Apparent diffusion coefficient (ADC) histogram analysis: differentiation of benign from malignant parotid gland tumors using readout-segmented diffusion-weighted imaging

Assessing the Histological Malignancy Grade of Olfactory Neuroblastoma Using the Apparent Diffusion Coefficient Histogram Analysis

Introduction Olfactory neuroblastoma (ONB) is a rare malignant tumor of the upper nasal cavity. The Hyams classification is an important histological grading system for diagnosing recurrence and predicting survival in ONB. This study aimed to evaluate the utility of apparent diffusion coefficient (ADC) histogram analysis in distinguishing between high-grade and low-grade ONB based on the Hyams classification system. Methods This retrospective study included 17 patients (11 males, six females; mean age 54 years, range 29-84) diagnosed with ONB who underwent pretreatment magnetic resonance imaging (MRI) including diffusion-weighted imaging between December 2017 and September 2022. Two board-certified radiologists outlined the regions of interest on ADC maps of the tumors. Mean, minimum, maximum ADC, standard deviation, skewness, kurtosis, and entropy were calculated from the ADC histograms. Patients were divided into low-grade (Hyams I-II) and high-grade (Hyams III-IV) groups based on histopathological evaluation by a board-certified pathologist. ADC histogram parameters were compared between the two groups using Mann-Whitney U tests. Two-sided p-values of < 0.05 were considered statistically significant. Results The study included 10 low-grade (two grade I, eight grade II) and seven high-grade (five grade III, one grade III/IV, one grade IV) ONB cases. Comparison between the low-grade and high-grade groups showed no statistically significant differences in any of the ADC histogram parameters analyzed: mean ADC (median 1.02 vs 0.95; p = 0.591), minimum ADC (0.84 vs 0.78; p = 0.494), maximum ADC (1.06 vs 1.19; p = 0.625), standard deviation (0.09 vs 0.14; p = 0.433), skewness (-0.48 vs -0.75; p = 0.133), kurtosis (2.79 vs 3.12; p = 0.161), and entropy (4.69 vs 5.06; p = 0.315). Conclusion This study demonstrated that ADC histogram analysis was unable to differentiate between high-grade and low-grade ONB based on the Hyams classification. The findings suggest that preoperative grading of ONB malignancy using ADC histogram parameters is challenging. Thus, grading based on preoperative imaging evaluation is difficult.

Differentiation of salivary gland tumours using diffusion-weighted image-based virtual MR elastography: a pilot study

OBJECTIVES

Differentiation among benign salivary gland tumours, Warthin tumours (WTs), and malignant salivary gland tumours is crucial to treatment planning and predicting patient prognosis. However, differentiation of those tumours using imaging findings remains difficult. This study evaluated the usefulness of elasticity determined from diffusion-weighted image (DWI)-based virtual MR elastography (MRE) compared with conventional magnetic resonance imaging (MRI) findings in differentiating the tumours.

METHODS

This study included 17 benign salivary gland tumours, 6 WTs, and 11 malignant salivary gland tumours scanned on neck MRI. The long and short diameters, T1 and T2 signal intensities, tumour margins, apparent diffusion coefficient (ADC) values, and elasticity from DWI-based virtual MRE of the tumours were evaluated. The interobserver agreement in measuring tumour elasticity and the receiver operating characteristic (ROC) curves were also assessed.

RESULTS

The long and short diameters and the T1 and T2 signal intensities showed no significant difference among the 3 tumour groups. Tumour margins and the mean ADC values showed significant differences among some tumour groups. The elasticity from virtual MRE showed significant differences among all 3 tumour groups and the interobserver agreement was excellent. The area under the ROC curves of the elasticity were higher than those of tumour margins and mean ADC values.

CONCLUSION

Elasticity values based on DWI-based virtual MRE of benign salivary gland tumours, WTs, and malignant salivary gland tumours were significantly different. The elasticity of WTs was the highest and that of benign tumours was the lowest. The elasticity from DWI-based virtual MRE may aid in the differential diagnosis of salivary gland tumours.

Differentiation of submandibular sialadenitis based on apparent diffusion coefficient

OBJECTIVES

This study aimed to quantify the submandibular gland in suppurative sialadenitis, primary Sjögren's syndrome (pSS) and radiation-induced sialadenitis using the apparent diffusion coefficient (ADC) for differential diagnosis.

