Assessing parotid gland tumor perfusion with a new imaging biomarker DDVD (diffusion-derived vessel density): promising initial results

Abstract

Purpose

DDVD (diffusion-derived vessel density) is an MRI surrogate of the area of micro-vessels per unit tissue area. DDVD is calculated according to: DDVD(b0b20) = Sb0/ROIarea0 – Sb20/ROIarea20, where Sb0 and Sb20 refer to the tissue signal when b is 0 or 20 s/mm². This study applied DDVD to assess the perfusion of parotid gland tumors.

Materials and Methods

MRI was performed at 3.0T. Diffusion-weighted images with b-values of 0, 20, 1000 s/mm² were acquired for 24 pleomorphic adenomas (PA), 16 Warthin’s tumors (WT), and 14 malignant tumors (MT). DDVDr was DDVD of the tumor divided by DDVD of tumor-free parotid gland tissue. A systematic literature search was conducted for parotid gland tumor perfusion imaging studies. Perfusion parameters of PA, MT, and WT were normalized by PA value, and thus the ratio for PA value was assumed to be 1. The ratio results of MT DDVDr and WT DDVDr further normalized by PA DDVDr were compared with the literature results. In addition, the ADC (apparent diffusion coefficient) was calculated with b=0, 1000 s/mm² images.

Results

Most of the tumors were hyper-vascular relative to native parotid gland tissue with DDVDr >1, with PA, MT, and WT having mean DDVDr values of 1.753±0.462, 2.731±2.254, and 4.324 ±3.203, respectively. DDVDr ratios of MT/PA and WT/PA agreed with the literature perfusion results derived with non-DWI methods, particularly consistent with CT perfusion blood volume results. PA, MT, and WT had ADC values of 1.485 ±0.36, 0.969± 0.194, and 0.772± 0.070 (×10^-3 mm²/s), respectively. WT had very high DDVDr and low ADC, while PA had moderately high DDVDr and very high ADC. Most of the MTs had moderately high DDVDr and low ADC. A combination of ADC and DDVDr can largely differentiate between PA and WT.

Conclusion

DDVD results approximately agree with parotid gland perfusion imaging literature data. A combination of DDVD and ADC may support parotid gland tumor tissue characterization.

Key Points

• As a straightforward diffusion MRI biomarker, DDVD can be used to assess parotid gland tumor perfusion.

• Warthin’s tumors have very high DDVD and low ADC.

• Pleomorphic adenomas have moderately high DDVDr and very high ADC.

• A combination of ADC and DDVDr can largely differentiate between pleomorphic adenoma and Warthin’s tumor.

Citation Format

• Yao D, King AD, Zhang R etal. Assessing parotid gland tumor perfusion with a new imaging biomarker DDVD (diffusion-derived vessel density): promising initial results. Rofo 2024; DOI 10.1055/a-2543-3305

Zusammenfassung

Zweck

DDVD (diffusion-derived “vessel density” ) ist ein MRT-Ersatzwert für die Fläche der Mikrogefäße pro Gewebeflächeneinheit. DDVD wird wie folgt berechnet: DDVD(b0b20) = Sb0/ROIarea0 – Sb20/ROIarea20, wobei sich Sb0 und Sb20 auf das Gewebesignal beziehen, wenn b 0 oder 20 s/mm² beträgt. In dieser Studie wurde DDVD zur Beurteilung der Durchblutung von Ohrspeicheldrüsentumoren eingesetzt.

Materialien und Methoden

Die MRT wurde bei 3,0 T durchgeführt. Diffusionsgewichtete Bilder mit b-Werten von 0, 20, 1000 s/mm² wurden von 24 pleomorphen Adenomen (PA), 16 Warthin-Tumoren (WT) und 14 malignen Tumoren (MT) aufgenommen. DDVDr war DDVD des Tumors geteilt durch DDVD des tumorfreien Ohrspeicheldrüsengewebes. Es wurde eine systematische Literaturrecherche nach Studien zur Perfusionsbildgebung von Ohrspeicheldrüsentumoren durchgeführt. Die Perfusionsparameter von PA, MT und WT wurden durch die PA-Messung normalisiert, und daher wurde das Verhältnis für die PA-Messung als 1 angenommen. Die Verhältnisergebnisse von MT DDVDr und WT DDVDr, weiter normalisiert durch PA DDVDr, wurden mit Literaturergebnissen verglichen. Zusätzlich wurde der ADC (scheinbarer Diffusionskoeffizient) mit Bildern mit b=0, 1000 s/mm² berechnet.

