Differentiation of Malignant Salivary Gland Tumors from Pleomorphic Adenomas and Warthin’s Tumors: Combined Diagnostic Value of Tumor Blood Flow and Apparent Diffusion Coefficient by Histogram Analysis

doi:10.21203/rs.3.rs-944191/v1

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Differentiation of Malignant Salivary Gland Tumors from Pleomorphic Adenomas and Warthin’s Tumors: Combined Diagnostic Value of Tumor Blood Flow and Apparent Diffusion Coefficient by Histogram Analysis

https://doi.org/10.21203/rs.3.rs-944191/v1

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We aimed to evaluate the usefulness of tumor blood flow (TBF) obtained by pseudocontinuous arterial spin labeling (pCASL) and apparent diffusion coefficient (ADC) for differentiating salivary gland malignant tumors (MTs) from pleomorphic adenomas (PAs) and Warthin’s tumors (WTs). We used pCASL imaging and ADC map to evaluate 65 patients, including 16 with MT, 30 with PA, and 19 with WT. We evaluated all tumors by histogram analyses and compared various characteristics by one-way analysis of variance followed by Tukey post-hoc tests. Diagnostic performance was evaluated by receiver operating characteristic (ROC) curve analysis. There were significant differences in the mean, 50th, 75th, and 90th percentiles of TBF among the tumor types, in the mean TBFs (ml/100g/min) between MTs (57.47 ± 35.14) and PAs (29.88 ± 22.53, p = 0.039) and between MTs and WTs (119.31 ± 50.11, p < 0.001), as well as in the mean ADCs (×10^− 3 mm²/sec) between MTs (1.08 ± 0.28) and PAs (1.60 ± 0.34, p < 0.001), but not in the mean ADCs between MTs and WTs (0.87 ± 0.23, p = 0.117). In the ROC curve analysis, the highest areas under the curves (AUCs) were achieved by the 10th and 25th percentiles of ADC (AUC = 0.885) for differentiating MTs from PAs and the 50th percentile of TBF (AUC = 0.855) for differentiating MTs from WTs. The AUCs of TBF, ADC, and combination of TBF and ADC were 0.850, 0.885, and 0.950 for MT and PA differentiation and 0.855, 0.814, and 0.905 for MT and WT differentiation, respectively. The combination of TBF and ADC evaluated by histogram analysis helped differentiate salivary gland MTs from PAs and WTs.

Oncology

Cancer Biology

tumor blood flow

pseudocontinuous arterial spin labeling

receiver operating characteristic

salivary gland

Parotid tumors represent nearly 70% of all salivary gland tumors, and 80% of them are benign [1]. The most frequent benign salivary gland tumors are pleomorphic adenomas (PAs), which comprise 45% of all salivary gland tumors, followed by Warthin’s tumors (WTs) [2]. On the other hand, malignant tumors (MTs) represent nearly 20% of parotid tumors, approximately 40% of submandibular tumors, and 70–90% of sublingual tumors [1, 3].

Malignant salivary tumors demonstrate a range of biological behaviors. About 40% of MTs are indolent, especially in young adults [3]. The other 40% of MTs are aggressive, especially in the elderly [3]. Clinical indicators suggesting MTs are rapid growth rate, pain, facial nerve involvement, and cervical adenopathy. However, a slow growth rate of asymptomatic mass does not exclude MTs [3]. Therefore, it is important to differentiate MTs from benign salivary gland tumors, such as PAs and WTs. Fine-needle aspiration cytology is widely accepted as a reliable way to diagnose salivary gland tumors before surgical resection, but it is not appropriate for tumors located in deep areas and is an intrinsically invasive procedure [4]. Noninvasive magnetic resonance imaging (MRI) techniques may improve the diagnostic performance of salivary gland tumors regardless of tumor locations. However, conventional MRI cannot clearly distinguish between benign and malignant salivary gland tumors [5]. For instance, the apparent diffusion coefficient (ADC) obtained by diffusion-weighted imaging (DWI) reportedly provided useful information for the differentiation of WTs and PAs but remained inconclusive for differentiation of benign and malignant salivary gland tumors [6–8].

