Histogram analysis of apparent diffusion coefficient maps in the prognosis of patients with locally advanced head and neck squamous cell carcinoma: Comparison of different region of interest selection methods

Sinonasal adenoid cystic carcinoma: preoperative apparent diffusion coefficient histogram analysis in prediction of prognosis and Ki-67 proliferation status

PURPOSE

To investigate the value of preoperative apparent diffusion coefficient (ADC) histogram analysis in predicting the prognosis of patients with sinonasal adenoid cystic carcinoma (ACC) and the correlation between ADC histogram parameters and Ki-67 labeling index (LI).

MATERIALS AND METHODS

The study enrolled 66 patients with sinonasal ACC who were surgically resected and confirmed by histopathology. The disease-free survival (DFS) was evaluated with clinical-pathologic and radiologic characteristics using the Cox proportion hazard model. Spearman correlation analysis was used to evaluate the correlation between ADC histogram parameters and Ki-67 LI. The predictive performance of ADC histogram parameters for Ki-67 LI was assessed using the receiver operating characteristic (ROC) curve.

RESULTS

Multivariable analysis showed Ki-67 LI (hazard ratio: 9.279; 95% confidence interval 1.099-78.338; P = 0.041) and ADCskewness (hazard ratio: 5.942; 95% confidence interval 1.832-19.268; P = 0.003) were significant independent predictors of DFS. The combination of these two variables achieved the predictive ability with a C-index of 0.717 (95% confidence interval 0.607-0.826). ADCmean and all ADC percentiles (10th, 50th, and 90th) significantly and inversely correlated with Ki-67 LI of ACC (Correlation coefficients = - 0.574 to - 0.591, Ps < 0.001). Among the ADC histogram parameters, the ADC50th showed superior performance for the differentiation of the high from low Ki-67 LI groups with an area under the curve (AUC) of 0.834 and an accuracy of 80.30%.

CONCLUSION

ADC histogram analysis had predictive value for DFS and Ki-67 LI, which may be a valuable biomarker for prognosis and proliferation status for ACC in clinical practice.

The value of whole-volume apparent diffusion coefficient histogram analysis in preoperatively distinguishing intracranial solitary fibrous tumor and transitional meningioma

Purpose

To investigate the value of whole-volume apparent diffusion coefficient (ADC) histogram analysis in preoperatively distinguishing intracranial solitary fibrous tumors (SFT) from transitional meningiomas (TM), thereby assisting the establishment of the treatment protocol.

Methods

Preoperative diffusion-weighted imaging datasets of 24 patients with SFT and 28 patients with TM were used to extract whole-volume ADC histogram parameters, including variance, skewness, kurtosis, and mean, as well as 1st (AP1), 10th (AP10), 50th (AP50), 90th (AP90), and 99th (AP99) percentiles of ADC using MaZda software. The independent t-test or Mann-Whitney U test was used to compare the differences between ADC histogram parameters of SFT and TM. Receiver operating characteristic (ROC) curves were generated to evaluate the performance of significant ADC histogram parameters. Spearman's correlation coefficients were calculated to evaluate correlations between these parameters and the Ki-67 expression levels.

Results

SFT exhibited significantly higher variance, and lower AP1 and AP10 (all P < 0.05) than TM. The best diagnostic performance was obtained by variance, with an area under the ROC curve of 0.848 (0.722-0.933). However, there was no significant difference in skewness, kurtosis, mean, or other percentiles of ADC between the two groups (all P > 0.05). Significant correlations were also observed between the Ki-67 proliferation index and variance (r = 0.519), AP1 (r = -0.425), and AP10 (r = -0.372) (all P < 0.05).

Conclusion

Whole-volume ADC histogram analysis is a feasible tool for non-invasive preoperative discrimination between intracranial SFT and TM, with variance being the most promising prospective parameter.

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.

Synthetic MRI in differentiating benign from metastatic retropharyngeal lymph node: combination with diffusion-weighted imaging

OBJECTIVES

This study aimed to evaluate the synthetic MRI (syMRI), its combination with diffusion-weighted imaging (DWI), and morphological features for discriminating benign from metastatic retropharyngeal lymph nodes (RLNs).