Ergebnisse

Die meisten Tumoren waren im Vergleich zum nativen Ohrspeicheldrüsengewebe hypervaskulär mit DDVDr >1, wobei PA, MT und WT mittlere DDVDr-Werte von 1,753 ± 0,462, 2,731 ± 2,254 bzw. 4,324 ± 3,203 aufwiesen. Die DDVDr-Verhältnisse von MT/PA und WT/PA stimmten mit in der Literatur angegebenen Perfusionsergebnissen überein, die mit Nicht-DWI-Methoden ermittelt wurden, und waren insbesondere mit den CT-Perfusionsblutvolumenergebnissen konsistent. PA, MT und WT hatten ADC-Werte von 1,485 ± 0,36, 0,969 ± 0,194 bzw. 0,772 ± 0,070 (× 10^-3 mm²/s). WT hatte sehr hohe DDVDr und niedrige ADC, während PA mäßig hohe DDVDr und sehr hohe ADC hatte. Die meisten MT hatten mäßig hohe DDVDr und niedrige ADC. Eine Kombination aus ADC und DDVDr kann PA und WT weitgehend trennen.

Schlussfolgerung

Die DDVD-Ergebnisse stimmen in etwa mit den Literaturdaten zur Ohrspeicheldrüsen-Perfusionsbildgebung überein. Eine Kombination aus DDVD und ADC kann die Charakterisierung des Ohrspeicheldrüsentumorgewebes unterstützen.

Kernaussagen

• Als unkomplizierter Diffusions-MRT-Biomarker kann DDVD zur Beurteilung der Durchblutung von Ohrspeicheldrüsentumoren verwendet werden.

• Warthin-Tumoren haben einen sehr hohen DDVD und einen niedrigen ADC.

• Pleomorphe Adenome haben einen mäßig hohen DDVDr und einen sehr hohen ADC.

• Eine Kombination aus ADC und DDVDr kann pleomorphe Adenome und Warthin-Tumoren weitgehend voneinander unterscheiden.

Keywords

parotid gland tumors - Warthin’s tumor - perfusion - pleomorphic adenoma - diffusion-weighted imaging

Seventy percent of parotid gland tumors (PGT) are benign and are mostly pleomorphic adenomas (PA) and Warthin’s tumors (WT). PA is the most common parotid gland tumor comprising 60% of cases. Warthin’s tumor (WT) is the second most common benign tumor comprising 15% to 20% of cases. 20% to 30% of tumors of the parotid gland are malignant [1] [2]. The initial diagnosis of a parotid gland tumor before surgical excision is confirmed by histopathological analysis of specimens obtained by ultrasound-guided fine-needle aspiration or core biopsy. The main role of CT or MRI is to map the location and extent of the tumor prior to surgical resection. Imaging can be additionally helpful for characterization in terms of supporting or questioning the provisional cytological diagnosis. When a parotid gland tumor is detected as an incidental imaging finding, which is common, it is helpful to indicate in the report whether the incidental parotid gland tumor is likely to be benign or malignant.

Perfusion imaging techniques such as dynamic contrast-enhanced (DCE) CT and DCE MRI have been investigated to evaluate functional parameters of parotid gland tumors. An in vivo marker of angiogenesis is valuable for diagnosis, risk stratification, and the monitoring of therapeutic success in parotid gland tumor patients. Serial perfusion imaging studies during radiotherapy can quantify a dynamic state of perfusion in the tumor in response to radiation.

The diffusion-weighted imaging (DWI)-derived surrogate biomarker DDVD (diffusion derived vessel density) works on the principle that on spin-echo-type echo-planar-imaging diffusion-weighted images, blood vessels (including micro-vessels) have a high signal when there is no motion probing gradient (b=0 s/mm²), while they have a low signal even when very low b-values (such as b=1 or 2 s/mm²) are applied. Thus, the signal difference between images when the motion probing gradient is “off” and “on” reflects the extent of tissue vessel density. DDVD is derived from the equation [3]:

DDVD(b0b2) = Sb0/ROIarea0 – Sb2/ROIarea2 [unit: arbitrary unit (au)/pixel] Eq. (1)

where ROIarea0 and ROIarea2 refer to the number of pixels in the selected region-of-interest (ROI) on b=0 s/mm² and b=2 s/mm² images, respectively. Sb0 refers to the measured sum signal intensity within the ROI when b=0 s/mm², and Sb2 refers to the measured sum signal intensity within the ROI when b=2 s/mm², thus Sb/ROIarea equates to the mean signal intensity within the ROI. Sb2 and ROIarea2 can also be approximated by other low b-value diffusion image data.

