Histogram analysis of apparent diffusion coefficient maps for differentiating malignant from benign parotid gland tumors

Characterization of parotid gland tumors using diffusion-relaxation correlation spectrum imaging: a preliminary study

AIM

To assess the performance of diffusion-relaxation correlation spectrum imaging (DR-CSI) in the characterization of parotid gland tumors.

MATERIALS AND METHODS

Twenty-five pleomorphic adenomas (PA) patients, 9 Warthin's tumors (WT) patients and 7 malignant tumors (MT) patients were prospectively recruited. DR-CSI (7 b-values combined with 5 TEs, totally 35 diffusion-weighted images) was scanned for pre-treatment assessment. Diffusion (D)-T2 signal spectrum summating all voxels were built for each patient, characterized by D-axis with range 0∼5 × 10-3 mm2/s, and T2-axis with range 0∼300ms. With boundaries of 0.5 and 2.5 × 10-3 mm2/s for D, all spectra were divided into three compartments labeled A (low D), B (mediate D) and C (high D). Volume fractions acquired from each compartment (VA, VB, VC) were compared among PA, WT and MT. Diagnostic performance was assessed using receiver operating characteristic analysis and area under the curve (AUC).

RESULTS

Each subtype of parotid tumors had their specific D-T2 spectrum. PA showed significantly lower VA (8.85 ± 4.77% vs 20.68 ± 10.85%), higher VB (63.40 ± 8.18% vs 43.05 ± 7.16%), and lower VC (27.75 ± 8.51% vs 36.27 ± 11.09) than WT (all p<0.05). VB showed optimal diagnostic performance (AUC 0.969, sensitivity 92.00%, specificity 100.00%). MT showed significantly higher VA (21.23 ± 12.36%), lower VB (37.09 ± 6.43%), and higher VC (41.68 ± 13.72%) than PA (all p<0.05). Similarly, VB showed optimal diagnostic performance (AUC 0.994, sensitivity 96.00%, specificity 100.00%). No significant difference of VA, VB and VC was found between WT and MT.

CONCLUSIONS

DR-CSI might be a promising and non-invasive way for characterizing parotid gland tumors.

Whole-lesion iodine map histogram analysis in the risk classification of gastrointestinal stromal tumors: comparison with single-slice iodine concentration measurements

PURPOSE

To evaluate and compare the diagnostic performances of whole-lesion iodine map (IM) histogram analysis and single-slice IM measurement in the risk classification of gastrointestinal stromal tumors (GISTs).

METHODS

Thirty-seven patients with GISTs, including 19 with low malignant underlying GISTs (LG-GISTs) and 18 with high malignant underlying GISTs (HG-GISTs), were evaluated with dual-energy computed tomography (DECT). Whole-lesion IM histogram parameters (mean; median; minimum; maximum; standard deviation; variance; 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile; kurtosis, skewness, and entropy) were computed for each lesion. In other sessions, iodine concentrations (ICs) were derived from the IM by placing regions of interest (ROIs) on the tumor slices and normalizing them to the iodine concentration in the aorta. Both quantitative analyses were performed on the venous phase images. The diagnostic accuracies of the two methods were assessed and compared.

RESULTS

The minimum, maximum, 1st, 10th, and 25th percentile of the whole-lesion IM histogram and the IC and normalized IC (NIC) of the single-slice IC measurement significantly differed between LG- and HG-GISTs (p < 0.001 - p = 0.042). The minimum value in the histogram analysis (AUC = 0.844) and the NIC in the single-slice measurement analysis (AUC = 0.886) showed the best diagnostic performances. The NIC of single-slice measurements had a diagnostic performance similar to that of the whole-lesion IM histogram analysis (p = 0.618).

CONCLUSIONS

Both whole-lesion IM histogram analysis and single-slice IC measurement can differentiate LG-GISTs and HG-GISTs with similar diagnostic performances.

Characterization of parotid gland tumors: Whole-tumor histogram analysis of diffusion weighted imaging, diffusion kurtosis imaging, and intravoxel incoherent motion - A pilot study

PURPOSE

To investigate the diagnostic performance of histogram features of diffusion parameters in characterizating parotid gland tumors.

METHOD

From December 2018 to January 2023, patients who underwent diffusion weighted imaging (DWI), diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) were consecutively enrolled in this retrospective study. The histogram features of diffusion parameters, including apparent diffusion coefficient (ADC), diffusion coefficient (Dk), diffusion kurtosis (K), pure diffusion coefficient (D), pseudo-diffusion coefficient (DP), and perfusion fraction (FP) were analyzed. The Mann-Whitney U test was used for comparison between benign parotid gland tumors (BPGTs) and malignant parotid gland tumors (MPGTs). Receiver operating characteristic curve and logistic regression analysis were used to identify the differential diagnostic performance. The Spearman's correlation coefficient was used to analyze the correlation between diffusion parameters and Ki-67 labeling index.

RESULTS

For diffusion MRI, twenty-three histogram features of diffusion parameters showed significant differences between BPGTs and MPGTs (all P < 0.05). Compared with the DWI model, the IVIM model and combined model had better diagnostic specificity (58 %, 94 %, and 88 %, respectively; both corrected P < 0.001) and accuracy (64 %, 89 %, and 86 %, respectively; both corrected P = 0.006). The combined model was superior to the single DWI model with improved IDI (IDI improvement 0.25). Significant correlations were found between Ki-67 and ADCmean, Dkmean, Kmean, and Dmean (r = -0.57 to 0.53; all P < 0.05).

CONCLUSIONS

Whole-tumor histogram analysis of IVIM and combined diffusion model could further improve the diagnostic performance for differentiating BPGTs from MPGTs.

Histogram analysis of ultrasonographic images in the differentiation of benign and malignant parotid gland tumors

OBJECTIVE

We evaluated the diagnostic value of histogram analysis (HA) using ultrasonographic (US) images for differentiation among pleomorphic adenoma (PA), adenolymphoma (AL), and malignant tumors (MT) of the parotid gland.

STUDY DESIGN

Preoperative US images of 48 patients with PA, 39 patients with AL, and 17 patients with MT were retrospectively analyzed for gray-scale histograms. Nine first-order texture features derived from histograms of the tumors were compared. Area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnostic performance of texture features. The Youden index maximum exponent was used to calculate sensitivity and specificity.

RESULTS

Statistically significant differences were discovered in Mean and Skewness HA values between PA and AL (P<0.001), and in Mean values between AL and MT (P<0.001). However, comparison of PA and MT showed no statistically significant differences (P>0.01). Excellent discrimination was detected between PA and AL (AUC=0.802), and between AL and MT (AUC=0.822). The combination of Mean plus Skewness improved discrimination between PA and AL (AUC=0.823) with sensitivity values reaching 1.00. However, Mean plus Skewness applied to differentiate PA from AL and Mean values applied to distinguish AL and MT resulted in low specificity, indicating many false positive interpretations.

CONCLUSIONS

Histogram analysis is useful for differentiating PA from AL and AL from MT but not PA from MT.

Differential diagnosis of parotid gland tumors using apparent diffusion coefficient, texture features, and their combination

OBJECTIVES

Warthin's tumors (WT) and pleomorphic adenomas (PA) are the commonest parotid gland tumors; however, their differentiation remains difficult. This study aimed to investigate the utility of the apparent diffusion coefficient (ADC) value, texture features, and their combination for the differential diagnosis of parotid gland tumors.

METHODS

Patients who underwent magnetic resonance imaging (MRI) between April 2008 and March 2021 for parotid gland tumors were included and divided into two groups according to the tumor type: WT and PA. The tumor types were used as predictor variables, while the ADC value, texture features, and their combination were the outcome variables. Texture features were measured on short tau inversion recovery (STIR) images and selected using the Fisher's coefficient method and probability of error, and average correlation coefficients. The Mann-Whitney U-test was used to analyze bivariate statistics. Receiver operating characteristic curve analysis was used to assess the ability of the ADC value, texture features, and their combination to distinguishing between the two tumor types.

RESULTS

A total of 22 patients were included, 11 in each group. The ADC value, 10 texture features, and their combination were significantly different between the two groups (p < .001). Moreover, all three variables had high area under the curve values of 0.93-0.96.

CONCLUSION

The ADC value, texture features, and their combination demonstrated good diagnostic ability to distinguish between WTs and PAs. This method may be used to aid the differential diagnosis of parotid gland tumors, thereby promoting timely and adequate treatment.