METHODS

Fifty-eight patients with a total of 63 RLNs (21 benign and 42 metastatic) were enrolled. The mean and standard deviation of syMRI-derived relaxometry parameters (T1, T2, PD; T1_SD, T2_SD, PD_SD) were obtained from two different regions of interest (namely, partial-lesion and full-lesion ROI). The parameters derived from benign and metastatic RLNs were compared using Student's t or chi-square tests. Logistic regression analysis was used to construct a multi-parameter model of syMRI, syMRI + DWI, and syMRI + DWI + morphological features. Areas under the curve (AUC) were compared using the DeLong test to determine the best diagnostic approach.

RESULTS

Benign RLNs had significantly higher T1, T2, PD, and T1_SD values compared with metastatic RLNs in both partial-lesion and full-lesion ROI (all p < 0.05). The T1_SD obtained from full-lesion ROI showed the best diagnostic performance among all syMRI-derived single parameters. The AUC of combined syMRI multiple parameters (T1, T2, PD, T1_SD) were higher than those of any single parameter from syMRI. The combination of synthetic MRI and DWI can improve the AUC regardless of ROI delineation. Furthermore, the combination of synthetic MRI, DWI-derived quantitative parameters, and morphological features can significantly improve the overall diagnostic performance.

CONCLUSIONS

The value of syMRI has been validated in differential diagnosis of benign and metastatic RLNs, and syMRI + DWI + morphological features can further improve the diagnostic efficiency for discriminating these two entities.

KEY POINTS

• Synthetic MRI was useful in differential diagnosis of benign and metastatic RLNs. • The combination of syMRI, DWI, and morphological features can significantly improve the diagnostic efficiency.

A Prognostic Model Generated from an Apparent Diffusion Coefficient Ratio Reliably Predicts the Outcomes of Oral Tongue Squamous Cell Carcinoma

This study aimed to develop an apparent diffusion coefficient (ADC) ratio-based prognostic model to predict the recurrence and disease-free survival (DFS) of oral tongue squamous cell carcinoma (OTSCC). A total of 188 patients with cT1-2 oral tongue squamous cell carcinoma were enrolled retrospectively. Clinical and laboratory data were extracted from medical records. The ADC values were measured at the regions of interest of the tumor and non-tumor tissues of the MRI images, and the ADC ratio was used for comparison between the patient with recurrence (n = 83 case, 44%) and patients without recurrence (n = 105 cases, 56%). Cox proportional hazards models were generated to analyze the risk factors of cancer recurrence. A nomogram was developed based on significant risk factors to predict 1-, 5- and 10-year DFS. The receiver operator characteristic (ROC) curves of predictors in the multivariable Cox proportional hazards prognostic model were generated to predict the recurrence and DFS. The integrated areas under the ROC curve were calculated to evaluate discrimination of the models. The ADC ratio, tumor thickness and lymph node ratio were reliable predictors in the final prognostic model. The final model had a 71.1% sensitivity and an 81.0% specificity. ADC ratio was the strongest predictor of cancer recurrence in prognostic performance. Discrimination and calibration statistics were satisfactory with C-index above 0.7 for both model development and internal validation. The calibration curve showed that the 5- and 10-year DFS predicted by the nomogram agreed with actual observations.

Histogram analysis of 18F-FDG PET imaging SUVs may predict the histologic grade of oral squamous cell carcinoma

OBJECTIVE

We tested the hypothesis that histogram analysis parameters of standardized uptake values (SUVs) obtained preoperatively using ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) are significantly influenced by differences in metabolic capacity due to the histologic grade of oral squamous cell carcinoma (OSCC).

STUDY DESIGN

The study included 62 patients who were clinically diagnosed with OSCC and received surgical treatment after an ¹⁸F-FDG PET examination. Histogram analysis was performed using all voxels contained in the tumor area of each patient with an SUV ≥2.5. The histogram parameters calculated were the mean and standard deviation of SUVs, maximum SUV, metabolic tumor volume, skewness, and kurtosis. Statistical analyses were performed using a Mann-Whitney U test to calculate the significance of differences in these parameters between groups with well- and moderately- or poorly-differentiated tumors. Statistical significance was assumed at P < .05.

RESULTS

Only a comparison of kurtosis in the histogram showed a significant difference between the well- and moderately/poorly-differentiated tumors (P = .0294).