SUBJECTS AND METHODS

This retrospective study included 16 patients with suppurative sialadenitis (n = 9), pSS (n = 3) and radiation-induced sialadenitis (n = 4) who underwent magnetic resonance imaging between June 2006 and May 2022. The ADC of the submandibular glands in each state was calculated, and the differences were analysed using a one-way analysis of variance and Tukey's post hoc test. Receiver operating characteristic curves were used to assess the ability of the ADC to distinguish each condition. Statistical significance was set at p < 0.05.

RESULTS

The mean ADC value (×10-3 mm2/s) ± standard deviation in the control (non-affected side of the suppurative sialadenitis group), suppurative sialadenitis, pSS and radiation-induced groups were 0.94 ± 0.16, 1.24 ± 0.16, 1.33 ± 0.13 and 1.5 ± 0.12, respectively (p < 0.001). The diagnostic value for distinguishing each group was ≥0.75.

CONCLUSION

ADC values are useful for quantitatively assessing and distinguishing submandibular glands in suppurative sialadenitis, primary Sjögren's syndrome and radiation-induced sialadenitis.

Presurgical MRI-Based Radiomics Models for Predicting Cerebellar Mutism Syndrome in Children With Posterior Fossa Tumors

BACKGROUND

Current studies have indicated that tumoral morphologic features are associated with cerebellar mutism syndrome (CMS), but the radiomics application in CMS is scarce.

PURPOSE

To develop a model for CMS discrimination based on multiparametric MRI radiomics in patients with posterior fossa tumors.

STUDY TYPE

Retrospective.

POPULATION

A total of 218 patients (males 132, females 86) with posterior fossa tumors, 169 of which were included in the MRI radiomics analysis. The MRI radiomics study cohort (169) was split into training (119) and testing (50) sets with a ratio of 7:3.

FIELD/SEQUENCE

All the MRI were acquired under 1.5/3.0 T scanners. T2-weighted image (T2W), T1-weighted (T1W), fluid attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI).

ASSESSMENT

Apparent diffusion coefficient (ADC) maps were generated from DWI. Each MRI dataset generated 1561 radiomics characteristics. Feature selection was performed with univariable logistic analysis, correlation analysis, and least absolute shrinkage and selection operator (LASSO) penalized logistic regression. Significant clinical features were selected with multivariable logistic analysis and used to constructed the clinical model. Radiomics models (based on T1W, T2W, FLAIR, DWI, ADC) were constructed with selected radiomics features. The mix model was based on the multiparametric MRI radiomics features.

STATISTICAL TEST

Multivariable logistic analysis was utilized during clinical features selection. Models' performance was evaluated using the area under the receiver operating characteristic (AUC) curve. Interobserver variability was assessed using Cohen's kappa. Significant threshold was set as P < 0.05.

RESULTS

Sex (aOR = 3.72), tumor location (aOR = 2.81), hydrocephalus (aOR = 2.14), and tumor texture (aOR = 5.08) were significant features in the multivariable analysis and were used to construct the clinical model (AUC = 0.79); totally, 33 radiomics features were selected to construct radiomics models (AUC = 0.63-0.93). Seven of the 33 radiomics features were selected for the mix model (AUC = 0.93).

DATA CONCLUSION

Multiparametric MRI radiomics may be better at predicting CMS than single-parameter MRI models and clinical model.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

2.

Value of pre-/post-contrast-enhanced T1 mapping and readout segmentation of long variable echo-train diffusion-weighted imaging in differentiating parotid gland tumors

PURPOSE

This study aimed to explore the value of pre-/post-contrast-enhanced T1 mapping and readout segmentation of long variable echo-train diffusion-weighted imaging (RESOLVE-DWI) for the differential diagnosis of parotid gland tumors.

METHODS

A total of 128 patients with histopathologically confirmed parotid gland tumors [86 benign tumors (BTs) and 42 malignant tumors (MTs)] were retrospectively recruited. BTs were further divided into pleomorphic adenomas (PAs, n = 57) and Warthin's tumors (WTs, n = 15). MRI examinations were performed before and after contrast injection to measure the longitudinal relaxation time (T1) value (T1p and T1e, respectively) and the apparent diffusion coefficient (ADC) value of the parotid gland tumors. The reduction in T1 (T1d) values and the percentage of T1 reduction (T1d%) were calculated.