Recently, arterial spin labeling (ASL) techniques, such as pulsed ASL or pseudocontinuous ASL (pCASL), were introduced for clinical applications [9]. This method has been applied for noninvasive measurement of tumor blood flow (TBF) by using the magnetization of protons in arterial blood as an intrinsic tracer without an exogenous contrast agent [9]. There have been only a few reports on the usefulness of ASL for differentiating salivary gland tumors so far [10–12]. The use of multiparametric MRI, such as DWI and ASL, may help radiologists by increasing their efficiency in the differential diagnosis of salivary gland tumors. In addition, appropriate automated software needs to be developed so that these advanced applications can be adjusted to facilitate the workflow of radiologists and to objectively evaluate quantitative data, such as ADC and TBF. Therefore, we have developed a custom software application in MATLAB 2020a for evaluation of TBF and the ADC of salivary gland tumors by using histogram analysis. The purpose of this study was to assess the combined diagnostic value of ADC and TBF for differentiating MTs in salivary glands from PAs and WTs.

A total of 65 subjects (age range, 11–86 years; mean 59 years; 34 males and 31 females) were finally included. There were 16 subjects with MTs, 30 with PAs, and 19 with WTs. The characteristics of patients are described in Table 1. The pathology of MTs was variable, including five carcinoma ex pleomorphic adenomas, two acinic-cell carcinomas, two adenocarcinomas, two adenoid cystic carcinomas, two mucoepidermoid carcinomas, one basal-cell adenocarcinoma, one epithelial myoepithelial carcinoma, and one salivary-duct carcinoma. One patient with PAs and eight patients with WTs had multiple or bilateral tumors. Among these patients, only the largest one was assessed.

Table 1

Patients’ characteristics
	MT (n = 16)	PA (n = 30)	WT (n = 19)
Sex
Male: Female	9:7	9:21	16:3
Age
Range (year)	11–82	24–86	56–83
Mean age (year)	60	53	68
Tumor diameter
Range (mm)	14–96	11–60	17–64
Mean (mm)	34.63	27.37	35.84
Tumor sub-site
Parotid gland	11	22	19
Submandibular gland	4	8	0
Sublingual gland	1	0	0
Diagnostic method
Resection	14	23	13
Fine-needle aspiration cytology	2	7	6
Abbreviations; MT, malignant tumor; PA, pleomorphic adenoma; WT, Warthin’s tumor

Comparison of the parameters for TBF and ADC between MTs, PAs, and WTs. Figures 1, 2, and 3 show representative cases of MTs, PAs, and WTs, respectively. Tables 2 and 3 show the parameter measurements of TBF and ADC, respectively, in MTs, PAs, and WTs.

Table 2

Measurements of TBF in MTs, PAs, and WTs
TBF parameters	Mean ± standard deviation			p-value
TBF parameters	MT	PA	WT	MT vs. PA	MT vs. WT	PA vs. WT
Max	97.70 ± 54.98	66.11 ± 29.27	166.03 ± 49.85	0.054	< 0.001 ^*	< 0.001 ^*
Min	22.47 ± 29.10	7.22 ± 14.45	63.74 ± 43.62	0.219	< 0.001 ^*	< 0.001 ^*
Mean	57.47 ± 35.14	29.88 ± 22.53	119.31 ± 50.11	0.039 ^*	< 0.001 ^*	< 0.001 ^*
10th percentile	34.37 ± 30.95	14.19 ± 18.84	88.96 ± 48.85	0.127	< 0.001 ^*	< 0.001 ^*
25th percentile	43.99 ± 32.73	20.84 ± 21.80	103.44 ± 50.52	0.09	< 0.001 ^*	< 0.001 ^*
50th percentile	56.36 ± 35.35	28.48 ± 23.62	120.80 ± 51.76	0.044 ^*	< 0.001 ^*	< 0.001 ^*
75th percentile	70.71 ± 40.83	37.65 ± 25.32	135.35 ± 52.47	0.021 ^*	< 0.001 ^*	< 0.001 ^*
90th percentile	81.82 ± 46.63	47.29 ± 25.87	147.45 ± 51.63	0.020 ^*	< 0.001 ^*	< 0.001 ^*
Skewness	0.10 ± 0.52	0.64 ± 0.68	−0.23 ± 0.72	0.029 ^*	0.289	< 0.001 ^*
Kurtosis	−0.42 ± 0.49	0.62 ± 1.15	0.39 ± 2.82	0.136	0.364	0.887
* P-value < 0.05
minimum; MT, Abbreviations; TBF, tumor blood flow (mL/100 g/min); max, maximum; min, malignant tumor; PA, pleomorphic adenoma; WT, Warthin’s tumor

