Diagnostic value of ADC in patients with prostate cancer: influence of the choice of b-values. Eur Radiol. 2012;22:1820-1828. [PMID: 22527373 DOI: 10.1007/s00330-012-2432-3]

Using deep learning to optimize the prostate MRI protocol by assessing the diagnostic efficacy of MRI sequences

PURPOSE

To explore diagnostic deep learning for optimizing the prostate MRI protocol by assessing the diagnostic efficacy of MRI sequences.

METHOD

This retrospective study included 840 patients with a biparametric prostate MRI scan. The MRI protocol included a T2-weighted image, three DWI sequences (b50, b400, and b800 s/mm2), a calculated ADC map, and a calculated b1400 sequence. Two accelerated MRI protocols were simulated, using only two acquired b-values to calculate the ADC and b1400. Deep learning models were trained to detect prostate cancer lesions on accelerated and full protocols. The diagnostic performances of the protocols were compared on the patient-level with the area under the receiver operating characteristic (AUROC), using DeLong's test, and on the lesion-level with the partial area under the free response operating characteristic (pAUFROC), using a permutation test. Validation of the results was performed among expert radiologists.

RESULTS

No significant differences in diagnostic performance were found between the accelerated protocols and the full bpMRI baseline. Omitting b800 reduced 53% DWI scan time, with a performance difference of + 0.01 AUROC (p = 0.20) and -0.03 pAUFROC (p = 0.45). Omitting b400 reduced 32% DWI scan time, with a performance difference of -0.01 AUROC (p = 0.65) and + 0.01 pAUFROC (p = 0.73). Multiple expert radiologists underlined the findings.

CONCLUSIONS

This study shows that deep learning can assess the diagnostic efficacy of MRI sequences by comparing prostate MRI protocols on diagnostic accuracy. Omitting either the b400 or the b800 DWI sequence can optimize the prostate MRI protocol by reducing scan time without compromising diagnostic quality.

AI-predicted mpMRI image features for the prediction of clinically significant prostate cancer

PURPOSE

To evaluate the feasibility of using mpMRI image features predicted by AI algorithms in the prediction of clinically significant prostate cancer (csPCa).

MATERIALS AND METHODS

This study analyzed patients who underwent prostate mpMRI and radical prostatectomy (RP) at the Affiliated Hospital of Jiaxing University between November 2017 and December 2022. The clinical data collected included age, serum prostate-specific antigen (PSA), and biopsy pathology. The reference standard was the prostatectomy pathology, and a Gleason Score (GS) of 3 + 3 = 6 was considered non-clinically significant prostate cancer (non-csPCa), while a GS ≥ 3 + 4 was considered csPCa. A pre-trained AI algorithm was used to extract the lesion on mpMRI, and the image features of the lesion and the prostate gland were analyzed. Two logistic regression models were developed to predict csPCa: an MR model and a combined model. The MR model used age, PSA, PSA density (PSAD), and the AI-predicted MR image features as predictor variables. The combined model used biopsy pathology and the aforementioned variables as predictor variables. The model's effectiveness was evaluated by comparing it to biopsy pathology using the area under the curve (AUC) of receiver operation characteristic (ROC) analysis.

RESULTS

A total of 315 eligible patients were enrolled with an average age of 70.8 ± 5.9. Based on RP pathology, 18 had non-csPCa, and 297 had csPCa. PSA, PSAD, biopsy pathology, and ADC value of the prostate outside the lesion (ADCprostate) varied significantly across different ISUP grade groups of RP pathology (P < 0.001). Other clinical variables and image features did not vary significantly across different ISUP grade groups (P > 0.05). The MR model included PSAD, the ratio of ADC value between the lesion and the prostate outside the lesion (ADClesion/prostate), the signal intensity ratio of DWI between the lesion and the prostate outside the lesion (DWIlesion/prostate), and the ratio of DWIlesion/prostate to ADClesion/prostate. The combined model included biopsy pathology, ADClesion/prostate, mean signal intensity of the lesion on DWI (DWIlesion), DWI signal intensity of the prostate outside the lesion (DWIprostate), and signal intensity ratio of DWI between the lesion and the prostate outside the lesion (DWIlesion/prostate). The AUC of the MR model (0.830, 95% CI 0.743, 0.916) was not significantly different from that of biopsy pathology (0.820, 95% CI 0.728, 0.912, P = 0.884). The AUC of the combined model (0.915, 95% CI 0.849, 0.980) was higher than that of the biopsy pathology (P = 0.042) and MR model (P = 0.031).

CONCLUSION

The aggressiveness of prostate cancer can be effectively predicted using AI-extracted image features from mpMRI images, similar to biopsy pathology. The prediction accuracy was improved by combining the AI-extracted mpMRI image features with biopsy pathology, surpassing the performance of biopsy pathology alone.