CONCLUSIONS

The distribution of metabolic capacity in oral squamous cell carcinoma tissues revealed on an ¹⁸F-FDG PET examination may help identify the histologic grade. This finding may provide valuable information for determining the subsequent treatment plan and predicting disease prognosis.

Differentiation of intracranial solitary fibrous tumor/hemangiopericytoma from atypical meningioma using apparent diffusion coefficient histogram analysis

This study aimed to investigate the value of apparent diffusion coefficient (ADC) histogram analysis in differentiating intracranial solitary fibrous tumor/hemangiopericytoma (SFT/HPC) from atypical meningioma (ATM). Retrospective analyzed the clinical, magnetic resonance imaging, and pathological data of 20 and 25 patients with SFT/HPC and ATM, respectively. Histogram analysis was performed on the axial ADC images using MaZda software, and nine histogram parameters were obtained, including mean, variance, skewness, kurtosis, and the 1^st (ADC1), 10^th (ADC10), 50^th (ADC50), 90^th (ADC90), and 99^th (ADC99) percentile ADC. Differences in ADC histogram parameters between SFT/HPC and ATM were compared by an independent t test or Mann-Whitney U test, while the statistically significant histogram parameters were further analyzed by drawing receiver operating characteristic (ROC) curves to evaluate the differential diagnostic performance. Among the nine ADC histogram parameters we extracted, the mean, ADC1, ADC10, ADC50, and ADC90 in the SFT/HPC group were greater than those of ATM, and significant differences were observed (all P < 0.05). ROC analysis showed that the ADC1 generated the highest area under the curve (AUC) value of 0.920 in distinguishing the two tumors, when using 91.00 as the optimal threshold. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value in distinguishing between SFT/HPC and ATM were 84.00%, 85.00%, 84.44%, 87.50%, and 81.00%, respectively. ADC histogram analysis can be a reliable tool to differentiate between SFT/HPC and ATM, with the ADC1 being the most promising potential parameter.

Radiomic Analysis of Tumour Heterogeneity Using MRI in Head and Neck Cancer Following Chemoradiotherapy: A Feasibility Study

Objectives

To evaluate interval changes in heterogeneity on diffusion-weighted apparent diffusion coefficient (ADC) maps and T1-weighted post-gadolinium (T1w post gad) MRI in head and neck carcinoma (HNSCC), with and without chemo-radiotherapy (CRT) response.

Methods

This prospective observational cohort study included 24 participants (20 men, age 62.9 ± 8.8 years) with stage III and IV HNSCC. The primary tumour (n = 23) and largest lymph node (n = 22) dimensions, histogram parameters and grey-level co-occurrence matrix (GLCM) parameters were measured on ADC maps and T1w post gad sequences, performed pretreatment and 6 and 12 weeks post CRT. The 2-year treatment response at primary and nodal sites was recorded. The Wilcoxon signed-rank test was used to compare interval changes in parameters after stratifying for treatment response and failure (p < 0.001 statistical significance).

Results

23/23 primary tumours and 18/22 nodes responded to CRT at 2 years. Responding HNSCC demonstrated a significant interval change in ADC histogram parameters (kurtosis, coefficient of variation, entropy, energy for primary tumour; kurtosis for nodes) and T1w post gad GLCM (entropy and contrast in the primary tumour and nodes) by 6 weeks post CRT (p < 0.001). Lymph nodes with treatment failure did not demonstrate an interval alteration in heterogeneity parameters.

Conclusions

ADC maps and T1w post gad MRI demonstrate the evolution of heterogeneity parameters in successfully treated HNSCC by 6 weeks post CRT; however, this is not observed in lymph nodes failing treatment.

Advances in Knowledge

Early reduction in heterogeneity is demonstrated on MRI when HNSCC responds to CRT.

Associations Between ADC and Tumor Infiltrating Lymphocytes, Tumor-Stroma Ratio and Vimentin Expression in Head and Neck Squamous Cell Cancer

RATIONALE AND OBJECTIVES

The present study used diffusion-weighted imaging (DWI) to elucidate possible associations with tumor-infiltrating lymphocytes (TIL), tumor-stroma ratio and vimentin expression in head and neck squamous cell cancer (HNSCC).