Current status of diffusion-weighted imaging in differentiating parotid tumors

Recently, diffusion-weighted imaging (DWI) is an essential magnetic resonance imaging (MRI) protocol for head and neck imaging in clinical practice as it plays an important role in lesion detection, tumor extension evaluation, differential diagnosis, therapeutic effect prediction, therapy evaluation, and recurrence diagnosis. Especially in the parotid gland, several studies have already attempted to achieve accurate differentiation between benign and malignant tumors using DWI. A conventional single-shot echo-planar-based DWI is widely used for head and neck imaging, whereas advanced DWI sequences, such as intravoxel incoherent motion, diffusion kurtosis imaging, periodically rotated overlapping parallel lines with enhanced reconstruction, and readout-segmented echo-planar imaging (readout segmentation of long variable echo-trains), have been used to characterize parotid tumors. The mean apparent diffusion coefficient values are easily measured and useful for assessing cellularity and histological characteristics, whereas advanced image analyses, such as histogram analysis, texture analysis, and machine and deep learning, have been rapidly developed. Furthermore, a combination of DWI and other MRI protocols has reportedly improved the diagnostic accuracy of parotid tumors. This review article summarizes the current state of DWI in differentiating parotid tumors.

MRI features and whole-lesion apparent diffusion coefficient histogram analysis of brain metastasis from non-small cell lung cancer for differentiating epidermal growth factor receptor mutation status

AIM

To explore the utility of magnetic resonance imaging (MRI) characteristics and whole-lesion apparent diffusion coefficient histogram analysis of brain metastasis from non-small cell lung cancer (NSCLC) in the differentiation of epidermal growth factor receptor (EGFR) mutation status.

MATERIALS AND METHODS

Forty-eight patients with brain metastases from NSCLC were enrolled in this retrospective study. Patients were subtyped into EGFR mutation (23 cases) and wild-type (25 cases) groups. Whole-lesion histogram metrics were derived from the apparent diffusion coefficient (ADC) maps, and imaging features were evaluated according to conventional MRI. Student's t-test or Mann-Whitney U-test, chi-squared test, and receiver operating characteristic (ROC) curve analysis were performed to discriminate the two groups and to determine the diagnostic efficacy of ADC histogram parameters.

RESULTS

EGFR mutation group had more multiple brain metastases, less peritumoural brain oedema (PTBO), and lower peritumoural brain oedema index (PTBO-I) than EGFR wild-type group (all p<0.05). In addition, 90th and 75th percentiles of ADC and maximum ADC in the EGFR mutation group were significantly higher than in the EGFR wild-type group (all p<0.05). Ninetieth percentile of ADC had the highest area under the curve (AUC; 0.711), and it was found to outperform 75th percentile of ADC (AUC, 0.662; p=0.039) and maximum ADC (AUC, 0.681).

CONCLUSIONS

Whole-lesion ADC histogram analysis and MRI features of brain metastasis from NSCLC are expected to be potential biomarkers to non-invasively differentiate the EGFR mutation status.

Development and validation of CT-based radiomics nomogram for the classification of benign parotid gland tumors

PURPOSE

Accurate preoperative diagnosis of parotid tumor is essential for the formulation of optimal individualized surgical plans. The study aims to investigate the diagnostic performance of radiomics nomogram based on contrast-enhanced computed tomography (CT) images in the differentiation of the two most common benign parotid gland tumors.

METHODS

One hundred and ten patients with parotid gland tumors including 76 with pleomorphic adenoma (PA) and 34 with adenolymphoma (AL) confirmed by histopathology were included in this study. Radiomics features were extracted from contrast-enhanced CT images of venous phase. A radiomics model was established and a radiomics score (Rad-score) was calculated. Clinical factors including clinical data and CT features were assessed to build a clinical factor model. Finally, a nomogram incorporating the Rad-score and independent clinical factors was constructed. Receiver operator characteristics (ROC) curve was generated and the area under the ROC curve (AUC) was calculated to quantify the discriminative performance of each model on both the training and validation cohorts. Decision curve analysis (DCA) was conducted to evaluate the clinical usefulness of each model.

RESULTS

The radiomics model showed good discrimination in the training cohort [AUC, 0.89; 95% confidence interval (CI), 0.80-0.98] and validation cohort (AUC, 0.89; 95% CI, 0.77-1.00). The radiomics nomogram showed excellent discrimination in the training cohort (AUC, 0.98; 95% CI, 0.96-1.00) and validation cohort (AUC, 0.95; 95% CI, 0.88-1.00) and displayed better discrimination efficacy compared with the clinical factor model (AUC, 0.93; 95% CI, 0.88-0.99) in the training cohort (p < 0.05). The DCA demonstrated that the combined radiomics nomogram provided superior clinical usefulness than clinical factor model and radiomics model.

CONCLUSIONS

The CT-based radiomics nomogram combining Rad-score and clinical factors exhibits excellent predictive capability for differentiating parotid PA from AL, which might hold promise in assisting radiologists and clinicians in the exact differential diagnosis and formulation of appropriate treatment strategy.

Discriminating between benign and malignant salivary gland tumors using diffusion-weighted imaging and intravoxel incoherent motion at 3 Tesla

PURPOSE

The purpose of this study was to retrospectively evaluate the diagnostic performances of diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM) for discriminating between benign and malignant salivary gland tumors (SGTs).

MATERIALS AND METHODS

Sixty-seven patients with 71 SGTs who underwent MRI examination at 3 Tesla were included. There were 34 men and 37 women with a mean age of 57 ± 17 (SD) years (age range: 20-90 years). SGTs included 21 malignant tumors (MTs) and 50 benign SGTs (33 pleomorphic adenomas [PAs] and 17 Warthin's tumors [WTs]). For each SGT, DWI and IVIM parameters, mean, skewness, and kurtosis of apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*) and perfusion volume fraction (f) were calculated and further compared between SGTs using univariable analysis. Areas under the curves (AUC) of receiver operating characteristic of significant parameters were compared using the Delong test.

RESULTS

Significant differences in ADC_mean, D_mean and D*_mean were found between SGTs (P < 0.001). The highest AUC values were obtained for ADC_mean (0.949) for identifying PAs and D*_mean (0.985) for identifying WTs and skewness and kurtosis did not outperform mean. To discriminate benign from malignant SGTs with thresholds set to maximize Youden index, IVIM and DWI produced accuracies of 85.9% (61/71; 95% CI: 75.6-93.0) and 77.5% (55/71; 95% CI: 66.0-86.5) but misdiagnosed MTs as benign in 28.6% (6/21) and 61.9% (13/21) of SGTs, respectively. After maximizing specificity to 100% for benign SGTs, the accuracies of IVIM and DWI decreased to 76.1% (54/71; 95% CI: 64.5-85.4) and 64.8% (46/71; 95% CI: 52.5-75.8) but no MTs were misdiagnosed as benign. IVIM and DWI correctly diagnosed 66.0% (33/50) and 50.0% (25/50) of benign SGTs and 46.5% (33/71) and 35.2% (25/71) of all SGTs, respectively.

CONCLUSION

IVIM is more accurate than DWI for discriminating between benign and malignant SGTs because of its advantage in detecting WTs. Thresholds set by maximizing specificity for benign SGTs may be advantageous in a clinical setting.

Unusual case of skull base adenoid cystic carcinoma presenting as skull base osteomyelitis: case report

Background

Adenoid cystic carcinoma is a rare malignancy. Tumours of palatal region with minor salivary gland origin do not generally present at an early stage as the tumour is submucosal with symptoms prevalent only when there is evidence of perineural spread of the tumour. We report a case of adenoid cystic carcinoma of the palate with rare presentation of left ear discharge and diplopia on left lateral gaze. We discuss the case with emphasis on imaging evaluation mimicking a case of infective etiology with adjacent skull base osteomyelitis on initial presentation. However, on follow-up and further evaluation the patient was diagnosed as adenoid cystic carcinoma of hard palate on left side.

Case presentation

A 25-year-old male patient has presented to Jagadguru Sri Shivarathreeswara Hospital in August 2019 with complaints of left ear discharge and diplopia on left lateral gaze since 1 week. The clinical and imaging findings was suggestive of infective etiology and the patient was treated for the same with IV antibiotics. Repeat magnetic resonance imaging was then done which revealed definitive reduction in the severity of inflammation suggestive of response to therapy. Patient was then discharged and was followed up. Three months later, the patient came with complaints of mass in left nasal cavity. Patient was then referred for contrast enhanced computed tomography neck strongly suggestive of neoplastic etiology. The patient was then operated and histopathological examination of the biopsy revealed adenoid cystic carcinoma.

