Value of diffusion MR imaging in differentiation of recurrent head and neck malignancies from post treatment changes

Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma

OBJECTIVE

To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS

This retrospective study included 285 patients (mean age ± standard deviation, 62 ± 12 years; 220 male, 77.2%), including 215 for training (n = 161) and internal validation (n = 54) and 70 others for external validation, with newly developed contrast-enhancing lesions at the primary cancer site on the surveillance MRI following definitive treatment of HNSCC between January 2014 and October 2019. Of the 215 and 70 patients, 127 and 34, respectively, had local tumor recurrence. Radiomics models using radiomics scores were created separately for T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI), and ADC maps using non-zero coefficients from the least absolute shrinkage and selection operator in the training set. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of each radiomics score and known clinical parameter (age, sex, and clinical stage) in the internal and external validation sets.

RESULTS

Five radiomics features from T2WI, six from CE-T1WI, and nine from ADC maps were selected and used to develop the respective radiomics models. The area under ROC curve (AUROC) of ADC radiomics score was 0.76 (95% confidence interval [CI], 0.62-0.89) and 0.77 (95% CI, 0.65-0.88) in the internal and external validation sets, respectively. These were significantly higher than the AUROC values of T2WI (0.53 [95% CI, 0.40-0.67], p = 0.006), CE-T1WI (0.53 [95% CI, 0.40-0.67], p = 0.012), and clinical parameters (0.53 [95% CI, 0.39-0.67], p = 0.021) in the external validation set.

CONCLUSION

The radiomics model using ADC maps exhibited higher diagnostic performance than those of the radiomics models using T2WI or CE-T1WI and clinical parameters in the diagnosis of local tumor recurrence in HNSCC following definitive treatment.

Normalized Parameters of Dynamic Contrast-Enhanced Perfusion MRI and DWI-ADC for Differentiation between Posttreatment Changes and Recurrence in Head and Neck Cancer

BACKGROUND AND PURPOSE

Differentiating recurrence from benign posttreatment changes has clinical importance in the imaging follow-up of head and neck cancer. This study aimed to investigate the utility of normalized dynamic contrast-enhanced MR imaging and ADC for their differentiation.

MATERIALS AND METHODS

This study included 51 patients with a history of head and neck cancer who underwent follow-up dynamic contrast-enhanced MR imaging with DWI-ADC, of whom 25 had recurrences and 26 had benign posttreatment changes. Quantitative and semiquantitative dynamic contrast-enhanced MR imaging parameters and ADC of the ROI and reference region were analyzed. Normalized dynamic contrast-enhanced MR imaging parameters and normalized DWI-ADC parameters were calculated by dividing the ROI by the reference region.

RESULTS

Normalized plasma volume, volume transfer constant between extravascular extracellular space and blood plasma per minute (K ^trans), area under the curve, and wash-in were significantly higher in patients with recurrence than in those with benign posttreatment change (P = .003 to <.001). The normalized mean ADC was significantly lower in patients with recurrence than in those with benign posttreatment change (P < .001). The area under the receiver operating characteristic curve of the combination of normalized dynamic contrast-enhanced MR imaging parameters with significance (normalized plasma volume, normalized extravascular extracellular space volume per unit tissue volume, normalized K ^trans, normalized area under the curve, and normalized wash-in) and normalized mean ADC was 0.97 (95% CI, 0.93-1).

CONCLUSIONS

Normalized dynamic contrast-enhanced MR imaging parameters, normalized mean ADC, and their combination were effective in differentiating recurrence and benign posttreatment changes in head and neck cancer.

ADC for Differentiation between Posttreatment Changes and Recurrence in Head and Neck Cancer: A Systematic Review and Meta-analysis

BACKGROUND

Previous studies reported that the ADC values of recurrent head and neck cancer lesions are lower than those of posttreatment changes, however, the utility of ADC to differentiate them has not been definitively summarized and established.

PURPOSE

Our aim was to evaluate the diagnostic benefit of ADC calculated from diffusion-weighted imaging in differentiating recurrent lesions from posttreatment changes in head and neck cancer.