Table 3

Measurements of ADC in MTs, PAs, and WTs
ADC parameters	Mean ± standard deviation			p-value
ADC parameters	MT	PA	WT	MT vs. PA	MT vs. WT	PA vs. WT
Max	1.39 ± 0.31	1.95 ± 0.37	1.29 ± 0.42	< 0.001 ^*	0.684	< 0.001 ^*
Min	0.76 ± 0.23	1.23 ± 0.38	0.51 ± 0.16	< 0.001 ^*	0.050	< 0.001 ^*
Mean	1.08 ± 0.28	1.60 ± 0.34	0.87 ± 0.23	< 0.001 ^*	0.117	< 0.001 ^*
10th percentile	0.90 ± 0.23	1.40 ± 0.33	0.68 ± 0.15	< 0.001 ^*	0.052	< 0.001 ^*
25th percentile	0.98 ± 0.27	1.49 ± 0.33	0.76 ± 0.17	< 0.001 ^*	0.057	< 0.001 ^*
50th percentile	1.08 ± 0.31	1.59 ± 0.34	0.85 ± 0.22	< 0.001 ^*	0.075	< 0.001 ^*
75th percentile	1.16 ± 0.31	1.71 ± 0.36	0.97 ± 0.30	< 0.001 ^*	0.220	< 0.001 ^*
90th percentile	1.25 ± 0.30	1.80 ± 0.37	1.09 ± 0.39	< 0.001 ^*	0.393	< 0.001 ^*
Skewness	−0.03 ± 0.64	0.07 ± 0.47	0.29 ± 0.46	0.805	0.168	0.32
Kurtosis	0.29 ± 1.16	−0.04 ± 0.95	0.55 ± 1.15	0.594	0.749	0.155
* P-value < 0.05
Abbreviations; ADC, apparent diffusion coefficient (×10^− 3 mm²/sec); max, maximum; min, minimum; MT, malignant tumor; PA, pleomorphic adenoma; WT, Warthin’s tumor

There were significant differences in the mean, 50th, 75th, and 90th percentiles of TBF among all three types of tumors (all p < 0.05). The mean TBF was significantly higher in MTs (57.47 ± 35.14 mL/100 g/min) than in PAs (29.88 ± 22.53 mL/100 g/min, p = 0.039) and significantly lower in MTs than in WTs (119.31 ± 50.11 mL/100 g/min, p < 0.001). The 50th percentile of TBF was significantly higher in MTs (56.36 ± 35.35 mL/100 g/min) than in PAs (28.48 ± 23.62 mL/100 g/min, p = 0.044) and significantly lower in MTs than in WTs (120.80 ± 51.76 mL/100 g/min, p < 0.001). The 75th percentile of TBF was significantly higher in MTs (70.71 ± 40.83 mL/100 g/min) than in PAs (37.65 ± 25.32 mL/100 g/min, p = 0.021) and significantly lower in MTs than in WTs (135.35 ± 52.47 mL/100 g/min, p < 0.001). The 90th percentile of TBF was significantly higher in MTs (81.82 ± 46.63 mL/100 g/min) than in PAs (47.29 ± 25.87 mL/100 g/min, p = 0.020) and significantly lower in MTs than in WTs (147.45 ± 51.63 mL/100 g/min, p < 0.001).

There was a significant difference in the mean ADCs between MTs (1.08 ± 0.28 × 10^− 3 mm²/sec) and PAs (1.60 ± 0.34 × 10^− 3 mm²/sec, p < 0.001) but not between MTs and WTs (0.87 ± 0.23 × 10^− 3 mm²/sec, p = 0.117). There were no ADC parameters that showed significant differences for all three combinations of tumor types (MT and PA, MT and WT, and PA and WT).

Comparison of diagnostic performance for TBF and ADC in differentiating MTs, PAs, and WTs.

Tables 4, 5, and 6 show the diagnostic performance of each parameter determined by the receiver operating characteristic (ROC) curve analysis. When differentiating MTs from PAs, the 10th and 25th percentiles of the ADC both had the best diagnostic performance out of all TBF and ADC parameters, with areas under the curve (AUCs) of 0.885 and 0.885, respectively, which is considered medium diagnostic performance. The best detected cutoff points were 1.15 × 10^− 3 mm²/sec and 1.26 × 10^− 3 mm²/sec, respectively, yielding sensitivity and specificity for both cutoff values of 73.3% and 93.8%, respectively.