Quantitative diffusion MRI in prostate cancer: Image quality, what we can measure and how it improves clinical assessment

Diffusion-weighted imaging is a dependable method for detection of clinically significant prostate cancer. In prostate tissue, there are several compartments that can be distinguished from each other, based on different water diffusion decay signals observed. Alterations in cell architecture, such as a relative increase in tumor infiltration and decrease in stroma, will influence the observed diffusion signal in a voxel due to impeded random motion of water molecules. The amount of restricted diffusion can be assessed quantitatively by measuring the apparent diffusion coefficient (ADC) value. This is traditionally calculated using a monoexponential decay formula represented by the slope of a line produced between the logarithm of signal intensity decay plotted against selected b-values. However, the choice and number of b-values and their distribution, has a significant effect on the measured ADC values. There have been many models that attempt to use higher-order functions to better describe the observed diffusion signal decay, requiring an increased number and range of b-values. While ADC can probe heterogeneity on a macroscopic level, there is a need to optimize advanced diffusion techniques to better interrogate prostate tissue microstructure. This could be of benefit in clinical challenges such as identifying sparse tumors in normal prostate tissue or better defining tumor margins. This paper reviews the principles of diffusion MRI and novel higher order diffusion signal analysis techniques to improve the detection of prostate cancer.

Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques

Apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) is an indispensable imaging technique in clinical neuroimaging that quantitatively assesses the diffusivity of water molecules within tissues using diffusion-weighted imaging (DWI). This study focuses on developing a robust machine learning (ML) model to predict the aggressiveness of gliomas according to World Health Organization (WHO) grading by analyzing patients' demographics, higher-order moments, and grey level co-occurrence matrix (GLCM) texture features of ADC. A population of 722 labeled MRI-ADC brain image slices from 88 human subjects was selected, where gliomas are labeled as glioblastoma multiforme (WHO-IV), high-grade glioma (WHO-III), and low-grade glioma (WHO I-II). Images were acquired using 3T-MR systems and a region of interest (ROI) was delineated manually over tumor areas. Skewness, kurtosis, and statistical texture features of GLCM (mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence, and shade) were calculated using ADC values within ROI. The ANOVA f-test was utilized to select the best features to train an ML model. The data set was split into training (70%) and testing (30%) sets. The train set was fed into several ML algorithms and selected most promising ML algorithm using K-fold cross-validation. The hyper-parameters of the selected algorithm were optimized using random grid search technique. Finally, the performance of the developed model was assessed by calculating accuracy, precision, recall, and F1 values reported for the test set. According to the ANOVA f-test, three attributes; patient gender (1.48), GLCM energy (9.48), and correlation (13.86) that performed minimum scores were excluded from the dataset. Among the tested algorithms, the random forest classifier(0.8772 ± 0.0237) performed the highest mean-cross-validation score and selected to build the ML model which was able to predict tumor categories with an accuracy of 88.14% over the test set. The study concludes that the developed ML model using the above features except for patient gender, GLCM energy, and correlation, has high prediction accuracy in glioma grading. Therefore, the outcomes of this study enable to development of advanced tumor classification applications that assist in the decision-making process in a real-time clinical environment.

Accurate diagnosis of pulmonary inflammatory myofibroblastic tumor by imaging technology before operation: A case report

RATIONALE

Pulmonary inflammatory myofibroblastic tumor (IMT) is a rare borderline tumor, which has the potential of malignant including invasion of surrounding tissues, distant metastasis and recurrence. However, the preoperative diagnosis is difficult and it can also be difficult to distinguish from malignancy in small tissue samples. Preoperative accurate diagnosis has important clinical significance for patients to choose treatment measures and improve the quality of rehabilitation. We was examined by computed tomography (CT) plain scan plus enhanced scan, magnetic resonance diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) imaging technology in an adult female, compared with lung cancer and pulmonary cryptococcus infection for diagnosis of pulmonary IMT.

PATIENT CONCERNS

A 32-year-old female patient was admitted to the hospital "physical examination revealed nodules in the right upper lung for 1 week".

DIAGNOSES

The patient was diagnosed with Pulmonary inflammatory myofibroblastic tumor.

INTERVENTIONS

Single-port thoracoscopic lobectomy was performed after multidisciplinary consultation.

OUTCOMES

DWI and ADC improves the accuracy of preoperative diagnosis and well guides the formulation of treatment measures. The combined CT, DWI, and ADC magnetic resonance imaging technology has more important significance in the diagnosis and differential diagnosis of IMT and lung malignant tumors.

LESSONS

Although accurate preoperative diagnosis of pulmonary IMT is difficult. Chest CT examination combined with DWI and ADC imaging technology has high clinical significance for the diagnosis of IMT.

Tumor Area Highlighting Using T2WI, ADC Map, and DWI Sequence Fusion on bpMRI Images for Better Prostate Cancer Diagnosis

Prostate cancer is the second most common cancer in men worldwide. The results obtained in magnetic resonance imaging examinations are used to decide the indication, type, and location of a prostate biopsy and contribute information about the characterization or aggressiveness of detected cancers, including tumor progression over time. This study proposes a method to highlight prostate lesions with a high and very high risk of being malignant by overlaying a T2-weighted image, apparent diffusion coefficient map, and diffusion-weighted image sequences using 204 pairs of slices from 80 examined patients. It was reviewed by two radiologists who segmented suspicious lesions and labeled them according to the prostate imaging-reporting and data system (PI-RADS) score. Both radiologists found the algorithm to be useful as a “first opinion”, and they gave an average score on the quality of the highlight of 9.2 and 9.3, with an agreement of 0.96.