MATERIALS AND METHODS

30 patients with primary HNSCC of different localizations were involved in the study. DWI was obtained on a 3 T scanner. Apparent diffusion coefficients (ADC) images were analyzed with a whole lesion measurement using a histogram approach. TIL- and vimentin-expression was calculated on biopsy samples before any form of treatment.

RESULTS

Tumor-stroma ratio correlated with ADC kurtosis (r = 0.46, p = 0.01) and ADC skewness (r = 0.42, p = 0.02). Several ADC parameters were significantly different between stroma rich und tumor rich tumors. ADC entropy correlated significantly with the expression of TIL within the tumor compartment (r = 0.44, p = 0.01). No associations were identified between ADC parameters and vimentin expression.

CONCLUSION

ADC skewness and kurtosis histogram parameters can reflect tumor compartments in HNSCC. ADC entropy was associated with TIL of the tumor compartment but not with those of the stroma compartment, which emphasizes the ability of ADC histogram parameters to reflect distinctive differences of tumors.

Apparent Diffusion Coefficient Histogram Analysis for the Preoperative Evaluation of Ki-67 Expression in Pituitary Macroadenoma

PURPOSE

To explore the value of an apparent diffusion coefficient (ADC) histogram in predicting the Ki-67 proliferation index in pituitary macroadenomas.

MATERIAL AND METHODS

This retrospective study analyzed the pathological and imaging data of 102 patients with pathologically confirmed pituitary macroadenoma. Immunohistochemistry staining was used to assess Ki-67 expression in tumor tissue samples, and a high Ki-67 labeling index was defined as 3%. The ADC images of the maximum slice of tumors were selected and the region of interest (ROI) of each slice was delineated using the MaZda software (version 4.7, Technical University of Lodz, Institute of Electronics, Łódź, Poland) and analyzed by ADC histogram. Histogram characteristic parameters were compared between the high Ki-67 group (n = 42) and the low Ki-67 group (n = 60). The important parameters were further analyzed by receiver operating characteristic (ROC).

RESULTS

The mean value, and the 1st, 10th, 50th, 90th, and 99th percentiles were found to be negatively correlated with Ki-67 expression (all P < 0.05), with correlation coefficients of -0.292, -0.352, -0.344, -0.289, -0.253 and -0.267, respectively. The mean ADC and the 1st, 10th, 50th, 90th, and 99th quantiles extracted from the histogram were significantly lower in the high Ki-67 group than in the low Ki-67 group (all P < 0.05). The area under the ROC curve was 0.699-0.720; however, there were no significant between-group differences in variance, skewness and kurtosis (all P > 0.05).

CONCLUSION

An ADC histogram can be a reliable tool to predict the Ki-67 proliferation status in patients with pituitary macroadenomas.

Utility of diffusion MRI characteristics of cervical lymph nodes as disease classifier between patients with head and neck squamous cell carcinoma and healthy volunteers

Diffusion MRI characteristics assessed by apparent diffusion coefficient (ADC) histogram analysis in head and neck squamous cell carcinoma (HNSCC) have been reported as helpful in classifying tumours based on diffusion characteristics. There is little reported on HNSCC lymph nodes classification by diffusion characteristics. The aim of this study was to determine whether pretreatment nodal microstructural diffusion MRI characteristics can classify diseased nodes of patients with HNSCC from normal nodes of healthy volunteers. Seventy-nine patients with histologically confirmed HNSCC prior to chemoradiotherapy, and eight healthy volunteers, underwent diffusion-weighted (DW) MRI at a 1.5-T MR scanner. Two radiologists contoured lymph nodes on DW (b = 300 s/m² ) images. ADC, distributed diffusion coefficient (DDC) and alpha (α) values were calculated by monoexponential and stretched exponential models. Histogram analysis metrics of drawn volume were compared between patients and volunteers using a Mann-Whitney test. The classification performance of each metric between the normal and diseased nodes was determined by receiver operating characteristic (ROC) analysis. Intraclass correlation coefficients determined interobserver reproducibility of each metric based on differently drawn ROIs by two radiologists. Sixty cancerous and 40 normal nodes were analysed. ADC histogram analysis revealed significant differences between patients and volunteers (p ≤0.0001 to 0.0046), presenting ADC distributions that were more skewed (1.49 for patients, 1.03 for volunteers; p = 0.0114) and 'peaked' (6.82 for patients, 4.20 for volunteers; p = 0.0021) in patients. Maximum ADC values exhibited the highest area under the curve ([AUC] 0.892). Significant differences were revealed between patients and volunteers for DDC and α value histogram metrics (p ≤0.0001 to 0.0044); the highest AUC were exhibited by maximum DDC (0.772) and the 25th percentile α value (0.761). Interobserver repeatability was excellent for mean ADC (ICC = 0.88) and the 25th percentile α value (ICC = 0.78), but poor for all other metrics. These results suggest that pretreatment microstructural diffusion MRI characteristics in lymph nodes, assessed by ADC and α value histogram analysis, can identify nodal disease.