Table 4

Receiver operating characteristic curve analysis of the parameters for differentiating MT from PA
Parameters	AUC	Cutoff value	Sensitivity (%)	Specificity (%)	Youden index
TBF max	0.665	99.80	50.0	86.7	0.367
TBF min	0.731	6.89	56.3	83.3	0.396
TBF mean	0.740	66.82	50.0	93.3	0.433
TBF 10th percentile	0.738	6.26	93.8	46.7	0.404
TBF 25th percentile	0.742	13.48	93.8	53.3	0.471
TBF 50th percentile	0.744	20.06	93.8	50.0	0.438
TBF 75th percentile	0.740	77.91	50.0	93.3	0.433
TBF 90th percentile	0.721	83.98	50.0	93.3	0.433
TBF skewness	0.710	0.71	46.7	100.0	0.467
TBF kurtosis	0.798	0.00	66.7	93.8	0.604
ADC max	0.877	1.73	76.7	93.8	0.704
ADC min	0.856	1.01	70.0	93.8	0.638
ADC mean	0.879	1.30	80.0	87.5	0.675
ADC 10th percentile	0.885	1.15	73.3	93.8	0.671
ADC 25th percentile	0.885	1.26	73.3	93.8	0.671
ADC 50th percentile	0.867	1.38	73.3	93.8	0.671
ADC 75th percentile	0.883	1.38	80.0	87.5	0.675
ADC 90th percentile	0.873	1.48	80.0	87.5	0.675
ADC skewness	0.592	0.17	43.3	81.3	0.246
ADC kurtosis	0.598	−0.15	68.8	53.3	0.221
Abbreviations; TBF, tumor blood flow (mL/100 g/min); ADC, apparent diffusion coefficient (×10^− 3 mm²/sec); max, maximum; min, minimum; MT, malignant tumor; PA, pleomorphic adenoma

Table 5

Receiver operating characteristic curve analysis of the parameters for differentiating MT from WT
Parameters	AUC	Cutoff value	Sensitivity (%)	Specificity (%)	Youden index
TBF max	0.829	151.61	73.7	87.5	0.612
TBF min	0.813	30.21	73.7	75.0	0.487
TBF mean	0.842	102.51	63.2	93.8	0.569
TBF 10th percentile	0.845	20.97	100.0	56.3	0.563
TBF 25th percentile	0.836	75.10	68.4	87.5	0.559
TBF 50th percentile	0.855	78.02	84.2	75.0	0.592
TBF 75th percentile	0.836	111.49	68.4	87.5	0.559
TBF 90th percentile	0.832	124.57	73.7	87.5	0.612
TBF skewness	0.651	0.34	43.8	89.5	0.332
TBF kurtosis	0.556	0.11	31.6	93.8	0.253
ADC max	0.671	1.15	93.8	57.9	0.516
ADC min	0.814	0.62	81.3	78.9	0.602
ADC mean	0.743	0.85	81.3	68.4	0.497
ADC 10th percentile	0.806	0.80	62.5	89.5	0.520
ADC 25th percentile	0.783	0.76	75.0	73.7	0.487
ADC 50th percentile	0.763	0.80	87.5	63.2	0.507
ADC 75th percentile	0.730	0.91	87.5	63.2	0.507
ADC 90th percentile	0.727	1.02	87.5	68.4	0.559
ADC skewness	0.701	0.08	68.4	75.0	0.434
ADC kurtosis	0.592	0.13	63.2	62.5	0.257
Abbreviations; TBF, tumor blood flow (mL/100 g/min); ADC, apparent diffusion coefficient (×10^− 3 mm²/sec); max, maximum; min, minimum; MT, malignant tumor; WT, Warthin’s tumor

Table 6

Receiver operating characteristic curve analysis of the parameters for differentiating PA from WT
Parameters	AUC	Cutoff value	Sensitivity (%)	Specificity (%)	Youden index
TBF max	0.954	85.41	100.0	76.7	0.767
TBF min	0.944	6.67	94.7	83.3	0.781
TBF mean	0.954	68.30	84.2	93.3	0.775
TBF 10th percentile	0.946	20.28	100.0	76.7	0.767
TBF 25th percentile	0.946	27.39	100.0	73.3	0.733
TBF 50th percentile	0.951	71.21	84.2	93.3	0.775
TBF 75th percentile	0.963	70.65	89.5	90.0	0.795
TBF 90th percentile	0.968	82.30	89.5	93.3	0.828
TBF skewness	0.821	0.27	70.0	89.5	0.595
TBF kurtosis	0.695	−0.30	80.0	63.2	0.432
ADC max	0.870	1.27	100.0	68.4	0.684
ADC min	0.960	0.76	93.3	94.7	0.881
ADC mean	0.960	1.04	96.7	84.2	0.809
ADC 10th percentile	0.984	0.79	100.0	89.5	0.895
ADC 25th percentile	0.979	0.95	96.7	89.5	0.861
ADC 50th percentile	0.962	0.97	100.0	78.9	0.789
ADC 75th percentile	0.933	1.15	93.3	84.2	0.775
ADC 90th percentile	0.898	1.20	96.7	78.9	0.756
ADC skewness	0.635	0.41	47.4	76.7	0.240
ADC kurtosis	0.674	0.06	73.7	66.7	0.404
Abbreviations; AUC, area under the curve; TBF, tumor blood flow (mL/100 g/min); max, maximum; min, minimum; ADC, apparent diffusion coefficient (×10^− 3 mm²/sec); PA, pleomorphic adenoma; WT, Warthin’s tumor

