Utility of computed diffusion-weighted MRI for predicting aggressiveness of prostate cancer

Computed diffusion-weighted imaging in patients with transient neurovascular symptoms with and without ischemic infarction

PURPOSE

Detection of ischemic lesions in patients with transient neurovascular symptoms is relevant for the estimation of the risk of a subsequent stroke and etiological classification. To improve detection rates, different technical approaches have been used, such as diffusion-weighted imaging (DWI) with high b-values or higher magnetic field strength. Here, we sought to investigate the value of computed DWI (cDWI) with high b-values in these patients.

METHODS

From an MRI report database we identified patients with transient neurovascular symptoms who underwent repeated MRI including DWI. cDWI was calculated with a monoexponential model with high b-values (2000, 3000, and 4000 s/mm2) and compared to the routinely used standard DWI with regard to presence of ischemic lesions and lesion detectability.

RESULT

Overall 33 patients with transient neurovascular symptoms (71 [IQR 57-83.5] years; 21 [63.6%] male) were included. On DWI, acute ischemic lesions were observed in 22 (78.6%). Acute ischemic lesions were observed in 17 (51.5%) patients on initial DWI, and in 26 (78.8%) patients on follow-up DWI. Lesion detectability was rated significantly better on cDWI at 2000s/mm2 compared to standard DWI. In 2 (9.1%) patients, cDWI at 2000s/mm2 revealed an acute ischemic lesion proven on follow-up standard DWI which was not detected with certainty on the initial standard DWI.

CONCLUSION

cDWI might be a valuable addition to routinely acquired standard DWI in patients with transient neurovascular symptoms since its use might result in improved ischemic lesion detection. A b-value of 2000s/mm2 seems most promising for clinical practice.

Nomogram Models for Distinguishing Intraductal Carcinoma of the Prostate From Prostatic Acinar Adenocarcinoma Based on Multiparametric Magnetic Resonance Imaging

OBJECTIVE

To compare multiparametric magnetic resonance imaging (MRI) features of intraductal carcinoma of the prostate (IDC-P) with those of prostatic acinar adenocarcinoma (PAC) and develop prediction models to distinguish IDC-P from PAC and IDC-P with a high proportion (IDC ≥ 10%, hpIDC-P) from IDC-P with a low proportion (IDC < 10%, lpIDC-P) and PAC.

MATERIALS AND METHODS

One hundred and six patients with hpIDC-P, 105 with lpIDC-P and 168 with PAC, who underwent pretreatment multiparametric MRI between January 2015 and December 2020 were included in this study. Imaging parameters, including invasiveness and metastasis, were evaluated and compared between the PAC and IDC-P groups as well as between the hpIDC-P and lpIDC-P subgroups. Nomograms for distinguishing IDC-P from PAC, and hpIDC-P from lpIDC-P and PAC, were made using multivariable logistic regression analysis. The discrimination performance of the models was assessed using the receiver operating characteristic area under the curve (ROC-AUC) in the sample, where the models were derived from without an independent validation sample.

RESULTS

The tumor diameter was larger and invasive and metastatic features were more common in the IDC-P than in the PAC group (P < 0.001). The distribution of extraprostatic extension (EPE) and pelvic lymphadenopathy was even greater, and the apparent diffusion coefficient (ADC) ratio was lower in the hpIDC-P than in the lpIDC-P group (P < 0.05). The ROC-AUCs of the stepwise models based solely on imaging features for distinguishing IDC-P from PAC and hpIDC-P from lpIDC-P and PAC were 0.797 (95% confidence interval, 0.750-0.843) and 0.777 (0.727-0.827), respectively.

CONCLUSION

IDC-P was more likely to be larger, more invasive, and more metastatic, with obviously restricted diffusion. EPE, pelvic lymphadenopathy, and a lower ADC ratio were more likely to occur in hpIDC-P, and were also the most useful variables in both nomograms for predicting IDC-P and hpIDC-P.