RESULTS

The T1d and ADC values of the BTs were considerably higher than those of the MTs (all P <.05). The area under the curve (AUC) of the T1d and ADC values for differentiating between BTs and MTs of the parotid was 0.618 and 0.804, respectively (all P <.05). The AUC of the T1p, T1d, T1d%, and ADC values for differentiating between PAs and WTs was 0.926, 0.945, 0.925, and 0.996, respectively (all P >.05). The ADC and T1d% + ADC values performed better in differentiating between PAs and MTs than the T1p, T1d, and T1d% (AUC values: 0.902, 0.909, 0.660, 0.726, and 0.736, respectively). The T1p, T1d, T1d%, and T1d% + T1p values all had high diagnosis efficacy in differentiating WTs from MTs (AUC values: 0.865, 0.890, 0.852, and 0.897, respectively, all P >.05).

CONCLUSION

T1 mapping and RESOLVE-DWI can be used to differentiate parotid gland tumors quantitatively and can be complementary to each other.

Whole-tumor ADC histogram analysis for differentiating endometriosis-related tumors: seromucinous borderline tumor, clear cell carcinoma and endometrioid carcinoma

PURPOSE

To investigate the feasibility of whole-tumor apparent diffusion coefficient (ADC) histogram analysis for improving the differentiation of endometriosis-related tumors: seromucinous borderline tumor (SMBT), clear cell carcinoma (CCC) and endometrioid carcinoma (EC).

METHODS

Clinical features, solid component ADC (ADC_SC) and whole-tumor ADC histogram-derived parameters (volume, the ADC_mean, 10th, 50th and 90th percentile ADCs, inhomogeneity, skewness, kurtosis and entropy) were compared among 22 SMBTs, 42 CCCs and 21 ECs. Statistical analyses were performed using chi-square test, one-way ANOVA or Kruskal-Wallis test, and receiver operating characteristic curves.

RESULTS

A significantly higher ADC_SC and smaller volume were associated with SMBT than with CCC/EC. The ADC_mean was significantly higher in CCC than in EC. The 10th percentile ADC was significantly lower in EC than in SMBT/CCC. The 50th and 90th percentile ADCs were significantly higher in CCC than in SMBT/EC. For differentiating SMBT from CCC, AUCs of the ADC_SC, volume, and 50th and 90th percentile ADCs were 0.97, 0.86, 0.72 and 0.81, respectively. For differentiating SMBT from EC, AUCs of the ADC_SC, volume and 10th percentile ADC were 0.97, 0.71 and 0.72, respectively. For differentiating CCC from EC, AUCs of the ADC_mean and 10th, 50th and 90th percentile ADCs were 0.79, 0.72, 0.81 and 0.85, respectively.

CONCLUSION

Whole-tumor ADC histogram analysis was valuable for differentiating endometriosis-related tumors, and the 90th percentile ADC was optimal in differentiating CCC from EC.

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.

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.

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.

Texture analysis of magnetic resonance image to differentiate benign from malignant myxoid soft tissue tumors: A retrospective comparative study

It is important to differentiate between benign and malignant myxoid tumors to establish the treatment plan, determine the optimal surgical extent, and plan postoperative surveillance, but differentiation may be complicated by imaging-feature overlap. Texture analysis is used for quantitative assessment of imaging characteristics based on mathematically calculated pixel heterogeneity and has been applied to the discrimination of benign from malignant soft tissue tumors (STTs). In this study, we aimed to assess the diagnostic value of the texture features of conventional magnetic resonance images for the differentiation of benign from malignant myxoid STTs. Magnetic resonance images of 39 patients with histologically confirmed myxoid STTs of the extremities were analyzed. Qualitative features were assessed and compared between the benign and malignant groups. Texture analysis was performed, and texture features were selected based on univariate analysis and Fisher’s coefficient. The diagnostic value of the texture features was assessed using receiver operating curve analysis. T1 heterogeneity showed a statistically significant difference between benign and malignant myxoid STTs, with substantial inter-reader reliability. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of T1 heterogeneity were 55.6%, 83.3%, 88.2%, 45.5%, and 64.1%, respectively. Among the texture features, T2w-WavEnLL_s-3 showed good diagnostic performance, and T2w-WavEnLL_s-4 and GeoW4 showed fair diagnostic performance. The logistic regression model including T1 heterogeneity and T2_WavEnLL_s-4 showed good diagnostic performance. However, there was no statistically significant difference between the overall qualitative assessment by a radiologist and the predictor model. Geometry-based and wavelet-derived texture features from T2-weighted images were significantly different between benign and malignant myxoid STTs. However, the texture features had a limited additive value in differentiating benign from malignant myxoid STTs.