When differentiating MTs from WTs, the 50th percentile of TBF had the best diagnostic performance out of all TBF and ADC, with an AUC of 0.855, which is considered medium diagnostic performance. The best detected cutoff point was 78.02 mL/100 g/min, yielding a sensitivity and a specificity of 84.2% and 75.0%, respectively.

When differentiating PAs from WTs, the 10th percentile of ADC had the best diagnostic performance out of all TBFs and ADCs, with an AUC of 0.984, which is considered high diagnostic performance. The best detected cutoff point was 0.79 × 10^− 3 mm²/sec, yielding a sensitivity and a specificity of 100.0% and 89.5%, respectively. Figure 4 summarizes the diagnostic performance of the parameters. TBF, ADC, and the combination of TBF and ADC showed medium to high diagnostic performances, with AUCs of 0.850, 0.885, and 0.950 for differentiating MTs from PAs, 0.855, 0.814, and 0.905 for differentiating MTs from WTs, and 0.968, 1.000, and 1.000 for differentiating PAs from WTs, respectively.

Interobserver agreement of TBF and ADC measurements.

Table 7 shows the intraclass correlation coefficients (ICCs) of the measurements by the two observers. Excellent agreements were observed for all parameters except for the skewness of ADC, which showed good agreement.

Table 7

Interobserver agreement
Parameters	ICC
TBF max	0.966
TBF min	0.827
TBF mean	0.995
TBF 10th percentile	0.919
TBF 25th percentile	0.962
TBF 50th percentile	0.997
TBF 75th percentile	0.998
TBF 90th percentile	0.991
TBF skewness	0.957
TBF kurtosis	0.930
ADC max	0.918
ADC min	0.983
ADC mean	0.997
ADC 10th percentile	0.991
ADC 25th percentile	0.999
ADC 50th percentile	0.995
ADC 75th percentile	0.993
ADC 90th percentile	0.989
ADC skewness	0.713
ADC kurtosis	0.892
Abbreviations; ICC, intraclass correlation coefficient; TBF, tumor blood flow; max, maximum; min, minimum; ADC, apparent diffusion coefficient

In this study, the diagnostic performance of the combination of TBF and ADC for differentiating MTs from PAs and WTs increased relative to the performance of each parameter alone. However, in differentiating PAs from WTs, the diagnostic performance of ADC alone showed perfect discrimination, and therefore, the value of adding the combination of ADC and TBF was low. To our best knowledge, this is the first study to evaluate the usefulness of the combination of pCASL and the ADC map by histogram analysis for differentiating malignant salivary gland tumors from PAs and WTs.

Kato et al. reported that qualitative analysis showed that TBF was significantly higher in WTs than PAs and MTs, but did not show a significant difference between PAs and MTs [10]. However, we demonstrated that the mean, 50th, 75th, and 90th percentiles of TBF could differentiate MTs, PAs, and WTs. We speculate that the differences in ASL methods may explain why their results differed from ours. They placed the regions of interest (ROIs) on both a tumor and the contralateral normal parotid gland parenchyma at the same level and then evaluated tumor-to-parotid signal intensity ratios from ASL images supposing that those ratios are surrogates of TBF [10]. They measured the relative ratio of salivary gland tumors to normal parotid glands, whereas we measured the TBF values of tumors quantitatively. Consequently, histogram analysis may overcome the limitations of qualitative analysis. Moreover, they used an alternating radio-frequency ASL sequence with gradient echo-type single-shot echo-planar imaging (MP-EPISTAR), which suffers from susceptibility artifacts more seriously than pCASL sequences that use 3D turbo spin echo (TSE) acquisition [10]. In addition, MP-EPISTAR used in the study of Kato et al. has a lower signal-to-noise ratio than that of pCASL [11]. Thus, the pCASL technique may be more suitable for imaging compared to the ASL sequence that Kato et al. used for differentiation among MTs, PAs, and WTs.

