Diffusion kurtosis imaging and dynamic contrast-enhanced MRI for the differentiation of parotid gland tumors

Bi-regional dynamic contrast-enhanced MRI for prediction of microvascular invasion in solitary BCLC stage A hepatocellular carcinoma

OBJECTIVES

To construct a combined model based on bi-regional quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), as well as clinical-radiological (CR) features for predicting microvascular invasion (MVI) in solitary Barcelona Clinic Liver Cancer (BCLC) stage A hepatocellular carcinoma (HCC), and to assess its ability for stratifying the risk of recurrence after hepatectomy.

METHODS

Patients with solitary BCLC stage A HCC were prospective collected and randomly divided into training and validation sets. DCE perfusion parameters were obtained both in intra-tumoral region (ITR) and peritumoral region (PTR). Combined DCE perfusion parameters (CDCE) were constructed to predict MVI. The combined model incorporating CDCE and CR features was developed and evaluated. Kaplan-Meier method was used to investigate the prognostic significance of the model and the survival benefits of different hepatectomy approaches.

RESULTS

A total of 133 patients were included. Total blood flow in ITR and arterial fraction in PTR exhibited the best predictive performance for MVI with areas under the curve (AUCs) of 0.790 and 0.792, respectively. CDCE achieved AUCs of 0.868 (training set) and 0.857 (validation set). A combined model integrated with the α-fetoprotein, corona enhancement, two-trait predictor of venous invasion, and CDCE could improve the discrimination ability to AUCs of 0.966 (training set) and 0.937 (validation set). The combined model could stratify the prognosis of HCC patients. Anatomical resection was associated with a better prognosis in the high-risk group (p < 0.05).

CONCLUSION

The combined model integrating DCE perfusion parameters and CR features could be used for MVI prediction in HCC patients and assist clinical decision-making.

CRITICAL RELEVANCE STATEMENT

The combined model incorporating bi-regional DCE-MRI perfusion parameters and CR features predicted MVI preoperatively, which could stratify the risk of recurrence and aid in optimizing treatment strategies.

KEY POINTS

Microvascular invasion (MVI) is a significant predictor of prognosis for hepatocellular carcinoma (HCC). Quantitative DCE-MRI could predict MVI in solitary BCLC stage A HCC; the combined model improved performance. The combined model could help stratify the risk of recurrence and aid treatment planning.

MR-Neurography of the facial nerve in parotid tumors: intra-parotid nerve visualization and surgical correlation

PURPOSE

One of the most severe complications in surgery of parotid tumors is facial palsy. Imaging of the intra-parotid facial nerve is challenging due to small dimensions. Our aim was to assess, in patients with parotid tumors, the ability of high-resolution 3D double-echo steady-state sequence with water excitation (DE3D-WE) (1) to visualize the extracranial facial nerve and its tracts, (2) to evaluate their relationship to the parotid lesion and (3) to compare MRI and surgical findings.

METHODS

A retrospective study was conducted including all patients with parotid tumors, who underwent MRI from April 2022 to December 2023. Two radiologists independently reviewed DE3D-WE images, assessing quality of visualization of the facial nerve bilaterally and localizing the nerve's divisions in relation to the tumor. MRI data were compared with surgical findings.

RESULTS

Forty consecutive patients were included (M:F = 22:18; mean age 56.3 ± 17.4 years). DE3D-WE could excellently visualize the nerve main trunk and the temporofacial division in all cases. The cervicofacial branch was visible in 99% of cases and visibility was good. Distal divisions were displayed in 34% of cases with a higher visibility on the tumor side (p < 0.05). Interrater agreement was high (weighted kappa 0.94 ± 0.01 [95% CI 0.92-0.97]). Compared to surgery accuracy of MRI in localizing the nerve was 100% for the main trunk, 96% for the temporofacial and 89% for the cervicofacial branches.

CONCLUSIONS

Facial nerve MR-neurography represents a reliable tool. DE3D-WE can play an important role in surgical planning of patients with parotid tumors, reducing the risk of nerve injury.

