglio MM, Cong X, Otremba MD, Judson BL: Parotid gland lymphoma: Prognostic analysis of 2140 patients . The Laryngoscope 2013, 123 (5):1199-1203. Allevi F, Borzi P, Valsecchi F, Cucurullo M, Bolognesi F, Rabbiosi D, Biglioli F: The extracapsular dissection technique in the management of benign tumors of the parotid gland: our experience in 194 patients . The Journal of Laryngology & Otology 2024, 138 (5):565-569. GUDMUNDSSON JK, AJAN A, ABTAHI J: The accuracy of fine-needle aspiration cytology for diagnosis of parotid gland masses: a clinicopathological study of 114 patients . J APPL ORAL SCI 2016, 24 (6):561-567. Xu Z, Zheng S, Pan A, Cheng X, Gao M: A multiparametric analysis based on DCE-MRI to improve the accuracy of parotid tumor discrimination . EUR J NUCL MED MOL I 2019, 46 (11):2228-2234. Abdel Razek AAK, Samir S, Ashmalla GA: Characterization of Parotid tumors With Dynamic Susceptibility Contrast Perfusion-Weighted Magnetic Resonance Imaging and Diffusion-Weighted MR Imaging . J COMPUT ASSIST TOMO 2017, 41 (1):131-136. Sakamoto J, Imaizumi A, Sasaki Y, Kamio T, Wakoh M, Otonari-Yamamoto M, Sano T: Comparison of accuracy of intravoxel incoherent motion and apparent diffusion coefficient techniques for predicting malignancy of head and neck tumors using half-Fourier single-shot turbo spin-echo diffusion-weighted imaging . MAGN RESON IMAGING 2014, 32 (7):860-866. Semelka RC, Commander CW, Jay M, Burke LMB, Ramalho M: Presumed Gadolinium Toxicity in Subjects With Normal Renal Function . INVEST RADIOL 2016, 51 (10):661-665. McDonald RJ, McDonald JS, Kallmes DF, Jentoft ME, Murray DL, Thielen KR, Williamson EE, Eckel LJ: Intracranial Gadolinium Deposition after Contrast-enhanced MR Imaging . RADIOLOGY 2015, 2 75 (3):772-782. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M: Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging . RADIOLOGY 1988, 168 (2):497-505. Tao J, Yin Z, Li X, Zhang Y, Zhang K, Yang Y, Fang S, Wang S: Correlation between IVIM parameters and microvessel architecture: direct comparison of MRI images and pathological slices in an orthotopic murine model of rhabdomyosarcoma . EUR RADIOL 2023, 33 (12):8576-8584. Sheng Y, Dang X, Zhang H, Rui W, Wang J, Cheng H, Qiu T, Zhang Y, Ding Y, Yao Z et al : Correlations between intravoxel incoherent motion – derived fast diffusion and perfusion fraction parameters and VEGF- and MIB-1-positive rates in brain gliomas: an intraoperative MR-navigated, biopsy-based histopathologic study . EUR RADIOL 2023, 33 (8):5236-5246. Sumi M, Van Cauteren M, Sumi T, Obara M, Ichikawa Y, Nakamura T: Salivary Gland tumors : Use of Intravoxel Incoherent Motion MR Imaging for Assessment of Diffusion and Perfusion for the Differentiation of Benign from Malignant tumors . RADIOLOGY 2012, 263 (3):770-777. Yabuuchi H, Kamitani T, Sagiyama K, Yamasaki Y, Hida T, Matsuura Y, Hino T, Murayama Y, Yasumatsu R, Yamamoto H: Characterization of parotid gland tumors : added value of permeability MR imaging to DWI and DCE-MRI . EUR RADIOL 2020, 30 (12):6402-6412. Ma G, Xu X, Zhu L, Jiang J, Su G, Hu H, Bu S, Wu F: Intravoxel Incoherent Motion Magnetic Resonance Imaging for Assessing Parotid Gland tumors : Correlation and Comparison with Arterial Spin Labeling Imaging . KOREAN J RADIOL 2021, 22 (2):243. Markiet K, Glinska A, Nowicki T, Szurowska E, Mikaszewski B: Feasibility of Intravoxel Incoherent Motion (IVIM) and Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in Differentiation of Benign Parotid Gland tumors . Biology 2022, 11 (3):399. Zhang R, King AD, Wong LM, Bhatia KS, Qamar S, Mo FKF, Vlantis AC, Ai QYH: Discriminating between benign and malignant salivary gland tumors using diffusion-weighted imaging and intravoxel incoherent motion at 3 Tesla . DIAGN INTERV IMAG 2023, 104 (2):67-75. Chen Y, Huang N, Zheng Y, Wang F, Cao D, Chen T: Characterization of parotid gland tumors : Whole-tumor histogram analysis of diffusion weighted imaging, diffusion kurtosis imaging, and intravoxel incoherent motion – A pilot study . EUR J RADIOL 2024, 170 :111199. Haacke EM, Xu Y, Cheng YCN, Reichenbach JR: Susceptibility weighted imaging (SWI) . MAGN RESON MED 2004, 52 (3):612-618. Haller S, Haacke EM, Thurnher MM, Barkhof F: Susceptibility-weighted Imaging: Technical Essentials and Clinical Neurologic Applications . RADIOLOGY 2021, 299 (1):3-26. Park MJ, Kim HS, Jahng GH, Ryu CW, Park SM, Kim SY: Semiquantitative Assessment of Intratumoral Susceptibility Signals Using Non-Contrast-Enhanced High-Field High-Resolution Susceptibility-Weighted Imaging in Patients with Gliomas: Comparison with MR Perfusion Imaging . AM J NEURORADIOL 2009, 30 (7):1402-1408. Martín-Noguerol T, Santos-Armentia E, Ramos A, Luna A: An update on susceptibility ‐ weighted imaging in brain gliomas . EUR RADIOL 2024, 34 (10):6763-6775. Radbruch A, Wiestler B, Kramp L, Lutz K, Bäumer P, Weiler M, Roethke M, Sahm F, Schlemmer H, Wick W et al : Differentiation of glioblastoma and primary CNS lymphomas using susceptibility weighted imaging . EUR J RADIOL 2013, 82 (3):552-556. Zhang W, Zuo Z, Huang X, Jin G, Su D: Value of Diffusion-Weighted Imaging Combined with Susceptibility-Weighted Imaging in Differentiating Benign from Malignant Parotid Gland Lesions . MED SCI MONITOR 2018, 24 :4610-4616. Jiang J, Zhu L, Chen W, Chen L, Su G, Xu X, Wu F: Added value of susceptibility-weighted imaging to diffusion-weighted imaging in the characterization of parotid gland tumors . EUR ARCH OTO-RHINO-L 2020, 277 (10):2839-2846. Xu Z, Chen M, Zheng S, Chen S, Xiao J, Hu Z, Lu L, Yang Z, Lin D: Differential diagnosis of parotid gland tumors : Application of SWI combined with DWI and DCE-MRI . EUR J RADIOL 2022, 146 :110094. PARK SM, KIM HS, JAHNG GH, RYU CW, KIM SY: Combination of high-resolution susceptibility-weighted imaging and the apparent diffusion coefficient: added value to brain tumour imaging and clinical feasibility of non-contrast MRI at 3 T . BRIT J RADIOL 2010, 83 (990):466-475. Yamamoto T, Kimura H, Hayashi K, Imamura Y, Mori M: Pseudo-continuous arterial spin labeling MR images in Warthin tumors and pleomorphic adenomas of the parotid gland: qualitative and quantitative analyses and their correlation with histopathologic and DWI and dynamic contrast enhanced MRI findings . NEURORADIOLOGY 2018, 60 (8):803-812. Monestier L, Del Grande J, Haddad R, Santini L, Michel J, Varoquaux A, Fakhry N: Correlation between MRI (DWI and DCE) and cellularity of parotid gland pleomorphic adenomas . EUR ARCH OTO-RHINO-L 2024, 281 (5):2655-2665. Sumi M, Nakamura T: Head and neck tumors : combined MRI assessment based on IVIM and TIC analyses for the differentiation of tumors of different histological types . EUR RADIOL 2014, 24 (1):223-231. Jalnefjord O, Montelius M, Starck G, Ljungberg M: Optimization of b ‐ value schemes for estimation of the diffusion coefficient and the perfusion fraction with segmented intravoxel incoherent motion model fitting . MAGN RESON MED 2019, 82 (4):1541-1552. Shi L, Wang YXJ, Yu C, Zhao F, Kuang PD, Shao GL: CT and Ultrasound Features of Basal Cell Adenoma of the Parotid Gland: A Report of 22 Cases with Pathologic Correlation . AM J NEURORADIOL 2012, 33 (3):434-438. Dong Kyung Lee KWCC: Basal cell adenoma of the parotid gland: characteristics of 2-phase helical computed tomography and magnetic resonance imaging . J COMPUT ASSIST TOMO 2005, 29 (6):884-888. Mungai F, Verrone GB, Bonasera L, Bicci E, Pietragalla M, Nardi C, Berti V, Mazzoni LN, Miele V: Imaging biomarkers in the diagnosis of salivary gland tumors : the value of lesion/parenchyma ratio of perfusion-MR pharmacokinetic parameters . La radiologia medica 2021, 126 (10):1345-1355. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers invited by journal 14 May, 2025 Editor assigned by journal 13 May, 2025 Editor invited by journal 14 Apr, 2025 Submission checks completed at journal 11 Apr, 2025 First submitted to journal 11 Apr, 2025 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-6364778","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457326929,"identity":"a611cd14-9bb3-48e8-b8d7-7e67d139b3f9","order_by":0,"name":"Ningning Di","email":"","orcid":"","institution":"Binzhou Medical University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ningning","middleName":"","lastName":"Di","suffix":""},{"id":457326930,"identity":"86980101-ff6d-438c-8397-ab8e87563a47","order_by":1,"name":"Wenna Cheng","email":"","orcid":"","institution":"Binzhou Medical University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenna","middleName":"","lastName":"Cheng","suffix":""},{"id":457326931,"identity":"fdc54dfc-6295-4f4a-a6d5-89340097eeda","order_by":2,"name":"Huacheng Chen","email":"","orcid":"","institution":"Weifang Traditional Chinese Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huacheng","middleName":"","lastName":"Chen","suffix":""},{"id":457326932,"identity":"38ca6535-2a04-4c2c-8c8f-e37dbd459ea2","order_by":3,"name":"Junfang Fang","email":"","orcid":"","institution":"Binzhou Medical University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Junfang","middleName":"","lastName":"Fang","suffix":""},{"id":457326933,"identity":"c9f1dc5d-136c-4c2c-aca7-5e225ba7f812","order_by":4,"name":"Chang