Histogram analysis of diffusion-weighted imaging and dynamic contrast-enhanced MRI for predicting occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma

OBJECTIVES

To investigate the feasibility of whole-tumor histogram analysis of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI for predicting occult lymph node metastasis (LNM) in early-stage oral tongue squamous cell cancer (OTSCC).

MATERIALS AND METHODS

This retrospective study included 55 early-stage OTSCC (cT1-2N0M0) patients; 34 with pathological LNM and 21 without. Eight whole-tumor histogram features were extracted from quantitative apparent diffusion coefficient (ADC) maps and two semi-quantitative DCE parametric maps (wash-in and wash-out). The clinicopathological factors and histogram features were compared between the two groups. Stepwise logistic regression was used to identify independent predictors. Receiver operating characteristic curves were generated to assess the performances of significant variables and a combined model for predicting occult LNM.

RESULTS

MRI-determined depth of invasion and ADC_entropy was significantly higher in the LNM group, with respective areas under the curve (AUCs) of 0.67 and 0.69, and accuracies of 0.73 and 0.73. ADC_10th. ADC_uniformity and wash-in_skewness were significantly lower in the LNM group, with respective AUCs of 0.68, 0.71, and 0.69, and accuracies of 0.65, 0.71, and 0.64. Histogram features from wash-out maps were not significantly associated with cervical node status. In the logistic regression analysis, ADC_10th, ADC_uniformity, and wash-in_skewness were independent predictors. The combined model yielded the best predictive performance, with an AUC of 0.87 and an accuracy of 0.82.

CONCLUSIONS

Whole-tumor histogram analysis of ADC and wash-in maps is a feasible tool for preoperative evaluation of cervical node status in early-stage OTSCC.

KEY POINTS

• Histogram analysis of parametric maps from DWI and DCE-MRI may assist the prediction of occult LNM in early-stage OTSCC. • ADC_10th, ADC_uniformity, and wash-in_skewness were independent predictors. • The combined model exhibited good predictive performance, with an accuracy of 0.82.

Prognostic functional MR imaging parameters in head and neck squamous cell carcinoma: A systematic review

OBJECTIVE

Functional MR imaging has demonstrated potential for predicting treatment response. This systematic review gives an extensive overview of the current level of evidence for pre-treatment MR-based perfusion and diffusion imaging parameters that are prognostic for treatment outcome in head and neck squamous cell carcinoma (HNSCC) (PROSPERO registrationCRD42020210689).

MATERIALS AND METHODS

According to the PRISMA statements, Medline, Embase and Scopus were queried for articles with a maximum date of October 19th, 2020. Studies investigating the predictive performance of pre-treatment MR-based perfusion and/or diffusion imaging parameters in HNSCC treatment response were included. All prognosticators were extracted from the primary tumor. Risk of bias was assessed using the QUIPS tool. Results were summarized in tables and forest plots.

RESULTS

31 unique studies met the inclusion criteria; among them, 11 articles described perfusion (n = 529 patients) and 28 described diffusion (n = 1626 patients) MR-imaging, eight studies were included in both categories. Higher K^trans and K_ep were associated with better treatment response for OS and DFS, respectively. Study findings for V_p and V_e were inconsistent or not significant. High-level controversy was observed between studies examining the MR diffusion parameters mean and median ADC.

CONCLUSION

For HNSCC patients, the accurate and consistent results of pre-treatment MR-based perfusion parameters K^trans and K_ep are potential for clinical applicability predictive of OS and DFS and treatment decision guidance. Significant heterogeneity in study designs might affect high discrepancy in study results for parameters extracted from diffusion imaging. Furthermore, recommendations for future research were summarized.