Apparent diffusion coefficient values effectively predict cell proliferation and determine oligodendroglioma grade

This study aims to evaluate the value of conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) values in differentiating oligodendroglioma of various grades and explore the correlation between ADC and Ki-67. The preoperative MRI data of 99 patients with World Health Organization (WHO) grades 2 (n = 42) and 3 (n = 57) oligodendroglioma confirmed by surgery and pathology were retrospectively analyzed. Conventional MRI features, ADCmean, ADCmin, and normalized ADC (nADC) were compared between the two groups. A receiver operating characteristic curve was used to evaluate each parameter's diagnostic efficacy in differentiating the two tumor types. Each tumor's Ki-67 proliferation index was also measured to explore its relationship with the ADC value. Compared with WHO2 grade tumors, WHO3 grade tumors had a larger maximum diameter and more significant cystic degeneration/necrosis, edema, and moderate/severe enhancement (all P < 0.05). The ADCmin, ADCmean, and nADC values of the WHO3 and WHO2 grade tumors were significantly different, and the ADCmin value most accurately distinguished the two tumor types, yielding an area under the curve value of 0.980. When 0.96 × 10^-3 mm²/s was used as the differential diagnosis threshold, the sensitivity, specificity, and accuracy of the two groups were 100%, 93.00%, and 96.96%, respectively. The ADCmin (r = -0.596), ADCmean (r =  - 0.590), nADC (r =  - 0.577), and Ki-67 proliferation index values had significantly negative correlations (all P < 0.05). Conventional MRI features and ADC values are beneficial in the noninvasive prediction of the WHO grade and tumor proliferation rate of oligodendroglioma.

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.

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.

Why Is a b-value Range of 1500-2000 s/mm² Optimal for Evaluating Prostatic Index Lesions on Synthetic Diffusion-Weighted Imaging?

Objective

It is uncertain why a b-value range of 1500–2000 s/mm² is optimal. This study was aimed at qualitatively and quantitatively analyzing the optimal b-value range of synthetic diffusion-weighted imaging (sDWI) for evaluating prostatic index lesions.

Materials and Methods

This retrospective study included 92 patients who underwent DWI and targeted biopsy for magnetic resonance imaging (MRI)-suggested index lesions. We generated sDWI at a b-value range of 1000–3000 s/mm² using dedicated software and true DWI data at b-values of 0, 100, and 1000 s/mm². We hypothesized that lesion conspicuity would be best when the background (i.e., MRI-suggested benign prostatic [bP] and periprostatic [pP] regions) signal intensity (SI) is suppressed and becomes homogeneous. To prove this hypothesis, we performed both qualitative and quantitative analyses. For qualitative analysis, two independent readers analyzed the b-value showing the best visual conspicuity of an MRI-suggested index lesion. For quantitative analysis, the readers assessed the b-value showing the same bP and pP region SI. The 95% confidence interval (CI) or interquartile range of qualitatively and quantitatively selected optimal b-values was assessed, and the mean difference between qualitatively and quantitatively selected b-values was investigated.

Results

The 95% CIs of optimal b-values from qualitative and quantitative analyses were 1761–1805 s/mm² and 1640–1771 s/mm² (median, 1790 s/mm² vs. 1705 s/mm²; p = 0.003) for reader 1, and 1835–1895 s/mm² and 1705–1841 s/mm² (median, 1872 s/mm² vs. 1763 s/mm²; p = 0.022) for reader 2, respectively. Interquartile ranges of qualitatively and quantitatively selected optimal b-values were 1735–1873 s/mm² and 1573–1867 s/mm² for reader 1, and 1775–1945 s/mm² and 1591–1955 s/mm² for reader 2, respectively. Bland–Altman plots consistently demonstrated a mean difference of less than 100 s/mm² between qualitatively and quantitatively selected optimal b-values.

Conclusion

b-value range showing a homogeneous background signal may be optimal for evaluating prostatic index lesions on sDWI. Our qualitative and quantitative data consistently recommend b-values of 1500–2000 s/mm².

Differentiating Giant Cell Glioblastoma from Classic Glioblastoma With Diffusion-Weighted Imaging

BACKGROUND

Differential diagnosis of giant cell glioblastoma (GC) and classic glioblastoma (GBM) using conventional radiological modalities is difficult. This study aimed to use diffusion-weighted imaging (DWI) to distinguish GC from GBM and thereby improve the accuracy of preoperative assessment of patients with GB.