Xu","email":"","orcid":"","institution":"Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Xu","suffix":""},{"id":457326934,"identity":"d4c2ce38-0c1c-4c13-9748-5ff3ab5ec13b","order_by":5,"name":"Xingyue Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYHCChANAgoefvfkAQwIpWmQke44lEK0FDGwMbuQYEKfU4EbCwwM/d9TySPac+fzh4Q47Bv72bvyWSc5ISDjYe+Y40C+92yQSzyQzSJw5uwGvFn6JhIQDvG3HgLac3caQ2MbMYCCRi18LG1DLwb9ALUC/PP6Q2FZPWAvIlsO8bTUgLQwSiW2HCWuR7HmQcFi27QDQYcfMgFqO8xD0i8HxnOSPb9vq7IFR+fjjz7ZqOf72XvxaGARyEoDkYTifB79ysGeOHwCSdYQVjoJRMApGwcgFAMU+T65rWC7sAAAAAElFTkSuQmCC","orcid":"","institution":"Binzhou Medical University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xingyue","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2025-04-03 01:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6364778/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6364778/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83124359,"identity":"f03661c8-0682-4cbd-b602-f62f0d2df974","added_by":"auto","created_at":"2025-05-20 09:31:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":881004,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative images of adenoid cystic carcinoma (A1–A5), WT (B1–B5), PA\u0026nbsp; (C1–C5), and BCA (D1–D5). With reference to the axial IVIM-b0 map (Fig1. A1–D1), the regions of interest (ROIs) were manually drawn on areas with relatively low intensity in D map (Fig1. A2–D2), and high intensity in the D* map (Fig1. A3–D3) and f map (Fig1. A4–D4). The D values of adenoid cystic carcinoma and WT were lower (Fig1. A2, B2) than those of PA and BCA (Fig1. C2, D2) (0.267, 0.140, and 0.464, 0.626, ×10\u003csup\u003e-3\u003c/sup\u003emm\u003csup\u003e2\u003c/sup\u003e/s), but showed higher f values (Fig1. A4, B4) than BCA (Fig. D4) (0.937, 0.916, and 0.876). SWI shows high-grade ITSS of grade 3 in adenoid cystic carcinoma (Fig1. A5), moderate ITSS of grade2 in WT (Fig1. B5), and no ITSS in PA (Fig1. C5) and BCA (Fig1. D5). \u003cem\u003eWT:\u003c/em\u003e Warthin tumor, \u003cem\u003ePA:\u003c/em\u003e pleomorphic adenoma, \u003cem\u003eBCA:\u003c/em\u003e basal cell adenoma, \u003cem\u003eITSS:\u003c/em\u003e intratumoral susceptibility signal intensity.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6364778/v1/85222099c8ecef565863b7c8.png"},{"id":83124364,"identity":"51cc985b-9a21-4eb6-a9f4-43027b9bfc1e","added_by":"auto","created_at":"2025-05-20 09:31:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":195603,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the diagnostic ability of different parameters for discriminating malignant tumors from benign tumors(A) , from pleomorphic adenomas (B) and from basal cell adenoma (C), and discriminating Warthin tumor from basal cell adenoma (D). AUC = area under curve\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6364778/v1/ee1260f0fc6f01803551236e.png"},{"id":83126639,"identity":"9700bec8-f151-435b-8451-0c7f14b9af0f","added_by":"auto","created_at":"2025-05-20 09:47:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3735872,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6364778/v1/bd426025-2fa6-4017-861c-6ef780ce1763.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differentiation of parotid gland tumors using intravoxel incoherent motion magnetic resonance imaging integrated with susceptibility-weighted imaging","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParotid gland tumors, the most common type of salivary gland tumour, account for 3\u0026ndash;4% of all head and neck tumors. Pleomorphic adenoma (PA), a benign type of parotid gland, accounts for approximately 75% of cases, followed by Warthin\u0026rsquo;s tumour (WT) and basal cell adenoma (BCA). Malignant neoplasms, which seldom originate from the parotid gland, account for \u0026le;\u0026thinsp;3% of all cases of head and neck cancer[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVarious treatment strategies have been proposed for different histopathological types of parotid gland tumors. Radical surgery and extracapsular dissection are recommended for the management of PA owing to the relatively high frequency of local recurrence and potential malignant transformation[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Conservative follow-up or partial parotidectomy is sufficient for the management of WT or BCA, given the low risk of malignancy[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Parotidectomy with potential removal of the facial nerve followed by adjuvant radiotherapy is recommended for the treatment of malignant parotid gland tumors (MT). Chemotherapy or radiation therapy remains the standard of care for lymphoma[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Thus, the proper diagnosis of parotid gland tumors is crucial for selecting an appropriate treatment strategy in clinical settings. Fine-needle aspiration (FNA) biopsy is an effective and minimally invasive method for defining the pathology of parotid gland tumors; however, insufficient samples from the heterogeneous tumour area may result in misdiagnosis[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. FNA biopsy can facilitate tumour dissemination and metastasis, which may lead to facial nerve palsy in some patients. Thus, non-invasive and accurate pre-operative differentiation of parotid gland tumors is a crucial part of treatment planning.\u003c/p\u003e \u003cp\u003eMultiple advanced magnetic resonance (MR)-derived parameters that reflect tumour diffusion or perfusion information have been developed in recent years to evaluate the diagnostic efficiency of parotid gland tumors. Diffusion-weighted imaging (DWI), dynamic susceptibility contrast-enhanced (DSC), and dynamic contrast-enhanced (DCE) imaging have been frequently applied in clinical practice[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, microcapillary perfusion may affect the apparent diffusion coefficient (ADC) derived from DWI, thereby limiting its differentiation potential [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. DCE and DSC are gadolinium-based contrast agent (GBCA)-enhanced MRI perfusion techniques. An association between GBCA and nephrogenic systemic fibrosis has been reported in previous studies[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. GBCA-dependent examinations have confirmed long-term gadolinium retention in the central nervous system[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]; this has limited the clinical application of DSC and DCE to some extent.\u003c/p\u003e \u003cp\u003eIntravoxel incoherent motion (IVIM) MR was developed to quantitatively analyse the molecular diffusion of water and the microcirculation of blood in the capillary network. The pure diffusion coefficient (D) is related to water mobility and can be used to evaluate pure incoherent motion without disturbing blood perfusion. The pseudo-diffusion coefficient (D*) and perfusion fraction (f) facilitate the assessment of the microperfusion pattern while avoiding the disadvantages of Gd contrast[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Thus, IVIM is a promising method for evaluating the diffusion and perfusion patterns in tumors that can reflect the cellular structures and microvessels[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Few studies have confirmed the efficiency of IVIM in differentiating parotid gland tumors[\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, most previous studies mainly focused on the differential diagnosis of MT, PA, and WT. Thus, studies that aimed to discriminate BCA from other parotid gland tumors using IVIM are scarce.\u003c/p\u003e \u003cp\u003eSusceptibility-weighted imaging (SWI), a sensitive MRI technique used to detect the susceptibility differences in tissues, can noninvasively characterise haemorrhages, veins, and calcification structures. Consequently, SWI has been widely used in clinical practice[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The intratumoural susceptibility signal intensity (ITSS), a semi-quantitative parameter derived from SWI, has been defined as a hypo-signal with a linear or dot-like structure in the maximum intensity projection (MIP) image of SWI. Given its ability to provide information on tumour vascularisation and haemorrhage[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], ITSS plays an important role in tumour grading, differentiation, phenotyping, and post-treatment assessment and follow-up [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Only three studies have applied SWI to the assessment of parotid gland tumors to date[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Two of these studies focused on differentiating malignant from benign tumors, while one study classified malignant tumors separately from WT and PA, but did not consider BCA as an independent subgroup. To the best of our knowledge, no previous study has combined IVIM and SWI for diagnosing parotid gland tumors.\u003c/p\u003e \u003cp\u003eTherefore, the present study aimed to evaluate the added value of combining SWI with IVIM in characterizing parotid gland tumors, particularly in terms of differentiating BCA from other parotid gland tumors.