Histogram parameters derived from T1 and T2 weighted images correlate with tumor infiltrating lymphocytes and tumor-stroma ratio in head and neck squamous cell cancer

PURPOSE

The present study used histogram analysis values derived from T1- and T2- weighted (w) images to elucidate possible associations with Tumor-infiltrating lymphocytes (TIL) and Vimentin expression in head and neck squamous cell cancer (HNSCC).

MATERIALS AND METHODS

Overall, 28 patients (n = 8 female patient, 28.6%) with primary HNSCC of different localizations were involved in the study. Magnetic resonance imaging (MRI) was obtained on a 3 T MRI. The images were analyzed with a whole lesion measurement using a histogram approach. TIL- and vimentin-expression was calculated on biopsy samples before any form of treatment.

RESULTS

Several T1-derived parameters correlated with the expression of TIL within the stroma compartment: mean (r = 0.42, p = 0.025), p10 (r = 0.50, p = 0.007), p25 (r = 0.42, p = 0.025), median (r = 0.39, p = 0.036), and mode (r = 0.39, p = 0.04). No T2-derived parameter correlated with the TIL within the stroma compartment. Several T2-derived parameters correlated with the expression of TIL within the tumor compartment: mean (r = -0.52, p = 0.004), max (r = -0.43, p = 0.02), p10 (r = -0.38, p = 0.04), p25 (r = -0.53, p = 0.004), p75 (r = -0.52, p = 0.004), p90 (r = -0.48, p = 0.009), median (r = -0.52, p = 0.004), mode (r = -0.40, p = 0.03). Kurtosis derived from T2w images had significant higher values in tumor-rich tumors, compared to stroma-rich tumors, (mean 5.5 ± 0.5 versus 4.2 ± 1.2, p = 0.028).

CONCLUSIONS

Histogram analysis parameters derived from T1w and T2w images might be able to reflect tumor compartments and TIL expression in HNSCC.

Apparent diffusion coefficient agreement and reliability using different region of interest methods for the evaluation of head and neck cancer post chemo-radiotherapy

Objectives:

Post chemoradiotherapy (CRT) interval changes in apparent diffusion coefficient (ADC) have prognostic value in head and neck squamous cell cancer (HNSCC). The impact of using different region of interest (ROI) methods on interobserver agreement and their ability to reliably detect the changes in the ADC values was assessed.

Methods:

Following ethical approval, 25 patients (mean age 59.5 years, 21 male) with stage 3–4 HNSCC undergoing CRT were recruited for this prospective cohort study. Diffusion weighted MRI (DW-MRI) was performed pre-treatment and at 6 and 12 weeks following CRT. Two radiologists independently delineated ROIs using whole volume (ROI_v), largest area (ROI_a) or representative area (ROI_r) methods at primary tumour (n = 22) and largest nodal (n = 24) locations and recorded the ADC_mean. When no clear focus of increased DWI signal was evident at follow-up, a standardised ROI was placed (non-measurable or NM). Bland-Altman plots and interclass correlation coefficient (ICC) were assessed. Paired t-tests evaluated interval changes in pre- and post-treatment ADC_mean at each location, which were compared to the smallest detectable difference (SDD).

Results:

Excellent agreement was obtained for all ROI methods at pre-treatment (ICC 0.94–0.98) and 6-week post-treatment (ICC 0.94–0.98). At 12-week post-treatment, agreement was excellent (ICC 0.91–0.94) apart from ROI_r (ICC 0.86) and the NM nodal disease (ICC 0.87). There were significant interval increases in ADC_mean between pre-treatment and post-treatment studies, which were greater than the SDD for all ROIs.

Conclusions:

ADC_mean values can be reproducibly obtained in HNSCC using the different ROI techniques on pre- and post-CRT MRI, and this reliably detects the interval changes.

An analysis of Ki-67 expression in stage 1 invasive ductal breast carcinoma using apparent diffusion coefficient histograms

Background

To investigate the value of apparent diffusion coefficient (ADC) histograms in differentiating Ki-67 expression in T1 stage invasive ductal breast carcinoma (IDC).