Introducing new plan evaluation indices for prostate dose painting IMRT plans based on apparent diffusion coefficient images

BACKGROUND

Dose painting planning would be more complicated due to different levels of prescribed doses and more complex evaluation with conventional plan quality indices considering uniform dose prescription. Therefore, we tried to introduce new indices for evaluating the dose distribution conformity and homogeneity of treatment volumes based on the tumoral cell density and relative volumes of each lesion in prostate IMRT.

METHODS

CT and MRI scans of 20 male patients having local prostate cancer were used for IMRT DP planning. Apparent diffusion coefficient (ADC) images were imported to a MATLAB program to identify lesion regions based on ADC values automatically. Regions with ADC values lower than 750 mm²/s and regions with ADC values higher than 750 and less than 1500 mm²/s were considered CTV_70Gy (clinical tumor volume with 70 Gy prescribed dose), and CTV_60Gy, respectively. Other regions of the prostate were considered as CTV_53Gy. New plan evaluation indices based on evaluating the homogeneity (IOE(H)), and conformity (IOE(C)) were introduced, considering the relative volume of each lesion and cellular density obtained from ADC images. These indices were compared with conventional homogeneity and conformity indices and IOEs without considering cellular density. Furthermore, tumor control probability (TCP) was calculated for each patient, and the relationship of the assessed indices were evaluated with TCP values.

RESULTS

IOE (H) and IOE (C) with considering cellular density had significantly lower values compared to conventional indices and IOEs without considering cellular density. (P < 0.05). TCP values had a stronger relationship with IOE(H) considering cell density (R² = -0.415), and IOE(C) without considering cell density (R² = 0.624).

CONCLUSION

IOE plan evaluation indices proposed in this study can be used for evaluating prostate IMRT dose painting plans. We suggested to consider cell densities in the IOE(H) calculation formula and it's appropriate to calculate IOE(C) without considering cell density values.

Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques

Background

Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors.

Methods

This prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed.

Results

According to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process.

Conclusions

This study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies.

Reproducibility of apparent diffusion coefficient measurement in normal prostate peripheral zone at 1.5T MRI

PURPOSE

The purpose of this study was to quantify the influence of factors of variability on apparent diffusion coefficient (ADC) estimation in the normal prostate peripheral zone (PZ).

MATERIALS AND METHODS

Fifty healthy volunteers underwent in 2017 (n = 17) or 2020 (n = 33) two-point (0, 800 s/mm²) prostate diffusion-weighted imaging in the morning on 1.5 T scanners A and B from different manufacturers. Additional five-point (50, 150, 300, 500, 800 s/mm²) acquisitions were performed on scanner B in the morning and evening. ADC was measured in PZ at midgland using ADC maps reconstructed with various b-value combinations. ADC distributions from 2017 and 2020 were compared using Wilcoxon rank sum test. ADC obtained in the same volunteers were compared using Bland Altman methodology. The 95% confidence interval upper limit of the repeatability/reproducibility coefficient defined the lowest detectable ADC difference.

RESULTS

Forty-nine participants with a mean age of 24.6 ± 3.8 [SD] years (range: 21-37 years) were finally included. ADC distributions from 2017 and 2020 were not significantly different and were combined. Despite high individual variability, there was no significant bias (10 × 10^-6 mm²/s, P = 0.58) between ADC measurements made on both scanners. On scanner B, differences in lowest b-values chosen within the 0-500 s/mm² range for two-point ADC computation induced significant biases (56-109 × 10^-6 mm²/s, P < 0.0001). ADC was significantly lower in the morning (bias: 33 × 10^-6 mm²/s, P = 0.006). The number of b-values had little influence on ADC values. The lowest detectable ADC difference varied from 85 × 10^-6 to 311 × 10^-6 mm²/s across scanners, b-value combinations and periods of the day.

CONCLUSIONS

The MRI scanner, the lowest b-value used and the period of the day induce substantial variability in ADC computation.

SUVmax/ADC Ratio as a Molecular Imaging Biomarker for Diagnosis of Biopsy-Naïve Primary Prostate Cancer

Background:

Gallium-68-prostate-specific membrane antigen (⁶⁸Ga-PSMA) positron emission tomography/computed tomography (PET/CT) has recently been shown to be very high accuracy in biopsy-naïve prostate cancer (PCa) detection and can potentially improve the low specificity noted with diffusion-weighted magnetic resonance imaging (DW-MRI), especially in instances of prostate inflammation. We aimed to compare the diagnostic accuracy of DW-MRI and PSMA PET/CT using apparent diffusion coefficient (ADC) and maximum standardized uptake (SUV_max) values in the diagnosis of PCa.

Patients and Methods:

A retrospective study comparing and analyzing the diagnostic accuracy of prebiopsy DW-MRI and ⁶⁸Ga-PSMA PET/CTs done in patients with suspected PCa (raised prostate specific antigen [PSA] and/or positive digital rectal examination) from January 2019 to December 2020. The standard of reference was transrectal ultrasound-guided biopsies.