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.

Influence of post-label delay time on the performance of 3D pseudo-continuous arterial spin labeling magnetic resonance imaging in the characterization of parotid gland tumors

OBJECTIVE

To evaluate the influence of post-label delay times (PLDs) on the performance of 3D pseudo-continuous arterial spin labeling (pCASL) magnetic resonance imaging for characterizing parotid gland tumors and to explore the optimal PLDs for the differential diagnosis.

MATERIALS AND METHOD

Fifty-eight consecutive patients with parotid gland tumors were enrolled, including 33 patients with pleomorphic adenomas (PAs), 16 patients with Warthin's tumors (WTs), and 9 patients with malignant tumors (MTs). 3D pCASL was scanned for each patient five times, with PLDs of 1025 ms, 1525 ms, 2025 ms, 2525 ms, and 3025 ms. Tumor blood flow (TBF) was calculated, and compared among different PLDs and tumor groups. Performance of TBF at different PLDs was evaluated using receiver operating characteristic analysis.

RESULTS

With an increasing PLD, TBF tended to gradually increase in PAs (p < 0.001), while TBF tended to slightly increase and then gradually decrease in WTs (p = 0.001), and PAs showed significantly lower TBF than WTs at all 5 PLDs (p < 0.05). PAs showed significantly lower TBF than MTs at 4 PLDs (p < 0.05), except at 3025 ms (p = 0.062). WTs showed higher TBF than MTs at all 5 PLDs; however, differences did not reach significance (p > 0.05). Setting a TBF of 64.350 mL/100g/min at a PLD of 1525 ms, or a TBF of 23.700 mL/100g/min at a PLD of 1025 ms as the cutoff values, optimal performance could be obtained for differentiating PAs from WTs (AUC = 0.905) or from MTs (AUC = 0.872).

CONCLUSIONS

Short PLDs (1025 ms or 1525 ms) are suggested to be used in 3D pCASL for characterizing parotid gland tumors in clinical practice.

KEY POINTS

• With 5 different PLDs, 3D pCASL can reflect the variation of blood flow in parotid gland tumors. • 3D pCASL is useful for characterizing PAs from WTs or MTs. • Short PLDs (1025 ms or 1525 ms) are suggested to be used in 3D pCASL for characterizing parotid gland tumors in clinical practice.

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.

Carcinoma ex pleomorphic adenoma of major salivary glands: CT and MR imaging findings

OBJECTIVES

To describe the CT and MR imaging characteristics of primary carcinoma ex pleomorphic adenoma (Ca-ex-PA) in major salivary glands and present more information for recognizing this malignancy.

METHODS

212 patients with primary Ca-ex-PA in major salivary glands (169 in the parotid gland, 36 in the submandibular gland, 7 in the sublingual gland) underwent CT and MR imaging (plain and contrast-enhanced scans) prior to surgical management and histopathological examination. The CT and MR imaging findings of this condition were retrospectively reviewed and correlated with their pathological types: non-invasive carcinoma (Type I, 37 cases), minimally invasive carcinoma (Type II, 18 cases), and widely invasive carcinoma (TypeIII, 157 cases). The binary logistic regression analysis was used to analyze the independent influencing factors of morphology and boundary for differentiating between Type I/II and Type III of Ca-ex-PA, and the sensitivity, specificity and positive predictive value were calculated. Differences in apparent diffusion coefficient (ADC) values between Type I/II and Type III of Ca-ex-PA were calculated by independent sample t-tests.