METHODS

The clinical, magnetic resonance imaging, and pathologic data of 12 patients with GC and 21 patients with GBM were retrospectively analyzed. Independent sample t tests were used to compare the minimum apparent diffusion coefficient (ADC_min) and the normalized apparent diffusion coefficients (nADC) of the 2 tumor types. Receiver operating curve (ROC) analysis was used to assess the diagnostic efficacy of ADC_min and nADC values.

RESULTS

Compared with that of the classic GBM group, the ADC_min (0.98 ± 0.14 vs. 0.80 ± 0.19×10^-3 mm²/second, P = 0.007) and nADC (1.42 ± 0.25 vs. 1.17 ± 0.25, P = 0.011) of the GC group were significantly higher. ROC curve analysis showed that the maximum area under the curve of ADC_min and nADC were 0.800 ± 0.080 and 0.778 ± 0.082, respectively. The sensitivity, specificity, and accuracy distinguishing GC and classic GBM was best (83.33%, 76.19%, and 78.79%, respectively) when ADC_min = 0.84×10^-3 mm²/second (maximum area under the ROC, 0.800). Its positive and negative predictive values under this condition were 88.89% and 66.67%, respectively.

CONCLUSIONS

By distinguishing GC from classic GBM, the ADC_min parameter of DWI can improve the accuracy of the preoperative differential diagnosis of the 2 tumor types.

Diagnostic benefit of high b-value computed diffusion-weighted imaging in acute brainstem infarction

BACKGROUND AND PURPOSE

Diffusion-weighted imaging (DWI) is a cornerstone in diagnostic of ischemic stroke. The aim of this study was to investigate the usefulness of high-b-value computed DWI (c-DWI) in comparison to standard DWI in patients with acute brainstem infarction.

MATERIALS AND METHODS

56 patients with acute brainstem infarction were retrospectively analysed by two readers. DWI was obtained with the b-values 0, 500 and 1000 s/mm² on either a 1.5 or 3 T magnetic resonance imaging (MRI) scanner. c-DWI was calculated with a monoexponential model with high b-values 2000, 3000, 4000 and 5000 s/mm². All c-DWI series with high-b-values were compared to the standard DWI sequence at b-value of 1000 s/mm² in terms of image artifacts, lesion extent and contrast.

RESULTS

There was no statistically significant difference between 1.5 and 3 T MRI regarding the measured ischemic lesion size. There were no statistically significant differences between the ischemic lesion sizes on DWI at b-values of 1000 s/mm² and on c-DWI at higher b-values. Overall, the contrast between the lesion and the surrounding normal areas improved with increasing b-value on the isotropic DWIs: maximum at b = 5000, followed by that at b 2000 and b 1000 s/mm², in order. The best relation between artifacts and lesion contrast was identified for b 2000 s/mm².

CONCLUSION

High b-value DWI derived from c-DWI has a higher visibility for ischemic brainstem lesions compared to standard DWI without additional time cost. The b-2000 image is recommended to use in clinical routine, higher b-value images lead to more imaging artifacts, which might result in misdiagnosis.

MRI in the Management of Prostate Cancer

Multiparametric MRI has a changing role in prostate cancer diagnosis. Internationally recognized consensus documents such as prostate imaging reporting and data system version have been developed and adapted to standardize the acquisition and reporting of prostate MRI. The improvement in scanning techniques and development of highly sensitive functional sequences have improved the detection of clinically significant prostate cancer as well as treatment planning and follow up. This has led to a recent NICE recommendation to use prostate MRI as the initial investigation in men with clinically suspected localized disease. The results of several recent international MRI prostate trials are influencing the way imaging is used to stratify which patients require a prostate biopsy as well as how MRI guidance is used to target biopsies.

Improved Visualization of Middle Ear Cholesteatoma with Computed Diffusion-weighted Imaging

Computed DWI (cDWI) is a mathematical technique that calculates arbitrary higher b value images from at least two different lower b values. In addition, the removal of high intensity noise with image processing on cDWI could improve cholesteatoma-background contrast-to-noise ratio (CNR). In the present study, noise reduction was performed by the cut-off values of apparent diffusion coefficient (ADC) less than 0 and 0.4 × 10⁻³ s/mm². The cholesteatoma to non-cholesteatoma CNR was increased using a noise reduction algorithm for clinical setting.