Results:

Sixty-seven patients were included in the study, mean age: 70 years (range 49–84), mean PSA: 23.2 ng/ml (range 2.97–45.6). Biopsy was positive for PCa in 56% (n = 38) and negative in 43% (n = 29). Of the benign results, benign hyperplasia was noted in 75% (n = 22) and prostatitis in 25% (n = 7). Of the PCa, 55% (n = 21) of were high International Society of Urological Pathology (ISUP) grade (4–5) and 45% (n = 17) low/intermediate ISUP grade (1–3). Overall the sensitivity/specificity/Accuracy for prediction of PCa of MRI using prostate imaging and reporting data system version 2 criteria and PSMA PET/CT using PCa molecular imaging standardized evaluation criteria was 92.1%/65.5%/80.5% and 76.3%/96.5%/85.1% respectively. Mean apparent diffusion co-efficient (mean ADC) value of benign lesions and PCa was 1.135 × 10^-3 mm²/s and 0.723 × 10^-3 mm²/s, respectively (P = 0.00001). Mean SUV_max and ADC of benign and PCa lesions was 4.01 and 16.4 (P = 0.000246). Mean SUV_max/ADC ratio of benign and malignant lesions was 3.8 × 103 versus 25.21 × 103 (P < 0.000026). Inverse correlation was noted between ADC and SUV_max values (R = −0.609), inverse correlation noted between ADC and Gleason's score (R = −0.198), and positive correlation of SUV_max and SUV_max/ADC with Gleason's score (R = 0.438 and R = 0.448). Receiver operating characteristic curve analysis revealed a SUV_max cutoff 6.03 (sensitivity/specificity - 76%/90%, area under the curve (AUC) - 0.935, Youden index (YI) - 0.66), ADC cutoff of 0.817 × 10⁻³ mm²/s (sensitivity/specificity – 79%/86%, AUC – 0.890, YI - 0.65), and SUV_max/ADC ratio cutoff of 7.43 × 103 (sensitivity/specificity – 87%/98%, AUC - 0.966, YI - 0.85) for PCa diagnosis.

Conclusion:

For diagnosis of biopsy-naïve PCas, the combination of diffusion-weighted MRI and PSMA PET/CT (i.e., SUV_max/ADC ratio) shows better diagnostic accuracy than either used alone and the combination of PET and MRI is especially useful when distinguishing cancer from prostatitis.

Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment

Magnetic resonance imaging (MRI) is increasingly used in the management of prostate cancer (PCa). Quantitative MRI (qMRI) parameters, derived from multi-parametric MRI, provide indirect measures of tumour characteristics such as cellularity, angiogenesis and hypoxia. Using Artificial Intelligence (AI), relevant information and patterns can be efficiently identified in these complex data to develop quantitative imaging biomarkers (QIBs) of tumour function and biology. Such QIBs have already demonstrated potential in the diagnosis and staging of PCa. In this review, we explore the role of these QIBs in monitoring treatment response during and after PCa radiotherapy (RT). Recurrence of PCa after RT is not uncommon, and early detection prior to development of metastases provides an opportunity for salvage treatments with curative intent. However, the current method of monitoring treatment response using prostate-specific antigen levels lacks specificity. QIBs, derived from qMRI and developed using AI techniques, can be used to monitor biological changes post-RT providing the potential for accurate and early diagnosis of recurrent disease.

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.

Diffusion-Weighted Magnetic Resonance Imaging as a Noninvasive Parameter for Differentiating Benign and Malignant Intraperitoneal Collections

Background and Objective: The imaging differentiation of benign from malignant intraperitoneal collections (IPCs) relies on the tumoral morphological modifications of the peritoneum, which are not always advocating for malignancy. We aimed to assess ascitic fluid with the apparent diffusion coefficient (ADC) to determine non-invasive, stand-alone, differentiation criteria for benign and malignant intraperitoneal effusions. Materials and Methods: Sixty-one patients with known IPCs who underwent magnetic resonance examinations for reasons such as tumor staging, undetermined abdominal mass and disease follow up were retrospectively included in this study. All subjects had a final diagnosis of the fluid based on pathological examinations, which were divided into benign (n = 37) and malignant (n = 24) IPCs groups. ADC values were measured separately by two radiologists, and the average values were used for comparing the two groups by consuming the independent samples t-test. The receiver operating characteristic analysis was performed to test the ADC values' diagnostic ability to distinguish malignant from benign collections. Results: The differentiation between benign and malignant IPCs based on ADC values was statistically significant (p = 0.0034). The mean ADC values were higher for the benign (3.543 × 10^-3 mm²/s) than for the malignant group (3.057 × 10^-3 mm²/s). The optimum ADC cutoff point for the diagnosis of malignant ascites was <3.241 × 10^-3 mm²/s, with a sensitivity of 77.78% and a specificity of 80%. Conclusions: ADC represents a noninvasive and reproducible imaging parameter that may help to assess intraperitoneal collections. Although successful in distinguishing malignant from benign IPCs, further research must be conducted in order to certify if the difference in ADC values is a consequence of the physical characteristics of the ascitic fluids or their appurtenance to a certain histopathological group.

Impact of different b-value combinations on radiomics features of apparent diffusion coefficient in cervical cancer

Background

The impact of variable b-value combinations on apparent diffusion coefficient (ADC)-based radiomics features has not been fully addressed in literature.

Purpose

To investigate the correlation between radiomics features extracted from ADC maps and various b-value combinations in cervical cancer.

Material and Methods

Diffusion-weighted images (b-values: 0, 600, 800, and 1000 s/mm2) of 20 patients with cervical cancer were included. Tumors were identified with the largest transversal cross-section and manually segmented by radiologist. For each b-value combination, 92 radiomics features were extracted and coefficient of variance (CV) was used to evaluate the robustness of radiomics features with different b-value combinations. Features with CV > 5% were normalized by the mean feature variation across the group.