Evaluation of Quantitative Dual-Energy Computed Tomography Parameters for Differentiation of Parotid Gland Tumors

RATIONALE AND OBJECTIVES

To assess the diagnostic performance of quantitative parameters from dual-energy CT (DECT) in differentiating parotid gland tumors (PGTs).

MATERIALS AND METHODS

101 patients with 108 pathologically proved PGTs were enrolled and classified into four groups: pleomorphic adenomas (PAs), warthin tumors (WTs), other benign tumors (OBTs), and malignant tumors (MTs). Conventional CT attenuation and DECT quantitative parameters, including iodine concentration (IC), normalized iodine concentration (NIC), effective atomic number (Zeff), electron density (Rho), double energy index (DEI), and the slope of the spectral Hounsfield unit curve (λHU), were obtained and compared between benign tumors (BTs) and MTs, and further compared among the four subgroups. Logistic regression analysis was used to assess the independent parameters and the receiver operating characteristic (ROC) curves were used to analyze the diagnostic performance.

RESULTS

Attenuation, Zeff, DEI, IC, NIC, and λHU in the arterial phase (AP) and venous phase (VP) were higher in MTs than in BTs (p < 0.001-0.047). λHU in VP and Zeff in AP were independent predictors with an area under the curve (AUC) of 0.84 after the combination. Furthermore, attenuation, Zeff, DEI, IC, NIC, and λHU in the AP and VP of MTs were higher than those of PAs (p < 0.001-0.047). Zeff and NIC in AP and λHU in VP were independent predictors with an AUC of 0.93 after the combination. Attenuation and Rho in the precontrast phase; attenuation, Rho, Zeff, DEI, IC, NIC, and λHU in AP; and the Rho in the VP of PAs were lower than those of WTs (p < 0.001-0.03). Rho in the precontrast phase and attenuation in AP were independent predictors with an AUC of 0.89 after the combination. MTs demonstrated higher Zeff, DEI, IC, NIC, and λHU in VP and lower Rho in the precontrast phase compared with WTs (p < 0.001-0.04); but no independent predictors were found.

CONCLUSION

DECT quantitative parameters can help to differentiate PGTs.

An ultrasound-based ensemble machine learning model for the preoperative classification of pleomorphic adenoma and Warthin tumor in the parotid gland

OBJECTIVES

The preoperative classification of pleomorphic adenomas (PMA) and Warthin tumors (WT) in the parotid gland plays an essential role in determining therapeutic strategies. This study aims to develop and validate an ultrasound-based ensemble machine learning (USEML) model, employing nonradiative and noninvasive features to differentiate PMA from WT.

METHODS

A total of 203 patients with histologically confirmed PMA or WT who underwent parotidectomy from two centers were enrolled. Clinical factors, ultrasound (US) features, and radiomic features were extracted to develop three types of machine learning model: clinical models, US models, and USEML models. The diagnostic performance of the USEML model, as well as that of physicians based on experience, was evaluated and validated using receiver operating characteristic (ROC) curves in internal and external validation cohorts. DeLong's test was used for comparisons of AUCs. SHAP values were also utilized to explain the classification model.

RESULTS

The USEML model achieved the highest AUC of 0.891 (95% CI, 0.774-0.961), surpassing the AUCs of both the US (0.847; 95% CI, 0.720-0.932) and clinical (0.814; 95% CI, 0.682-0.908) models. The USEML model also outperformed physicians in both internal and external validation datasets (both p < 0.05). The sensitivity, specificity, negative predictive value, and positive predictive value of the USEML model and physician experience were 89.3%/75.0%, 87.5%/54.2%, 87.5%/65.6%, and 89.3%/65.0%, respectively.

CONCLUSIONS

The USEML model, incorporating clinical factors, ultrasound factors, and radiomic features, demonstrated efficient performance in distinguishing PMA from WT in the parotid gland.