DDVD can thus be interpreted as a physiological surrogate of the area of micro-vessels per unit tissue area, which can be conceptually converted to a surrogate of the volume of micro-vessels per tissue unit volume if multiple slices are integrated. The clinical usefulness of DDVD as a straightforward diffusion imaging biomarker has been recently demonstrated [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]. Huang et al. [4] showed that DDVD analysis demonstrates liver parenchyma has an age-dependent decrease in micro-perfusion. This is in agreement with the known physiological age-dependent reduction in liver blood flow, which has been well documented using a variety of technical methods including histology, dye dilution, and indicator clearance. DDVD is a useful parameter for distinguishing between livers with and without fibrosis. Livers with more severe fibrosis tend to have even lower DDVD values than those with milder liver fibrosis [3] [5]. Li et al. [7] reported that the DDVD ratio of HCC to adjacent liver is 2.94 (median, 95%CI: 2.42–3.52), which is in agreement with DCE CT/MRI literature data. Li et al. [7] also demonstrated a trend of higher DDVD value for HCCs positive for microvascular invasion than for HCCs negative for microvascular invasion. Lu et al. [8] reported that rectal carcinoma with earlier clinical grades had a higher DDVDr (tumor to tumor-free rectal wall) than those with advanced clinical grades (2.245 for grade 0&I, 1.460 for grade II, 1.430 for grade III, 1.130 for grade IV). These are all consistent with the biological behaviors of HCC and rectal carcinoma. Moreover, He et al. [9] reported that DDVD analysis of the placenta allows excellent separation of normal and early preeclampsia pregnancies. Lu et al. [10] reported that the regional DDVD in the placenta is significantly higher in pregnant women with placenta accreta spectrum disorders than in women with a normal placenta and is especially higher in patients with placenta increta and percreta. In a qualitative study including 22 liver hemangiomas, 4 cases of focal nodular hyperplasia, and 24 HCCs, a correct diagnosis was made by a trained reader solely based on a DDVD pixelwise map in 90.9% of the liver hemangiomas and 96.4% of the mass-forming lesions (inclusive of focal nodular hyperplasia and HCC) [11]. Preliminary results of ours also show that DDVD can help to assess ischemic penumbra of brain stroke [12].

The analysis of DDVD requires only two b-values (with one being b=0 s/mm²), thus allowing a significantly shorter scanning time than DCE CT/MRI or intravoxel incoherent motion (IVIM) imaging. Compared with DCE imaging, the DDVD protocol does not involve contrast injection, data acquisition is much faster, and data post-processing is also relatively straightforward. This study evaluates the potential of DDVD to assess parotid gland tumor perfusion.

Materials and methods

The study was approved by the local institutional review board and informed consent was obtained. Study subjects included adult patients who had parotid gland PA, WT, or a malignant tumor (MT) confirmed by histopathological examination of surgical specimens or tissue samples obtained using biopsy/aspiration cytology, and who had undergone MRI with prescribed diffusion-weighted imaging (DWI) from June 2013 to March 2021 ([Fig. 1]). MRI was performed using a 3T MRI scanner (Achieva TX, Philips Healthcare), with a head and neck coil for radiofrequency transmission and a 16-channel Philips neurovascular phased-array coil for reception. In addition to standard structural MRI, fat-suppressed single-shot spin-echo echo-planar-imaging sequence DWI data were sampled in the axial plane. The DWI parameters included: TR/TE, 2000/50 ms; FOV 230×230 mm; 4 mm slice thickness; inter-slice gap: 0.06 mm; voxel size 2.7×2.7×6.0 mm; number-of-excitation (NEX)=1. Three b-values of 0, 20, 1000 s/mm² were acquired.

Fig. 1 Flow diagram of patient inclusion.

