DWI-associated entire-tumor histogram analysis for the differentiation of low-grade prostate cancer from intermediate-high-grade prostate cancer

Histogram-based analysis of diffusion-weighted imaging for predicting aggressiveness in papillary thyroid carcinoma

BACKGROUND

To assess the potential of apparent diffusion coefficient (ADC) map in predicting aggressiveness of papillary thyroid carcinoma (PTC) based on whole-tumor histogram-based analysis.

METHODS

A total of 88 patients with PTC confirmed by pathology, who underwent neck magnetic resonance imaging, were enrolled in this retrospective study. Whole-lesion histogram features were extracted from ADC maps and compared between the aggressive and non-aggressive groups. Multivariable logistic regression analysis was performed for identifying independent predictive factors. Receiver operating characteristic curve analysis was used to evaluate the performances of significant factors, and an optimal predictive model for aggressiveness of PTC was developed.

RESULTS

The aggressive and non-aggressive groups comprised 67 (mean age, 44.03 ± 13.99 years) and 21 (mean age, 43.86 ± 12.16 years) patients, respectively. Five histogram features were included into the final predictive model. ADC_firstorder_TotalEnergy had the best performance (area under the curve [AUC] = 0.77). The final combined model showed an optimal performance, with AUC and accuracy of 0.88 and 0.75, respectively.

CONCLUSIONS

Whole-lesion histogram analysis based on ADC maps could be utilized for evaluating aggressiveness in PTC.

Impact of measurement method on interobserver variability of apparent diffusion coefficient of lesions in prostate MRI

Purpose

To compare the inter-observer variability of apparent diffusion coefficient (ADC) values of prostate lesions measured by 2D-region of interest (ROI) with and without specific measurement instruction.

Methods

Forty lesions in 40 patients who underwent prostate MR followed by targeted prostate biopsy were evaluated. A multi-reader study (10 readers) was performed to assess the agreement of ADC values between 2D-ROI without specific instruction and 2D-ROI with specific instruction to place a 9-pixel size 2D-ROI covering the lowest ADC area. The computer script generated multiple overlapping 9-pixel 2D-ROIs within a 3D-ROI encompassing the entire lesion placed by a single reader. The lowest mean ADC values from each 2D-small-ROI were used as reference values. Inter-observer agreement was assessed using the Bland-Altman plot. Intraclass correlation coefficient (ICC) was assessed between ADC values measured by 10 readers and the computer-calculated reference values.

Results

Ten lesions were benign, 6 were Gleason score 6 prostate carcinoma (PCa), and 24 were clinically significant PCa. The mean±SD ADC reference value by 9-pixel-ROI was 733 ± 186 (10⁻⁶ mm²/s). The 95% limits of agreement of ADC values among readers were better with specific instruction (±112) than those without (±205). ICC between reader-measured ADC values and computer-calculated reference values ranged from 0.736–0.949 with specific instruction and 0.349–0.919 without specific instruction.

Conclusion

Interobserver agreement of ADC values can be improved by indicating a measurement method (use of a specific ROI size covering the lowest ADC area).

Textural differences based on apparent diffusion coefficient maps for discriminating pT3 subclasses of rectal adenocarcinoma

BACKGROUND

The accuracy of discriminating pT3a from pT3b-c rectal cancer using high-resolution magnetic resonance imaging (MRI) remains unsatisfactory, although texture analysis (TA) could improve such discrimination.

AIM

To investigate the value of TA on apparent diffusion coefficient (ADC) maps in differentiating pT3a rectal adenocarcinomas from pT3b-c tumors.

METHODS

This was a case-control study of 59 patients with pT3 rectal adenocarcinoma, who underwent diffusion-weighted imaging (DWI) between October 2016 and December 2018. The inclusion criteria were: (1) Proven pT3 rectal adenocarcinoma; (2) Primary MRI including high-resolution T2-weighted image (T2WI) and DWI; and (3) Availability of pathological reports for surgical specimens. The exclusion criteria were: (1) Poor image quality; (2) Preoperative chemoradiation therapy; and (3) A different pathological type. First-order (ADC values, skewness, kurtosis, and uniformity) and second-order (energy, entropy, inertia, and correlation) texture features were derived from whole-lesion ADC maps. Receiver operating characteristic curves were used to determine the diagnostic value for pT3b-c tumors.

RESULTS

The final study population consisted of 59 patients (34 men and 25 women), with a median age of 66 years (range, 41-85 years). Thirty patients had pT3a, 24 had pT3b, and five had pT3c. Among the ADC first-order textural differences between pT3a and pT3b-c rectal adenocarcinomas, only skewness was significantly lower in the pT3a tumors than in pT3b-c tumors. Among the ADC second-order textural differences, energy and entropy were significantly different between pT3a and pT3b-c rectal adenocarcinomas. For differentiating pT3a rectal adenocarcinomas from pT3b-c tumors, the areas under the curves (AUCs) of skewness, energy, and entropy were 0.686, 0.657, and 0.747, respectively. Logistic regression analysis of all three features yielded a greater AUC (0.775) in differentiating pT3a rectal adenocarcinomas from pT3b-c tumors (69.0% sensitivity and 83.3% specificity).

CONCLUSION

TA features derived from ADC maps might potentially differentiate pT3a rectal adenocarcinomas from pT3b-c tumors.

Thyroid-Associated Orbitopathy: Evaluating Microstructural Changes of Extraocular Muscles and Optic Nerves Using Readout-Segmented Echo-Planar Imaging-Based Diffusion Tensor Imaging

Objective

We aimed to investigate the ability of readout-segmented echo-planar imaging (rs-EPI)-based diffusion tensor imaging (DTI) in assessing the microstructural change of extraocular muscles (EOMs) and optic nerves in patients with thyroid-associated orbitopathy (TAO) as well as in evaluating disease activity.