Efficacy of apparent diffusion coefficient in predicting aggressive histological features of papillary thyroid carcinoma

PURPOSE

We aimed to evaluate preoperative diffusion-weighted magnetic resonance imaging (DWI) for predicting aggressive histological features in papillary thyroid cancer (PTC).

METHODS

This prospective study included 141 PTC patients, who underwent DWI prior to thyroidectomy; 88 patients with 88 PTC lesions were finally analyzed. Multiple comparisons of mean and minimum apparent diffusion coefficient (ADC) values (ADCmean and ADCmin) and ADC of the solid component (ADCsolid) between the lowly aggressive PTC, highly aggressive PTC without hobnail, and hobnail variant PTC groups were performed by one-way ANOVA or the Welch test. The nonparametric Kruskal-Wallis H-test was used to assess lesion size differences. Receiver-operating characteristic (ROC) curve analysis was also performed.

RESULTS

ADC values in the lowly aggressive PTC group were found to be significantly higher than those in the highly aggressive PTC without hobnail group (ADCmean: 1.35±0.20×10-3 mm2/s vs. 1.16±0.17×10-3 mm2/s, P = 0.003; ADCmin: 1.10±0.17×10-3 mm2/s vs. 0.88±0.16×10-3 mm2/s, P < 0.001; ADCsolid: 1.26±0.23×10-3 mm2/s vs. 1.04±0.17×10-3 mm2/s, P < 0.001). No significant differences for the ADCmean, ADCmin, and ADCsolid were observed between the lowly aggressive and hobnail variant PTC groups (all P > 0.05). Lesion sizes in the hobnail variant PTC group was significantly elevated compared with the lowly aggressive PTC group (2.19±1.21 cm vs. 0.93±0.37 cm, P < 0.001). Areas under the curves (AUCs) for ADCmean, ADCmin, and ADCsolid between the lowly aggressive PTC and highly aggressive PTC group without hobnail were 0.758, 0.851, and 0.787, respectively. The AUC for size between the lowly aggressive and hobnail variant PTC group was 0.896.

CONCLUSION

ADCmin from DWI could potentially provide quantitative information to differentiate lowly aggressive PTC from highly aggressive PTC lesions without hobnail variants.

Computed Diffusion-Weighted Imaging in Prostate Cancer: Basics, Advantages, Cautions, and Future Prospects

Computed diffusion-weighted MRI is a recently proposed post-processing technique that produces b-value images from diffusion-weighted imaging (DWI), acquired using at least two different b-values. This article presents an argument for computed DWI for prostate cancer by viewing four aspects of DWI: fundamentals, image quality and diagnostic performance, computing procedures, and future uses.

Ultrasound Shear Wave Elastography of the Normal Prostate: Interobserver Reproducibility and Comparison with Functional Magnetic Resonance Tissue Characteristics

The purpose of this study was to establish interobserver reproducibility of Young's modulus (YM) derived from ultrasound shear wave elastography (US-SWE) in the normal prostate and correlate it with multiparametric magnetic resonance imaging (mpMRI) tissue characteristics. Twenty men being screened for prostate cancer underwent same-day US-SWE (10 done by two blinded, newly-trained observers) and mpMRI followed by 12-core biopsy. Bland-Altman plots established limits of agreement for YM. Quantitative data from the peripheral zone (PZ) and the transitional zone (TZ) for YM, apparent diffusion coefficient (ADC, mm2/s from diffusion-weighted MRI), and Ktrans (volume transfer coefficient, min-1), Ve (extravascular-extracellular space, %), Kep (rate constant, /min), and initial area under the gadolinium concentration curve (IAUGC60, mmol/L/s) from dynamic contrast-enhanced MRI were obtained for slice-matched prostate sextants. Interobserver intraclass correlation coefficients were fair to good for individual regions (PZ = 0.57, TZ = 0.65) and for whole gland 0.67, (increasing to 0.81 when corrected for systematic observer bias). In the PZ, there were weak negative correlations between YM and ADC ( p = 0.008), and Ve ( p = 0.01) and a weak positive correlation with Kep ( p = 0.003). No significant intermodality correlations were seen in the TZ. Transrectal prostate US-SWE done without controlling manually applied probe pressure has fair/good interobserver reproducibility in inexperienced observers with potential to improve this to excellent by standardization of probe contact pressure. Within the PZ, increase in tissue stiffness is associated with reduced extracellular water (decreased ADC) and space (reduced Ve).

Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings

BACKGROUND

Radiomic analysis is defined as computationally extracting features from radiographic images for quantitatively characterizing disease patterns. There has been recent interest in examining the use of MRI for identifying prostate cancer (PCa) aggressiveness in patients on active surveillance (AS).

PURPOSE

To evaluate the performance of MRI-based radiomic features in identifying the presence or absence of clinically significant PCa in AS patients.

STUDY TYPE

Retrospective.

SUBJECTS MODEL

MRI/TRUS (transperineal grid ultrasound) fusion-guided biopsy was performed for 56 PCa patients on AS who had undergone prebiopsy.

FIELD STRENGTH/SEQUENCE

3T, T2 -weighted (T2 w) and diffusion-weighted (DW) MRI.

ASSESSMENT

A pathologist histopathologically defined the presence of clinically significant disease. A radiologist manually delineated lesions on T2 w-MRs. Then three radiologists assessed MRIs using PIRADS v2.0 guidelines. Tumors were categorized into four groups: MRI-negative-biopsy-negative (Group 1, N = 15), MRI-positive-biopsy-positive (Group 2, N = 16), MRI-negative-biopsy-positive (Group 3, N = 10), and MRI-positive-biopsy-negative (Group 4, N = 15). In all, 308 radiomic features (First-order statistics, Gabor, Laws Energy, and Haralick) were extracted from within the annotated lesions on T2 w images and apparent diffusion coefficient (ADC) maps. The top 10 features associated with clinically significant tumors were identified using minimum-redundancy-maximum-relevance and used to construct three machine-learning models that were independently evaluated for their ability to identify the presence and absence of clinically significant disease.

STATISTICAL TESTS

Wilcoxon rank-sum tests with P < 0.05 considered statistically significant.

RESULTS

Seven T2 w-based (First-order Statistics, Haralick, Laws, and Gabor) and three ADC-based radiomic features (Laws, Gradient and Sobel) exhibited statistically significant differences (P < 0.001) between malignant and normal regions in the training groups. The three constructed models yielded overall accuracy improvement of 33, 60, 80% and 30, 40, 60% for patients in testing groups, when compared to PIRADS v2.0 alone.

DATA CONCLUSION

Radiomic features could help in identifying the presence and absence of clinically significant disease in AS patients when PIRADS v2.0 assessment on MRI contradicted pathology findings of MRI-TRUS prostate biopsies.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Multiparametric MRI Apparent Diffusion Coefficient (ADC) Accuracy in Diagnosing Clinically Significant Prostate Cancer

AIM

To evaluate the accuracy of multiparametric magnetic resonance imaging apparent diffusion coefficient (mpMRI ADC) in the diagnosis of clinically significant prostate cancer (PCa).

PATIENTS AND METHODS

From January 2016 to December 2016, 44 patients who underwent radical prostatectomy for PCa and mpMRI lesions suggestive of cancer were retrospectively evaluated at definitive specimen. The accuracy of suspicious mpMRI prostate imaging reporting and data system (PI-RADS ≥3) vs. ADC values in the diagnosis of Gleason score ≥7 was evaluated.

RESULTS

Receiver operating characteristics (ROC) curve analysis gave back an ADC threshold of 0.747×10^-3 mm²/s to separate between Gleason Score 6 and ≥7. The diagnostic accuracy of ADC value (cut-off 0.747×10^-3 mm²/s) vs. PI-RADS score ≥3 in diagnosing PCa with Gleason score ≥7 was equal to 84% vs. 63.6% with an area under the curve (AUC) ROC of 0.81 vs. 0.71, respectively.

CONCLUSION

ADC evaluation could support clinicians in decision making of patients with PI-RADS score <3 at risk for PCa.