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePatients\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSixty-six patients diagnosed with parotid neoplasms between December 2018 and December 2024 were enrolled in the present study. The inclusion criteria were as follows: (a) age \u0026gt;18 years, (b) SWI and IVIM were performed pre-operatively, (c) a definite pathological diagnosis was confirmed by surgery or biopsy, (d) no treatment commenced before MRI studies, and (e) image quality sufficient for analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe final study cohort comprised 70 pathologically confirmed focal tumors of the parotid gland of 66 patients. The lesions were divided into the MT (n=10) and BT (n=60) groups, with the BT group being further divided into the PA (n=27), WT (n=17), basal cell adenoma (BCA; n=10), and other (n=6) groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was conducted according to the tenets of the Declaration of Helsinki. Each patient signed informed consent. Ethics approval was obtained from the ethical committee of Binzhou Medical University Hospital.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMRI examinations\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll MRI examinations were conducted using a 3.0 T MRI scanner (Discovery MR750, GE Healthcare) with an 8-channel head array coil. All 66 patients underwent water T2 imaging, SWI, and IVIM-MRI. The acquisition parameters of the water T2 IDeal image were as follows: repetition time (TR)/echo time (TE), 4129/85 ms; matrix, 256\u0026times;256; field-of-view (FOV), 240 mm\u003csup\u003e2\u003c/sup\u003e; number of slices, 24; slice thickness, 4\u0026nbsp;mm without gap; scan time, 2\u0026rsquo;41\u0026rsquo;\u0026rsquo;. SWI was performed using a three-dimensional full-flow compensated gradient-echo sequence with the following parameters: TR/TE, 41/22.5 ms; matrix,\u0026nbsp;256\u0026times;256; FOV, 240 mm\u003csup\u003e2\u003c/sup\u003e; number of slices, 36; slice thickness, 2\u0026nbsp;mm without gap; and\u0026nbsp;scan time, 2\u0026rsquo;41\u0026rsquo;\u0026rsquo;. IVIM was performed using a single-shot echo-planar imaging diffusion sequence in the axial plane, with 19 b-values (b = 0, 30, 50, 100, 150, 200, 300, 500, 800, 1000, 1300, 1500, 1700, 2000, 2500, 3000, 3500, 4000, and 4500 s/mm\u003csup\u003e2\u003c/sup\u003e) in three diffusion directions. The detailed parameters were as follows: TR/TE, 4500/111.5 ms; matrix, 128\u0026times;128; FOV, 240 mm\u003csup\u003e2\u003c/sup\u003e; number of slices, 16; slice thickness, 4\u0026nbsp;mm without gap; and scan time, 8\u0026rsquo;15\u0026rsquo;\u0026rsquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eImage processing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA GE AW4.6 workstation (General Electric Healthcare) was used to perform post-processing after obtaining the IVIM data. The MADC program of the Functool 9.4.05 software was used to process and automatically generate D, D*, and f. Based on the bi-exponential signal evaluation model, Le Bihan et al[13]\u0026nbsp;evaluated the relationship between signal attenuation and b values using the:\u003c/p\u003e\n\u003cp\u003eS\u003csub\u003eb\u003c/sub\u003e∕S\u003csub\u003e0\u003c/sub\u003e = (1－f) ∙ e\u003csup\u003e(\u0026minus;bD)\u003c/sup\u003e＋f\u0026nbsp;∙\u0026nbsp;e\u003csup\u003e(\u0026minus;bD*)\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003ewhere S\u003csub\u003eb\u003c/sub\u003e and S\u003csub\u003e0\u0026nbsp;\u003c/sub\u003erepresent the signal attenuation when the b values are given a value and 0 s/mm\u003csup\u003e2\u003c/sup\u003e, respectively. f denotes the perfusion fraction, representing the fraction of microcirculation over the entire incoherent signal. D and D* denote the true molecular diffusion and perfusion-related pseudo-diffusion coefficients, respectively. The IVIM parameters were obtained by implementing two steps. The first step involved calculating D using a reduced set of high b-values of \u0026gt;200 s/mm\u003csup\u003e2\u003c/sup\u003e with a mono-exponential fitting. The contribution of D* to the signal can be neglected for b\u0026nbsp;\u0026ge;200 s/mm\u003csup\u003e2\u003c/sup\u003e. The second step involved the extraction of D as a fixed parameter; f and D* were calculated\u0026nbsp;using full bi-exponential curve fitting with nonlinear regression.\u003c/p\u003e\n\u003cp\u003eRegion-of-interest (ROI) analysis was conducted to measure the IVIM parameters. The placement of each ROI was determined based on the consensus between two senior radiologists with 12 and 8 years of experience who were blinded to the pathological results. The D, D*, and f values for each tumour were determined by placing two or three circular ROIs (ROI\u0026ge;4 mm\u003csup\u003e2\u003c/sup\u003e) on the D, D*, and f maps within the solid areas of the tumour solid.\u0026nbsp;The regions with relatively low intensities in\u0026nbsp;the D maps (\u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1. 1-4B2\u003c/strong\u003e) that exhibited high intensity in the f (\u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1. 1-4C2\u003c/strong\u003e) and D* maps\u0026nbsp;were targeted (\u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1-4D2\u003c/strong\u003e). Necrotic, cystic, calcified, and\u0026nbsp;haemorrhagic areas,\u0026nbsp;as well as the edges, were avoided to minimise the partial volume effect during ROI placement. The parameters were defined using the average D, D*, and f values\u0026nbsp;of\u0026nbsp;the targeted ROIs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eITSS was introduced and defined as a low-signal intensity with a fine linear or dot-like structure observed in the tumour on SWI-MIP images in the semi-quantitative SWI analysis[24]. Regions of necrosis and calcification within the tumors\u0026nbsp;were excluded with reference to\u0026nbsp;T2 IDeal and SWI phase images. The degree of ITSS was divided into four groups as described in a previous study[30]: Grade 0, no ITSS; Grade 1, 1\u0026ndash;5 dot-like or fine linear ITSSs; Grade 2, 6\u0026ndash;10 dot-like or fine linear ITSS; and Grade 3,\u0026nbsp;\u0026ge;11 dot-like or fine linear ITSS within the tumour.\u0026nbsp;All SWI images were evaluated\u0026nbsp;by two radiologists. Disagreements between the two radiologists were resolved by reaching a consensus through discussion with a\u0026nbsp;senior radiologist (with 12 years of experience).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using SPSS (version 27.0; IBM Corp., Armonk, NY, USA) and MedCalc (version 20.0; MedCalc software). Statistical significance was set at \u003cem\u003eP\u003c/em\u003e \u0026lt;0.05. Based on their normality, an independent sample\u0026nbsp;\u003cem\u003et\u003c/em\u003e-test or the Mann\u0026ndash;Whitney U test was used to assess the differences between the malignant and benign groups or subgroups of benign tumors in terms of patient age, D, D*, and f values. The Chi-square test or Fisher\u0026rsquo;s exact test was used to assess the differences in sex distribution and ITSS grade among patients with parotid tumors. A combined model of IVIM parameters and ITSS was established through logistic regression analysis. The diagnostic performance of D, f, D*, ITSS, and the combined model was assessed using receiver operating characteristic (ROC) curve analyses.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSeventy parotid gland tumour lesions (66 patients) were divided into two groups, the malignant and benign groups. The malignant group comprised 10 lesions (10 patients, seven men and three women; average age 49.40\u0026plusmn;11.52 years, adenoid cystic carcinoma [n=3], mucoepidermoid carcinomas [n=3], acinic cell carcinomas [n=2], squamous cell carcinoma [n=1], and melanoma metastasis [n=1]). The benign group comprised 60 lesions (57 patients, 35 men and 22 women; average age 51.91\u0026plusmn;10.63 years, Pas [n=27], WTs [n=17], BCAs [n=10], myoepitheliomas [n=2], schwannoma [n=1], neuroinoma [n=1], eosinophilic adenoma [n=1], and lymphoepithelial cyst [n=1]). No significant differences were observed between the benign and malignant groups in terms of age (\u003cem\u003eP\u003c/em\u003e=0.735) or sex distribution (\u003cem\u003eP\u003c/em\u003e=0.490).