Materials and Methods

We enrolled 35 TAO patients and 22 healthy controls (HCs) who underwent pre-treatment rs-EPI-based DTI. Mean, axial, and radial diffusivity (MD, AD, and RD) and fractional anisotropy (FA) of the medial and lateral EOMs and optic nerve for each orbit were calculated and compared between TAO and HC groups and between active and inactive TAO groups. Factors such as age, sex, disease duration, mediation, and smoking history between groups were also compared. Logistic regression analysis was used to evaluate the predictive value of significant variables for disease activity.

Results

Disease duration was significantly shorter in active TAOs than in inactive ones (p < 0.001). TAO patients showed significantly lower FA and higher MD, AD, and RD than HCs for both medial and lateral EOMs (p < 0.001), but not the AD value of lateral EOMs (p = 0.619). Active patients had significantly higher FA, MD, and AD than inactive patients for medial EOMs (p < 0.005), whereas only FA differed significantly in the lateral EOMs (p = 0.018). The MD, AD, and RD of optic nerves were significantly lower in TAO patients than HCs (p < 0.05), except for FA (p = 0.129). Multivariate analysis showed that the MD of medial EOMs and disease duration were significant predictors for disease activity. The combination of these two parameters showed optimal diagnostic efficiency for disease activity (area under the curve, 0.855; sensitivity, 68.4%; specificity, 96.9%).

Conclusion

rs-EPI-based DTI is promising in assessing microstructural changes of EOMs and optic nerves and can help to indicate the disease activity of TAO, especially through the MD of medial EOMs.

Histogram analysis in prostate cancer: a comparison of diffusion kurtosis imaging model versus monoexponential model

BACKGROUND

There is still little research about histogram analysis of diffusion kurtosis imaging (DKI) using in prostate cancer at present.

PURPOSE

To verify the utility of histogram analysis of DKI model in detection and assessment of aggressiveness of prostate cancer, compared with monoexponential model (MEM).

MATERIAL AND METHODS

Twenty-three patients were enrolled in this study. For DKI model and MEM, the D_app, K_app, and apparent diffusion coefficient (ADC) were obtained by using single-shot echo-planar imaging sequence. The pathologies were confirmed by in-bore magnetic resonance (MR)-guided biopsy. Regions of interest (ROI) were drawn manually in the position where biopsy needle was put. The mean values and histogram parameters in cancer and noncancerous foci were compared using independent-samples T test. Receiver operating characteristic curves were used to investigate the diagnostic efficiency. Spearman's test was used to evaluate the correlation of parameters and Gleason scores.

RESULTS

The mean, 10th, 25th, 50th, 75th, and 90th percentiles of ADC and D_app were significantly lower in prostate cancer than non-cancerous foci (P < 0.001). The mean, 50th, 75th, and 90th percentiles of K_app were significantly higher in prostate cancer (P < 0.05). There was no significant difference between the AUCs of two models (0.971 vs. 0.963, P > 0.05). With the increasing Gleason scores, the 10th ADC decreased (ρ = -0.583, P = 0.018), but the 90th K_app increased (ρ = 0.642, P = 0.007).

CONCLUSION

Histogram analysis of DKI model is feasible for diagnosing and grading prostate cancer, but it has no significant advantage over MEM.

Clear Cell Renal Cell Carcinoma Growth Correlates with Baseline Diffusion-weighted MRI in Von Hippel-Lindau Disease

Background Identification of markers to aid in understanding the growth kinetics of Von Hippel-Lindau (VHL)-associated clear cell renal cell carcinoma (ccRCC) has the potential to allow individualization of patient care, thereby helping prevent unnecessary screening and optimizing intervention. Purpose To determine whether the degree of restricted diffusion at baseline MRI holds predictive potential for the growth rate of VHL-associated ccRCC. Materials and Methods Patients with VHL disease who underwent surgical resection of tumors between November 2014 and October 2017 were analyzed retrospectively in this HIPAA-compliant study. The change in ccRCC volume between two time points and apparent diffusion coefficient (ADC) at baseline was calculated by using segmentations by two readers at nephrographic-phase CT and diffusion-weighted MRI, respectively. Intraclass correlation coefficient was used to assess agreement between readers. Repeated-measures correlation was used to investigate relationships between ADC (histogram parameters) and tumor size at baseline with growth rate and volume doubling time (VDT). Predictive performance of the ADC parameter with highest correlation and tumor size at baseline was reviewed to differentiate tumors based on their VDT (≤1 year or >1 year). Results Forty-six patients (mean age, 46 years ± 7 [standard deviation]; 25 women) with 100 ccRCCs were evaluated. Interreader agreement resulted in mean κ scores of 0.89, 0.82, and 0.93 for mean ADC, baseline tumor volume, and follow-up tumor volume, respectively. ADC percentiles correlated negatively with tumor growth rate but correlated positively with VDT. Lower ADC values demonstrated stronger correlations. The 25th percentile ADC had the strongest correlation with growth rate (ρ = -0.52, P < .001) and VDT (ρ = 0.60, P < .001) and enabled prediction of VDT (≤1 year or >1 year) with an area under the receiver operating characteristic curve of 0.86 (sensitivity, 67%; specificity, 89%) (P < .001). Conclusion Apparent diffusion coefficient at baseline was negatively correlated with tumor growth rate. Diffusion-weighted MRI may be useful to identify clear cell renal cell carcinomas with higher growth rates. © RSNA, 2020See also the editorial by Goh and Prezzi in this issue.

Application of texture analysis based on T2-weighted magnetic resonance images in discriminating Gleason scores of prostate cancer

OBJECTIVE

To investigate the value of texture analysis in magnetic resonance images for the evaluation of Gleason scores (GS) of prostate cancer.

METHODS

Sixty-six prostate cancer patients are retrospective enrolled, which are divided into five groups namely, GS = 6, 3 + 4, 4 + 3, 8 and 9-10 according to postoperative pathological results. Extraction and analysis of texture features in T2-weighted MR imaging defined tumor region based on pathological specimen after operation are performed by texture software OmniKinetics. The values of texture are analyzed by single factor analysis of variance (ANOVA), and Spearman correlation analysis is used to study the correlation between the value of texture and Gleason classification. Receiver operating characteristic (ROC) curve is then used to assess the ability of applying texture parameters to predict Gleason score of prostate cancer.