Tumors were excluded when: (i) the tumor was almost entirely cystic/necrotic without a solid component for analysis; (ii) the tumor contained a large internal hemorrhage area; or (iii) tumor images were degraded by artifacts. The quantification applied ROI-based analysis. The contours for ROI analyses were drawn by a trainee radiologist and a trained engineering graduate. An experienced radiologist checked the quality of the contour before the final quantitative analysis. Contours were manually drawn on b =0 s/mm² covering the entire tumor and adjusted with reference to anatomical images, excluding necrotic and cystic regions ([Fig. 2]). Visible vessels near the tumor or parotid gland were also carefully excluded. The contours on b =0 s/mm² image were then automatically fitted to the other DW images (b =20, 1000 s/mm²). DDVD was obtained according to Eq. (1) , while Sb2/ROIarea2 was replaced by Sb20/ROIarea20, where ROIarea20 refers to the number of pixels in the selected ROI on the b=20 s/mm² image and Sb20 refers to the measured sum signal intensity within the ROI.

Fig. 2 DDVD pixel-by-pixel map shows higher perfusion of tumor relative to parotid gland tissue. A1, B1, C1: T2-weighted anatomical image (with fat suppression applied in A1 and C1). A2, B2, C2: diffusion-weighted image (DWI) acquired at b = 0 s/mm². Parotid gland (ROI) is identified with reference to anatomical image. A3, B3, C3: DWI acquired at b = 20 s/mm². A4, B4, C4: DWI acquired at b = 0 s/mm² and the overlayed pixel-by-pixel DDVD map for parotid gland. The tumor region is denoted by orange arrows and parotid gland tissue is denoted by green arrows. A is a case of left side PA, tumor shows higher DDVD value than the contra-lateral parotid gland DDVD value (a vessel [asterisk] is shown with very high DDVD). B is a case with bilateral WT, with tumor regions showing higher DDVD than the parotid gland tissue. C is a right MT (basal cell adenocarcinoma). This case of basal cell adenocarcinoma has a very high DDVD value. Note that DDVD pixel-by-pixel maps presented in this figure are for demonstration only. DDVD values in this study were calculated with the region-of-interest (ROI) approach.

As absolute MR signal intensity is influenced by various factors, including B1 spatial inhomogeneity, coil loading, and receiver gain, etc., in this study the ratio (DDVDr) of tumor DDVD to contra-lateral tumor-free parotid gland tissue DDVD was used to minimize these scaling factors. DDVDr was taken as:

DDVDr = [ROI-based mean DDVD(b0b20) of PGT]/[ROI-based mean DDVD(b0b20) of parotid gland] Eq. (2)

In addition to the new DDVD parameter, we also extracted the apparent diffusion coefficient (ADC), the most established diffusion parameter for characterizing parotid gland tumors. ADC was obtained as

where b₁₀₀₀ =1000 and b₀ =0 s/mm² , Sb₀ refers to the signal intensity within the ROI when b=0 s/mm², and Sb₁₀₀₀ refers to the signal intensity within the ROI when b=1000 s/mm².

The DDVD value and ADC value shared the same ROI for each slice, and the mean of all included slice measurements was regarded as the value of the DWI scan, with the last step being weighted by the ROI area of each slice. For a random selection of 12 cases with a suitable amount of normal parotid gland tissue available bilaterally for analysis, the DDVD value agreement of the left parotid and the right parotid had an intraclass correlation coefficient (ICC) of 0.75, and that of the ADC was 0.73. As the perfusion of parotid gland tumors in this study could not be directly correlated with physiological perfusion methods, the DDVD values were compared with perfusion results in the literature. The perfusion parameters of PA, MT, and WT were normalized by the PA value, and thus the ratio of the PA value was assumed to be 1. We systematically searched CT, MRI, and ultrasound perfusion studies which reported the perfusion parameters separately at least for PA and WT, while excluding those of apparently unreasonable results.

DDVD values are presented as mean, median, and 95% CI (confidence interval). Statistical analysis was performed using GraphPad Prism Software (GraphPad Software Inc., San Diego, CA, USA). Comparisons between groups were tested by Mann-Whitney U test. A P-value < 0.05 was considered statistically significant.