A recent report stated that metrics, such as percentiles, kurtosis, and skewness, calculated by histogram analysis are strong and reliable quantitative surrogate markers of tumor heterogeneity [13]. Thus, we consider that microenvironments of tumors could be masked by evaluating only a single parameter, such as the mean value. Yamamoto et al. demonstrated that the mean TBF value was significantly higher in WTs than in PAs by using the pCASL sequence with conventional ROI analysis [11]. They also showed that the higher mean TBF of WTs than of PAs was attributable to higher micro-vessel density in WTs than in PAs [11]. Furthermore, our results revealed that the 75th and 90th percentiles of TBF exhibited higher AUC values than the mean TBF. Consequently, histogram analysis appears to provide more detailed information about TBF.

Kato et al. reported that the mean ADC values were significantly higher in PAs than in WTs and MTs but were not significantly different between WTs and MTs [10]. Their results were consistent with our results showing that all ADC parameters except for skewness and kurtosis were significantly different between PAs and WTs and between PAs and MTs, but not between WTs and MTs. Razek et al. studied ADC values by histogram analysis for diagnosis of PAs, WTs, and MTs and reported significant differences in the means and skewness of ADC among all three tumors, although these differences between WTs and MTs were weaker than those between PAs and WTs and PAs and MTs [14]. Histopathologically, PAs comprise an abundant myxoma-like stroma [6, 11], which probably contributes to the highest ADC value among the three types of tumors in all parameters except for skewness and kurtosis in our study. In contrast, WTs showed the lowest ADC among all parameters except for skewness and kurtosis, which might reflect epithelial and lymphoid stromata with microscopic slit-like cysts filled with proteinous fluid [2, 6].

There were several limitations in this study. First, the study was conducted at a single institution with a relatively small number of subjects. Studies with a larger number of subjects would be required to confirm the efficacy of pCASL imaging and ADC mapping for evaluating salivary gland tumors. Second, we could not evaluate the whole pCASL image slices and ADC maps for each tumor. Particularly, MTs tend to have heterogenous characteristics. Thus, whole-tumor evaluation would be desirable in future studies.

In conclusion, the combination of TBF and ADC evaluated by histogram analysis was found to be helpful for differentiating MTs from PAs and WTs in salivary glands.

Subjects.

This study was approved by the ethics committee of our university, and the requirement for written informed consent was waived because of the retrospective study design. All study procedures were conducted according to the principles of World Medical Association Declaration of Helsinki. We retrospectively collected data from the patients who fulfilled the following inclusion criteria: (a) underwent an MRI scan to evaluate clinically suspected major salivary gland tumors between December 2015 and September 2020; (b) available preoperative 3T MRI, including pCASL images, DWI, T1-weighted images, contrast-enhanced T1-weighted images, and T2-weighted images; (c) tumor size > 10 mm; (d) pathologically proven tumors by fine-needle aspiration biopsy or surgical resection; and (e) diagnosed as an MT, PA, or WT of the salivary gland.

Conventional MRI protocol.

All patients underwent MRI on a 3T MRI system (Ingenia; Philips Medical Systems, Best, the Netherlands) with a Head/Neck coil. The pulse sequence parameters were as follows. T2-weighted imaging: repetition time (TR)/echo time (TE), 6528/90 ms; number of signals averaged (NSA), 1; field of view (FOV), 240 × 240 mm; matrix, 384 × 271; slice thickness, 4 mm; number of slices, 22; acceleration factor, 1.5; and scanning time, 1 min 57 s. T1-weighted imaging: TR/TE, 614/14 ms; NSA, 1; FOV, 240 × 240 mm; matrix, 352 × 246; slice thickness, 4 mm; number of slices, 22; acceleration factor, 2; and acquisition time, 2 min 34 s. DWI: TR/TE, 5000/88 ms; fat suppression, short-tau inversion recovery; inversion time, 250 ms; NSA, 2; b value, 0 and 1,000 s/mm²; FOV, 240 × 240 mm; matrix, 96 × 125; slice thickness, 4 mm; number of slices, 22; acceleration factor, 2; and acquisition time, 3 min 30 s. Contrast-enhanced 3D-T1-weighted imaging: slice orientation, sagittal; TR/TE, 5.3/2.4 ms; flip angle (FA), 10; fat suppression, spectral-attenuated inversion recovery; FOV, 250 × 225 mm; matrix, 256 × 256; slice thickness, 1 mm; number of slices, 180; acceleration factor, 1.8; and acquisition time, 3 min 24 s. The contrast-enhanced 2D-T1-weighted imaging parameters were the same as the non-contrast parameters.

pCASL MRI protocol.