Results

Out of a total of 92 radiomics features, 18 were classified as robust features with CV ≤5%. Among the rest (CV > 5%), 11, 23, and 40 features demonstrated 5%< CV ≤10%, 10%< CV ≤20%, and CV > 20%, respectively. A subset of features in each category (CV > 5%) showed strong correlation with the b-value combination variation, including 44% (7/16) features in gray level co-occurrence matrix, 62% (8/13) features in gray level dependence matrix, 64% (9/14) features in first order, 50% (8/16) features in gray level run length matrix, 57% (8/14) features in gray level size matrix, and 20% (1/5) features in neighborhood gray-tone difference matrix.

Conclusions

Variations in b-value combinations demonstrated impact on radiomics features extracted from ADC maps for cervical cancer. The radiomics features with CV <5% can be considered as robust features and are recommended to be used in multicenter radiomics studies.

Synthetic Apparent Diffusion Coefficient for High b-Value Diffusion-Weighted MRI in Prostate

Purpose

It has been reported that diffusion-weighted imaging (DWI) with ultrahigh b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher Materials and Methods. Fifteen patients (7 malignant and 8 benign) were included in this study retrospectively with the institutional ethical committee approval. All images were acquired at a 3T MR scanner. The ADC values were calculated using a monoexponential model. Synthetic ADC (sADC) for higher b-value increases the diagnostic power of prostate cancer. DWI with higher

Results

No significant difference was observed between actual ADC and sADC for b-value increases the diagnostic power of prostate cancer. DWI with higher p=0.002, paired t-test) in sDWI as compared to DWI. Malignant lesions showed significantly lower sADC as compared to benign lesions (p=0.002, paired t-test) in sDWI as compared to DWI. Malignant lesions showed significantly lower sADC as compared to benign lesions (Discussion/

Conclusion

Our initial investigation suggests that the ADC values corresponding to higher b-value can be computed using log-linear relationship derived from lower b-values (b ≤ 1000). Our method might help clinicians to decide the optimal b-value for prostate lesion identification.b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher

Diffusion-weighted MRI with ADC mapping for response prediction and assessment of oesophageal cancer: A systematic review

PURPOSE

The aim was to perform a systematic review on the value of diffusion-weighted MRI (DW-MRI) with apparent diffusion coefficient (ADC) mapping in the prediction and assessment of response to chemo- and/or radiotherapy in oesophageal cancer.

MATERIALS AND METHODS

A systematic search was performed on Pubmed, Embase, Medline and Cochrane databases. Studies that evaluated the ADC for response evaluation before, during or after chemo- and/or radiotherapy were included. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the quality of the included studies.

RESULTS

Fourteen studies, comprising 516 patients, in which the response to treatment in oesophageal cancer was evaluated on ADC maps were included. Acquisition parameter settings for DW-MRI and ROI placement varied substantially. The reference standard was RECIST or endoscopic assessment in eight non-surgery studies and histopathology after surgery in six studies. A high pre-treatment ADC significantly correlated with good response in three out of 12 studies; conversely, one study reported a significantly higher pre-treatment ADC in poor responders. In five out of eight studies good responders showed a significantly larger relative increase in ADC two weeks after the onset of treatment (range 23-59%) than poor responders (range 1.5-17%). After chemo- and/or radiotherapy ADC results varied considerably, amongst others due to large variation in the interval between completion of therapy and DW-MRI.

CONCLUSION

DW-MRI for response evaluation to chemo- and/or radiotherapy in oesophageal cancer shows variable methods and results. A large relative ADC increase after two weeks of treatment seems most predictive for good response.

Effects of the addition of quantitative apparent diffusion coefficient data on the diagnostic performance of the PI-RADS v2 scoring system to detect clinically significant prostate cancer

PURPOSE

To evaluate the impact of the addition of quantitative apparent diffusion coefficient (ADC) data into the diagnostic performance of the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) scoring system to predict clinically significant prostate cancer (CSPCa).

METHODS

We retrospectively included 91 consecutive patients who underwent prostate multiparametric magnetic resonance imaging (mp-MRI) and histopathological evaluation. Mp-MRI images were reported by the PI-RADSv2 scoring system and patients were divided into groups considering the likelihood of CSPCa. ADC value and ratio were obtained. Findings were correlated with histopathological data.

RESULTS

CSPCa was found in 41.8% of cases (n = 38). PI-RADSv2 score 3-5 yielded a sensitivity of 97.4% (95% confidence intervals 86.5-99.5), a specificity of 50.9% (37.9-63.9), and AUC of 0.74 (0.67-0.81) to predict CSPCa. ADC value < 750 µm²/s and an ADC ratio < 0.62 were the most accurate thresholds for differentiation of CSPCa, with AUC of 0.81 and 0.76, respectively. Combined PI-RADSv2 score 3-5 and ADC value < 750 µm²/s yielded a specificity of 84.9 (72.9-92.2), sensitivity of 70.3 (54.2-82.5), and AUC of 0.77 (0.68-0.86). Combined PI-RADSv2 score 3-5 and ADC ratio < 0.62 yielded a specificity of 86.5 (74.7-93.3), sensitivity of was 64.9 (48.8-78.2), and AUC of 0.75 (0.66-0.84).