RESULTS

Entropy value increases and energy value decreases as the elevation of Gleason score, both with statistical difference among five groups (F = 10.826, F = 2.796, P < 0.05). Energy value of group GS = 6 is significantly higher than that of groups GS = 8 and 9-10 (P < 0.005), which is similar between three groups (GS = 3 + 4, 8 and 9-10). The entropy and energy values correlate with GS (r = 0.767, r = -0.692, P < 0.05). Areas under ROC curves (AUC) of combination of entropy and energy are greater than that of using energy alone between groups GS = 6 and ≥7. Analogously, AUC of combination of entropy and energy are significantly higher than that of using entropy alone between groups GS≤3 + 4 and ≥4 + 3, as well as between groups GS≤4 + 3 and ≥8.

CONCLUSION

Texture analysis on T2-weighted images of prostate cancer can evaluate Gleason score, especially using the combination of entropy and energy rendering better diagnostic efficiency.

Discrimination Between Solitary Brain Metastasis and Glioblastoma Multiforme by Using ADC-Based Texture Analysis: A Comparison of Two Different ROI Placements

RATIONALE AND OBJECTIVES

To explore the value of texture analysis based on the apparent diffusion coefficient (ADC) value and the effect of region of interest (ROI) placements in distinguishing glioblastoma multiforme (GBM) from solitary brain metastasis (sMET).

MATERIALS AND METHODS

Sixty-two patients with pathologically confirmed GBM (n = 36) and sMET (n = 26) were retrospectively included. All patients underwent diffusion-weighted imaging with b values of 0 and 1000 s/mm², and the ADC maps were generated automatically. ROIs were placed on the largest whole single-slice tumor (ROI1) and the enhanced solid portion (ROI2) of the ADC maps, respectively. The texture feature metrics of the histogram and gray-level co-occurrence matrix were then extracted by using in-house software. The parameters of the texture analysis were compared between GBM and sMET, using the Mann-Whitney U test. A receiver operating characteristic (ROC) curve analysis was performed to determine the best parameters for distinguishing between GBM from sMET.

RESULTS

Homogeneity and the inverse difference moment (IDM) of GBM were significantly higher than those of sMET in both ROIs (ROI1, p = 0.014 for homogeneity and p = 0.048 for IDM; ROI2, p< 0.001 for homogeneity and p = 0.029 for IDM). According to the ROC curve analysis, the area under the ROC curve (AUC) of homogeneity in ROI1 (AUC, 0.682, sensitivity, 72.2%, specificity, 61.5%) was significantly lower than that of ROI2 (AUC, 0.886, sensitivity, 83.3%, specificity, 76.9%; p= 0.012), whereas the IDM showed no statistical significance between two ROIs (p> 0.05).

CONCLUSION

The ADC-based texture analysis can help differentiate GBM from sMET, and the ROI on the solid portion would be recommended to calculate the ADC-based texture metrics.

Histogram analysis of DCE-MRI for chemoradiotherapy response evaluation in locally advanced esophageal squamous cell carcinoma

AIMS

The aim of the study was to predict and assess treatment response by histogram analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to patients with locally advanced esophageal squamous cell carcinoma receiving chemoradiotherapy (CRT).

MATERIALS AND METHODS

Seventy-two patients with locally advanced esophageal squamous cell carcinoma who underwent DCE-MRI before and after chemoradiotherapy were enrolled and divided into the complete response (CR) group and the non-CR group based on RECIST. The histogram parameters (10th percentile, 90th percentile, median, mean, standard deviation, skewness, and kurtosis) of pre-CRT and post-CRT were compared using a paired Student's t test in the CR and non-CR groups, respectively. The histogram parameter differences between the CR and the non-CR groups were compared using an unpaired Student's t test. A receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic performance.

RESULTS

The histogram parameters of K^trans values were observed to have significantly decreased after chemoradiotherapy in the CR group. The CR responders showed significantly higher median, mean, and 10th and 90th percentile of pre-K^trans values than those of the non-CR group. The histogram analysis indicated the decreased heterogeneity in the CR group after CRT. Esophageal cancer with higher pre-K^trans and lower post-K^trans values indicated a good treatment response to CRT. Pre-K^trans-10th showed the best diagnostic performance in predicting the chemoradiotherapy response.

CONCLUSIONS

The histogram parameters of K^trans are useful in the assessment and prediction of the chemoradiotherapy response in patients with advanced esophageal squamous cell carcinoma. DCE-MRI could serve as an adjunctive imaging technique for treatment planning.

A machine learning-assisted decision-support model to better identify patients with prostate cancer requiring an extended pelvic lymph node dissection

OBJECTIVES

To develop a machine learning (ML)-assisted model to identify candidates for extended pelvic lymph node dissection (ePLND) in prostate cancer by integrating clinical, biopsy, and precisely defined magnetic resonance imaging (MRI) findings.

PATIENTS AND METHODS

In all, 248 patients treated with radical prostatectomy and ePLND or PLND were included. ML-assisted models were developed from 18 integrated features using logistic regression (LR), support vector machine (SVM), and random forests (RFs). The models were compared to the Memorial SloanKettering Cancer Center (MSKCC) nomogram using receiver operating characteristic-derived area under the curve (AUC) calibration plots and decision curve analysis (DCA).