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eComparison and diagnostic performance of D, D*, f, and ITSS in differentiating MT from BT\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe D value of MT was significantly lower than that of BT; however, the ITSS grade of MT was significantly higher than that of BT (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, \u003cem\u003eP\u003c/em\u003e=0.009). No significant differences were observed between the groups in terms of the D* or f values (\u003cem\u003eP\u003c/em\u003e=0.681 and \u003cem\u003eP\u003c/em\u003e=0.718, respectively) (\u003cstrong\u003eTable 1\u003c/strong\u003e). The area under the curve (AUC), sensitivity, and specificity were 0.828, 70%, and 83.33%, respectively, when the D\u0026nbsp;\u0026le;0.161\u0026times;10\u003csup\u003e-3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s was set as the cut-off value in the ROC analyses. Optimal diagnostic performance was achieved when the ITSS grade was set as\u0026nbsp;\u0026ge;2 (AUC=0.793, sensitivity=70%, and specificity=73.33%). The application of the combined model of D and ITSS enhanced\u0026nbsp;the diagnostic performance:\u0026nbsp;AUC, 0.858; sensitivity, 70%; and specificity, 90% (\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Fig\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;2A\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eComparison and diagnostic performance of D, D*, f, and ITSS among MT, WT, PA, and BCA\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eITSS achieved a higher value for differentiating MT than WT (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). However, the IVIM parameters D, D*, and f exhibited invalid diagnostic performance (\u003cem\u003eP\u003c/em\u003e \u0026ge;0.05) (\u003cstrong\u003eTable 2\u003c/strong\u003e). ROC analysis revealed that the use of ITSS\u0026nbsp;\u0026ge;1 as the cut-off value yielded AUC, sensitivity, and specificity of 0.803, 100%, and 41.18%, respectively (\u003cstrong\u003eTable 3\u003c/strong\u003e). \u003cstrong\u003eTable 4\u003c/strong\u003e summarizes the detailed ITSS grades of each pathological tumour type.\u003c/p\u003e\n\u003cp\u003eThe D values of MT were significantly lower, but the ITSS of MT was higher than that of PA (\u003cem\u003eP\u003c/em\u003e=0.001 and \u003cem\u003eP\u003c/em\u003e=0.011. respectively) (\u003cstrong\u003eTable 2\u003c/strong\u003e). ROC analysis revealed that setting D\u0026nbsp;\u0026le;0.230\u0026times;10\u003csup\u003e-3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s as the cut-off value yielded AUC, sensitivity, and specificity of 0.852, 80%, and 81.48%, respectively. Setting ITSS\u0026nbsp;\u0026ge;2 as the cut-off value yielded AUC, sensitivity, and specificity of 0.819, 70%, and 81.48%, respectively. Notably, the application of the combined model enhanced the diagnostic performance, with an AUC of 0.893\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Fig. 2B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe D values of MT were significantly lower than those of BCA; however, the f value of MT was higher than that of BCA (\u003cem\u003eP\u003c/em\u003e=0.002 and \u003cem\u003eP\u003c/em\u003e=0.028, respectively) \u003cstrong\u003e(Table 2\u003c/strong\u003e). ROC analysis demonstrated good diagnostic performance with AUC, sensitivity, and specificity of 0.911, 70%, and 100%, respectively, when D\u0026nbsp;\u0026le;0.161\u0026times;10\u003csup\u003e-3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s was set as the cut-off value. The AUC, sensitivity, and specificity were 0.790, 90%, and 70%, respectively, when f \u0026gt;0.876 was set as the cut-off value. The application of the combined model enhanced the diagnostic performances, with an AUC of 0.950 (\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Fig\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;2C\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe D values of WT were significantly lower than those of BCA; however, the f value of WT was higher than that of BCA (\u003cem\u003eP\u003c/em\u003e=0.005 and \u003cem\u003eP\u003c/em\u003e=0.04, respectively) (\u003cstrong\u003eTable 2\u003c/strong\u003e). ROC analysis revealed good diagnostic efficiency when D \u0026le;0.505\u0026times;10\u003csup\u003e-3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s was set as the cut-off value with AUC, sensitivity, and specificity of 0.829, 94.12%, and 60%, respectively. The AUC, sensitivity, and specificity were 0.741, 82.35%, and 70% when f \u0026gt;0.786 was set as the cut-off value. Notably, the application of the combined D and f value enhanced the diagnostic efficiency, with an AUC of 0.824 (\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Fig\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;2D\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe D values of WT were significant compared with those of PA (\u003cem\u003eP\u003c/em\u003e=0.014) (\u003cstrong\u003eTable 2\u003c/strong\u003e). ROC analyses revealed that AUC, sensitivity, and specificity were 0.721, 64.71%, and 81.48%, respectively, setting D\u0026nbsp;\u0026le;0.248\u0026times;10\u003csup\u003e-3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s was set as the cut-off value (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;1\u0026nbsp;\u003c/strong\u003eshows representative images of adenoid cystic carcinoma, WT, PA, and BCA. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eComparison of IVIM- and SWI- derived parameters between malignant and benign groups\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"423\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eD*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003ef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eITSS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.183\u0026plusmn;0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.135\u0026plusmn;0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.92\u0026plusmn;0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2.10\u0026plusmn;0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eBT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.439\u0026plusmn;0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.132\u0026plusmn;0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.883\u0026plusmn;0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.98\u0026plusmn;0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e＜0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.009*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eData are reported as mean\u0026plusmn;standard deviation. The unit of D and D* is\u0026times;10\u003csup\u003e-3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s. \u0026nbsp;\u003cem\u003eD\u0026nbsp;\u003c/em\u003etrue diffusion coefficient, \u003cem\u003eD\u003c/em\u003e* pseudo-diffusion coefficient, \u003cem\u003ef\u003c/em\u003e fraction of perfusion, \u003cem\u003eITSS\u003c/em\u003e intratumoral susceptibility signal intensity, \u003cem\u003eMT\u0026nbsp;\u003c/em\u003emalignant tumor, \u003cem\u003eBT\u003c/em\u003e benign tumor, \u003cem\u003eWT\u0026nbsp;\u003c/em\u003eWarthin tumor, * the statistically significant \u003cem\u003eP\u003c/em\u003e values.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eComparison of IVIM- and SWI- derived parameters among MT, WT, PA and BCA\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"756\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003ePA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eBCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 421px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMT vs WT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMT vs PA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMT vs BCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eWT vs PA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eWT vs BCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003ePA vs BCA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.183\u0026plusmn;0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.257\u0026plusmn;0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.520\u0026plusmn;0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.466\u0026plusmn;0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.014*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.005*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eD*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.135\u0026plusmn;0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.136\u0026plusmn;0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.146\u0026plusmn;0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.092\u0026plusmn;0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003ef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.92\u0026plusmn;0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.890\u0026plusmn;0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.903\u0026plusmn;0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.847\u0026plusmn;0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.028*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eITSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e2.10\u0026plusmn;0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.94\u0026plusmn;0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.93\u0026plusmn;0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.20\u0026plusmn;1.