Methods

The records of 111 patients with pathologically confirmed T1 stage IDC who underwent magnetic resonance imaging prior to surgery were retrospectively reviewed. The expression of Ki-67 in tumor tissue samples from the patients was assessed using immunohistochemical (IHC) staining, with a cut-off value of 25% for high Ki-67 labeling index (LI). ADC images of the maximum lay of tumors were selected, and the region of interest (ROI) of each lay was delineated using the MaZda software and analyzed by histogram. The correlations between the histogram characteristic parameters and the Ki-67 LI were investigated. Additionally, the histogram characteristic parameters of the high Ki-67 group (n=54) and the low Ki-67 group (n=57) were statistically analyzed to determine the characteristic parameters with significant difference. Receiver operator characteristic (ROC) analyses were further performed for the significant parameters.

Results

The mean value, and the 1st, 10th, 50th, 90th, and 99th percentiles were found to be negatively correlated with the expression of Ki-67 (all P values <0.001), with a correlation coefficient of -0.624, -0.749, -0.717, -0.621, -0.500, and -0.410, respectively. In the high Ki-67 group, the mean value, and the 1st, 10th, 50th, 90th, and 99th percentiles extracted by the histogram were significantly lower (all P values <0.05) than that of the low Ki-67 group, with areas under the ROC curves ranging from 0.717-0.856. However, the variance, skewness, and kurtosis did not differ between the two groups (all P values >0.05).

Conclusions

Histogram-derived parameters for ADC images can serve as a reliable tool in the prediction of Ki-67 proliferation status in patients with T1 stage IDC. Among the significant ADC histogram values, the 1st and 10th percentiles showed the best predictive values.

Investigation of the feasibility of synthetic MRI in the differential diagnosis of non-keratinising nasopharyngeal carcinoma and benign hyperplasia using different contoured methods for delineation of the region of interest

AIM

To assess the feasibility and preliminary diagnostic performances of relaxation times derived from synthetic magnetic resonance imaging (syMRI) for differentiating nasopharyngeal carcinoma from nasopharyngeal benign lymphoid hyperplasia, and to assess the influence of tissue segmentation method on relaxation estimates.

MATERIALS AND METHODS

Fifty participants with nasopharyngeal carcinoma (NPC) and 40 participants with benign hyperplasia (NPH) who underwent syMRI examination were enrolled prospectively. T1, T2, and proton density (PD) values were obtained from four different regions of interest (ROIs), namely, partial-section, single-section, three-sections, and whole-lesion. The metrics between NPC and NPH or among different ROIs were compared using Student's t-test or one-way ANOVA. The area under curve (AUC) was calculated to assess the performance of metrics obtained from different ROIs to differentiate NPC and NPH.

RESULTS

The T1, T2, and PD values for NPH were significantly higher than those for NPC, regardless of the type of ROI used, except for the PD value obtained from the whole-lesion ROI. The T2 values obtained from the single-section ROI showed the highest diagnostic accuracy in distinguishing NPC from NPH, with an AUC of 0.894, sensitivity of 0.900, and specificity of 0.800. Additionally, the T1, T2, and PD values for nasopharyngeal lesions showed no statistical difference among different kinds of ROI, except for the difference in T1 value between partial-section and other methods.

CONCLUSION

Quantitative analysis of syMRI has the potential to distinguish NPC from NPH. Moreover, different types of ROI showed limited influence on the relaxation time estimation for nasopharyngeal lesions.

Correlation between apparent diffusion coefficients and metabolic parameters in hypopharyngeal squamous cell carcinoma: A prospective study with integrated PET/MRI

PURPOSE

Apparent diffusion coefficients (ADCs) derived from diffusion-weighted magnetic resonance imaging (DW-MRI) and metabolic parameters derived from ¹⁸F-FDG positron emission tomography (PET) are promising prognostic indicators for head and neck squamous cell carcinoma (SCC). However, the relationship between them remains unclear. This study aimed to investigate the relationship between ADCs and metabolic parameters in hypopharyngeal SCC (HSCC) using integrated PET/MRI.