DATA SOURCES

MEDLINE, Scopus, and EMBASE data bases were searched for studies.

STUDY SELECTION

The review identified 6 prospective studies with a total of 365 patients (402 lesions) who were eligible for the meta-analysis.

DATA ANALYSIS

Forest plots were used to assess the mean difference in ADC values. Heterogeneity among the studies was evaluated using the Cochrane Q test and the I² statistic.

DATA SYNTHESIS

Among included studies, the overall mean of ADC values of recurrent lesions was 1.03 × 10^-3mm²/s and that of the posttreatment changes was 1.51 × 10^-3mm²/s. The ADC value of recurrence was significantly less than that of posttreatment changes in head and neck cancer (pooled mean difference: -0.45; 95% CI, -0.59-0.32, P < .0001) with heterogeneity among studies. The threshold of ADC values between recurrent lesions and posttreatment changes was suggested to be 1.10 × 10^-3mm²/s.

LIMITATIONS

Given the heterogeneity of the data of the study, the conclusions should be interpreted with caution.

CONCLUSIONS

The ADC values in recurrent head and neck cancers are lower than those of posttreatment changes, and the threshold of ADC values between them was suggested.

MRI Posttreatment Surveillance for Head and Neck Squamous Cell Carcinoma: Proposed MR NI-RADS Criteria

BACKGROUND AND PURPOSE

The current Neck Imaging Reporting and Data System (NI-RADS) criteria were designed for contrast-enhanced CT with or without PET. Prior studies have revealed the capability of DWI and T2 signal intensity in distinguishing locoregional tumor residual and recurrence from posttreatment benign findings in head and neck cancers. We aimed to propose MR imaging NI-RADS criteria by adding diffusion criteria and T2 signal intensity to the American College of Radiology NI-RADS template.

MATERIALS AND METHODS

This retrospective study included 69 patients with head and neck squamous cell carcinoma (HNSCC) who underwent posttreatment contrast-enhanced MRI imaging surveillance using a 1.5T scanner. The scans were interpreted by 2 neuroradiologists. Image analysis assessed the primary tumor site using the current American College of Radiology NI-RADS morphologic lexicon (mainly designed for contrast-enhanced CT with or without PET). NI-RADS rescoring was then performed based on our proposed criteria using T2 signal and diffusion features. The reference standard was a defined set of criteria, including clinical and imaging follow-up and pathologic assessment.

RESULTS

Imaging assessment of treated HNSCC at the primary tumor site using T2 signal intensity and diffusion features as modifying rules to NI-RADS showed higher sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (92.3%, 90.7%, 85.7%, 95.1%, and 91.3%, respectively) compared with the current NI-RADS lexicon alone (84.6%, 81.4%, 73.3%, 89.8%, and 82.6%, respectively).

CONCLUSIONS

The addition of diffusion features and T2 signal to the American College of Radiology NI-RADS criteria for the primary tumor site enhances the specificity, sensitivity, positive predictive value, negative predictive value, and NI-RADS accuracy.

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.

Post-treatment benign changes versus recurrence in non-lymphoid head and neck malignancies: can diffusion-weighted magnetic resonance imaging end up the diagnostic challenge?

Background

The aim of this prospective cohort study is to substantiate the added value of diffusion-weighted magnetic resonance imaging (DW-MRI) over conventional MRI assessment in the differentiation between locoregional recurrence/residual tumour and post-treatment benign changes in patients with non-lymphoid head and neck malignancies.

Thirty adult patients, each with a suspicious lesion on post-treatment imaging scans at the primary site of a previously treated non-lymphoid head and neck malignancy, were evaluated by MRI and diffusion-weighted imaging (DWI). The apparent diffusion coefficient (ADC) values of the lesions were calculated.