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.038*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.011*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eData are reported as mean\u0026plusmn;standard deviation. The unit of D and D* is\u0026times;10\u003csup\u003e-3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s. \u003cem\u003eD\u0026nbsp;\u003c/em\u003etrue diffusion coefficient, \u003cem\u003eD\u003c/em\u003e* pseudo-diffusion coefficient, \u003cem\u003ef\u003c/em\u003e fraction of perfusion, \u003cem\u003eITSS\u003c/em\u003e intratumoral susceptibility signal intensity, \u003cem\u003eMT\u0026nbsp;\u003c/em\u003emalignant tumor, \u003cem\u003eWT\u0026nbsp;\u003c/em\u003eWarthin tumor, \u003cem\u003ePA\u0026nbsp;\u003c/em\u003epleomorphic adenoma, \u003cem\u003eBCA\u0026nbsp;\u003c/em\u003ebasal cell adenoma, * the statistically significant \u003cem\u003eP\u003c/em\u003e values\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eDiagnostic performance of IVIM- and SWI-derived parameters in discriminating parotid gland tumors\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eCut-off value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eArea under curve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eSensitivity（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eSpecificity（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eDiscriminating MT from BT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e≦0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e83.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eITSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e≧2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e73.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eD+ITSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eDiscriminating MT from PA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e≦0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e81.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eITSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e≧2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e81.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eD+ITSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e96.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eDiscriminating MT from WT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eITSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e≧1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e41.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eDiscriminating MT from BCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e≦0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026gt;0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.0106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eD+f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eDiscriminating WT from PA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e≦0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e64.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e81.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.0058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 473px;\"\u003e\n \u003cp\u003eDiscriminating WT from BCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e≦0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e94.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026gt;0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e82.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.0173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eD+f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eComparison of ITSS grade among each pathological tumor type\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTumor pathology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 455px;\"\u003e\n \u003cp\u003eITSS grade\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eBT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003ePA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eBCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eITSS\u003c/em\u003e intratumoral susceptibility signal intensity, \u003cem\u003eMT\u0026nbsp;\u003c/em\u003emalignant tumor, \u003cem\u003eBT\u003c/em\u003e benign tumor, \u003cem\u003eWT\u0026nbsp;\u003c/em\u003eWarthin tumor, \u003cem\u003ePA\u0026nbsp;\u003c/em\u003epleomorphic adenoma, \u003cem\u003eBCA\u0026nbsp;\u003c/em\u003ebasal cell adenoma\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eProper pre-operative diagnosis of parotid gland tumors plays a critical role in personalised treatment planning. The present study confirmed that IVIM and SWI-derived parameters are promising non-invasive methods for differentiating malignant parotid gland tumors from benign tumors and subgroups. Furthermore, combining IVIM parameters with ITSS derived from SWI improved the diagnostic accuracy. To the best of our knowledge, this is the first study to combine IVIM and SWI to characterise parotid gland tumors and differentiate BCA from other parotid gland tumors.\u003c/p\u003e \u003cp\u003eIVIM is a functional diffusion MRI technique that can quantify the diffusion and microvascular perfusion of tissues. The IVIM-derived D values reflect the pure diffusion of water molecules, excluding the influence of microcirculation to a large extend. Consistent with the findings of previous studies[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the present study confirmed the diagnostic value of D in differentiating MT from BT. Subgroup analyses revealed that the D value of PA was significantly higher than than that of MT or WT (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014), indicating the lower cell density and higher pure diffusion in PA compared with that of MT or WT. Although the D values of MT were lower than those of WT (0.183\u0026thinsp;\u0026plusmn;\u0026thinsp;0.084 mm\u003csup\u003e2\u003c/sup\u003e/s vs 0.257\u0026thinsp;\u0026plusmn;\u0026thinsp;0.132 mm\u003csup\u003e2\u003c/sup\u003e/s), no significant differences were observed between them (\u003cem\u003eP\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0.05). This may be attributed to the lymphoid components in WT containing dense small lymphocytes, which greatly limit pure diffusion. Thus, WT may exhibit D values lower than those of MT (0.61\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e \u0026plusmn;0.11 mm\u003csup\u003e2\u003c/sup\u003e/s vs 0.96\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e \u0026plusmn;0.22 mm\u003csup\u003e2\u003c/sup\u003e/s) as demonstrated in the study conducted by Sumi et al[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIVIM-derived D* and f, which are non-intravenous contrast medium-dependent perfusion parameters, reflect information regarding microcirculation perfusion at the capillary level. These parameters have been identified as biomarkers for tumour angiogenesis and microvasculature[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Angiogenesis is an oblique feature of tumour growth that results in hyper-microvessels in MT; the capillary density of WT is higher than that of PA [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, D* and f values have been used to differentiate these tumors owing to their varied vascular statuses. However, their diagnostic efficiency varies. Sumi et al.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]reported that the f values of WT were significantly higher than those of PA, consistent with the findings of the study conducted by Ma et al.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], who demonstrated that the D* and f values of WT were significant higher than those of PA and MT, respectively. In contrast, Markit et al.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] reported that the f values of WT were lower than those of PA. Zhang et al.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]also found that D* increased significantly from PA to MT and WT; however, the f values exhibited no significant differences. However, D* and f values can cannot be used to differentiate among MT, WT, and PA[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The present study yielded a similar result: limited diagnostic efficiency of the D* and f values in differentiating between MT, BT, WT, and PA. These conflicting results may be attributed to the differences in the histopathological subtypes of parotid gland tumors included in these studies, the relatively low microvascular density in low-grade carcinoma or lymphoma in MT, and the high vascularisation in WT and solid-type PA, which led to a bias in statistical analysis[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Moreover, the different b-values used in different studies may have affected the diagnostic ability of IVIM[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Wide-high b-values (19 b values with an upper limit of 4500 s/mm\u003csup\u003e2\u003c/sup\u003e) were applied in IVIM in the present study, which is different from other studies that used relatively narrow b-values (ranging from 10 to 14 b values with an upper limit of 800\u0026ndash;2500 s/mm\u003csup\u003e2\u003c/sup\u003e). A study conducted by Shen et al.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]revealed that IVIM-derived D* values showed better efficiency in evaluating glioma angiogenesis and perfusion status when using a wide-high b value (22 b values with an upper limit of 5000 s/mm\u003csup\u003e2\u003c/sup\u003e) scheme compared with that observed with a narrow-low b value (15 b values with an upper limit of 1500 s/mm\u003csup\u003e2\u003c/sup\u003e) pattern. Thus, the high b-values applied herein provide further insights into the IVIM-derived perfusion parameters of f and D* in differentiating parotid gland tumors.