CONCLUSION

Quantitative ADC data might not be beneficial to be used routinely in mp-MR imaging as criteria to detect clinically significant lesions due to the reduced sensitivity. Instead, when prostate lesions present a PI-RADSv2 score ≥ 3, additional quantitative ADC criteria can be helpful to increase the PI-RADS score specificity.

Correlations between Apparent Diffusion Coefficient and Gleason Score in Prostate Cancer: A Systematic Review

BACKGROUND

Reported data regarding the associations between apparent diffusion coefficient (ADC) of diffusion-weighted imaging (DWI) and Gleason score in prostate cancer (PC) are inconsistent.

OBJECTIVE

The aim of the present systematic review was to analyze relationships between ADC and Gleason score in PC.

DESIGN, SETTING, AND PARTICIPANTS

MEDLINE library, SCOPUS, and EMBASE databases were screened for relationships between ADC and Gleason score in PC up to April 2018. Overall, 39 studies with 2457 patients were identified. Data on the following parameters were extracted from the literature: number of patients, cancer localization, and correlation coefficients between ADC and Gleason score.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Associations between ADC and Gleason score were analyzed by the Spearman's correlation coefficient.

RESULTS AND LIMITATIONS

In overall sample, the pooled correlation coefficient between ADC and Gleason score was -0.45 (95% confidence interval [CI]=[-0.50; -0.40]). In PC in the transitional zone, the pooled correlation coefficient was -0.22 (95% CI=[-0.47; 0.03]). In PC in the peripheral zone, the pooled correlation coefficient was -0.48 (95% CI=[-0.54; -0.42]).

CONCLUSIONS

In PC located in the peripheral zone, ADC correlated moderately with Gleason score. In PC located in the transitional zone, ADC correlated weakly with Gleason score.

PATIENT SUMMARY

We reviewed studies using apparent diffusion coefficient for the prediction of Gleason score in prostate cancer patients.

Multi-model Analysis of Diffusion-weighted Imaging of Normal Testes at 3.0 T: Preliminary Findings

RATIONALE AND OBJECTIVES

This study aimed to establish diffusion quantitative parameters (apparent diffusion coefficient [ADC], DDC, α, D_app, and K_app) in normal testes at 3.0 T.

MATERIALS AND METHODS

Sixty-four healthy volunteers in two age groups (A: 10-39 years; B: ≥ 40 years) underwent diffusion-weighted imaging scanning at 3.0 T. ADC₁₀₀₀, ADC₂₀₀₀, ADC₃₀₀₀, DDC, α, D_app, and K_app were calculated using the mono-exponential, stretched-exponential, and kurtosis models. The correlations between parameters and the age were analyzed. The parameters were compared between the age groups and between the right and the left testes.

RESULTS

The average ADC₁₀₀₀, ADC₂₀₀₀, ADC₃₀₀₀, DDC, α, D_app, and K_app values did not significantly differ between the right and the left testes (P > .05 for all). The following significant correlations were found: positive correlations between age and testicular ADC₁₀₀₀, ADC₂₀₀₀, ADC₃₀₀₀, DDC, and D_app (r = 0.516, 0.518, 0.518, 0.521, and 0.516, respectively; P < .01 for all) and negative correlations between age and testicular α and K_app (r = -0.363, -0.427, respectively; P < .01 for both). Compared to group B, in group A, ADC₁₀₀₀, ADC₂₀₀₀, ADC₃₀₀₀, DDC, and D_app were significantly lower (P < .05 for all), but α and K_app were significantly higher (P < .05 for both).

CONCLUSIONS

Our study demonstrated the applicability of the testicular mono-exponential, stretched-exponential, and kurtosis models. Our results can help establish a baseline for the normal testicular parameters in these diffusion models. The contralateral normal testis can serve as a suitable reference for evaluating the abnormalities of the other side. The effect of age on these parameters requires further attention.

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.

Optimization of prostate MRI acquisition and post-processing protocol: a pictorial review with access to acquisition protocols

The aim of this review article is to provide insight into the optimization of 1.5-Testla (T) and 3-T prostate magnetic resonance imaging (MRI). An approach for optimization of data quantification, especially diffusion-weighted imaging (DWI), is provided. Benefits and limitations of various pulse sequences are discussed. Importable MRI protocols and access to imaging datasets is provided. Careful optimization of prostate MR acquisition protocol allows the acquisition of high-quality prostate MRI using clinical 1.5-T/3-T MR scanners with an overall acquisition time < 15 min.

Multiparametric magnetic resonance imaging for transition zone prostate cancer: essential findings, limitations, and future directions

OBJECTIVE

Review the multiparametric MRI (mpMRI) findings of transition zone (TZ) prostate cancer (PCa) using T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI and to integrate mpMRI findings with clinical history, laboratory values, and histopathology.

CONCLUSION

TZ prostate tumors are challenging to detect clinically and at MRI. mpMRI using the combination of sequences has the potential to improve accuracy of TZ cancer detection and staging.

Apparent diffusion coefficient in the analysis of prostate cancer: determination of optimal b-value pair to differentiate normal from malignant tissue

PURPOSE

Determining optimal b-value pair for differentiation between normal and prostate cancer (PCa) tissues.