Conclusions

Tumours of palatal region with minor salivary gland origin do not generally present at an early stage as the tumour is submucosal with symptoms prevalent only when there is evidence of perineural spread of the tumour. In our case patient presented with lateral rectus palsy, involvement of meckel’s cave, trigeminal nerve involvement and cavernous sinus involvement which are strong indicators of the perineural and locoregional spread of the tumour. Hence, it is important for the radiologist and clinician to strongly suspect and evaluate for a primary lesion of the head and neck when such a radiological presentation has been demonstrated.

Apparent Diffusion Coefficient (ADC) Histogram Analysis in Parotid Gland Tumors: Evaluating a Novel Approach for Differentiation between Benign and Malignant Parotid Lesions Based on Full Histogram Distributions

The aim of this study was to assess the diagnostic value of ADC distribution curves for differentiation between benign and malignant parotid gland tumors and to compare with mean ADC values. 73 patients with parotid gland tumors underwent head-and-neck MRI on a 1.5 Tesla scanner prior to surgery and histograms of ADC values were extracted. Histopathological results served as a reference standard for further analysis. ADC histograms were evaluated by comparing their similarity to a reference distribution using Chi2-test-statistics. The assumed reference distribution for benign and malignant parotid gland lesions was calculated after pooling the entire ADC data. In addition, mean ADC values were determined. For both methods, we calculated and compared the sensitivity and specificity between benign and malignant parotid gland tumors and three subgroups (pleomorphic adenoma, Warthin tumor, and malignant lesions), respectively. Moreover, we performed cross-validation (CV) techniques to estimate the predictive performance between ADC distributions and mean values. Histopathological results revealed 30 pleomorphic adenomas, 22 Warthin tumors, and 21 malignant tumors. ADC histogram distribution yielded a better specificity for detection of benign parotid gland lesions (ADChistogram: 75.0% vs. ADCmean: 71.2%), but mean ADC values provided a higher sensitivity (ADCmean: 71.4% vs. ADChistogram: 61.9%). The discrepancies are most pronounced in the differentiation between malignant and Warthin tumors (sensitivity ADCmean: 76.2% vs. ADChistogram: 61.9%; specificity ADChistogram: 81.8% vs. ADCmean: 68.2%). Using CV techniques, ADC distribution revealed consistently better accuracy to differentiate benign from malignant lesions (“leave-one-out CV” accuracy ADChistogram: 71.2% vs. ADCmean: 67.1%). ADC histogram analysis using full distribution curves is a promising new approach for differentiation between primary benign and malignant parotid gland tumors, especially with respect to the advantage in predictive performance based on CV techniques.

Differentiation of Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram

Objectives

We aimed to develop and validate radiomic nomograms to allow preoperative differentiation between benign- and malignant parotid gland tumors (BPGT and MPGT, respectively), as well as between pleomorphic adenomas (PAs) and Warthin tumors (WTs).

Materials and Methods

This retrospective study enrolled 183 parotid gland tumors (68 PAs, 62 WTs, and 53 MPGTs) and divided them into training (n = 128) and testing (n = 55) cohorts. In total, 2553 radiomics features were extracted from fat-saturated T2-weighted images, apparent diffusion coefficient maps, and contrast-enhanced T1-weighted images to construct single-, double-, and multi-sequence combined radiomics models, respectively. The radiomics score (Rad-score) was calculated using the best radiomics model and clinical features to develop the radiomics nomogram. The receiver operating characteristic curve and area under the curve (AUC) were used to assess these models, and their performances were compared using DeLong’s test. Calibration curves and decision curve analysis were used to assess the clinical usefulness of these models.

Results

The multi-sequence combined radiomics model exhibited better differentiation performance (BPGT vs. MPGT, AUC=0.863; PA vs. MPGT, AUC=0.929; WT vs. MPGT, AUC=0.825; PA vs. WT, AUC=0.927) than the single- and double sequence radiomics models. The nomogram based on the multi-sequence combined radiomics model and clinical features attained an improved classification performance (BPGT vs. MPGT, AUC=0.907; PA vs. MPGT, AUC=0.961; WT vs. MPGT, AUC=0.879; PA vs. WT, AUC=0.967).

Conclusions

Radiomics nomogram yielded excellent diagnostic performance in differentiating BPGT from MPGT, PA from MPGT, and PA from WT.

Machine learning-based radiomics for histological classification of parotid tumors using morphological MRI: a comparative study

OBJECTIVES

To evaluate the effectiveness of machine learning models based on morphological magnetic resonance imaging (MRI) radiomics in the classification of parotid tumors.

METHODS

In total, 298 patients with parotid tumors were randomly assigned to a training and test set at a ratio of 7:3. Radiomics features were extracted from the morphological MRI images and screened using the Select K Best and LASSO algorithm. Three-step machine learning models with XGBoost, SVM, and DT algorithms were developed to classify the parotid neoplasms into four subtypes. The ROC curve was used to measure the performance in each step. Diagnostic confusion matrices of these models were calculated for the test cohort and compared with those of the radiologists.

RESULTS

Six, twelve, and eight optimal features were selected in each step of the three-step process, respectively. XGBoost produced the highest area under the curve (AUC) for all three steps in the training cohort (0.857, 0.882, and 0.908, respectively), and for the first step in the test cohort (0.826), but produced slightly lower AUCs than SVM in the latter two steps in the test cohort (0.817 vs. 0.833, and 0.789 vs. 0.821, respectively). The total accuracies of XGBoost and SVM in the confusion matrices (70.8% and 59.6%) outperformed those of DT and the radiologist (46.1% and 49.2%).

CONCLUSION

This study demonstrated that machine learning models based on morphological MRI radiomics might be an assistive tool for parotid tumor classification, especially for preliminary screening in absence of more advanced scanning sequences, such as DWI.

KEY POINTS

• Machine learning algorithms combined with morphological MRI radiomics could be useful in the preliminary classification of parotid tumors. • XGBoost algorithm performed better than SVM and DT in subtype differentiation of parotid tumors, while DT seemed to have a poor validation performance. • Using morphological MRI only, the XGBoost and SVM algorithms outperformed radiologists in the four-type classification task for parotid tumors, thus making these models a useful assistant diagnostic tool in clinical practice.

Apparent Diffusion Coefficient Map–Based Radiomics Features for Differential Diagnosis of Pleomorphic Adenomas and Warthin Tumors From Malignant Tumors

Purpose

The magnetic resonance imaging (MRI) findings may overlap due to the complex content of parotid gland tumors and the differentiation level of malignant tumor (MT); consequently, patients may undergo diagnostic lobectomy. This study assessed whether radiomics features could noninvasively stratify parotid gland tumors accurately based on apparent diffusion coefficient (ADC) maps.

Methods

This study examined diffusion-weighted imaging (DWI) obtained with echo planar imaging sequences. Eighty-eight benign tumors (BTs) [54 pleomorphic adenomas (PAs) and 34 Warthin tumors (WTs)] and 42 MTs of the parotid gland were enrolled. Each case was randomly divided into training and testing cohorts at a ratio of 7:3 and then was compared with each other, respectively. ADC maps were digitally transferred to ITK SNAP (www.itksnap.org). The region of interest (ROI) was manually drawn around the whole tumor margin on each slice of ADC maps. After feature extraction, the Synthetic Minority Oversampling TEchnique (SMOTE) was used to remove the unbalance of the training dataset. Then, we applied the normalization process to the feature matrix. To reduce the similarity of each feature pair, we calculated the Pearson correlation coefficient (PCC) value of each feature pair and eliminated one of them if the PCC value was larger than 0.95. Then, recursive feature elimination (RFE) was used to process feature selection. After that, we used linear discriminant analysis (LDA) as the classifier. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the ADC.

Results

The LDA model based on 13, 8, 3, and 1 features can get the highest area under the ROC curve (AUC) in differentiating BT from MT, PA from WT, PA from MT, and WT from MT on the validation dataset, respectively. Accordingly, the AUC and the accuracy of the model on the testing set achieve 0.7637 and 73.17%, 0.925 and 92.31%, 0.8077 and 75.86%, and 0.5923 and 65.22%, respectively.