\u003c/p\u003e \u003cp\u003eIn addition, to comparisons of MT, WT, and PA, the present study included BCA as a separate subgroup and emphasised comparisons with other tumors. The D values of BCA were significantly higher than those of MT or WT (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, respectively), implying a higher cell density in BCA. Furthermore, the f value could help distinguish BCA from MT and WT. The f value of BCA was lower than those of MT and WT owing to the more microvascular nature of parotid gland carcinomas and WT. PA have lower microvessel counts[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], whereas BCA have abundant small capillaries and venues[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This discrepancy in the vasculature may have contributed to the higher f values in the BCA[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, the present study gained contrary result that BCA showed lower f values than PA (0.847\u0026thinsp;\u0026plusmn;\u0026thinsp;0.079 vs 0.903\u0026thinsp;\u0026plusmn;\u0026thinsp;0.081), although there was no significant difference(P\u0026thinsp;=\u0026thinsp;0.070). In a pathological analysis, Monestier et al.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]found that the hypercellular subtype of PA was more vascular than the hypocellular subtype. Lee et al.[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] found that the typical solid type of BCA usually has numerous endothelial-lined vascular channels with prominent small capillaries and venules; however, the trabecular and tubular types often feature a markedly sparse vasculature in the myxoid area. Therefore, the variation in vascularity among the PA and BCA subtypes might explain the differences in the f values between the two groups in our study.\u003c/p\u003e \u003cp\u003eMT exhibited a significantly higher ITSS, an SWI-derived semi-quantitative parameter, compared with that of BT (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) in the present study, which is consistent with the findings of previous studies[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This may be attributed to MT exhibiting greater tumour angiogenesis characterised by the presence of immature and tortuous vascular structure that are more susceptible to bleeding. Therefore, MT exhibited a higher ITSS compared with those of the WT and PA groups, with the subgroup analysis indicating significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011, respectively). Jiang et al. reported that MT exhibited ITSS higher than that of PA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, they found no significant difference in the ITSS between MT and BT or WT mice (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), which contradicts our results. This discrepancy might be attributed to the lymphoma portion of MT in the study cohort. No lymphoma was included in the MT group in the present study. However, lymphoma accounted for 36.4% (4/11) of cases of MT in the study by Jiang et al.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Neovascularization is not a prominent feature of lymphoma; this may explain the rarely visible ITSS in lymphoma[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Mungai et al.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]reported that the MR perfusion pharmacokinetic parameters K\u003csup\u003etrans\u003c/sup\u003e, Ve, and Vp of parotid gland lymphomas were lower than those of other malignant tumors and even lower than those of WT and benign parotid tumors. This finding further indicates the lower microvasculature in gland lymphomas. Thus, the lymphoma portion may have affected the diagnostic efficiency of the ITSS.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this study is the first to combine IVIM and SWI parameters to differentiate between parotid gland tumors. The diagnostic efficiency of D for differentiating MT from BT (AUC, 0.828 vs 0.858) and PA (AUC, 0.852 vs 0.893) was enhanced when combined with ITSS; however, the difference was not significant. Thus, SWI can add diagnostic information to IVIM to discriminate parotid tumors.\u003c/p\u003e \u003cp\u003eIn addition to the small cohort size, the present study has some limitations. First, MT has several histopathologic subtypes and histologic grades. The small sample size for each subtype or grade limited further subgroup differentiation. Furthermore, the large discrepancy between the number of MT and BT may have led to a bias in the statistical analysis. Further studies with larger sample sizes must be conducted to clarify the value of IVIM and SWI in subtype comparisons and confirm the findings of the present study. Second, a validated diagnostic model with internal and external datasets was not used owing to the limited sample size. Further multicentre studies with sample sizes must be conducted to validate the findings of the present study. Third, images often exhibit distortion in the high b-value phase of IVIM with the SS-EPI sequence, which increases the bias for parameter measurements. The RS-EPI sequence may overcome this disadvantage and improve the image quality of IVIM.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIn conclusion, the present study confirmed that IVIM plays an important role in differentiating parotid gland tumors, although an overlap between MT and WT was observed. Notably, this overlap could be compensated for by SWI. The addition of SWI to IVIM enhanced the diagnostic efficiency in differentiating parotid gland tumors. Thus, SWI should be integrated with IVIM to make use of the supplementary diagnostic information.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cem\u003eIVIM\u003c/em\u003e: intravoxel incoherent motion magnetic;\u003cem\u003e\u0026nbsp;D\u003c/em\u003e:\u003cem\u003e\u0026nbsp;\u003c/em\u003etrue diffusion coefficient; D* : pseudo-diffusion coefficient; \u003cem\u003ef\u003c/em\u003e: fraction of perfusion; \u003cem\u003eSWI\u003c/em\u003e: susceptibility-weighted imaging; \u003cem\u003eITSS\u003c/em\u003e: intratumoral susceptibility signal intensity; \u003cem\u003eMT\u003c/em\u003e:\u003cem\u003e\u0026nbsp;\u003c/em\u003emalignant tumor; \u003cem\u003eBT\u003c/em\u003e: \u0026nbsp;benign tumor; \u003cem\u003eWT\u003c/em\u003e:\u003cem\u003e\u0026nbsp;\u003c/em\u003eWarthin tumor; PA:\u003cem\u003e\u0026nbsp;\u003c/em\u003epleomorphic adenoma; \u003cem\u003eBCA\u003c/em\u003e:\u003cem\u003e\u0026nbsp;\u003c/em\u003ebasal cell adenoma; \u003cem\u003eROC\u003c/em\u003e: receiver operating characteristic; \u003cem\u003eAUC\u003c/em\u003e: area under the curve; \u003cem\u003eFNA\u003c/em\u003e: fine needle aspiration; \u003cem\u003eDWI\u003c/em\u003e: diffusion weighted imaging; \u003cem\u003eDSC\u003c/em\u003e: dynamic susceptibility contrast enhanced; \u003cem\u003eDCE\u003c/em\u003e: dynamic contrast enhanced; \u003cem\u003eADC\u003c/em\u003e: apparent diffusion coefficient; \u003cem\u003eGBCA\u003c/em\u003e: gadolinium based contrast agent, \u003cem\u003eROI\u003c/em\u003e: region of interest\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eWenna Cheng contributed equal work to the first author Ningning Di in this manuscript and was listed as the joint first author. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted according to the tenets of the Declaration of Helsinki. Informed consent was obtained from each patient. The project was approved by the ethical committee of Binzhou Medical University Hospital.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Natural Science Foundation of Shandong Province (Grant No.ZR2019BH025) and Science and Technology Research Program for Colleges and Universities in Shandong Province (Grant No.J18KB115).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNND designed the study, acquired funding, acquired data, and wrote the main manuscript text. WNC analyzed data and revised the article. CHC and FJF acquired and analyzed data. XYJ and CX conceived and supervised the study. All authors have read and agreed to the published version of the manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZb\u0026auml;ren P, Sch\u0026uuml;pbach J, Nuyens M, Stauffer E, Greiner R, H\u0026auml;usler R: \u003cstrong\u003eCarcinoma of the parotid gland\u003c/strong\u003e. \u003cem\u003eThe American Journal of Surgery\u003c/em\u003e 2003, \u003cstrong\u003e186\u003c/strong\u003e(1):57-62.\u003c/li\u003e\n\u003cli\u003ePark YM, Kang MS, Kim DH, Koh YW, Kim S, Lim J, Choi EC: \u003cstrong\u003eSurgical extent and role of adjuvant radiotherapy of surgically resectable, low-grade parotid cancer\u003c/strong\u003e. \u003cem\u003eORAL ONCOL\u003c/em\u003e 2020, \u003cstrong\u003e107\u003c/strong\u003e:104780.