Results

The results are shown in Tables 1 and 2 and in Figs. 3 (Supplementary document 1) and 4. Most of the parotid gland tumors were hypervascular relative to the parotid gland tissue with DDVDr >1. [Table 1] shows that DDVDr was on average WT (4.324±3.203) > MT (2.731±2.254, Mann-Whitney U test p=0.032 for WT vs. MT comparison) > PA (1.753±0.462, Mann-Whitney U test p=0.016 for WT vs. PA comparison). Among the MTs, the case of adenoid cystic carcinoma had a lower DDVDr of 0.61, while one case of basal cell adenocarcinoma and one case of acinic cell carcinoma both had a very high DDVDr (>7.6). Two cases of metastatic carcinoma (one metastatic papillary carcinoma from a primary thyroid tumor and the another was metastatic undifferentiated carcinoma) had a moderately high DDVDr.

Table 1 DDVDr values of various parotid gland tumors.

Tumor category	Case no.	Range	Average	Median	SD	95%CI
Pleomorphic adenoma	24	1.025–2.543	1.753	1.791	0.4616	1.451–2.096
Malignant tumors	13	0.7859–7.705	2.731	1.950	2.254	1.465–2.899
Warthin’s tumor	16	1.280–11.42	4.324	3.045	3.203	2.290–6.220
Adenoid cystic carcinoma	1	0.611	0.611	0.611	NA	NA
epithelial myoepithelial carcinoma	1	1.465	1.465	1.465	NA	NA
Lymphoepithelioma-like carcinoma	2	0.786–2.175	1.481	1.481	0.982	0.786–2.175
Mucoepidermoid carcinoma	6	1.465–2.201	1.829	1.881	0.3103	1.465–2.201
Metastatic carcinoma	2	1.834–2.899	2.367	2.367	0.7534	1.834–2.899
Basal cell adenocarcinoma	1	7.666	7.666	7.666	NA	NA
Acinic cell carcinoma	1	7.705	7.705	7.705	NA	NA

A comparison of DDVDr in this study and literature results of parotid tumor perfusion is shown in [Fig. 3]. DDVDr ratios of both MT and WT were similar to the mean of CT measured blood volume of these tumors [13] [14] [15]. The DDVDr ratio MT/PA was close to the histology microvessel density ratio reported by Zhao et al. [16], and the DDVDr ratio WT/PA was close to the mean of the histology microvessel density ratio of three studies [16] [17] [18]. [Fig. 3] shows that DDVD was more similar to perfusion volume, rather than perfusion velocity (reflected by blood flow measured by DCE CT) or micro-vessel permeability (reflected by Ktrans measured by DCE MRI). When the MT/PA ratio mean and the WT/PA ratio mean were calculated for 1) CT blood volume, 2) contrast-enhanced delta CT absorption density, 3) ultrasound superb microvascular imaging vascular index, and 4) immunohistochemical for microvascular density count, these means were very similar to the MT/PA ratio mean and the WT/PA ratio mean of DDVDr.

Fig. 3 A comparison of DDVDr ratio and literature reports of parotid tumor perfusion. Perfusion parameters of PA, MT, and WT were normalized by the PA value, and thus the ratio for PA value was assumed to be 1. For results from the current study used in this graph, the DDVDr ratio of PA is assumed to be 1 (i.e., 1.753/1.753=1), the DDVDr ratio of MT assumed to be 1.558 (i.e., 2.731/1.753=1.558), and DDVDr ratio of WT assumed to be 2.467 (i.e., 4.324/1.753=2.467). Further as an example, Dong et al. [Dentomaxillofac Radiol. 2014;43:20130237.] reported a CT perfusion blood volume (BV) of 2.8 ± 1.3, 5.5 ± 3.0, and 10.3 ± 4.5 ml/100g for PA, MT, and WT, respectively; thus in this graph, the ratio value for MT is normalized to be 1.964 (i.e., 5.5/2.8), and the ratio value for WT is normalized to be 3.679 (i.e., 10.3/2.8). Besides DDVD data, each sign (dots, etc) represents the result of one literature report. The CT perfusion imaging parameter of BV has a very similar mean ratio value to that of DDVDr, both for MT and WT. MT or WT multi-mean is an aggregation of CT of blood volume, delta CT, ultrasound vascular index, and histology microvascular density (MVD), with the mean values also being similar to that of DDVDr (denoted by dotted red lines). Bar: mean value of the group (see Supplementary document 1 for more information regarding the literature ).