RESULTS

On CT and MR imaging, there were 190/212 cases (89.6%) identified as lobular, 203/212 cases (95.8%) with enhancement, and 173/212 cases (81.6%) with inhomogeneous after contrast administration.Calcification within the mass was shown in 76 of 192 cases (39.6%) on plain CT examination. Of 55 neoplasms with Type I and II, 38 (69.1%) were presented as round or oval and 42 (76.4%) as well-defined margins. Of 157 neoplasms with Type III, 103 (65.6%) were presented as irregular form and 110 (70.1%) as uneven margins or with partial uneven margins.The sensitivity, specificity and positive predictive value for distinguishing Type I/II and Type III tumors according to the morphology and boundary were 78.34%, 63.64% and 86.01%, respectively. The mean ADC value of Ca-ex-PA (22 cases) in major salivary glands was about 0.93 × 10^-3 mm² s^-1, and there was no significant difference in mean ADC value between Type I/II and Type III of this neoplasm. Cervical lymph node metastasis and distance metastasis were found in 67 patients (31.6%, Type III) and 32 patients (15.1%, Type I in 1; Type II in 1; and Type III in 30), respectively.

CONCLUSIONS

Most Ca-ex-PA is characterized by an irregular, lobular, and inhomogeneous enhanced neoplasm with uneven margin or partial uneven margin on CT and MR imaging, which is frequently corresponding with Type III. And a round or oval mass with well-defined margin usually correlates with Type I and II. Morphology and boundary are important basis for distinguishing Type I/II and Type III tumors. Calcification within the neoplasm shown on CT may be regarded as a specific sign for indicating this malignancy. Low ADC value is an important manifestation of this neoplasm.Ca-ex-PA with Type III is more likely to have cervical lymph node metastasis and distant metastasis.

MRI-Based radiomics nomogram for differentiation of benign and malignant lesions of the parotid gland

OBJECTIVES

Preoperative differentiation between benign parotid gland tumors (BPGT) and malignant parotid gland tumors (MPGT) is important for treatment decisions. The purpose of this study was to develop and validate an MRI-based radiomics nomogram for the preoperative differentiation of BPGT from MPGT.

METHODS

A total of 115 patients (80 in training set and 35 in external validation set) with BPGT (n = 60) or MPGT (n = 55) were enrolled. Radiomics features were extracted from T1-weighted and fat-saturated T2-weighted images. A radiomics signature model and a radiomics score (Rad-score) were constructed and calculated. A clinical-factors model was built based on demographics and MRI findings. A radiomics nomogram model combining the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The diagnostic performance of the three models was evaluated and validated using ROC curves on the training and validation datasets.

RESULTS

Seventeen features from MR images were used to build the radiomics signature. The radiomics nomogram incorporating the clinical factors and radiomics signature had an AUC value of 0.952 in the training set and 0.938 in the validation set. Decision curve analysis showed that the nomogram outperformed the clinical-factors model in terms of clinical usefulness.

CONCLUSIONS

The above-described radiomics nomogram performed well for differentiating BPGT from MPGT, and may help in the clinical decision-making process.

KEY POINTS

• Differential diagnosis between BPGT and MPGT is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, clinical data, and MRI features facilitates differentiation of BPGT from MPGT with improved diagnostic efficacy.

Diffusion-Weighted Imaging of the Head and Neck (Including Temporal Bone)

Diffusion techniques provide valuable information when performing head and neck imaging. This information can be used to detect the presence or absence of pathology, refine differential diagnosis, determine the location for biopsy, assess response to treatment, and prognosticate outcomes. For example, when certain technical factors are taken into consideration, diffusion techniques prove indispensable in assessing for residual cholesteatoma following middle ear surgery. In other scenarios, pretreatment apparent diffusion coefficient values may assist in prognosticating outcomes in laryngeal cancer and likelihood of response to radiation therapy. As diffusion techniques continue to advance, so too will its clinical utility.

MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation

Background

The differentiation between benign and malignant parotid lesions is crucial to defining the treatment plan, which highly depends on the tumor histology. We aimed to evaluate the role of MRI-based radiomics using both T2-weighted (T2-w) images and Apparent Diffusion Coefficient (ADC) maps in the differentiation of parotid lesions, in order to develop predictive models with an external validation cohort.

Materials and Methods

A sample of 69 untreated parotid lesions was evaluated retrospectively, including 37 benign (of which 13 were Warthin’s tumors) and 32 malignant tumors. The patient population was divided into three groups: benign lesions (24 cases), Warthin’s lesions (13 cases), and malignant lesions (32 cases), which were compared in pairs. First- and second-order features were derived for each lesion. Margins and contrast enhancement patterns (CE) were qualitatively assessed. The model with the final feature set was achieved using the support vector machine binary classification algorithm.