METHODS

Forty-three patients with diagnosis or PCa symptoms were included. Apparent diffusion coefficient (ADC) was estimated using minimum and maximum b-values of 0, 50, 100, 150, 200, 500s/mm2 and 500, 800, 1100, 1400, 1700 and 2000s/mm2, respectively. Diagnostic performances were evaluated when Area-under-the-curve (AUC)>95%.

RESULTS

15 of the 35 b-values pair surpassed this AUC threshold. The pair (50, 2000s/mm2) provided the highest AUC (96%) with ADC cutoff 0.89×10-³mm²/s, sensitivity 95.5%, specificity 93.2% and accuracy 94.4%.

CONCLUSIONS

The best b-value pair was b=50, 2000s/mm2.

Quantified analysis of histological components and architectural patterns of gleason grades in apparent diffusion coefficient restricted areas upon diffusion weighted MRI for peripheral or transition zone cancer locations

PURPOSE

To quantify and compare the histological components and architectural patterns of Gleason grades in cancerous areas with restriction on apparent diffusion coefficient (ADC) maps.

MATERIALS AND METHODS

Twelve consecutive cases with 14 separate ADC restriction areas, positive for cancer in the peripheral zone (PZ) and transition zone (TZ) were included. All had 3 Tesla MRI and radical prostatectomy. Ten regions of interest (ROIs) within and outside the 14 ADC restriction areas positive for cancer were selected. For each ROI, we performed quantitative analysis of (a) prostate benign and malignant histological component surface ratios, including stroma, glands, epithelium, lumen, cellular nuclei; (b) percent of Gleason grades and measures of ADC values. Means of histological components according to ADC restriction for cancerous area were compared with analyses of variance with repeated measures.

RESULTS

Independent predictors of the probability of cancer were median epithelium/ROI ratio (P = 0.001) and nuclei/ROI ratio (P = 0.03). Independent predictors of the probability of ADC restriction were malignant glands/ROI and luminal space/ROI (P < 0.0001). Effect of malignant glands/ROI area was different according to the localization of the ROI (P = 0.03). We observed an overall difference between the means for all of the histological components for the comparison of true positive and false negative (P < 0.0001), except for the percent of Gleason grade 4 (P = 0.18). In TZ cancers, a predominant grade 3 pattern was associated with low ADC values. In PZ cancers, a predominant grade 4 pattern was associated with low ADC values.

CONCLUSION

Determinants of low ADC were high ratio of malignant glands/ROI area which may be seen in Gleason grades 3 or 4 cancers.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1786-1796.

Evaluation of different mathematical models and different b-value ranges of diffusion-weighted imaging in peripheral zone prostate cancer detection using b-value up to 4500 s/mm2

OBJECTIVES

To evaluate the diagnostic performance of different mathematical models and different b-value ranges of diffusion-weighted imaging (DWI) in peripheral zone prostate cancer (PZ PCa) detection.

METHODS

Fifty-six patients with histologically proven PZ PCa who underwent DWI-magnetic resonance imaging (MRI) using 21 b-values (0-4500 s/mm2) were included. The mean signal intensities of the regions of interest (ROIs) placed in benign PZs and cancerous tissues on DWI images were fitted using mono-exponential, bi-exponential, stretched-exponential, and kurtosis models. The b-values were divided into four ranges: 0-1000, 0-2000, 0-3200, and 0-4500 s/mm2, grouped as A, B, C, and D, respectively. ADC, <D>, D*, f, DDC, α, Dapp, and Kapp were estimated for each group. The adjusted coefficient of determination (R2) was calculated to measure goodness-of-fit. Receiver operating characteristic curve analysis was performed to evaluate the diagnostic performance of the parameters.

RESULTS

All parameters except D* showed significant differences between cancerous tissues and benign PZs in each group. The area under the curve values (AUCs) of ADC were comparable in groups C and D (p = 0.980) and were significantly higher than those in groups A and B (p< 0.05 for all). The AUCs of ADC and Kapp in groups B and C were similar (p = 0.07 and p = 0.954), and were significantly higher than the other parameters (p< 0.001 for all). The AUCs of ADC in group D was slightly higher than Kapp (p = 0.002), and both were significantly higher than the other parameters (p< 0.001 for all).

CONCLUSIONS

ADC derived from conventional mono-exponential high b-value (3200 s/mm2) models is an optimal parameter for PZ PCa detection.

Impact of the use of an endorectal coil for 3 T prostate MRI on image quality and cancer detection rate

This work aims to assess the impact of an additional endorectal coil on image quality and cancer detection rate within the same patients. At a single academic medical center, this transversal study included 41 men who underwent T2- and diffusion-weighted imaging at 3 T using surface coils only or in combination with an endorectal coil in the same session. Two blinded readers (A and B) randomly evaluated all image data in separate sessions. Image quality with respect to localization and staging was rated on a five-point scale. Lesions were classified according to their prostate imaging reporting and data system (PIRADS) score version 1. Standard of reference was provided by whole-mount step-section analysis. Mean image quality scores averaged over all localization-related items were significantly higher with additional endorectal coil for both readers (p < 0.001), corresponding staging-related items were only higher for reader B (p < 0.001). With an endorectal coil, the rate of correctly detecting cancer per patient was significantly higher for reader B (p < 0.001) but not for reader A (p = 0.219). The numbers of histologically confirmed tumor lesions were rather similar for both settings. The subjectively rated 3-T image quality was improved with an endorectal coil. In terms of diagnostic performance, the use of an additional endorectal coil was not superior.