RESULTS

A total of 59/248 (23.8%) lymph node invasions (LNIs) were identified at surgery. The predictive accuracy of the ML-based models, with (+) or without (-) MRI-reported LNI, yielded similar AUCs (RFs⁺ /RFs^- : 0.906/0.885; SVM⁺ /SVM^- : 0.891/0.868; LR⁺ /LR^- : 0.886/0.882) and were higher than the MSKCC nomogram (0.816; P < 0.001). The calibration of the MSKCC nomogram tended to underestimate LNI risk across the entire range of predicted probabilities compared to the ML-assisted models. The DCA showed that the ML-assisted models significantly improved risk prediction at a risk threshold of ≤80% compared to the MSKCC nomogram. If ePLNDs missed was controlled at <3%, both RFs⁺ and RFs^- resulted in a higher positive predictive value (51.4%/49.6% vs 40.3%), similar negative predictive value (97.2%/97.8% vs 97.2%), and higher number of ePLNDs spared (56.9%/54.4% vs 43.9%) compared to the MSKCC nomogram.

CONCLUSIONS

Our ML-based model, with a 5-15% cutoff, is superior to the MSKCC nomogram, sparing ≥50% of ePLNDs with a risk of missing <3% of LNIs.

Differentiating Transition Zone Cancers From Benign Prostatic Hyperplasia by Histogram Analysis of Apparent Diffusion Coefficient Maps With Standard and Ultrahigh b-value Diffusion-weighted MR Imaging

OBJECTIVE

To compare the diagnostic performance of standard and ultrahigh b-value Diffusion-weighted Imaging (DWI) using volumetric histogram analysis in differentiating transition zone (TZ) cancer from benign prostatic hyperplasia (BPH).

METHODS

57 TZ cancer and 61 BPH patients received standard (1000 s/mm) and ultrahigh b-value (2000 s/mm) DWI. The diagnostic ability of ADC histogram parameters derived from two DWI for differentiating TZ cancer from BPH was determined by receiver operating characteristic curve.

RESULTS

Median, minimum, the 10th, 25th percentile ADC in both ADC1000 and ADC2000 and skewness in ADC2000 had significant differences between TZ cancer and BPH (for all, P < 0.05).The 10th percentile ADC showed highest area under the ROC curve (AUC) in both ADC1000 and ADC2000.The 10th percentile ADC of ADC2000 showed significantly higher AUC than did ADC1000 (P = 0.0385).

CONCLUSIONS

The 10th percentile ADC obtained from ultrahigh b-value DWI performed better for differentiating TZ cancer from BPH.

Prostate MRI using an external phased array wearable pelvic coil at 3T: comparison with an endorectal coil

PURPOSE

To evaluate T2w and DWI image quality using a wearable pelvic coil (WPC) compared with an endorectal coil (ERC).

METHODS

Twenty men consecutively presenting to our prostate cancer MRI clinic were prospectively consented to be scanned using a wearable pelvic coil then an endorectal coil and pelvic phased array coil at 3T. Eighteen patients were suitable for inclusion. Axial T2w images were obtained using the WPC and ERC, and DWI images were obtained using the WPC, ERC, and PPA. Analysis was performed in consensus by two readers with experience in prostate MRI. The readers scored the T2w images using six qualitative criteria and the DWI images using five criteria. Signal-to-noise ratio (SNR) was also measured.

RESULTS

T2w artifact severity was greater for an ERC than a WPC (p = 0.003). There was no significant difference in T2w qualititatve image quality by other measures. The distinction of zonal anatomy on DWI was superior for an ERC compared with both a WPC and a PPA (p = 0.018 and p < 0.001 respectively), and there was no significant difference in DWI image quality by other measures. SNR was significantly higher for ERC imaging for both T2w and DWI.

CONCLUSION

WPC imaging provides comparable image quality to that of an ERC, potentially reducing the need for an ERC. WPC imaging shows reduced T2w artifact severity and inferior DWI zonal anatomy distinction compared with an ERC. Imaging with a WPC produces a lower SNR than an ERC.

Histogram analysis of diffusion kurtosis imaging of nasopharyngeal carcinoma: Correlation between quantitative parameters and clinical stage

Purpose

To evaluate the correlation between histogram parameters derived from diffusion-kurtosis (DK) imaging and the clinical stage of nasopharyngeal carcinoma (NPC).

Results

High T-stage (T3/4) NPC showed significantly higher K_app-mean (P = 0.018), K_app-median (P = 0.029) and K_app-90th (P = 0.003) than low T-stage (T1/2) NPC. High N-stage NPC (N2/3) showed significantly lower Dapp-mean (P = 0.002), Dapp-median (P = 0.002) and Dapp-10th (P < 0.001) than low N-stage NPC (N0/1). High AJCC-stage NPC (III/IV) showed significantly lower D_app-10th (P = 0.038) than low AJCC-stage NPC (I/II). ROC analyses indicated that Kapp-90th was optimal for predicting high T-stage (AUC, 0.759; sensitivity, 0.842; specificity, 0.607), while Dapp-10th was best for predicting high N- and AJCC-stage (N-stage, AUC, 0.841; sensitivity, 0.875; specificity, 0.807; AJCC-stage, AUC, 0.671; sensitivity, 0.800; specificity, 0.588).

Materials and Methods

DK imaging data of forty-seven consecutive NPC patients were retrospectively analyzed. Apparent diffusion for Gaussian distribution (D_app) and apparent kurtosis coefficient (K_app) were generated using diffusion-kurtosis model. Histogram parameters, including mean, median, 10th, 90th percentiles, skewness and kurtosis of D_app and K_app were calculated. Patients were divided into low and high T, N and clinical stage based on American Joint Committee on Cancer (AJCC) staging system. Differences of histogram parameters between low and high T, N and AJCC stages were compared using t test. Multiple receiver operating characteristic (ROC) curves were used to determine and compare the value of significant parameters in predicting high T, N and AJCC stage, respectively.

Conclusions

DK imaging-derived parameters correlated well with clinical stage of NPC, therefore could serve as an adjunctive imaging technique for evaluating NPC.

New prostate cancer prognostic grade group (PGG): Can multiparametric MRI (mpMRI) accurately separate patients with low-, intermediate-, and high-grade cancer?