Results

Diffusion-weighted MRI yielded an accuracy of 90%, a sensitivity of 88.9%, a specificity of 91.7%, a positive predictive value of 94.1% and a negative predictive value of 84.6%. The mean ADC value of the lesions was lower in the “locoregional recurrence/residual tumour” group (1.08 × 10−3 mm2/s) compared to the “post-treatment benign changes” group (1.95 × 10−3 mm2/s); P < 0.001. An ADC cutoff value of 1.43 × 10−3 mm2/s achieved the same accuracy as the visual assessment by DW-MRI.

Conclusion

Incorporating the DWI sequence into the post-treatment imaging assessment protocol brings a substantial added value to conventional MRI assessment in patients with non-lymphoid head and neck malignancies. This valuable merit of DW-MRI can help avoid or, at least, largely minimize unnecessary or unfeasible tissue sampling. An ADC cutoff value of 1.43 × 10−3 mm2/s can also be utilized to aid in the assessment process.

Predictive Value of Diffusion, Glucose Metabolism Parameters of PET/MR in Patients With Head and Neck Squamous Cell Carcinoma Treated With Chemoradiotherapy

Purpose: This study aims to evaluate the predictive value of the pretreatment, metabolic, and diffusion parameters of a primary tumor assessed with PET/MR on patient clinical outcomes.

Methods: Retrospective evaluation was performed using PET/MR image data sets acquired using the single tracer injection dual imaging of 68 histologically proven head and neck cancer patients 4 weeks before receiving definitive chemoradiotherapy (CRT). PET/MR was performed before the CRT and 12 weeks after the CRT for response evaluation. Image data (PET and MRI diffusion-weighted imaging [DWI]) was used to specify the maximum standard uptake value, the peak lean body mass corrected, SUV_max, the metabolic tumor volume, the total lesion glycolysis (SUV_max, SUL_peak, MTV, and TLG), and the mean apparent diffusion coefficient (ADC_mean) of the primary tumor. Based on the results of the therapeutic response evaluation, two patient subgroups were created: one with a viable tumor and another without. Metabolic and diffusion data, from the pretreatment PET/MR and the therapeutic response, were correlated using Spearman's correlation coefficient and Wilcoxon's test.

Results: After completing the CRT, a viable residual tumor was detected in 36/68 (53%) cases, and 32/68 (47%) patients showed complete remission. However, no significant correlation was found between the pretreatment parameter, ADC_mean (p = 0.88), and the therapeutic success. The PET parameters, SUV_max and SUL_peak, MTV, and TLG (p = 0.032, p = 0.01, p < 0.0001, p = 0.0004) were statistically significantly different between the two patient subgroups.

Conclusion: This study found that MRI-based (ADC_mean) data from FDG PET/MR pretreatment could not be used to predict therapeutic response although the PET parameters SUV_max, SUL_peak, MTV, and TLG proved to be more useful; thus, their inclusion in risk stratification may also be of additional value.

Quantitative Analysis of DCE-MRI and RESOLVE-DWI for Differentiating Nasopharyngeal Carcinoma from Nasopharyngeal Lymphoid Hyperplasia

To explore the ability of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) analysis and readout segmentation of long variable echo-trains diffusion weighted imaging (RESOLVE-DWI) to distinguish nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPLH). Twenty-five patients with NPC and 30 patients with NPLH were evaluated. Three quantitative DCE-MRI parameters (Ktrans, Kep and Ve) and the apparent diffusion coeffcient (ADC) of lesions were calculated. The two independent samples t test or Mann-Whitney U test was used to compare the parameters between NPC and NPLH group. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic ability for distinguishing NPC from NPLH. A P value less than 0.05 was considered statistically significant. The difference in Ktrans value between the NPC group and the NPLH group was statistically significant, and the value of the NPC group was larger than that of the NPLH group. There was no statistical difference in Kep and Ve between the two groups. The ADC value of NPC group was smaller than that of NPLH group, and the difference was statistically significant. ROC curve analysis showed that both Ktrans and ADC were effective in diagnosing NPC and the area under the curve (AUC) was 0.773 and 0.704, respectively. In addition, the combination of Ktrans and ADC demonstrated the obviously improved AUC of 0.884. DCE-MRI and RESOLVE-DWI are effective in differentiating NPC from NPLH, especially the combination of the two models.