\u003c/li\u003e\n\u003cli\u003eQuer M, Vander Poorten V, Takes RP, Silver CE, Boedeker CC, de Bree R, Rinaldo A, Sanabria A, Shaha AR, Pujol A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eSurgical options in benign parotid \u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e\u003cstrong\u003e: a proposal for classification\u003c/strong\u003e. \u003cem\u003eEUR ARCH OTO-RHINO-L\u003c/em\u003e 2017, \u003cstrong\u003e274\u003c/strong\u003e(11):3825-3836.\u003c/li\u003e\n\u003cli\u003eThielker J, Grosheva M, Ihrler S, Wittig A, Guntinas-Lichius O: \u003cstrong\u003eContemporary Management of Benign and Malignant Parotid \u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e. \u003cem\u003eFRONT SURG\u003c/em\u003e 2018, \u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e39\u003c/li\u003e\n\u003cli\u003eFeinstein AJ, Ciarleglio MM, Cong X, Otremba MD, Judson BL: \u003cstrong\u003eParotid gland lymphoma: Prognostic analysis of 2140 patients\u003c/strong\u003e. \u003cem\u003eThe Laryngoscope\u003c/em\u003e 2013, \u003cstrong\u003e123\u003c/strong\u003e(5):1199-1203.\u003c/li\u003e\n\u003cli\u003eAllevi F, Borzi P, Valsecchi F, Cucurullo M, Bolognesi F, Rabbiosi D, Biglioli F: \u003cstrong\u003eThe extracapsular dissection technique in the management of benign \u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e\u003cstrong\u003e of the parotid gland: our experience in 194 patients\u003c/strong\u003e. \u003cem\u003eThe Journal of Laryngology \u0026amp; Otology\u003c/em\u003e 2024, \u003cstrong\u003e138\u003c/strong\u003e(5):565-569.\u003c/li\u003e\n\u003cli\u003eGUDMUNDSSON JK, AJAN A, ABTAHI J: \u003cstrong\u003eThe accuracy of fine-needle aspiration cytology for diagnosis of parotid gland masses: a clinicopathological study of 114 patients\u003c/strong\u003e. \u003cem\u003eJ APPL ORAL SCI\u003c/em\u003e 2016, \u003cstrong\u003e24\u003c/strong\u003e(6):561-567.\u003c/li\u003e\n\u003cli\u003eXu Z, Zheng S, Pan A, Cheng X, Gao M: \u003cstrong\u003eA multiparametric analysis based on DCE-MRI to improve the accuracy of parotid tumor discrimination\u003c/strong\u003e. \u003cem\u003eEUR J NUCL MED MOL I\u003c/em\u003e 2019, \u003cstrong\u003e46\u003c/strong\u003e(11):2228-2234.\u003c/li\u003e\n\u003cli\u003eAbdel Razek AAK, Samir S, Ashmalla GA: \u003cstrong\u003eCharacterization of Parotid \u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e\u003cstrong\u003e With Dynamic Susceptibility Contrast Perfusion-Weighted Magnetic Resonance Imaging and Diffusion-Weighted MR Imaging\u003c/strong\u003e. \u003cem\u003eJ COMPUT ASSIST TOMO\u003c/em\u003e 2017, \u003cstrong\u003e41\u003c/strong\u003e(1):131-136.\u003c/li\u003e\n\u003cli\u003eSakamoto J, Imaizumi A, Sasaki Y, Kamio T, Wakoh M, Otonari-Yamamoto M, Sano T: \u003cstrong\u003eComparison of accuracy of intravoxel incoherent motion and apparent diffusion coefficient techniques for predicting malignancy of head and neck \u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e\u003cstrong\u003e using half-Fourier single-shot turbo spin-echo diffusion-weighted imaging\u003c/strong\u003e. \u003cem\u003eMAGN RESON IMAGING\u003c/em\u003e 2014, \u003cstrong\u003e32\u003c/strong\u003e(7):860-866.\u003c/li\u003e\n\u003cli\u003eSemelka RC, Commander CW, Jay M, Burke LMB, Ramalho M: \u003cstrong\u003ePresumed Gadolinium Toxicity in Subjects With Normal Renal Function\u003c/strong\u003e. \u003cem\u003eINVEST RADIOL\u003c/em\u003e 2016, \u003cstrong\u003e51\u003c/strong\u003e(10):661-665.\u003c/li\u003e\n\u003cli\u003eMcDonald RJ, McDonald JS, Kallmes DF, Jentoft ME, Murray DL, Thielen KR, Williamson EE, Eckel LJ: \u003cstrong\u003eIntracranial Gadolinium Deposition after Contrast-enhanced MR Imaging\u003c/strong\u003e. \u003cem\u003eRADIOLOGY\u003c/em\u003e 2015, \u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e75\u003c/strong\u003e(3):772-782.\u003c/li\u003e\n\u003cli\u003eLe Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M: \u003cstrong\u003eSeparation of diffusion and perfusion in intravoxel incoherent motion MR imaging\u003c/strong\u003e. \u003cem\u003eRADIOLOGY\u003c/em\u003e\u003cem\u003e \u003c/em\u003e1988, \u003cstrong\u003e168\u003c/strong\u003e(2):497-505.\u003c/li\u003e\n\u003cli\u003eTao J, Yin Z, Li X, Zhang Y, Zhang K, Yang Y, Fang S, Wang S: \u003cstrong\u003eCorrelation between IVIM parameters and microvessel architecture: direct comparison of MRI images and pathological slices in an orthotopic murine model of rhabdomyosarcoma\u003c/strong\u003e. \u003cem\u003eEUR RADIOL\u003c/em\u003e 2023, \u003cstrong\u003e33\u003c/strong\u003e(12):8576-8584.\u003c/li\u003e\n\u003cli\u003eSheng Y, Dang X, Zhang H, Rui W, Wang J, Cheng H, Qiu T, Zhang Y, Ding Y, Yao Z\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eCorrelations between intravoxel incoherent motion\u003c/strong\u003e\u003cstrong\u003e\u0026ndash;\u003c/strong\u003e\u003cstrong\u003ederived fast diffusion and perfusion fraction parameters and VEGF- and MIB-1-positive rates in brain gliomas: an intraoperative MR-navigated, biopsy-based histopathologic study\u003c/strong\u003e. \u003cem\u003eEUR RADIOL\u003c/em\u003e 2023, \u003cstrong\u003e33\u003c/strong\u003e(8):5236-5246.\u003c/li\u003e\n\u003cli\u003eSumi M, Van Cauteren M, Sumi T, Obara M, Ichikawa Y, Nakamura T: \u003cstrong\u003eSalivary Gland \u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e\u003cstrong\u003e: Use of Intravoxel Incoherent Motion MR Imaging for Assessment of Diffusion and Perfusion for the Differentiation of Benign from Malignant \u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e. \u003cem\u003eRADIOLOGY\u003c/em\u003e 2012, \u003cstrong\u003e263\u003c/strong\u003e(3):770-777.\u003c/li\u003e\n\u003cli\u003eYabuuchi H, Kamitani T, Sagiyama K, Yamasaki Y, Hida T, Matsuura Y, Hino T, Murayama Y, Yasumatsu R, Yamamoto H: \u003cstrong\u003eCharacterization of parotid gland \u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e\u003cstrong\u003e: added value of permeability MR imaging to DWI and DCE-MRI\u003c/strong\u003e. \u003cem\u003eEUR RADIOL\u003c/em\u003e 2020, \u003cstrong\u003e30\u003c/strong\u003e(12):6402-6412.\u003c/li\u003e\n\u003cli\u003eMa G, Xu X, Zhu L, Jiang J, Su G, Hu H, Bu S, Wu F: \u003cstrong\u003eIntravoxel Incoherent Motion Magnetic Resonance Imaging for Assessing Parotid Gland \u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e\u003cstrong\u003e: Correlation and Comparison with Arterial Spin Labeling Imaging\u003c/strong\u003e. \u003cem\u003eKOREAN J RADIOL\u003c/em\u003e 2021, \u003cstrong\u003e22\u003c/strong\u003e(2):243.\u003c/li\u003e\n\u003cli\u003eMarkiet K, Glinska A, Nowicki T, Szurowska E, Mikaszewski B: \u003cstrong\u003eFeasibility of Intravoxel Incoherent Motion (IVIM) and Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in Differentiation of Benign Parotid Gland \u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e. \u003cem\u003eBiology\u003c/em\u003e 2022, \u003cstrong\u003e11\u003c/strong\u003e(3):399.\u003c/li\u003e\n\u003cli\u003eZhang R, King AD, Wong LM, Bhatia KS, Qamar S, Mo FKF, Vlantis AC, Ai QYH: \u003cstrong\u003eDiscriminating between benign and malignant salivary gland \u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e\u003cstrong\u003e using diffusion-weighted imaging and intravoxel incoherent motion at 3 Tesla\u003c/strong\u003e. \u003cem\u003eDIAGN INTERV IMAG\u003c/em\u003e 2023, \u003cstrong\u003e104\u003c/strong\u003e(2):67-75.\u003c/li\u003e\n\u003cli\u003eChen Y, Huang N, Zheng Y, Wang F, Cao D, Chen T: \u003cstrong\u003eCharacterization of parotid gland \u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e\u003cstrong\u003e: Whole-tumor histogram analysis of diffusion weighted imaging, diffusion kurtosis imaging, and intravoxel incoherent motion \u003c/strong\u003e\u003cstrong\u003e\u0026ndash;\u003c/strong\u003e\u003cstrong\u003e A pilot study\u003c/strong\u003e. \u003cem\u003eEUR J RADIOL\u003c/em\u003e 2024, \u003cstrong\u003e170\u003c/strong\u003e:111199.\u003c/li\u003e\n\u003cli\u003eHaacke EM, Xu Y, Cheng YCN, Reichenbach JR: \u003cstrong\u003eSusceptibility weighted imaging (SWI)\u003c/strong\u003e. \u003cem\u003eMAGN RESON MED\u003c/em\u003e 2004, \u003cstrong\u003e52\u003c/strong\u003e(3):612-618.\u003c/li\u003e\n\u003cli\u003eHaller S, Haacke EM, Thurnher MM, Barkhof F: \u003cstrong\u003eSusceptibility-weighted Imaging: Technical Essentials and Clinical Neurologic Applications\u003c/strong\u003e. \u003cem\u003eRADIOLOGY\u003c/em\u003e 2021, \u003cstrong\u003e299\u003c/strong\u003e(1):3-26.\u003c/li\u003e\n\u003cli\u003ePark MJ, Kim HS, Jahng GH, Ryu CW, Park SM, Kim SY: \u003cstrong\u003eSemiquantitative Assessment of Intratumoral Susceptibility Signals Using Non-Contrast-Enhanced High-Field High-Resolution Susceptibility-Weighted Imaging in Patients with Gliomas: Comparison with MR Perfusion Imaging\u003c/strong\u003e. \u003cem\u003eAM J NEURORADIOL\u003c/em\u003e 2009, \u003cstrong\u003e30\u003c/strong\u003e(7):1402-1408.\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;n-Noguerol T, Santos-Armentia E, Ramos A, Luna A: \u003cstrong\u003eAn update on susceptibility\u003c/strong\u003e\u003cstrong\u003e‐\u003c/strong\u003e\u003cstrong\u003eweighted imaging in brain gliomas\u003c/strong\u003e. \u003cem\u003eEUR RADIOL\u003c/em\u003e 2024, \u003cstrong\u003e34\u003c/strong\u003e(10):6763-6775.