PURPOSE

Our objective is to determine the accuracy of multiparametric MRI (mpMRI) in predicting pathologic grade of prostate cancer (PCa) after radical prostatectomy (RP) using simple apparent diffusion coefficient metrics and, specifically, whether mpMRI can accurately separate disease into one of two risk categories (low vs. higher grade) or one of three risk categories (low, intermediate, or high grade) corresponding to the new prognostic grade group (PGG) criteria.

METHODS

This retrospective, HIPAA-compliant, IRB-approved study included 140 patients with PCa who underwent 3 T mpMRI with endorectal coil and transrectal ultrasound-guided (TRUS-G) biopsy before RP. MpMRI was used to classify lesions using a two-tier (low-grade/PGG 1 vs. high-grade/PGG 2-5) or a three-tier system (low-grade/PGG 1 vs. intermediate-grade/PGG 2 vs. high-grade/PGG 3-5). Accuracy of mpMRI was compared against RP for each system.

RESULTS

The predictive accuracy of mpMRI using the two-tier system is higher than when using three-tier system (0.77 and 0.45, respectively). There were similar rates of undergrading between mpMRI and TRUS-G biopsy compared to RP (16% & 21%; respectively); rate of overgrading was higher for mpMRI vs. TRUS-G biopsy compared to RP (42% & 17%, respectively). When mpMRI and TRUS-G biopsy are combined, rate of undergrading is 1.4% and overgrading is 11%.

CONCLUSIONS

MpMRI predictive accuracy is higher when using a two-tier vs. a three-tier system, suggesting that advanced metrics may be necessary to delineate intermediate- from high-grade disease. Rates of under- and overgrading decreased when mpMRI and TRUS-G biopsy are combined, suggesting that these techniques may be complementary in predicting tumor grade.

Histogram analysis of apparent diffusion coefficient maps for assessing thymic epithelial tumours: correlation with world health organization classification and clinical staging

OBJECTIVE

To investigate the value of apparent diffusion coefficients (ADCs) histogram analysis for assessing World Health Organization (WHO) pathological classification and Masaoka clinical stages of thymic epithelial tumours.

METHODS

37 patients with histologically confirmed thymic epithelial tumours were enrolled. ADC measurements were performed using hot-spot ROI (ADC_HS-ROI) and histogram-based approach. ADC histogram parameters included mean ADC (ADC_mean), median ADC (ADC_median), 10 and 90 percentile of ADC (ADC₁₀ and ADC₉₀), kurtosis and skewness. One-way ANOVA, independent-sample t-test, and receiver operating characteristic were used for statistical analyses.

RESULTS

There were significant differences in ADC_mean, ADC_median, ADC₁₀, ADC₉₀ and ADC_HS-ROI among low-risk thymoma (type A, AB, B1; n = 14), high-risk thymoma (type B2, B3; n = 9) and thymic carcinoma (type C, n = 14) groups (all p-values <0.05), while no significant difference in skewness (p = 0.181) and kurtosis (p = 0.088). ADC₁₀ showed best differentiating ability (cut-off value, ≤0.689 × 10^-3 mm² s^-1; AUC, 0.957; sensitivity, 95.65%; specificity, 92.86%) for discriminating low-risk thymoma from high-risk thymoma and thymic carcinoma. Advanced Masaoka stages (Stage III and IV; n = 24) tumours showed significant lower ADC parameters and higher kurtosis than early Masaoka stage (Stage I and II; n = 13) tumours (all p-values <0.05), while no significant difference on skewness (p = 0.063). ADC₁₀ showed best differentiating ability (cut-off value, ≤0.689 × 10^-3 mm² s^-1; AUC, 0.913; sensitivity, 91.30%; specificity, 85.71%) for discriminating advanced and early Masaoka stage epithelial tumours.

CONCLUSION

ADC histogram analysis may assist in assessing the WHO pathological classification and Masaoka clinical stages of thymic epithelial tumours. Advances in knowledge: 1. ADC histogram analysis could help to assess WHO pathological classification of thymic epithelial tumours. 2. ADC histogram analysis could help to evaluate Masaoka clinical stages of thymic epithelial tumours. 3. ADC₁₀ might be a promising imaging biomarker for assessing and characterizing thymic epithelial tumours.

Readout-segmented echo-planar diffusion-weighted imaging in the assessment of orbital tumors: comparison with conventional single-shot echo-planar imaging in image quality and diagnostic performance

Background Readout-segmented echo-planar imaging (RS-EPI) could improve the imaging quality of diffusion-weighted imaging (DWI) in various organs. However, whether it could improve the imaging quality and diagnostic performance for the patients with orbital tumors is still unknown. Purpose To compare the image quality and diagnostic performance of RS-EPI DWI with that of conventional single-shot EPI (SS-EPI) DWI in patients with orbital tumors. Material and Methods SS-EPI and RS-EPI DW images of 32 patients with pathologically diagnosed orbital tumors were retrospectively analyzed. Qualitative imaging parameters (imaging sharpness, geometric distortion, ghosting artifacts, and overall imaging quality) and quantitative imaging parameters (apparent diffusion coefficient [ADC], signal-to-noise ratio [SNR], contrast, and contrast-to-noise ratio [CNR]) were assessed by two independent radiologists, and compared between SS-EPI and RS-EPI DWI. Receiver operating characteristic curves were used to determine the diagnostic value of ADC in differentiating malignant from benign orbital tumors. Results RS-EPI DW imaging produced less geometric distortion and ghosting artifacts, and better imaging sharpness and overall imaging quality than SS-EPI DWI (for all, P < 0.001). Meanwhile, RS-EPI DWI produced significantly lower SNR ( P < 0.001) and ADC ( P < 0.001), and higher contrast ( P < 0.001) than SS-EPI DWI, while producing no difference in CNR ( P = 0.137). There was no significant difference on the diagnostic performance between SS-EPI and RS-EPI DWI, when using ADC as the differentiating index ( P = 0.529). Conclusion Compared with SS-EPI, RS-EPI DWI provided significantly better imaging quality and comparable diagnostic performance in differentiating malignant from benign orbital tumors.