Conclusion

The ADC-based radiomics features may be used to assist clinicians for differential diagnosis of PA and WT from MTs.

Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging in the differential diagnosis of parotid tumors

Objective

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) histograms were used to investigate whether their parameters can distinguish between benign and malignant parotid gland tumors and further differentiate tumor subgroups.

Materials and methods

A total of 117 patients (32 malignant and 85 benign) who had undergone DCE-MRI for pretreatment evaluation were retrospectively included. Histogram parameters including mean, median, entropy, skewness, kurtosis and 10th, 90th percentiles were calculated from time to peak (TTP) (s), wash in rate (WIR) (l/s), wash out rate (WOR) (l/s), and maximum relative enhancement (MRE) (%) mono-exponential models. The Mann–Whitney U test was used to compare the differences between the benign and malignant groups. The diagnostic value of each significant parameter was determined on Receiver operating characteristic (ROC) analysis. Multivariate stepwise logistic regression analysis was used to identify the independent predictors of the different tumor groups.

Results

For both the benign and malignant groups and the comparisons among the subgroups, the parameters of TTP and MRE showed better performance among the various parameters. WOR can be used as an indicator to distinguish Warthin’s tumors from other tumors. Warthin’s tumors showed significantly lower values on 10th MRE and significantly higher values on skewness TTP and 10th WOR, and the combination of 10th MRE, skewness TTP and 10th WOR showed optimal diagnostic performance (AUC, 0.971) and provided 93.12% sensitivity and 96.70% specificity. After Warthin’s tumors were removed from among the benign tumors, malignant parotid tumors showed significantly lower values on the 10th TTP (AUC, 0.847; sensitivity 90.62%; specificity 69.09%; P < 0.05) and higher values on skewness MRE (AUC, 0.777; sensitivity 71.87%; specificity 76.36%; P < 0.05).

Conclusion

DCE-MRI histogram parameters, especially TTP and MRE parameters, show promise as effective indicators for identifying and classifying parotid tumors. Entropy TTP and kurtosis MRE were found to be independent differentiating variables for malignant parotid gland tumors. The 10th WOR can be used as an indicator to distinguish Warthin’s tumors from other tumors.

Differential diagnosis of parotid gland tumours: Application of SWI combined with DWI and DCE-MRI

BACKGROUND

Parotid tumours (PTs) have a variety of pathological types, and the surgical procedures differ depending on the tumour type. However, accurate diagnosis of PTs from the current preoperative examinations is unsatisfactory.

METHODS

This retrospective study was approved by the Ethics Committee of our hospital, and the requirement for informed consent was waived. A total of 73 patients with PTs, including 55 benign and 18 malignant tumours confirmed by surgical pathology, were enrolled. All patients underwent diffusion-weighted imaging (DWI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), susceptibility-weighted imaging (SWI), T2-weighted imaging (T2WI), and T1-weighted imaging (T1WI). The signal uniformity and capsule on T2WI, apparent diffusion coefficient (ADC) derived from DWI, semi-quantitative parameter time-intensity curve (TIC) pattern, and quantitative parameters including transfer constant (K_trans), extravascular extracellular volume fraction (V_e), wash-out constant (K_ep) calculated from DCE-MRI, and intratumoural susceptibility signal (ITSS) obtained from SWI were assessed and compared between benign and malignant PTs. Logistic regression analysis was used to select the predictive parameters for the classification of benign and malignant parotid gland tumours, and receiver operating characteristic (ROC) curve analysis was used to evaluate their diagnostic performance.

RESULTS

Malignant PTs tended to exhibit a type C TIC pattern, whereas benign tumours tended to be type A and B (p < 0.001). Benign PTs had less ITSS than malignant tumours (p < 0.001). Multivariate analyses showed that ADC, V_e, and ITSS were predictors of tumour classification. ROC analysis showed that the area under the curve (AUC) of ADC, V_e, ITSS, and ADC combined with V_e were 0.623, 0.615, 0.826, and 0.782, respectively, in differentiating between malignant and benign PTs. When ITSS was added, the AUCs of ADC, V_e, and ADC combined with V_e increased to 0.882, 0.848, and 0.930, respectively.

CONCLUSION

SWI offers incremental diagnostic value to DWI and DCE-MRI in the characterisation of parotid gland tumours.

The Value of Magnetic Resonance Imaging Histograms in the Preoperative Differential Diagnosis of Endometrial Stromal Sarcoma and Degenerative Hysteromyoma

Objective: The present study aimed to explore the application value of magnetic resonance imaging (MRI) histograms with multiple sequences in the preoperative differential diagnosis of endometrial stromal sarcoma (ESS) and degenerative hysteromyoma (DH).

Methods: The clinical and preoperative MRI data of 20 patients with pathologically confirmed ESS and 24 patients with pathologically confirmed DH were retrospectively analyzed, forming the two study groups. Mazda software was used to select the MRI layer with the largest tumor diameter in T2WI, the apparent diffusion coefficient (ADC), and enhanced T1WI (T₁CE) images. The region of interest (ROI) was outlined for gray-scale histogram analysis. Nine parameters—the mean, variance, kurtosis, skewness, 1st percentile, 10th percentile, 50th percentile, 90th percentile, and 99th percentile—were obtained for intergroup analysis, and the receiver operating curves (ROCs) were plotted to analyze the differential diagnostic efficacy for each parameter.

Results: In the T2WI histogram, the differences between the two groups in seven of the parameters (mean, skewness, 1st percentile, 10th percentile, 50th percentile, 90th percentile, and 99th percentile) were statistically significant (P < 0.05). In the ADC histogram, the differences between the two groups in three of the parameters (skewness, 10th percentile, and 50th percentile) were statistically significant (P < 0.05). In the T₁CE histogram, no significant differences were found between the two groups in any of the parameters (all P > 0.05). Of the nine parameters, the 50th percentile was found to have the best diagnostic efficacy. In the T2WI histogram, ROC curve analysis of the 50th percentile yielded the best area under the ROC curve (AUC; 0.742), sensitivity of 70%, and specificity of 83.3%. In the ADC histogram, ROC curve analysis of the 50th percentile yielded the best area under the ROC curve (AUC; 0.783), sensitivity of 81%, and specificity of 76.9%.

Conclusion: The parameters of the mean, 10th percentile and 50th percentile in the T2WI histogram have good diagnostic efficacy, providing new methods and ideas for clinical diagnosis.

Apparent diffusion coefficient measurement of the parotid gland parenchyma

The measurements of apparent diffusion coefficient (ADC) with diffusion weighted magnetic resonance imaging (DW-MRI) is becoming a popular diagnostic and research tool for examination of parotid glands. However, there is little agreement between the reported ADC values of the parotid gland in published literature. In this review 43 studies on ADC measurement of the parotid glands were included. The analyses indicated several possible culprits of the observed ADC discrepancies. For example, DW-MRI examinations under gustatory stimulation gives higher ADC values compared to the unstimulated parotid gland (P=0.003). The diffusion weighting factors (b-values) can either increase (b-value <200 s/mm²) or decrease ADC values (b-values >1,000 s/mm²). The timing of follow-up DW-MRI after radiotherapy (RT) indicates correlation to the found ADC values (R² =0.39). Interestingly, the choice of regions of interest (ROI) appears not to affect the measurements of ADC (P=0.75). It can be concluded that there is a critical need for standardization of ADC measurement of the parotid glands to allow valid inter-study comparisons and eventually to reach consensus on the use of ADC as biomarker.

Longitudinal Multiparametric MRI Assessment of Irradiated Salivary Gland in a Rat Model: Correlated With Histological Findings

BACKGROUND

Several magnetic resonance imaging (MRI) sequences have been applied to assess injured glands but without histological validation.

PURPOSE

To evaluate longitudinal changes in multiparametric MRI (mp-MRI) of irradiated salivary glands in a rat model and investigate correlations between mp-MRI and histological findings.

STUDY TYPE

Prospective.

ANIMAL MODEL

Submandibular glands of 36 rats were radiated using a single dose of 15 Gy X-ray (irradiation [IR] group), and 6 other rats were enrolled into sham-IR group. mp-MRI were scanned 1 day after sham-IR (n = 6), or 1, 2, 4, 8, 12, 24 weeks after IR (n = 36, 6 per subgroup).