Results

Models for discriminating between Warthin’s and malignant tumors, benign and Warthin’s tumors and benign and malignant tumors had an accuracy of 86.7%, 91.9% and 80.4%, respectively. After the feature selection process, four parameters for each model were used, including histogram-based features from ADC and T2-w images, shape-based features and types of margins and/or CE. Comparable accuracies were obtained after validation with the external cohort.

Conclusions

Radiomic analysis of ADC, T2-w images, and qualitative scores evaluating margins and CE allowed us to obtain good to excellent diagnostic accuracies in differentiating parotid lesions, which were confirmed with an external validation cohort.

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.

Prediction of Platinum-based Chemotherapy Response in Advanced High-grade Serous Ovarian Cancer: ADC Histogram Analysis of Primary Tumors

RATIONALE AND OBJECTIVES

To investigate the feasibility of apparent diffusion coefficient (ADC) histogram analysis of primary advanced high-grade serous ovarian cancer (HGSOC) to predict patient response to platinum-based chemotherapy.

MATERIALS AND METHODS

A total of 70 patients with 102 advanced stage HGSOCs (International Federation of Gynecology and Obstetrics (FIGO) stages III-IV) who received standard treatment of primary debulking surgery followed by the first line of platinum-based chemotherapy were retrospectively enrolled. Patients were grouped as platinum-resistant and platinum-sensitive according to whether relapse occurred within 6 months. Clinical characteristics, including age, pretherapy CA125 level, International Federation of Gynecology and Obstetrics stage, residual tumor, and histogram parameters derived from whole tumor and solid component such as ADC_mean; 10th, 20th, 25th, 30th, 40th, 50th, 60th, 70th, 75th, 80th, 90th percentiles; skewness and kurtosis, were compared between platinum-resistant and platinum-sensitive groups.

RESULTS

No significantly different clinical characteristics were observed between platinum-sensitive and platinum-resistant patients. There were no significant differences in any whole-tumor histogram-derived parameters between the two groups. Significantly higher ADC_mean and percentiles and significantly lower skewness and kurtosis from the solid-component histogram parameters were observed in the platinum-sensitive group when compared with the platinum-resistant group. ADC_mean, skewness and kurtosis showed moderate prediction performances, with areas under the curve of 0.667, 0.733 and 0.616, respectively. Skewness was an independent risk factor for platinum resistance.

CONCLUSION

Pretreatment ADC histogram analysis of primary tumors has the potential to allow prediction of response to platinum-based chemotherapy in patients with advanced HGSOC.

Radiomics-based comparison of MRI and CT for differentiating pleomorphic adenomas and Warthin tumors of the parotid gland: a retrospective study

OBJECTIVE

The objective of this study was to compare the diagnostic performance of magnetic resonance imaging (MRI) and computed tomography (CT) in differentiating pleomorphic adenomas from Warthin tumors using radiomics.

STUDY DESIGN

We retrospectively reviewed 626 patients who underwent preoperative MRI or CT for parotid tumor diagnosis. Patient groups were balanced by propensity score matching (PSM) and 123 radiomic features were extracted from tumor images. Radiomic signatures (rad-scores) were generated using a least absolute shrinkage and selection operator logistic regression model. The Canny edge detector was used to define tumor borders (border index). The diagnostic performance of rad-score and border index before and after PSM was evaluated with area under the receiver operating characteristic curve analysis.

RESULTS

For differentiation of pleomorphic adenomas and Warthin tumors, rad-score and border index areas under the curve for MRI after PSM were 0.911 (95% confidence interval [CI], 0.871-0.951) and 0.716 (95% CI, 0.646-0.787), respectively; those for CT were 0.876 (95% CI, 0.829-0.923) and 0.608 (95% CI, 0.527-0.690), respectively. Tumor border index on MRI, but not CT, had superior diagnostic performance (P < .05); MRI- and CT-based rad-scores showed similar performance (P >.05).

CONCLUSIONS

MRI is superior to CT for tumor margin examination; however, the radiomics features of both modalities showed no difference.

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.

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.

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.