The pulse sequence parameters for 3D TSE pCASL were as follows: TR/TE, 6000/40 ms; FA, 90°; labeling duration, 1650 ms; post-label delay, 1800 ms; number of shots, 3; FOV, 240 × 240 mm; matrix, 80 × 80; slice thickness, 4 mm; number of slices, 22; acceleration factor, 2.5; and acquisition time, 5 min 36 s. The labeling plane was set parallel to the imaging volume and perpendicular to the common carotid artery.

TBF was calculated according to the following equation [8]:

\(\text{T}\text{B}\text{F}=\frac{6000 \bullet {\lambda } \bullet \left({\text{S}\text{I}}_{\text{c}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}-{\text{S}\text{I}}_{\text{l}\text{a}\text{b}\text{e}\text{l}}\right) \bullet {\text{e}}^{\frac{\text{P}\text{L}\text{D}}{{\text{T}}_{1, \text{b}\text{l}\text{o}\text{o}\text{d}}}}}{2 \bullet {\alpha } \bullet {\text{T}}_{1, \text{b}\text{l}\text{o}\text{o}\text{d} }\bullet {\text{S}\text{I}}_{\text{P}\text{D} }\bullet (1-{\text{e}}^{-\frac{{\tau }}{{\text{T}}_{1,\text{b}\text{l}\text{o}\text{o}\text{d}}}})}\) [mL/100 g/min]

where λ is the blood/tumor-tissue water partition coefficient (1.0 g/mL), and SI_control and SI_label are the time-averaged signal intensities in the control and label images, respectively. T_1,blood is the longitudinal relaxation time of blood (1650 ms), α is the labeling efficiency (0.85), SI_PD is the signal intensity of a proton density-weighted image, and τ is the label duration (1650 ms). The value ofλwas 1.0 mL/g. To calculate TBF, we used the same model and conditions as those used for calculating blood flow in the brain.

Image analysis.

Image analysis was performed by using a custom software application developed in MATLAB 2020a. The custom software displays the ADC map and the pCASL map for the same patient side by side on the monitor. A slice image of each map for display can be moved. Two board-certified neuroradiologists (F.T and R.K) reviewed all MRI sequences. First, we identified the tumors on T1-weighted images, T2-weighted images, and contrast-enhanced T1-weighted images. The ROIs were manually drawn around the tumor margin in the maximum diameters on the ADC map by using the software. The ROIs were within an entire solid part of a tumor as much as visually traced, avoiding areas of necrosis, cyst, or hemorrhage. Then, the segmented ROI was copied from the ADC map and pasted to the pCASL image by using the software. The histogram features for each image were determined using those histograms. The following 10 objective features were determined as histogram features in the custom software: (1) minimum (min), (2) mean, (3) maximum (max), (4) 10th percentile, (5) 25th percentile, (6) 50th percentile, (7) 75th percentile, (8) 90th percentile, (9) skewness, and (10) kurtosis. The histogram features of TBF and ADC were measured twice in each ROI, and these measurements were averaged.

Statistical analysis.

Statistical analysis was performed by using SPSS v. 25.0 software (IBM SPSS Statistics for Windows, IBM Corp., Armonk, NY). All 10 parameters of the TBF and ADC values were assessed. Significant differences among the groups were analyzed by one-way analysis of variance followed by Tukey post-hoc tests. A p-value of < 0.05 was considered to be indicative of statistical significance.

ROC curve analyses were performed to investigate the diagnostic performance of the parameters in differentiating among PAs, WTs, and MTs. We considered AUC values < 0.7, 0.7–0.9, and > 0.9 to indicate low, medium, and high diagnostic performance, respectively. Cutoff values were calculated with the maximum of the Youden index (Youden index = sensitivity + specificity − 1). A p-value of < 0.05 was considered significant to be indicative of statistical significance.

Interobserver agreement on TBF and ADC values between two readers was evaluated by ICC. ICCs are considered excellent if > 0.74 [15].

Acknowledgements

We thank Toru Ogura Ph.D. from Clinical Research Support Center, Mie University Hospital, for his valuable assistance with the statistical analyses of the present study.