Correlation of diffusion tensor imaging parameters and Gleason scores of prostate cancer

The aim of the present study was to explore the association between the parameters of diffusion tensor imaging (DTI), including fractional anisotropy (FA) values, apparent diffusion coefficient (ADC) values and the diffusion tensor tractography (DTT) map, with the Gleason score of prostate cancer (PCa). A retrospective study of 50 cases of PCa confirmed by biopsy or surgical pathology was performed. Conventional magnetic resonance imaging and DTI scans were conducted in these cases. The 50 cases of PCa were divided into three groups, including low, intermediate and high grade, according to the Gleason score. Post-DTI processing was performed using Neuro 3D software, in order to measure the FA and ADC values, and map the prostate fibers. Differences in FA and ADC values among the various PCa groups were examined using analysis of variance, while the correlation of FA and ADC values with the Gleason score was studied using Pearson correlation analysis. The obtained DTT map clearly demonstrated the spatial structure of the prostate fibers. The fibers of the cancer area were dense without interruption in the low-grade group, sparse and disordered in the intermediate-grade group, and were disordered, sparse or even absent in the high-grade group. The FA values were 0.284±0.313, 0.293±0.347 and 0.369±0.347, respectively, with statistically significant differences observed among the three groups (F=234.533; P<0.05) and between each group (P<0.05). In addition, the FA value of PCa was positively correlated with the Gleason score (r=0.884; P<0.05). The ADC values of the low-, intermediate- and high-grade groups were 1.070±0.072×10^-3, 0.961±0.081×10^-3 and 0.821±0.048×10^-3, respectively, which demonstrated statistically significant differences among the three groups (F=49.987; P<0.05) and between each group (P<0.05). Furthermore, the ADC values of PCa were negatively correlated with Gleason score (r=-0.810; P<0.05). In conclusion, there was an association between DTI parameters and Gleason score, which may be used to evaluate the grading and prognosis of PCa.

Whole-lesion apparent diffusion coefficient histogram analysis: significance in T and N staging of gastric cancers

BACKGROUND

Whole-lesion apparent diffusion coefficient (ADC) histogram analysis has been introduced and proved effective in assessment of multiple tumors. However, the application of whole-volume ADC histogram analysis in gastrointestinal tumors has just started and never been reported in T and N staging of gastric cancers.

METHODS

Eighty patients with pathologically confirmed gastric carcinomas underwent diffusion weighted (DW) magnetic resonance imaging before surgery prospectively. Whole-lesion ADC histogram analysis was performed by two radiologists independently. The differences of ADC histogram parameters among different T and N stages were compared with independent-samples Kruskal-Wallis test. Receiver operating characteristic (ROC) analysis was performed to evaluate the performance of ADC histogram parameters in differentiating particular T or N stages of gastric cancers.

RESULTS

There were significant differences of all the ADC histogram parameters for gastric cancers at different T (except ADC_min and ADC_max) and N (except ADC_max) stages. Most ADC histogram parameters differed significantly between T1 vs T3, T1 vs T4, T2 vs T4, N0 vs N1, N0 vs N3, and some parameters (ADC_5%, ADC_10%, ADC_min) differed significantly between N0 vs N2, N2 vs N3 (all P < 0.05). Most parameters except ADC_max performed well in differentiating different T and N stages of gastric cancers. Especially for identifying patients with and without lymph node metastasis, the ADC_10% yielded the largest area under the ROC curve of 0.794 (95% confidence interval, 0.677-0.911). All the parameters except ADC_max showed excellent inter-observer agreement with intra-class correlation coefficients higher than 0.800.

CONCLUSION

Whole-volume ADC histogram parameters held great potential in differentiating different T and N stages of gastric cancers preoperatively.

Diffusion Kurtosis Imaging Helps to Predict Upgrading in Biopsy-Proven Prostate Cancer With a Gleason Score of 6

OBJECTIVE

The purpose of this study was to investigate whether diffusion kurtosis imaging (DKI) is useful for predicting upgrades in Gleason score (GS) in biopsy-proven prostate cancer with a GS of 6.

MATERIALS AND METHODS

A total of 46 patients with biopsy-proven GS 6 prostate cancer, 3-T DWI results, and surgical pathologic results were retrospectively included in the study. DWI data were postprocessed with monoexponential and DK models to quantify the apparent diffusion coefficient (ADC), apparent diffusion for gaussian distribution (D_app), and apparent kurtosis coefficient (K_app). The volume of the lesions, prostate-specific antigen (PSA) level, and diffusion variables (ADC_min, D_appmin, K_appmax, ADC_mean, D_appmean, and K_appmean) were evaluated. PSA and DKI were combined as a parameter in a logistic regression model. The utility of these parameters in predicting an upgrade in GS was analyzed with ROC regression.

RESULTS

The rate of GS upgrade was 50.0% (23/46). The GS upgrade group had significantly lower ADC_min (p = 0.007), ADC mean (p = 0.003), D appmin (p < 0.001), and D_appmean (p = 0.001) values and significantly higher K_appmax (p = 0.003), K_appmean (p = 0.005), and PSA (p = 0.004) values than the group that did not have an upgrade. Among single parameters, K_appmax had the highest ROC AUC value (0.819, p < 0.05), and among all the parameters and models, PSA-K_appmax had the highest AUC (0.868, p < 0.05) and Youden index (0.6522).

CONCLUSION

The results showed that DKI may help in prediction of GS upgrade in biopsy-proven GS 6 prostate cancer. The comprehensive consideration of DKI and PSA may be a promising approach to predicting GS upgrade.

Benign and malignant orbital lymphoproliferative disorders: Differentiating using multiparametric MRI at 3.0T

PURPOSE

To determine the optimal combination of parameters derived from 3T multiparametric (conventional magnetic resonance imaging [MRI], diffusion-weighted [DW] and dynamic contrast-enhanced [DCE]) MRI for differentiating malignant from benign orbital lymphoproliferative disorders (OLPDs).