[Table 2] shows that the ADC value was on average PA (1.485±0.361) > MT (0.970±0.194, Mann-Whitney U test p<0.0001 for PA vs. MT comparison) > WT (0.772±0.070, Mann-Whitney U test p<0.006 for MT vs. WT comparison). [Fig. 4] A shows that a combination of ADC and DDVDr can largely differentiate PA from WT. WT had a very high DDVDr (with the lowest case being 1.28) and a low ADC, while PA had a modestly high DDVDr and a very high ADC. [Fig. 4] B shows that most MTs had a moderately high DDVDr and a low ADC. The case of basal cell adenocarcinoma and the case of acinic cell carcinoma had a very high DDVDr and also had a moderately high ADC. [Fig. 4] C shows that when the DDVD is very high the tumor is likely to be a WT, and when the ADC is very high the tumor is likely to be a PA. However, when a WT had only modestly high DDVD and when a PA had only modestly high ADC (<1.25 ×10^-3mm²/s), WT and PA overlapped with MT.

Table 2 ADC values (×10^-3 mm²/s) of various parotid gland tumors.

Tumor category	Case No.	Range:	Average	median	SD	95%CI
Warthin’s tumor	16	0.658–0.910	0.772	0.756	0.070	0.713–0.848
Malignant tumors	13	0.635–1.244	0.970	1.006	0.194	0.8283–1.126
Pleomorphic adenoma	24	0.793–2.248	1.485	1.456	0.361	1.228–1.650
Lymphoepithelioma-like carcinoma	2	0.635–0.829	0.732	0.7319	0.137	0.6352–0.8286
Metastatic carcinoma	2	0.695–0.828	0.762	0.762	0.094	0.695–0.828
Adenoid cystic carcinoma	1	0.838	0.838	0.838	NA	NA
epithelial myoepithelial carcinoma	1	0.9460	0.9460	0.946	NA	NA
Mucoepidermoid carcinoma	6	0.841–1.239	1.050	1.064	0.129	0.841–1.239
Acinic cell carcinoma	1	1.126	1.126	1.126	NA	NA
Basal cell adenocarcinoma	1	1.244	1.244	1.244	NA	NA

Fig. 4 A combination of DDVDr and ADC to characterize the tumors. The dotted line denotes the DDVDr value of =1. Y-axis: DDVD ratio. X-axis: ADC in ×10^-3 mm²/s. A WT and PA data. B MT data. C WT, MT, and PA data.

Based on the data in [Fig. 4] A, the probability of WT as differentiated from PA can be modelled as:

probability of WT (compared to PA): ln(p/(1−p))= 50.914 − 74.317 × ADC ×10³ + 5.637 × DDVDr Eq. (3)

Based on the data in [Fig. 4] C, the probability of PA and WT as differentiated from MT can be modelled as:

probability of PA&WT (compared to MT): ln(p/(1−p))= −1.411 + 2.008 × ADC ×10³ + 0.118 × DDVDr Eq. (4) .

According to Eq. (4) , the probability of case 1, case 2, and case 3 in [Fig. 4] C being MT was 0.5172, respectively [ Eq. (3) and Eq. (4) are further explained in Supplementary document 2].

ADC value can also be calculated using the three b-values b=0, 20, 1000 s/mm². ADC values calculated with the three b-values b=0, 20, 1000 s/mm² show a similar pattern as ADC values calculated with the two b-values b=0, 1000 s/mm² (details shown in Supplementary document 3 ).