\u003c/li\u003e\n\u003cli\u003eRadbruch A, Wiestler B, Kramp L, Lutz K, B\u0026auml;umer P, Weiler M, Roethke M, Sahm F, Schlemmer H, Wick W\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eDifferentiation of glioblastoma and primary CNS lymphomas using susceptibility weighted imaging\u003c/strong\u003e. \u003cem\u003eEUR J RADIOL\u003c/em\u003e 2013, \u003cstrong\u003e82\u003c/strong\u003e(3):552-556.\u003c/li\u003e\n\u003cli\u003eZhang W, Zuo Z, Huang X, Jin G, Su D: \u003cstrong\u003eValue of Diffusion-Weighted Imaging Combined with Susceptibility-Weighted Imaging in Differentiating Benign from Malignant Parotid Gland Lesions\u003c/strong\u003e. \u003cem\u003eMED SCI MONITOR\u003c/em\u003e 2018, \u003cstrong\u003e24\u003c/strong\u003e:4610-4616.\u003c/li\u003e\n\u003cli\u003eJiang J, Zhu L, Chen W, Chen L, Su G, Xu X, Wu F: \u003cstrong\u003eAdded value of susceptibility-weighted imaging to diffusion-weighted imaging in the characterization of parotid gland \u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e. \u003cem\u003eEUR ARCH OTO-RHINO-L\u003c/em\u003e 2020, \u003cstrong\u003e277\u003c/strong\u003e(10):2839-2846.\u003c/li\u003e\n\u003cli\u003eXu Z, Chen M, Zheng S, Chen S, Xiao J, Hu Z, Lu L, Yang Z, Lin D: \u003cstrong\u003eDifferential diagnosis of parotid gland \u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e\u003cstrong\u003e: Application of SWI combined with DWI and DCE-MRI\u003c/strong\u003e. \u003cem\u003eEUR J RADIOL\u003c/em\u003e 2022, \u003cstrong\u003e146\u003c/strong\u003e:110094.\u003c/li\u003e\n\u003cli\u003ePARK SM, KIM HS, JAHNG GH, RYU CW, KIM SY: \u003cstrong\u003eCombination of high-resolution susceptibility-weighted imaging and the apparent diffusion coefficient: added value to brain tumour imaging and clinical feasibility of non-contrast MRI at 3 T\u003c/strong\u003e. \u003cem\u003eBRIT J RADIOL\u003c/em\u003e 2010, \u003cstrong\u003e83\u003c/strong\u003e(990):466-475.\u003c/li\u003e\n\u003cli\u003eYamamoto T, Kimura H, Hayashi K, Imamura Y, Mori M: \u003cstrong\u003ePseudo-continuous arterial spin labeling MR images in Warthin \u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e\u003cstrong\u003e and pleomorphic adenomas of the parotid gland: qualitative and quantitative analyses and their correlation with histopathologic and DWI and dynamic contrast enhanced MRI findings\u003c/strong\u003e. \u003cem\u003eNEURORADIOLOGY\u003c/em\u003e 2018, \u003cstrong\u003e60\u003c/strong\u003e(8):803-812.\u003c/li\u003e\n\u003cli\u003eMonestier L, Del Grande J, Haddad R, Santini L, Michel J, Varoquaux A, Fakhry N: \u003cstrong\u003eCorrelation between MRI (DWI and DCE) and cellularity of parotid gland pleomorphic adenomas\u003c/strong\u003e. \u003cem\u003eEUR ARCH OTO-RHINO-L\u003c/em\u003e 2024, \u003cstrong\u003e281\u003c/strong\u003e(5):2655-2665.\u003c/li\u003e\n\u003cli\u003eSumi M, Nakamura T: \u003cstrong\u003eHead and neck \u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e\u003cstrong\u003e: combined MRI assessment based on IVIM and TIC analyses for the differentiation of \u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e\u003cstrong\u003e of different histological types\u003c/strong\u003e. \u003cem\u003eEUR RADIOL\u003c/em\u003e 2014, \u003cstrong\u003e24\u003c/strong\u003e(1):223-231.\u003c/li\u003e\n\u003cli\u003eJalnefjord O, Montelius M, Starck G, Ljungberg M: \u003cstrong\u003eOptimization of b\u003c/strong\u003e\u003cstrong\u003e‐\u003c/strong\u003e\u003cstrong\u003evalue schemes for estimation of the diffusion coefficient and the perfusion fraction with segmented intravoxel incoherent motion model fitting\u003c/strong\u003e. \u003cem\u003eMAGN RESON MED\u003c/em\u003e 2019, \u003cstrong\u003e82\u003c/strong\u003e(4):1541-1552.\u003c/li\u003e\n\u003cli\u003eShi L, Wang YXJ, Yu C, Zhao F, Kuang PD, Shao GL: \u003cstrong\u003eCT and Ultrasound Features of Basal Cell Adenoma of the Parotid Gland: A Report of 22 Cases with Pathologic Correlation\u003c/strong\u003e. \u003cem\u003eAM J NEURORADIOL\u003c/em\u003e 2012, \u003cstrong\u003e33\u003c/strong\u003e(3):434-438.\u003c/li\u003e\n\u003cli\u003eDong Kyung Lee KWCC: \u003cstrong\u003eBasal cell adenoma of the parotid gland: characteristics of 2-phase helical computed tomography and magnetic resonance imaging\u003c/strong\u003e. \u003cem\u003eJ COMPUT ASSIST TOMO\u003c/em\u003e 2005, \u003cstrong\u003e29\u003c/strong\u003e(6):884-888.\u003c/li\u003e\n\u003cli\u003eMungai F, Verrone GB, Bonasera L, Bicci E, Pietragalla M, Nardi C, Berti V, Mazzoni LN, Miele V: \u003cstrong\u003eImaging biomarkers in the diagnosis of salivary gland \u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e\u003cstrong\u003e: the value of lesion/parenchyma ratio of perfusion-MR pharmacokinetic parameters\u003c/strong\u003e. \u003cem\u003eLa radiologia medica\u003c/em\u003e 2021, \u003cstrong\u003e126\u003c/strong\u003e(10):1345-1355.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Differential diagnosis, Parotid gland tumor, Intravoxel incoherent motion, Susceptibility-weighted imaging","lastPublishedDoi":"10.21203/rs.3.rs-6364778/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6364778/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e To evaluate the value of combining susceptibility-weighted imaging (SWI) with intravoxel incoherent motion (IVIM) magnetic resonance (MR) imaging in the characterisation of parotid gland tumors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods\u003c/strong\u003e Seventy parotid gland tumors of 66 patients who had undergone SWI and IVIM were retrospectively reviewed. The true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and fraction of perfusion (f) values calculated from IVIM imaging and the intra-tumoural susceptibility signal (ITSS) obtained from SWI were assessed and compared using independent Student’s \u003cem\u003et\u003c/em\u003e, Mann–Whitney \u003cem\u003eU\u003c/em\u003e, Chi-square, or Fisher’s exact tests. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic performance for the classification of parotid gland tumors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eThe D values of malignant parotid gland tumors (MT) were significantly lower than those of benign parotid gland tumors (BT) (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). The ITSS of MT was significantly higher than that of BT (\u003cem\u003eP=\u003c/em\u003e0.009). Subgroup analyses revealed that the D values of MT and Warthin tumors (WT) were significantly lower than those of pleomorphic adenoma (PA) (\u003cem\u003eP\u003c/em\u003e=0.001 and \u003cem\u003eP\u003c/em\u003e=0.014, respectively) and basal cell adenoma (BCA) (\u003cem\u003eP\u003c/em\u003e=0.002 and \u003cem\u003eP\u003c/em\u003e=0.005, respectively); however, the f values of MT and WT were significantly higher than those of BCA (\u003cem\u003eP\u003c/em\u003e=0.028 and \u003cem\u003eP\u003c/em\u003e=0.04, respectively). The ITSS of MT was significantly higher than those of WT and PA (\u003cem\u003eP\u003c/em\u003e=0.038 and \u003cem\u003eP\u003c/em\u003e=0.011, respectively). Integration with ITSS enhanced the diagnostic performance of D in differentiating MT from BT (area under the curve [AUC]0.858 vs 0.828) and PA (AUC 0.893 vs 0.852). Compared with the application of a single parameter (D or f), the application of the combination model enhanced the diagnostic performance for differentiating MT from BCA (AUC 0.950 vs 0.911 vs 0.790). The D* values exhibited no significant difference in terms of discriminating between the parotid gland tumors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e IVIM plays a critical role in discriminating MT from benign parotid tumors and characterizing the subgroups of benign parotid tumors. SWI offers additional diagnostic differentiation information for IVIM and servers a supplementary imaging marker for the characterization of parotid gland tumors.\u003c/p\u003e","manuscriptTitle":"Differentiation of parotid gland tumors using intravoxel incoherent motion magnetic resonance imaging integrated with susceptibility-weighted imaging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-20 09:31:38","doi":"10.21203/rs.3.rs-6364778/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-05-16T00:53:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152579674379739528432594951489084402300","date":"2025-05-14T22:02:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-14T18:31:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-13T08:24:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-14T15:00:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-12T03:03:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-04-12T03:02:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d69aaecf-888a-463d-b9fc-7900c58314d4","owner":[],"postedDate":"May 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-20T09:31:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-20 09:31:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6364778","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6364778","identity":"rs-6364778","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}