MATERIALS AND METHODS

Forty patients with OLPDs (18 benign and 22 malignant) underwent conventional 3.0T MR, DW, and DCE-MRI examination for presurgery evaluation. Conventional MRI features (including tumor laterality, shape, number of involved quadrants, signal intensity on T₁ -weighted imaging (WI) and T₂ WI, flow void sign on T₂ WI, and findings suggestive of sinusitis) were reviewed, and multivariate logistic regression analysis was used to identify the most significant conventional MRI features. Apparent diffusion coefficient (ADC) and DCE-MRI derived parameters (area under curve [AUC], time to peak [TTP], maximum rise slope [Slope_max ]) were measured and compared between two groups. Receiver operating characteristic (ROC) curve analyses were used to determine the diagnostic ability of each combination that was established based on identified qualitative and quantitative parameters.

RESULTS

Multivariate logistic regression analysis showed that the presence of flow void sign on T₂ WI significantly associated with benign OLPDs (P = 0.034). Malignant OLPDs demonstrated significantly lower ADC (P = 0.001) and AUC (P = 0.002) than benign mimics. ROC analyses indicted that, ADC alone showed the optimal sensitivity (threshold value, 0.886 × 10^-3 mm² /s; sensitivity, 90.9%), while a combination of no presence of flow void sign on T₂ WI + ADC ≤ 0.886 × 10^-3 mm² /s + AUC ≤ 7.366 showed optimal specificity (88.9%) in differentiating benign from malignant OLPDs.

CONCLUSION

Multiparametric MRI can help to differentiate malignant from benign OLPDs. DWI offers optimal sensitivity, while the combination of conventional MRI, DWI, and DCE-MRI offers optimal specificity.

LEVEL OF EVIDENCE

3 J. Magn. Reson. Imaging 2017;45:167-176.

Effects of regions of interest methods on apparent coefficient measurement of the parotid gland in early Sjögren's syndrome at 3T MRI

BACKGROUND

The apparent diffusion coefficient (ADC) has been used to assess parotid gland abnormalities in Sjögren's syndrome (SS) patients; however, few data exist on the influence of region of interest (ROI) methods on ADC measurements.

PURPOSE

To assess the influence of ROI methods on ADC measurement, and their diagnostic ability in detecting parotid gland abnormalities in early SS patients.

MATERIAL AND METHODS

Thirteen early SS patients underwent parotid gland diffusion-weighted imaging scans at a 3.0 T MR unit. Two readers independently measured the parotid gland ADC value using three different ROIs (whole-gland [WG], single-slice [SS], and reader-based circular [RBC]). The ADC value based on three different ROIs (ADC-ROI_WG, ADC-ROI_SS, ADC-ROI_RBC) were compared between the SS group and a matched healthy control (HC) group (n = 19). Receiver operating characteristic (ROC) curves and intra-class correlation coefficients (ICC) were used to determine the diagnostic ability and reproducibility of the parameters.

RESULTS

The ADC-ROI_WG, ADC-ROI_SS, and ADC-ROI_RBC in the SS group were all significantly higher than those in HC group (all P < 0.05). The ADC-ROI_WG showed better diagnostic ability than did ADC-ROI_RBC (P = 0.0200), while no significant difference was found between ADC-ROI_WG and ADC-ROI_SS (P = 0.4636). The ROI_WG method showed the best inter- and intra-reader agreement (ICC, 0.902 and 0.928, respectively), followed by ROI_SS and ROI_RBC.

CONCLUSION

The ROI methods can influence the parotid gland ADC measurements and their diagnostic ability. Considering our results, we suggest using in clinical practice single-slice ROIs to measure the ADC of the parotid gland.

Comparison of image quality and patient discomfort in prostate MRI: pelvic phased array coil vs. endorectal coil

PURPOSE

To compare image quality (IQ) and patient discomfort during prostate MRI using a pelvic phased array (PPA) coil and an endorectal (ER) coil.

MATERIALS AND METHODS

Ninety-eight patients (median age, 65.7; range 42.1-78.1) underwent prostate MRI on a 3T scanner including T2w and DWI acquired with PPA and an ER coil within the same exam. Acquisition time was kept similar for both acquisitions. Two radiologists evaluated aspects of IQ on a 5-point Likert scale and classified image artifacts. All patients completed a questionnaire on discomfort/pain regarding the ER coil using a visual analogue scale from 1 to 10.

RESULTS

There was no significant difference in overall IQ for T2w images for both readers (reader 1, 3.27 ± 0.91 and 3.07 ± 0.84, p = 0.057; reader 2, 3.70 ± 0.75 and 3.77 ± 0.81, p = 0.555) for PPA and ER coils, respectively. Overall IQ for DWI acquired with PPA and ER coils was rated similar by reader 1 (3.03 ± 1.10 and 3.08 ± 0.80, respectively, (p = 0.67)), while reader 2 preferred ER coil images (3.27 ± 0.81 and 3.66 ± 0.85 (p < 0.05)). Susceptibility artifacts were more frequent in ER than in PPA coil images (109 vs. 75). Discomfort and pain experienced during insertion of the ER coil was low altogether (VAS score, 3.5 ± 2.1 for "discomfort" and 2.4 ± 2.4 for "pain").

CONCLUSION

T2-weighted images may be acquired with comparable IQ using a PPA coil as compared to an ER coil, while DWI images showed better IQ using the ER coil for one of two readers. The insertion of the ER coil caused low to moderate discomfort and pain in patients.

Diffusion Weighted Imaging for Differentiating Benign from Malignant Orbital Tumors: Diagnostic Performance of the Apparent Diffusion Coefficient Based on Region of Interest Selection Method

Objective

To evaluate the differences in the apparent diffusion coefficient (ADC) measurements based on three different region of interest (ROI) selection methods, and compare their diagnostic performance in differentiating benign from malignant orbital tumors.