Discussion

It has been noted that the blood vessels including micro-vessels have a high signal when there is no motion probing gradient (b=0 s/mm²) and a low signal when even very low b-values are applied [3]. For a spin-echo-type echo-planar imaging sequence, the second motion probing gradient after the 180-degree radiofrequency pulse could not fully re-focus the flowing spins in vessel and micro-vessels after being de-phased by the first motion probing gradient before the 180-degree radiofrequency pulse (see Supplementary Abb. 1 in [12]). Our DDVD and ADC results for PA, MT, and WT are consistent with those of previous studies. Histology studies show WTs also have abundant tumor vascularity [16] [17] [19], a feature that is also readily detected using perfusion imaging ([Fig. 3]). Until the knowledge of how the DDVD value can be converted to perfusion parameters obtained by other methods is better known, we calculated the DDVDr ratios of MT and WT to PA in this study and compared this ratio to published data obtained with other non-DWI methods. [Fig. 3] shows that the DDVDr ratios of both MT and WT were very similar to the CT-measured blood volume of these tumors and were also consistent with histological microvessel density results. [Fig. 3] tentatively suggests that, at least in relative terms, the DDVD may be used to derive reliable parotid gland tumor perfusion value. While PA showed lower blood perfusion relative to MT and WT as shown in [Table 1], PA still showed hyper-perfusion relative to the native parotid gland. In this study, one case of basal cell adenocarcinoma and one case of acinic cell carcinoma had a distinctively higher DDVD than the rest of the other MTs. We cannot confirm whether this is a common feature of these two types of tumor or whether these were coincidental findings of this study.

As shown in this study, it has been well documented that the group mean of the ADC value decreases from PAs to MTs to WTs [20] [21]. For PAs, a high ADC value is attributed to its long T2 of myxomatous and chondroid contents but is not determined by its free water content [22] [23]. For WTs, a low ADC for the solid portion of WTs is attributed to a mature lymphocytic component [19] that is similar to lymphoid tissue elsewhere in the body [19] [24]. Lymphoid tissues have a T2 around 70 ms [24]. This is consistent with the recently proposed theory that the parotid gland tumor T2 relaxation time also contributes to the ADC values [23] [24]. With a 3.0T scanner, Baohong et al. [25] reported that the T2 was 142.9±53.8 ms for PA, which is in a very high range and such a high T2 is associated with an elevated ADC value. On the other hand, Baohong et al. reported a T2 of 83.27±23.47 for WT, and it has been noted that a T2 range of 60–80 is associated with a low ADC [24] [26]. Baohong et al. reported a T2 of 97.5 ± 45.2 ms for MT, which is a moderately high T2 and thus these tumors are associated with a moderately low ADC (relative to native gland tissue).

IVIM is another potential noninvasive DWI technique to assess tissue perfusion. IVIM data acquisition is time-consuming, and the results highly depend on the scan parameters (such as TE and number and distribution of the b-values) and the data-fitting approach. Data fitting instability is a common issue for IVIM studies. Conflicting results for parotid gland tumor IVIM results have been reported. Zhang et al. [27] reported that WT, MT, and PT had an IVIM-D_fast of 31.87 ± 6.47, 16.69 ± 6.75, and 7.98 (×10^-3 mm²/s), respectively, which is similar in trend to the DDVD data in the current study. However, Zhang et al. [27] did not show a difference in IVIM-PF (IVIM-derived perfusion fraction) for the three groups of tumors (WT: 12.29±3.84%, MT:11.02±0.04%: PT: 9.73%, p=0.225). On the other hand, Ma et al. [21] reported that WT, MT, and PT had an IVIM-PF of 16.8 ±2.5%, 11.3± 4.0%, 9.9 ± 3.8% (p<0.001), and an IVIM-D_fast of 58.0 ± 35.499, 47.7 ±46.8, 33.6 ± 22.2 (×10^-3 mm²/s), respectively. Markiet et al. [20] reported an IVIM-D_fast of 135.9 (interquartile range: 145.5) and 47.05 (20.7) (×10^-3 mm²/s) for WT and PA, respectively, and IVIM-PF of 24.8 (8.5) and 34.4 (14.5) for WT and PA, respectively (which is the opposite of the expectation). Recent studies showed that, in addition to physiological perfusion, IVIM-PF is also heavily affected by a tissue’s T2 relaxation time, with a longer T2 leading to a “depressed” PF value [28]. For example, HCCs mostly have higher perfusion values compared with the adjacent normal liver tissue, reflecting their hypervascular nature. Paradoxically, most authors reported a decreased IVIM-PF of an HCC relative to the adjacent liver [7]. The conflicting IVIM results of Markiet et al. [20], Ma et al. [21], and Zhang et al. [27] are likely complicated by both data fitting instability and the T2 contribution to IVIM results. Another noninvasive MRI technique called arterial spin labelling has also been used for the quantification of parotid gland tumor perfusion. [Fig. 3] suggests that arterial spin labelling tends to overestimate the relative perfusion particularly for WT. Arterial spin labelling for parotid gland tumors also reported less stable results. For example, Kato et al. [29] reported an arterial spin labelling-derived WT/PA ratio of 23.2, while Razek [30] reported lower WT perfusion relative to PA (WT/PA ratio= 1.38). The current study shows that by using DDVD as a simple and straightforward biomarker, the limitations of IVIM and arterial spin labelling can be largely overcome for parotid gland tumor perfusion assessment.