FIELD STRENGTH/SEQUENCE

A 3.0-T/Diffusion-weighted imaging (DWI), readout-segmented echo-planar imaging (EPI) sequence; intravoxel incoherent motion DWI, single-shot EPI sequence; T1 mapping, dual-flip-angle gradient-echo sequence with volumetric interpolated breath-hold examination; T2 mapping, turbo spin-echo sequence.

ASSESSMENT

Parameters including apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D^* ), perfusion fraction (f), T1 and T2 value were obtained. Histological examinations, including hematoxylin and eosin staining (for acinar cell fraction [AC%] detection), Masson's trichrome staining (for degree of fibrosis [F%] determination) and CD34-immunohistochemical staining (for microvessel density [MVD] calculation), were performed at corresponding time points.

STATISTICAL TESTS

One-way analysis of variance was used to compare the mp-MRI and histological parameters among different groups. Spearman correlation analysis was applied to determine the correlation between mp-MRI and histological parameters. Two-sided P ≤ 0.05 was considered statistically significant.

RESULTS

Changes of mp-MRI parameters (ADC, D, D^* , f, T1, T2) and histological results (AC%, F%, MVD) among the seven groups were all significant. ADC, D, and T2 values negatively correlated with AC% (ADC, r = -0.728; D, r = -0.773; T2, r = -0.600), f positively correlated with MVD (r = 0.496), and T1 values positively correlated with F% (r = 0.714). DATA CONCLUSION: mp-MRI might be able to noninvasively and quantitatively evaluate the dynamic pathological changes within the irradiated salivary glands.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 2.

Quantitative dynamic contrast-enhanced MRI and readout segmentation of long variable echo-trains diffusion-weighted imaging in differentiating parotid gland tumors

PURPOSE

To evaluate the ability of quantitative dynamic contrast-enhanced (DCE)-MRI and readout segmentation of long variable echo-trains diffusion-weighted imaging (RESOLVE-DWI) in differentiating parotid tumors (PTs) with different histological types.

METHODS

In this retrospective study, 123 patients with 145 histologically proven PTs who underwent both RESOLVE-DWI and DCE-MRI were enrolled including 51 pleomorphic adenomas (PAs), 52 Warthin's tumors (WTs), 27 other benign neoplasms (OBNs), and 15 malignant tumors (MTs). Quantitative parameters of DCE-MRI (K^trans, K_ep, and V_e) and the apparent diffusion coefficient (ADC) of lesions were calculated and analyzed. Kruskal-Wallis tests with Dunn-Bonferroni correction, logistic regression analyses, and receiver operating characteristic curve were used for statistical analyses.

RESULTS

PAs exhibited a lowest K^trans among these four PTs. WTs demonstrated the highest K_ep and lowest V_e values. WTs and MTs showed lower ADC_min values than PAs and OBNs. The combination of K_ep and V_e provided 98.1% sensitivity, 85% specificity, and 98.7% accuracy for differentiating WTs from the other three PTs. The ADC_min cutoff value of ≤ 0.826 yielded 80.0% sensitivity, 92.3% specificity, and 90.3% accuracy for the differentiation of MTs from PAs and OBNs. K^trans with a cutoff value of ≤ 0.185 achieved a sensitivity, specificity, and accuracy of 84.3, 70.4, and 79.5%, respectively, for discriminating PAs from OBNs.

CONCLUSION

The combination of quantitative DCE-MRI and RESOLVE-DWI is beneficial for characterizing four histological types of PTs.

Management of Salivary Gland Malignancy: ASCO Guideline

PURPOSE

To provide evidence-based recommendations for practicing physicians and other healthcare providers on the management of salivary gland malignancy.

METHODS

ASCO convened an Expert Panel of medical oncology, surgical oncology, radiation oncology, neuroradiology, pathology, and patient advocacy experts to conduct a literature search, which included systematic reviews, meta-analyses, randomized controlled trials, and prospective and retrospective comparative observational studies published from 2000 through 2020. Outcomes of interest included survival, diagnostic accuracy, disease recurrence, and quality of life. Expert Panel members used available evidence and informal consensus to develop evidence-based guideline recommendations.

RESULTS

The literature search identified 293 relevant studies to inform the evidence base for this guideline. Six main clinical questions were addressed, which included subquestions on preoperative evaluations, surgical diagnostic and therapeutic procedures, appropriate radiotherapy techniques, the role of systemic therapy, and follow-up evaluations.

RECOMMENDATIONS

When possible, evidence-based recommendations were developed to address the diagnosis and appropriate preoperative evaluations for patients with a salivary gland malignancy, therapeutic procedures, and appropriate treatment options in various salivary gland histologies.Additional information is available at www.asco.org/head-neck-cancer-guidelines.

Diffusion-weighted imaging with histogram analysis of the apparent diffusion coefficient maps in the diagnosis of parotid tumours

The aim of this study was to investigate the role of diffusion-weighted imaging (DWI) with histogram analysis of apparent diffusion coefficient (ADC) maps in the characterization of parotid tumours. This prospective study included 39 patients with parotid tumours. All patients underwent magnetic resonance imaging with DWI, and ADC maps were generated. The whole lesion was selected to obtain histogram-related parameters, including the mean (ADC_mean), minimum (ADC_min), maximum (ADC_max), skewness, and kurtosis of the ADC. The final diagnosis included pleomorphic adenoma (PA; n=18), Warthin tumour (WT; n=12), and salivary gland malignancy (SGM; n=9). ADC_mean (×10^-3mm²/s) was 1.93±0.34 for PA, 1.01±0.11 for WT, and 1.26±0.54 for SGM. There was a significant difference in whole lesion ADC_mean among the three study groups. Skewness had the best diagnostic performance in differentiating PA from WT (P=0.001; best detected cut-off 0.41, area under the curve (AUC) 0.990) and in discriminating WT from SGM (P=0.03; best detected cut-off 0.74, AUC 0.806). The whole lesion ADC_mean value had best diagnostic performance in differentiating PA from SGM (P=0.007; best detected cut-off 1.16×10^-3mm²/s, AUC 0.948). In conclusion, histogram analysis of ADC maps may offer added value in the differentiation of parotid tumours.

A triple-classification radiomics model for the differentiation of pleomorphic adenoma, Warthin tumour, and malignant salivary gland tumours on the basis of diffusion-weighted imaging

AIM

To develop and validate a triple-classification radiomics model for the preoperative differentiation of pleomorphic adenoma (PA), Warthin tumour (WT), and malignant salivary gland tumour (MSGT) based on diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

Data from 217 patients with histopathologically confirmed salivary gland tumours (100 PAs, 68 WTs, and 49 MSGTs) from January 2015 to March 2019 were analysed retrospectively and divided into a training set (n=173), and a validation set (n=44). A total of 396 radiomic features were extracted from the DWI of all patients. Analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) regression were used to select radiomic features, which were then constructed using three classification models, namely, logistic regression method (LR), support vector machine (SVM), and K-nearest neighbor (KNN). The diagnostic performance of the radiomics model was quantified by the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) of the training and validation data sets.

RESULTS

The 20 most valuable features were investigated based on the LASSO regression. LR and SVM methods exhibited better diagnostic ability than KNN for multiclass classification. LR and SVM had the best performance and yielded the AUC values of 0.857 and 0.824, respectively, in the training data set and the AUC values of 0.932 and 0.912, respectively, in the validation data set of MSGT diagnosis.

CONCLUSION

DWI-based triple-classification radiomics model has predictive value in distinguishing PA, WT, and MSGT, which can be used for preoperative auxiliary diagnosis in clinical practice.

Cross-sectional imaging and cytologic investigations in the preoperative diagnosis of parotid gland tumors - An updated literature review

An accurate preoperative diagnosis of parotid tumors is essential for the selection and planning of surgical treatment. Various modern cross-sectional imaging and cytologic investigations can support the differential diagnosis of parotid tumors. The aim of this study was to achieve a comprehensive and updated review of modern imaging and cytologic investigations used in parotid tumor diagnosis, based on the latest literature data. This literature review could serve as a guide for clinicians in selecting different types of investigations for the preoperative differential diagnosis of parotid tumors. Magnetic resonance imaging (MRI) with its dynamic and advanced sequences is the first-line imaging investigation used in differentiating parotid tumors. Computed tomography (CT) and positron emission tomography (PET)-CT provide limited indications in differentiating parotid tumors. Fine needle aspiration biopsy and core needle biopsy can contribute with satisfactory results to the cytological diagnosis of parotid tumors. Dynamic MRI with its dynamic contrast-enhanced and diffusion-weighted sequences provides the best accuracy for the preoperative differential diagnosis of parotid tumors. CT allows the best evaluation of bone invasion, being useful when MRI cannot be performed, and PET-CT has value in the follow-up of cancer patients. The dual cytological and imaging approach is the safest method for an accurate differential diagnosis of parotid tumors.