Materials and Methods

Diffusion-weighted imaging data of sixty-four patients with orbital tumors (33 benign and 31 malignant) were retrospectively analyzed. Two readers independently measured the ADC values using three different ROIs selection methods including whole-tumor (WT), single-slice (SS), and reader-defined small sample (RDSS). The differences of ADC values (ADC-ROI_WT, ADC-ROI_SS, and ADC-ROI_RDSS) between benign and malignant group were compared using unpaired t test. Receiver operating characteristic curve was used to determine and compare their diagnostic ability. The ADC measurement time was compared using ANOVA analysis and the measurement reproducibility was assessed using Bland-Altman method and intra-class correlation coefficient (ICC).

Results

Malignant group showed significantly lower ADC-ROI_WT, ADC-ROI_SS, and ADC-ROI_RDSS than benign group (all p < 0.05). The areas under the curve showed no significant difference when using ADC-ROI_WT, ADC-ROI_SS, and ADC-ROI_RDSS as differentiating index, respectively (all p > 0.05). The ROI_SS and ROI_RDSS required comparable measurement time (p > 0.05), while significantly shorter than ROI_WT (p < 0.05). The ROI_SS showed the best reproducibility (mean difference ± limits of agreement between two readers were 0.022 [-0.080–0.123] × 10^-3 mm²/s; ICC, 0.997) among three ROI methods.

Conclusion

Apparent diffusion coefficient values based on the three different ROI selection methods can help to differentiate benign from malignant orbital tumors. The results of measurement time, reproducibility and diagnostic ability suggest that the ROI_SS method are potentially useful for clinical practice.

Differentiation between malignant and benign thyroid nodules and stratification of papillary thyroid cancer with aggressive histological features: Whole-lesion diffusion-weighted imaging histogram analysis

PURPOSE

To explore the usefulness of whole-lesion histogram analysis of apparent diffusion coefficient (ADC) derived from reduced field-of-view (r-FOV) diffusion-weighted imaging (DWI) in differentiating malignant and benign thyroid nodules and stratifying papillary thyroid cancer (PTC) with aggressive histological features.

MATERIALS AND METHODS

This Institutional Review Board-approved, retrospective study included 93 patients with 101 pathologically proven thyroid nodules. All patients underwent preoperative r-FOV DWI at 3T. The whole-lesion ADC assessments were performed for each patient. Histogram-derived ADC parameters between different subgroups (pathologic type, extrathyroidal extension, lymph node metastasis) were compared. Receiver operating characteristic curve analysis was used to determine optimal histogram parameters in differentiating benign and malignant nodules and predicting aggressiveness of PTC.

RESULTS

Mean ADC, median ADC, 5^th percentile ADC, 25^th percentile ADC, 75^th percentile ADC, 95^th percentile ADC (all P < 0.001), and kurtosis (P = 0.001) were significantly lower in malignant thyroid nodules, and mean ADC achieved the highest AUC (0.919) with a cutoff value of 1842.78 × 10^-6 mm² /s in differentiating malignant and benign nodules. Compared to the PTCs without extrathyroidal extension, PTCs with extrathyroidal extension showed significantly lower median ADC, 5^th percentile ADC, and 25^th percentile ADC. The 5^th percentile ADC achieved the highest AUC (0.757) with cutoff value of 911.5 × 10^-6 mm² /s for differentiating between PTCs with and without extrathyroidal extension.

CONCLUSION

Whole-lesion ADC histogram analysis might help to differentiate malignant nodules from benign ones and show the PTCs with extrathyroidal extension. J. Magn. Reson. Imaging 2016;44:1546-1555.

Radiological indeterminate vestibular schwannoma and meningioma in cerebellopontine angle area: differentiating using whole-tumor histogram analysis of apparent diffusion coefficient

PURPOSE

To assess the role of whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating radiological indeterminate vestibular schwannoma (VS) from meningioma in cerebellopontine angle (CPA).

MATERIALS AND METHODS

Diffusion-weighted (DW) images (b = 0 and 1000 s/mm²) of pathologically confirmed and radiological indeterminate CPA meningioma (CPAM) (n = 27) and VS (n = 12) were retrospectively collected and processed with mono-exponential model. Whole-tumor regions of interest were drawn on all slices of the ADC maps to obtain histogram parameters, including the mean ADC (ADC_mean), median ADC (ADC_median), 10th/25th/75th/90th percentile ADC (ADC₁₀, ADC₂₅, ADC₇₅ and ADC₉₀), skewness and kurtosis. The differences of ADC histogram parameters between CPAM and VS were compared using unpaired t-test. Multiple receiver operating characteristic (ROC) curves analysis was used to determine and compare the diagnostic value of each significant parameter.

RESULTS

Significant differences were found on the ADC_mean, ADC_median, ADC₁₀, ADC₂₅, ADC₇₅ and ADC₉₀ between CPAM and VS (all p values < 0.001), while no significant difference was found on kurtosis (p = 0.562) and skewness (p = 0.047). ROC curves analysis revealed, a cut-off value of 1.126 × 10^-3 mm²/s for the ADC₉₀ value generated highest area under curves (AUC) for differentiating CPAM from VS (AUC, 0.975; sensitivity, 100%; specificity, 88.9%).

CONCLUSIONS

Histogram analysis of ADC maps based on whole tumor can be a useful tool for differentiating radiological indeterminate CPAM from VS. The ADC₉₀ value was the most promising parameter for differentiating these two entities.

Improve the image quality of orbital 3 T diffusion-weighted magnetic resonance imaging with readout-segmented echo-planar imaging

The aim of our study is to compare the image quality of readout-segmented echo-planar imaging (rs-EPI) and that of standard single-shot EPI (ss-EPI) in orbital 3 T diffusion-weighted (DW) magnetic resonance (MR) imaging in healthy subjects. Forty-two volunteers underwent two sets of orbital DW imaging scan at a 3 T MR unit, and image quality was assessed qualitatively and quantitatively. As a result, we found that rs-EPI could provide better image quality than standard ss-EPI, while no significant difference was found on the apparent diffusion coefficient between the two sets of DW images.