CLINICAL RELEVANCE STATEMENT

This study developed a machine learning model for preoperative diagnosis of pleomorphic adenoma and Warthin tumor in the parotid gland based on clinical, ultrasound, and radiomic features. Furthermore, it outperformed physicians in an external validation dataset, indicating its potential for clinical application.

KEY POINTS

• Differentiating pleomorphic adenoma (PMA) and Warthin tumor (WT) affects management decisions and is currently done by invasive biopsy. • Integration of US-radiomic, clinical, and ultrasound findings in a machine learning model results in improved diagnostic accuracy. • The ultrasound-based ensemble machine learning (USEML) model consistently outperforms physicians, suggesting its potential applicability in clinical settings.

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.

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.

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.

Diagnostic efficacy of diffusion-weighted imaging and semiquantitative and quantitative dynamic contrast-enhanced magnetic resonance imaging in salivary gland tumors

BACKGROUND

Increased use of functional magnetic resonance imaging (MRI) methods such as diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI consisting of sequential contrast series, allows us to obtain more information on the microstructure, cellularity, interstitial distance, and vascularity of tumors, which has increased the discrimination power for benign and malignant salivary gland tumors (SGTs). In the last few years, quantitative DCE MRI data containing T1 perfusion parameters (K_trans, K_ep and V_e), were reported to contribute to the differentiation of benign or malignant subtypes in SGTs.

AIM

To evaluate the diagnostic efficacy of DWI and semiquantitative and quantitative perfusion MRI parameters in SGTs.

METHODS

Diffusion MRI [apparent diffusion coefficient (ADC) value] with a 1.5 T MR machine, semiquantitative perfusion MRI [time intensity curve (TIC) pattern], and quantitative perfusion MRI examinations (K_trans, K_ep and V_e) of 73 tumors in 67 patients with histopathological diagnosis performed from 2017 to 2021 were retrospectively evaluated. In the ADC value and semiquantitative perfusion MRI measurements, cystic components of the tumors were not considered, and the region of interest (ROI) was manually placed through the widest axial section of the tumor. TIC patterns were divided into four groups: Type A = T_peak > 120 s; type B = T_peak ≤ 120 s, washout ratio (WR) ≥ 30%; type C = T_peak ≤ 120 s, WR < 30%; and type D = flat TIC. For the quantitative perfusion MRI analysis, a 3D ROI was placed in the largest solid component of the tumor, and the K_trans, K_ep and V_e values were automatically generated.

RESULTS

The majority of SGTs were located in the parotid glands (86.3%). Of all the SGTs, 68.5% were benign and 31.5% were malignant. Significant differences were found for ADC values among pleomorphic adenomas (PMAs), Warthin's tumors (WTs), and malignant tumors (MTs) (P < 0.001). PMAs had type A and WTs had type B TIC pattern while the vast majority of MTs and other benign tumors (OBTs) (54.5% and 45.5%, respectively) displayed type C TIC pattern. PMAs showed no washout, while the highest mean WR was observed in WTs (59% ± 11%). K_trans values of PMAs, WTs, OBTs, and MTs were not significantly different. K_ep values of PMAs and WTs were significantly different from those of OBTs and MTs. Mean V_e value of WTs was significantly different from those of PMAs, OBTs, and MTs (P < 0.001).

CONCLUSION

The use of quantitative DCE parameters along with diffusion MRI and semiquantitative contrast-enhanced MRI in SGTs could improve the diagnostic accuracy.

Advantages of contrast-enhanced CT combined with DCE-MRI in identifying malignant parotid tumor

OBJECTIVE

To study the value of single and combined application of contrast-enhanced computerized tomography (CT) and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) in diagnosing parotid tumors.