Author contributions

Study concept and design: T.T., M.U., and M.M. Acquisition of data: K.I. and M.O., development of the software for image analysis: R.N. Analysis and interpretation of data: F.T. and M.M. Drafting of the manuscript: F.T. and M.M. Statistical analysis: F.T. Study supervision: M.M. and H.S. All authors reviewed the manuscript.

Funding

This work was supported by a grant from the Japan Society for the Promotion of Science KAKENHI (grant numbers: 16 K10314 and 20 K08104).

Competing interests

Makoto Obara is an employee ofPhilips Japan. The other authors declare no competing interests.

Data availability

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

Ethics Statement

This study was approved by the ethics committee of Mie University School of Medicine, and the requirement for written informed consent was waived because of the retrospective study design. All study procedures were conducted according to the principles of World Medical Association Declaration of Helsinki.

Spiro, R. H. Salivary neoplasms: overview of a 35-year experience with 2,807 patients. Head Neck Surg. 8, 177-184; 10.1002/hed.2890080309 (1986).
Kato, H., Fujimoto, K., Matsuo, M., Mizuta, K. & Aoki, M. Usefulness of diffusion-weighted MR imaging for differentiating between Warthin’s tumor and oncocytoma of the parotid gland. Jpn. J. Radiol. 35, 78-85; 10.1007/s11604-016-0608-5 (2017).
Guzzo, M., et al. Major and minor salivary gland tumors. Crit. Rev. Oncol. Hematol. 74, 134-148; 10.1016/j.critrevonc.2009.10.004 (2010).
AlGhamdi, G. Z., et al. Correlation between fine needle aspiration cytology (FNAC) and permanent histopathology results in salivary gland masses. Cureus 13, e13976; 10.7759/cureus.13976 (2021).
Schlakman, B. N. & Yousem, D. M. MR of intraparotid masses. AJNR Am. J. Neuroradiol. 14, 1173-1180 (1993).
Matsushima, N., Maeda, M., Takamura, M. & Takeda, K. Apparent diffusion coefficients of benign and malignant salivary gland tumors. Comparison to histopathological findings. J. Neuroradiol. 34, 183-189; 10.1016/j.neurad.2007.04.002 (2007).
Eida, S., Sumi, M., Sakihama, N., Takahashi, H. & Nakamura, T. Apparent diffusion coefficient mapping of salivary gland tumors: prediction of the benignancy and malignancy. AJNR Am. J. Neuroradiol. 28, 116-121 (2007).
Coudert, H., Mirafzal, S., Dissard, A., Boyer, L. & Montoriol, P. F. Multiparametric magnetic resonance imaging of parotid tumors: A systematic review. Diagn. Interv. Imaging 102, 121-130; 10.1016/j.diii.2020.08.002 (2021).
Alsop, D. C., et al. Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: A consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magn. Reson. Med. 73, 102-116; 10.1002/mrm.25197 (2015).
Kato, H., et al. Perfusion imaging of parotid gland tumours: usefulness of arterial spin labeling for differentiating Warthin’s tumours. Eur. Radiol. 25, 3247-3254; 10.1007/s00330-015-3755-7 (2015).
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 60, 803-812; 10.1007/s00234-018-2046-9 (2018).
Takumi, K., et al. Differentiating malignant from benign salivary gland lesions: a multiparametric non-contrast MR imaging approach. Sci. Rep. 11, 2780; 10.1038/s41598-021-82455-2 (2021).
Just, N. Improving tumour heterogeneity MRI assessment with histograms. Br. J. Cancer 111, 2205-2213; 10.1038/bjc.2014.512 (2014).
Razek, A. A. K. A., GadelHak, B. N., El, I. A., Elrazzak, G. A. E. A. & Mowafey, B. Diffusion-weighted imaging with histogram analysis of the apparent diffusion coefficient maps in the diagnosis of parotid tumours. Int. J. Oral Maxillofac. Surg. 21, S0901-5027(21)00132-6; 10.1016/j.ijom.2021.03.019 (2021).
Shrout P. E. & Fleiss J. L. Intraclass correlations: uses in assessing rater reliability. Psychol. Bull. 86, 420-428; 10.1037//0033-2909.86.2.420 (1979).

Competing interest reported. Makoto Obara is an employee of Philips Japan. The other authors declare no competing interests.

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Differentiation of Malignant Salivary Gland Tumors from Pleomorphic Adenomas and Warthin’s Tumors: Combined Diagnostic Value of Tumor Blood Flow and Apparent Diffusion Coefficient by Histogram Analysis

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Methods

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Conventional MRI protocol.

pCASL MRI protocol.

Image analysis.

Statistical analysis.

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