MATERIALS AND METHODS

Twenty-seven patients with biopsy-proven HSCC underwent integrated ¹⁸F-FDG neck PET/MRI. ADCs of HSCC, including the mean and minimum ADC values (ADC_mean and ADC_min), were measured manually on ADC maps. Metabolic parameters of HSCC, including maximum and mean standardized uptake values (SUV_max and SUV_mean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), were calculated automatically on PET images. Spearman correlation coefficients were used to assess the relationships between ADCs and metabolic parameters in HSCC tumors as well as in tumor groups with different histological grading, clinical staging, and anatomical subsites. P values < 0.05 were considered statistically significant.

RESULTS

No significant correlation was observed between ADCs and ¹⁸F-FDG PET metabolic parameters in the entire cohort, except for a significant inverse correlation between ADC_mean and MTV (r = -0.556, P = 0.003). Furthermore, a significant inverse correlation was observed between ADC_mean and MTV of HSCC in the moderately to well differentiated group (r_ADCmean/MTV = -0.692, P = 0.006), stage III group (r_ADCmean/MTV = -0.758, P = 0.003), and pyriform sinus group (r_ADCmean/MTV = -0.665, P = 0.007), whereas no significant correlation was observed in the poorly differentiated group, stage IV group, or non-pyriform sinus group.

CONCLUSIONS

Inverse correlation between ADC_mean and MTV in the HSCC population was observed and the correlativity depended on histological grading, clinical staging, and anatomical subsites of HSCC.

Rationalization of Using the MR Diffusion Imaging in B12 Deficiency

Context:

The structural imaging of brain does not demonstrate any changes in the vast majority of patients with vitamin B12 deficiency, even in the advanced stages.

Aims:

We investigated the microstructural changes in the brain with diffusion imaging among patients with biochemical evidence of B12 deficiency.

Patients and Methods:

We retrospectively analyzed all diffusion-weighted MRI images between the periods 2014–2016 who had biochemical evidence of B12. The age-sex matched controls were chosen from the group with normal B12 levels. Patients with pathological findings in conventional MRI images were excluded from the study.

Results:

About 37 patients were recruited (22 women, 15 men; mean age, 34.1 ± 9.9 years; age range). They were about thirty-four age-and sex-matched controls (with normal B12 levels), which were also included in the study. The mean apparent diffusion coefficient (ADC) value of amygdala (773.8 ± 49.9 vs. 742.2 ± 24.2, P = 0.01), hypothalamus (721.3 ± 39.2 vs. 700.2 ± 38.2, P = 0.02), striate cortex (737.6 ± 77.6 vs. 704.3 ± 58.2, P = 0.04), suprafrontal gyrus (740.7 ± 46.9 vs. 711.6 ± 40.7, P = 0.007) and medulla oblongata-olivary nucleus (787.3 ± 56.4 vs. 759.7 ± 46.2, P = 0.02) were significantly higher in B12 deficiency group compared to controls, whereas ADC values were similar at hippocampus, thalamus, insula, corpus striatum, cingulate gyrus, occipital gyrus, dentate nucleus, cerebral pedicle, tegmentum, pons, and posterior medulla oblongata.

Conclusions:

Our study indicates that a significant increase in ADC values occurs in multiple brain regions in patients with vitamin B12.

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.

Whole-lesion ADC histogram analysis is not able to reflect microvessel density in HNSCC

Diffusion-weighted imaging (DWI) is a functional imaging technique sensitive to microstructure in tissues. It is widely acknowledged to reflect cellularity in tumors. A small part of DWI is also sensitive to perfusion-related information and might therefore be also be able to reflect microvessel density in tumor tissues. Aim of the present study was to elucidate possible correlations between microvessel density and apparent diffusion coefficient (ADC) values in head and neck squamous cell carcinoma (HNSCC).Thirty-four patients with histologically proven primary HNSCC were included in the study. DWI was performed with a 3 T magnetic resonance imaging (MRI) (b-values 0 and 800 s/mm) and histogram analysis was calculated with a whole lesion measurement. In every case, microvessel density was estimated with CD105-stained specimens.There were no statistically significant correlations between ADC histogram parameters and microvessel density. The calculated correlation coefficients ranged from r = -0.27, P = .13 for entropy and vessel area to r = 0.16, P = .40 for ADCmin and vessel count.Whole-lesion histogram analysis of ADC values cannot reflect microvessel density in HNSCC.