METHODS

In this retrospective study, 82 patients with parotid gland mass who received contrast-enhanced CT and DCE-MRI detection in The First People's Hospital of Huzhou from March 2018 to March 2022 were selected as study subjects. The nature of the parotid tumor was pathologically examined following the surgery. According to the pathological diagnosis results, these patients were divided into a benign group (n=59) and a malignant group (n=23). All patients underwent contrast-enhanced CT and DCE-MRI examinations. The diagnostic accuracy rates of contrast-enhanced CT, DCE-MRI and the joint application were compared. The CT or MRI images of benign and malignant parotid tumors were compared. The correlation of parotid cancer with the imaging features was analyzed. Diagnostic efficiency of contrast-enhanced CT, DCE-MRI and joint application for parotid cancer was assessed by receiver operating characteristic curve.

RESULTS

In terms of diagnostic accuracy, there was a significant difference between contrast-enhanced CT combined with DCE-MRI and contrast-enhanced CT alone (95.12% vs. 81.71%, P<0.001), and between the joint application and DCE-MRI alone (95.12% vs. 86.58%, P=0.004). Results of contrast-enhanced CT revealed statistical differences in tumor boundary, tumor size, calcification and cystic degeneration between benign and malignant tumors (P<0.05), but no obvious difference in lymph node enlargement between the two groups. MRI results showed that there were differences in the DCE-MRI time-signal intensity curve and ADC value between benign and malignant tumors (P<0.05). Correlation analysis results showed that the malignant tumor was negatively correlated with tumor boundary, calcification, cystic degeneration and ADC values, and it was positively correlated with DCE-MRI time-signal intensity curve and tumor size (P<0.05). Analysis of diagnostic efficacy showed that contrast-enhanced CT combined with DCE-MRI were significantly better than contrast-enhanced CT alone in terms of sensitivity and specificity (P<0.05). Moreover, the sensitivity of the joint application was also higher than that of MRI alone, while no obvious difference was found for specificity between joint application and MRI alone. The areas under the curve of contrast-enhanced CT combined with DCE-MRI in diagnosing malignant parotid tumor was remarkably greater than that of CT or MRI alone (P<0.05).

CONCLUSION

Contrast-enhanced CT combined with DCE-MRI can significantly improve the diagnostic accuracy, sensitivity and specificity for malignant parotid tumor, and the joint application was able to point out the direction of targeted surgical treatment plans.

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.

Neurofibromatosis type 2 versus sporadic vestibular schwannoma: The utility of MR diffusion and dynamic contrast-enhanced imaging

BACKGROUND AND PURPOSE

The goal of this study was to assess the utility of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to distinguish sporadic vestibular schwannomas (VSs) from those related to neurofibromatosis type 2 (NF2).

METHODS

We retrospectively reviewed 265 patients pathologically diagnosed with VSs between January 2015 and October 2020 in a single institution. There were 28 patients (male: 19, female: 9; age 11-67 years) including 23 sporadic and five NF2-related VSs, who had pretreatment DWI and DCE-MRI. Normalized mean apparent diffusion coefficient (nADCmean) and DCE-MRI parameters along with tumor characteristics were compared between sporadic and NF2-related VSs as appropriate. The diagnostic performances were calculated based on the receiver operating characteristic curve analysis for the values that showed significant differences. To identify significant modalities, multivariate logistic regression analysis was performed using nADCmean and the combination of statistically significant DCE-MRI parameters.

RESULTS

NADCmean, fractional volume of extracellular space (Ve), and forward volume transfer constant (Ktrans) were significantly different between sporadic and NF2-related VSs (nADCmean: median 1.62 vs. 1.16, P = .002; Ve: median 0.40 vs. 0.66, P = .007; Ktrans: median 0.17 vs. 0.33, P = .007), whereas fractional plasma volume (Vp), reverse reflux rate constant (Kep), and tumor characteristics were not. The diagnostic performances of nADCmean, Ve, and Ktrans were 0.93, 0.90, and 0.90 area under the curves with cutoffs of 1.46, 0.51, and 0.29, respectively. nADCmean and the combination of Ve and Ktrans were both chosen as significant differentiators by multivariate logistic regression analysis (P = .027).

CONCLUSIONS

DWI and DCE-MRI are both promising modalities to distinguish sporadic and NF2-related VSs.