Texture analysis in the characterization of parotid salivary gland lesions: A study on MR diffusion weighted imaging

BACKGROUND AND PURPOSE

Parotid lesions show overlaps of morphological findings, apparent diffusion coefficient (ADC) values and types of time/intensity curve. This research aimed to evaluate the role of diffusion weighted imaging texture analysis in differentiating between benign and malignant parotid lesions and in characterizing pleomorphic adenoma (PA), Warthin tumor (WT), epithelial malignancy (EM), and lymphoma (LY).

METHODS

Texture analysis of 54 parotid lesions (19 PA, 14 WT, 14 EM, and 7 LY) was performed on ADC map images. An ANOVA test was used to estimate both the difference between benign and malignant lesions and the texture feature differences among PA, WT, EM, and LY. A P-value≤0.01 was considered to be statistically significant. A cut-off value defined by ROC curve analysis was found for each statistically significant texture parameter. The diagnostic accuracy was obtained for each texture parameter with AUC ≥ 0.5. The agreement between each texture parameter and histology was calculated using the Cohen's kappa coefficient.

RESULTS

The mean kappa values were 0.61, 0.34, 0.26, 0.17, and 0.48 for LY, EM, WT, PA, and benign vs. malignant lesions respectively. Long zone emphasis cut-off values >1.870 indicated EM with an accuracy of 81 % and values >2.630 revealed LY with an accuracy of 93 %. Long run emphasis values >1.050 and >1.070 indicated EM and LY with a diagnostic accuracy of 79% and 93% respectively.

CONCLUSIONS

Long zone emphasis and long run emphasis texture parameters allowed the identification of LY and the differentiation between benign and malignant lesions. WT and PA were not accurately recognized.

Performance of diffusion-weighted imaging for the diagnosis of parotid gland malignancies: A meta-analysis

PURPOSE

This study aimed to assess the diagnostic performance of diffusion-weighted imaging (DWI) for parotid gland malignancies.

METHODS

Four databases (PubMed, the Cochrane Library, Embase, and Web of Science) were searched systematically and retrospectively by two researchers until May 18, 2020. The methodological quality of the studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate random effects model was used to pool the sensitivity and specificity data for the apparent diffusion coefficient (ADC). Summary receiver operating characteristic curve was constructed, and the area under the curve (AUC) was calculated. The positive (LR+) and negative likelihood ratios (LR-) were also calculated. Subgroup and meta-regression analyses were performed to evaluate heterogeneity within studies.

RESULTS

Sixteen studies involving 1004 patients were included. The pooled sensitivity, specificity, and AUC for the ADC to distinguish malignant from begin parotid lesions were 89 %, 76 %, and 0.91, respectively. The LR + was 3.7 and LR- was 0.15, respectively. Subgroup analyses revealed that the applied cut-off b values and study size were sources of heterogeneity for the ADC. There were publication bias concerns.

CONCLUSIONS

Our meta-analysis suggests that the ADC value provides excellent sensitivity and moderate specificity for the diagnosis of malignant lesions in the parotid gland. However, substantial heterogeneity was found. Therefore, additional larger, prospective studies in combination with standard techniques focusing on parotid tumors should be conducted to determine the true performance of DWI for the differential diagnosis of parotid lesions.

Investigation of Synthetic Relaxometry and Diffusion Measures in the Differentiation of Benign and Malignant Breast Lesions as Compared to BI-RADS

BACKGROUND

Breast cancer is the most common malignant tumor in women and a quantitative contrast-free method is highly desirable for its diagnosis.

PURPOSE

To investigate the performance of quantitative MRI in differentiating malignant from benign breast lesions and to compare with the Breast Imaging Reporting and Data System (BI-RADS).

STUDY TYPE

Retrospective.

SUBJECTS

Eighty patients (56 with malignant lesions and 24 with benign lesions).

FIELD STRENGTH/SEQUENCE

Diffusion-weighted imaging (DWI) with a single-shot echo planar sequence and synthetic MRI with magnetic resonance image compilation (MAGiC) were performed at 3T.

ASSESSMENT

T₁ relaxation time (T₁ ), T₂ relaxation time (T₂ ), and proton density (PD) from synthetic MRI and apparent diffusion coefficient (ADC) from DWI were analyzed by two radiologists (Reader A, Reader B). Univariable and multivariable models were developed to optimize differentiation between malignant and benign lesions and their performances compared to BI-RADS.

STATISTICAL TESTS

The diagnostic performance was evaluated using multivariate logistic regression analysis and area under the receiver operating characteristic (ROC) curves (AUC).

RESULTS

T₂ , PD, and ADC values for malignant lesions were significantly lower than those in benign breast lesions for both radiologists (all P < 0.05). The combined T₂ , PD, and ADC model had the best performance for differentiating malignant and benign lesions with AUC, sensitivity, specificity, positive predictive value, and negative predictive values of 0.904, 94.6%, 87.5%, 94.6%, and 87.5%, respectively. The corresponding results for BI-RADS were no AUC, 94.6%, 75.0%, 89.8%, and 85.7%, respectively.

DATA CONCLUSION

The approach that combined synthetic MRI and DWI outperformed BI-RADS in the differential diagnosis of malignant and benign breast lesions and was achieved without contrast agents. This approach may serve as an alternative and effective strategy for the improvement of breast lesion differentiation.

LEVEL OF EVIDENCE

3.

TECHNICAL EFFICACY STAGE

3.

Multiparametric magnetic resonance imaging of parotid tumors: A systematic review

PURPOSE

The purpose of this systematic review was to provide an overview of the contribution of multiparametric magnetic resonance imaging (MRI) in the diagnosis of parotid tumors (PT) and recommendations based on current evidences.

MATERIAL AND METHODS

We performed a retrospective systematic search of PubMed, EMBASE, and Cochrane Library databases from inception to January 2020, using the keywords "magnetic resonance imaging" and "salivary gland neoplasms".

RESULTS

The initial search returned 2345 references and 90 were deemed relevant for this study. A total of 54 studies (60%) reported the use of diffusion-weighted imaging (DWI) and 28 studies (31%) the use of dynamic contrast-enhanced (DCE) imaging. Specific morphologic signs of frequent benign PT and suggestive signs of malignancy on conventional sequences were reported in 37 studies (41%). DWI showed significant differences in apparent diffusion coefficient (ADC) values between benign and malignant PT, and especially between pleomorphic adenomas and malignant PT, with cut-off ADC values between 1.267×10^-3mm²/s and 1.60×10^-3mm²/s. Perfusion curves obtained with DCE imaging allowed differentiating among pleomorphic adenomas, Warthin's tumors, malignant PT and cystic lesions. The combination of morphological MRI sequences, DCE imaging and DWI helped increase the diagnostic accuracy of MRI.

CONCLUSION

Multiparametric MRI, including morphological MRI sequences, DWI and DCE imaging, is the imaging modality of choice for the characterization of focal PT and provides features that are highly suggestive of a specific diagnosis.

Diagnostic performance of whole-lesion apparent diffusion coefficient histogram analysis metrics for differentiating benign and malignant breast lesions: a systematic review and diagnostic meta-analysis

BACKGROUND

Although whole-lesion apparent diffusion coefficient (ADC) histogram has been increasingly used for breast lesions, it has not been routinely used in clinical practice as an emergent promising imaging tool.

PURPOSE

To evaluate the performance of whole-lesion ADC histogram analysis metrics for differentiating benign and malignant breast lesions.

MATERIAL AND METHODS

A systematic PubMed/EMBASE/Cochrane electronic database search was performed for original diagnostic studies from 1 January 1970 to 2 January 2019. Summary estimates of diagnostic accuracy were generated and meta-regression was performed to explore sources of heterogeneity according to study and magnetic resonance imaging characteristics.