There are limitations to this study. This is a preliminary proof-of-concept study for DDVD application in parotid gland perfusion assessment. We had only 15 patients with MT. MT is heterogeneous because we had a very limited sample size for each subtype. WT and PA can be differentiated by a combination of DDVD and ADC, requiring data acquisition of three sets of DWI images (b=0, b=20 and b=1000 in the current study). The differentiation of MT from WT and PA is less satisfactory. This could be due to the diversity of MT subtypes and their varying histological features. The standard deviation of the DDVDr of all PAs, MTs, and WTs was large. This may reflect the inhomogeneous nature of individual tumors. Note that none of the currently available imaging technologies can “satisfactorily” differentiate between PA, MT, and WT, thus biopsy remains commonly practiced. The goal of DDVD analysis was not to surpass other imaging technologies, particularly contrast-enhanced CT/MRI. Instead, DDVD offers a cost-effective approach that can readily provide in vivo tissue perfusion information and eliminate contrast agent administration in many clinical scenarios. For example, the CoVs (coefficients of variation, standard deviation divided by mean) for the DDVDr in this study is comparable to the perfusion CT blood volume results reported by Niazi et al. [14]. DDVD still can be useful in some scenarios. As shown in [Fig. 4]C (while the sample size was limited in the current study) when DDVDr is >3, the likelihood of the tumor being a WT is high, and only WTs had DDVDr >8. One advantage of the DDVD protocol is that it can be conveniently combined with ADC values. Tentative analysis of our limited data shows that a combination of DDVD and ADC had an AUC (receiver operating characteristic area under the curve) of 0.937 for differentiating between PA and MT and an AUC of 0.906 for differentiating between WT and MT, which compares favorably with perfusion CT blood volume AUC of 0.824 for differentiating between PA and MT and an AUC of 0.809 for differentiating between WT and MT as described by Dong et al. [13]. Perfusion MRI results tend to be even less stable than the perfusion CT results as shown in [Fig. 3]. DDVD may also be a useful tool for monitoring perfusion changes after non-surgical treatment, such radiation therapy for parotid tumors. It is also likely that the diagnostic performance of DDVD in parotid gland tumor characterization will improve with future technical optimization. The MRI data in this study had only NEX=1. Our recent experience shows that increasing the NEX can improve DDVD value stability (i.e., repeatability and reproducibility), and since the DDVD protocol is very fast, a higher NEX will be practically feasible. Future studies with a more optimized DW imaging parameter setup and with a larger sample size for various types of MT are highly desirable.

In conclusion, DDVD analysis in this study suggests that the majority of parotid gland tumors are hypervascular relative to parotid gland tissue. Consistent with most of the earlier reports, WTs have the highest perfusion with the highest DDVD value and with a low ADC value. PA has the feature of a modestly high DDVD value with a very high ADC value. Most of the MTs have a modestly high DDVD value with a low ADC value. As a ready-to-implement DWI biomarker, DDVD may be clinically applicable for parotid gland tumor perfusion assessment with further technical optimization.

Clinical Relevance

• In terms of relative ratio, parotid gland tumor perfusion measured with DDVD is consistent with the literature results for CT perfusion and histological micro-vessel density.

• DDVD analysis shows that perfusion was the highest for Warthin’s tumor, followed by malignant tumors, with pleomorphic adenomas showing the lowest perfusion among these three types of parotid gland tumors.

• A combination of DDVD and ADC allows good differentiation between Warthin’s tumor and pleomorphic adenoma.

Conflict of Interest

Y.X.J.W. is the founder of Yingran Medicals Ltd., which develops medical image-based diagnostics software. B.H.X. contributed to the development of Yingran Medicals Ltd. The other authors of this manuscript declare no potential conflicts of interest with respect to the research, authorship, and/or publication of the article.

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Publikationsverlauf

Eingereicht: 14. Oktober 2024

Angenommen nach Revision: 17. Februar 2025

Artikel online veröffentlicht:
13. März 2025