RESULTS

Five original articles involving 493 patients were included in the meta-analysis. The pooled sensitivity and specificity of whole-lesion ADC histogram analysis were 0.85 (95% confidence interval [CI] = 0.81-0.89) and 0.79 (95% CI = 0.72-0.84) for distinguishing benign and malignant breast lesions, respectively. The area under the curve (AUC) was 0.9178. No publication bias was detected (P = 0.51). In subgroup analysis, the summary sensitivity and specificity of 50th percentile ADC value were 0.81 (95% CI = 0.71-0.88) and 0.86 (95% CI = 0.74-0.94), respectively. Meta-regression analysis indicated no covariates were sources of heterogeneity (P > 0.05).

CONCLUSION

Whole-lesion ADC histogram analysis demonstrated good diagnostic performance for differentiating between benign and malignant breast lesions, with 50th percentile ADC value showing higher diagnostic accuracy than other histogram parameters. Given the limited number of studies included in the analysis, the findings from our meta-analysis will need further confirmation in future research.

Feasibility study of using simultaneous multi-slice RESOLVE diffusion weighted imaging to assess parotid gland tumors: comparison with conventional RESOLVE diffusion weighted imaging

Background

To evaluate the feasibility of using simultaneous multi-slice (SMS) readout segmentation of long variable echo-trains (RESOLVE) diffusion-weighted imaging (DWI) to assess parotid gland tumors, compared with conventional RESOLVE DWI.

Methods

From September 2018 to December 2018, 20 consecutive patients with parotid tumors who underwent MRI scan for pre-surgery evaluation were enrolled. SMS-RESOLVE DWI and conventional RESOLVE DWI were scanned with matched imaging parameters, respectively. The scan time of two DWI sequences was recorded. Qualitative (anatomical structure differentiation, lesion display, artifact, and overall image quality) and quantitative (apparent diffusion coefficient, ADC; ratio of signal-to-noise ratio, SNR ratio; ratio of contrast-to-noise ratio, CNR ratio) assessments of image quality were performed, and compared between SMS-RESOLVE DWI and conventional RESOLVE DWI by using Paired t-test. Two-sided P value less than 0.05 indicated significant difference.

Results

The scan time was 3 min and 41 s for SMS-RESOLVE DWI, and 5 min and 46 s for conventional RESOLVE DWI. SMS-RESOLVE DWI produced similar qualitative image quality with RESOLVE DWI (anatomical structure differentiation, P = 0.164; lesion display, P = 0.193; artifact, P = 0.330; overall image quality, P = 0.083). Meanwhile, there were no significant difference on ADC_Lesion (P = 0.298), ADC_Masseter (P = 0.122), SNR ratio (P = 0.584) and CNR ratio (P = 0.217) between two DWI sequences.

Conclusion

Compared with conventional RESOLVE DWI, SMS-RESOLVE DWI could provide comparable image quality using markedly reduced scan time. SMS could increase the clinical usability of RESOLVE technique for DWI of parotid gland.

Differentiation of salivary gland tumor using diffusion-weighted imaging with a fractional order calculus model

OBJECTIVE

To evaluate the feasibility of using imaging parameters (D, β and μ) obtained from fractional order calculus (FROC) diffusion model to differentiate salivary gland tumors.

METHODS

15 b-value (0-2000 s/mm²) diffusion-weighted imaging (DWI) was scanned in 62 patients with salivary gland tumors (47 benign and 15 malignant). Diffusion coefficient D, fractional order parameter β (which correlates with tissue heterogeneity) and a microstructural quantity μ of the solid portion within the tumor were calculated, and compared between benign and malignant groups, or among pleomorphic adenoma (PA), Warthin's tumor (WT), and malignant tumor (MT) groups. Performance of FROC parameters for differentiation was assessed using receiver operating characteristic analysis.

RESULTS

None of the FROC parameters exhibited significant differences between benign and malignant group (D, p = 0.150; β, p = 0.967; μ, p = 0.693). WT showed significantly lower D (p < 0.001) and β (p < 0.001), while higher μ (p = 0.001) than PA. Combination of D, β and μ showed optimal diagnostic performance (area under the curve, AUC, 0.998). MT showed significantly lower D (p = 0.001) and β (p = 0.025) than PA, while no significant difference was found on μ (p = 0.064). Combination of D and β showed optimal diagnostic performance (AUC, 0.933). Significant difference was found on β (p = 0.027) between MT and WT, while not on D (p = 0.806) and μ (p = 0.789). Setting a βof 0.615 as the cut-off value, optimal diagnostic performance could be obtained (AUC = 0.806).

CONCLUSION

A non-Gaussian FROC diffusion model can serve as a noninvasive and quantitative imaging technique for differentiating salivary gland tumors.

ADVANCES IN KNOWLEDGE

(1) PA showed higher D and β and lower μ than WT. (2) PA had higher D and β than MT. (3) WT demonstrated lower β than MT. (4) β, as a new FROC parameter, could offer an added value to the differentiation.

Added value of susceptibility-weighted imaging to diffusion-weighted imaging in the characterization of parotid gland tumors

PURPOSE

To assess the added value of susceptibility-weighted imaging (SWI) to diffusion-weighted imaging (DWI) in the characterization of parotid gland tumors.

METHODS

Seventy-eight patients with pathologically confirmed parotid gland tumors, who underwent DWI and SWI for pre-surgery evaluation, were enrolled. Apparent diffusion coefficient (ADC) and degree of intratumoral susceptibility signal intensity (ITSS) were measured and compared between benign and malignant groups, and among pleomorphic adenoma (PA), Warthin tumor (WT) and malignant tumor (MT). Independent sample t test, one-way analysis of variance and receiver operating characteristic curve analysis were used for statistical analyses.

RESULTS

Benign parotid gland tumor showed a significantly higher mean ADC value than malignant tumors (0.836 ± 0.350 vs 0.592 ± 0.163, p = 0.001). Setting an average ADC value of 0.679 as the cut-off value, optimal differentiating performance could be obtained (AUC, 0.700; sensitivity, 62.69%; specificity, 81.82%) for differentiating malignant from benign tumors. PA showed significantly higher mean ADC and less ITSS than WT (ADC, p < 0.001; ITSS, p = 0.033) and MT (ADC, p < 0.001; ITSS, p = 0.024), while the difference between WT and MT was not significant (ADC, p = 0.826; ITSS, p = 0.539). After integration with ITSS, the diagnostic performance of ADC was improved for differentiating PA from WT (AUC 0.921 vs 0.873) and from MT (AUC 0.906 vs 0.882).

CONCLUSION

SWI could provide added information to DWI and serve as a supplementary imaging marker for the characterization of parotid gland tumors.

The role of diffusion-weighted and dynamic contrast enhancement perfusion-weighted imaging in the evaluation of salivary glands neoplasms

OBJECTIVES

To evaluate the association of magnetic resonance diffusion-weighted imaging (DwI) and dynamic contrast-enhanced perfusion-weighted imaging (DCE-PwI) with a temporal resolution of 5 s, wash-in < 120 s, and wash-out ratio > 30% in the evaluation of salivary glands neoplasms.

METHODS

DwI and DCE-PwI of 92 salivary glands neoplasms were assessed. The apparent diffusion coefficient (ADC) was calculated by drawing three regions of interest with an average area of 0.30-0.40 cm² on three contiguous axial sections. The time/intensity curve was generated from DCE-PwI images by drawing a region of interest that included at least 50% of the largest lesion section. Vessels, calcifications, and necrotic/haemorrhagic or cystic areas within solid components were excluded. The association of ADC ≥ 1.4 × 10^-3 mm²/s with type A curves (progressive wash-in) and ADC 0.9-1.4 × 10^-3 mm²/s with type C curves (rapid wash-in/slow wash-out) were tested as parameters of benignity and malignancy, respectively. Type B curve (rapid wash-in/rapid wash-out) was not used as a reference parameter.

RESULTS

ADC ≥ 1.4 × 10^-3 mm²/s and type A curves were observed only in benign neoplasms. ADC of 0.9-1.4 × 10^-3 mm²/s and type C curves association showed specificity of 94.9% and positive predictive value of 81.8% for epithelial malignancies. The association of ADC < 0.9 × 10^-3 mm²/s with type B and C curves showed diagnostic accuracy of 94.6% and 100% for Warthin tumour and lymphoma, respectively.

CONCLUSIONS

ADC ≥ 1.4 × 10^-3 mm²/s and type A curves association was indicative of benignity. Lymphomas exhibited ADC < 0.7 × 10^-3 mm²/s and type C curves. The association of ADC < 0.9 × 10^-3 mm²/s and type B and C curves had accuracy 94.6% and 88.5% for Warthin tumour and epithelial malignancies, respectively.

The histogram analysis of apparent diffusion coefficient in differential diagnosis of parotid tumor

OBJECTIVES

Use apparent diffusion coefficient (ADC) histogram to investigate whether the parameters of ADC histogram can distinguish between benign and malignant tumors and further differentiate the tumor subgroups.

METHODS AND MATERIALS

This study retrospectively enrolls 161 patients with parotid gland tumors. Histogram parameters including mean, inhomogeneity, skewness, kurtosis and 10th, 25th, 50th, 75th, 90th percentiles are derived from ADC mono-exponential model. Mann-Whitney U test is used to compare the differences between benign and malignant groups. Kruskal-Wallis test with post-hoc Dunn-Bonferroni method is used for subgroup classification, then receiver operating characteristic curve analysis is performed in mean ADC value to obtain the appropriate cutoff values.

RESULTS

Except for kurtosis and 90th percentile, there are significant differences in all other ADC parameters between benign and malignant groups. In subgroup classification of benign tumors, there are significant differences in all ADC parameters between pleomorphic adenoma and Warthin's tumor (area under curve 0.988; sensitivity 93.8%; specificity 94.7%; all ps < 0.05). Pleomorphic adenoma has high value in mean than basal cell adenoma (area under curve 0.819; sensitivity 76.9%; specificity 76.9%; p < 0.05). Basal cell adenoma has high values in mean (area under curve 0.897; sensitivity 92.3%; specificity 78.9%; all ps < 0.05) and 10th, 25th, 50th percentiles than Warthin's tumor. In subgroup classification of malignant tumors, low-risk parotid carcinomas have higher values than hematolymphoid tumors in mean (area under curve 0.912; sensitivity 84.6%; specificity 100%, all ps < 0.05) and 10th, 25th percentiles.

CONCLUSION

ADC histogram parameters, especially mean and 10th, 25th percentiles, can potentially be an effective indicator for identifying and classifying parotid tumors.

Multiparametric Magnetic Resonance Imaging for the Diagnosis and Differential Diagnosis of Parotid Gland Tumors

The majority of salivary gland tumors occur in the parotid glands. Characterization (ie, benign or malignant, and histological type), location (deep or superficial), and invasion into the neighboring tissues of parotid tumors determine preoperative treatment planning. MRI gives more information than other imaging methods about the internal structure, localization, and relationship with other tissues of parotid tumors. Functional MRI methods (diffusion-weighted imaging, dynamic contrast-enhanced MRI, perfusion-weighted MRI, MR spectroscopy, etc.) have been increasingly used recently to increase the power of radiologists to characterize the tumors. Although they increase the workload of radiologists, the combined use of functional MRI methods improves accuracy in the differentiation of the tumors. There are a wide range of studies in the literature dealing with the combined use of different functional imaging methods in combination with conventional sequences. The aim of the present review is to evaluate conventional and functional/advanced MR methods, as well as multiparametric MRI applications combining them in the diagnosis of parotid gland tumors. Evidence Level: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;52:11-32.

Epithelial salivary gland tumors: Utility of radiomics analysis based on diffusion-weighted imaging for differentiation of benign from malignant tumors

OBJECTIVE

To evaluate the utility of radiomics analysis for differentiating benign and malignant epithelial salivary gland tumors on diffusion-weighted imaging (DWI).

METHODS

A retrospective dataset involving 218 and 51 patients with histology-confirmed benign and malignant epithelial salivary gland tumors was used in this study. A total of 396 radiomic features were extracted from the DW images. Analysis of variance (ANOVA) and least-absolute shrinkage and selection operator regression (LASSO) were used to select optimal radiomic features. The selected features were used to build three classification models namely, logistic regression method (LR), support vector machine (SVM), and K-nearest neighbor (KNN) by using a five-fold cross validation strategy on the training dataset. The diagnostic performance of each classification model was quantified by receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) in the training and validation datasets.

RESULTS

Eight most valuable features were selected by LASSO. LR and SVM models yielded optimally diagnostic performance. In the training dataset, LR and SVM yielded AUC values of 0.886 and 0.893 via five-fold cross validation, respectively, while KNN model showed relatively lower AUC (0.796). In the testing dataset, a similar result was found, where AUC values for LR, SVM, and KNN were 0.876, 0.870, and 0.791, respectively.

CONCLUSIONS

Classification models based on optimally selected radiomics features computed from DW images present a promising predictive value in distinguishing benign and malignant epithelial salivary gland tumors and thus have potential to be used for preoperative auxiliary diagnosis.

Diagnostic performance and inter-operator variability of apparent diffusion coefficient analysis for differentiating pleomorphic adenoma and carcinoma ex pleomorphic adenoma: comparing one-point measurement and whole-tumor measurement including radiomics approach

BACKGROUND AND PURPOSE

The purpose of this study was to compare the diagnostic performance between apparent diffusion coefficient (ADC) analysis of one-point measurement and whole-tumor measurement, including radiomics for differentiating pleomorphic adenoma (PA) from carcinoma ex pleomorphic adenoma (CXPA), and to evaluate the impact of inter-operator segmentation variability.

MATERIALS AND METHODS

One hundred and fifteen patients with PA and 22 with CXPA were included. Four radiologists with different experience independently placed one-point and whole-tumor ROIs and a radiomics-predictive model was constructed from the extracted imaging features. We calculated the area under the receiver-operator characteristic curve (AUC) for the diagnostic performance of imaging features and the radiomics-predictive model.

RESULTS

AUCs of the imaging features from whole-tumor varied between readers (0.50-0.89). The most experienced radiologist (Reader 1) produced significantly high AUCs than less experienced radiologists (Reader 3 and 4; P = 0.01 and 0.009). AUCs were higher for the radiomics-predictive model (0.82-0.87) than for one-point (0.66-0.79) in all readers.

CONCLUSION

Some imaging features of whole-tumor and radiomics-predictive model had higher diagnostic performance than one-point. The diagnostic performance of imaging features from whole-tumor alone varied depending on operator experience. Operator experience appears less likely to affect diagnostic performance in the radiomics-predictive model.

Effects of region of interest sizes on apparent diffusion coefficient measurements of pleomorphic adenoma, Warthin tumor, and normal parotid parenchyma

Background

Tumor apparent diffusion coefficient (ADC) measurements may be influenced by region of interest (ROI) sizes; however, this effect has not been systematically studied in parotid tumors. Our purpose was to determine the effects of ROI size on ADC measurements for the differentiation of pleomorphic adenoma (PA), Warthin tumor (WT), and normal parotid parenchyma.

Methods

Sixty-five patients including 37 with PA (lesions, n=37) and 28 with WT (lesions, n=36) were examined with diffusion-weighted imaging (DWI). Participants with normal contralateral parenchyma of the parotid gland constituted the control group (n=56). The mean ADC values and standard deviations (SDs) of the ADC (ADC_SD) of 12 concentric round ROIs (areas: 9, 28, 34, 50, 60, 82, 93, 98, 115, 130, 136, and 149 mm²) for tumors and normal tissue were measured by using custom-made software. Homogeneity index, which was defined by the ADC_SD/mean ADC, was also calculated. One-way repeated analyses of variance (ANOVAs) were performed on the mean ADCs, ADC_SDs, and homogeneity indices of the 12 ROIs in each group. The three parameters at different ROIs among PA, WT, and normal parotid parenchyma were compared using Kruskal-Wallis tests.

Results

There was excellent agreement for the ADC measurements with the 12 ROIs for PA [intraclass correlation coefficient (ICC), 0.98], WT (ICC, 0.99), and normal parotid parenchyma (ICC, 0.95). No significant differences were observed in the mean ADCs of the 12 ROIs for each of the three groups (P=0.744-0.990). Among the three groups, the mean ADC of normal parotid parenchyma [(0.94±0.003)×10^-3 mm²/s] was significantly lower than that of both PA [(1.72±0.01)×10^-3 mm²/s] and WT [(1.16±0.01)×10^-3 mm²/s] in the 12 ROIs, whereas the PA group had the highest mean ADC values. No significant differences were found in the mean ADC_SDs with each ROI between PA and WT (all P>0.017). PAs had lower homogeneity indices compared with WTs and normal parotid parenchyma (all P<0.01).

Conclusions

The effect of ROI size on ADC measurements could be excluded from the differentiation of PA, WT, and normal parotid parenchyma. Homogeneity index was a useful parameter in discriminating between the three groups.