Review of Prostate Imaging Reporting and Data System version 2

Prostate MRI has been a hot topic in recent years in large part due to the high incidence of prostate cancer worldwide. Advances in technology have allowed multiparametric MRI to improve lesion detection and characterization in prostate imaging. Additionally, prostate MRI has shown great promise in the detection of clinically significant cancer. In 2012, the European Society of Urogenital Radiology established clinical guidelines for multiparametric MRI of the prostate to facilitate a greater level of standardization and consistency, which became known as the Prostate Imaging Reporting and Data System (PI-RADS). Subsequently, the American College of Radiology, European Society of Urogenital Radiology and the AdMeTech Foundation jointly created PI-RADS version 2. This article focuses on summarizing the key points of PI-RADS version 2.

Differentiating Transition Zone Cancers From Benign Prostatic Hyperplasia by Quantitative Multiparametric Magnetic Resonance Imaging

OBJECTIVE

The aim of this study was to evaluate the value of quantitative diffusion and perfusion parameters to aid in discriminating between transition zone carcinomas and benign prostatic hyperplasia (BPH).

MATERIALS AND METHODS

Twenty-four transition zone cancers and BPH nodules were contoured on T2-weighted magnetic resonance imaging (MRI), apparent diffusion coefficient (ADC) maps, and raw dynamic contrast-enhanced (DCE) MRI. Benign prostatic hyperplasia nodules were then stratified into 2 groups based on the presence or absence of a capsule. Apparent diffusion coefficient values, per-voxel Ktrans, kep, vp, and ve were all compared across all groups.

RESULTS

Average ADCs (×10 mm/s) were 1019.22, 1338.11, and 1272.46 for cancer, encapsulated BPH, and nonencapsulated BPH, respectively. Both subgroups of BPH were found to be significantly different than that of cancer (P < 0.05). No individual DCE-MRI parameter was significantly different between cancer and either BPH group. The area under the curve for ADC alone was 0.83, and no individual DCE imaging parameter improved the area under the curve of ADC.

CONCLUSIONS

Apparent diffusion coefficient may play a role in distinguishing TZ cancers from non-encapsulated BPH nodules that closely resemble cancer.

New RESOLVE-Based Diffusional Kurtosis Imaging in MRI-Visible Prostate Cancer: Effect of Reduced b Value on Image Quality and Diagnostic Effectiveness

OBJECTIVE

The purpose of this article was to investigate whether a new readout segmentation of long variable echo-trains (RESOLVE)-based diffusional kurtosis imaging (DKI) with reduced b value technique can affect image quality and diagnostic effectiveness in MRI-visible prostate cancer (PCA).

SUBJECTS AND METHODS

Prostatic RESOLVE DKI (0-1400 s/mm2) was prospectively performed for 12 volunteers. The optimal protocol was then performed in 108 MRI-visible PCAs to determine whether it can compete against a preferred b-value set (0-2000 s/mm(2)) regarding image quality and diagnostic effectiveness. Images were interpreted by two independent radiologists using the prostate imaging reporting and data system (PI-RADS). Readers' concordance and diagnostic effectiveness were tested with the Fleiss kappa and area under the ROC curve (Az) analyses.

RESULTS

A b value of 1400 s/mm(2) generated a larger apparent diffusion coefficient of gaussian distribution (Dapp) (1.35 ± 0.31 vs 1.30 ± 0.30 mm(2)/s; p < 0.001) and apparent kurtosis coefficient (Kapp) (1.11 ± 0.26 vs 1.00 ± 0.21; p < 0.001) in PCA than did a b value of 2000 s/mm(2). Interreader agreement using PI-RADS was relatively low when Dapp and Kapp maps were excluded from image interpretations (κ = 0.39-0.41 vs κ = 0.66-0.68 with Dapp and Kapp maps). Interreader agreement in staging PCA was relatively high (κ > 0.80) and was not influenced by reducing the b value. The power of Dapp and Kapp to differentiate PCA from normal tissue (Az = 0.97-0.98), tissue with a Gleason score less than or equal to 3 + 4 from tissue with a Gleason score greater than 3 + 4 (Az = 0.77-0.82), and PCA stage lower than pT3 from stage pT3 and higher PCA (Az = 0.70-0.75) was not significantly degraded by reducing the b value.

CONCLUSION

We found that b values significantly influenced image quality, PI-RADS score, and DKI outputs but did not degrade the diagnostic effectiveness of DKI parameters to detect and classify PCA.

The expanding landscape of diffusion-weighted MRI in prostate cancer

The added value of diffusion-weighted magnetic resonance imaging (DW-MRI) for the detection, localization, and staging of primary prostate cancer has been extensively reported in original studies and meta-analyses. More recently, DW-MRI and related techniques have been used to noninvasively assess prostate cancer aggressiveness and estimate its biological behavior. The present article aims to summarize the potential applications of DW-MRI for noninvasive optimization of pretherapeutic risk assessment, patient management decisions, and evaluation of treatment response.

Quantitative Analysis of Prostate Multiparametric MR Images for Detection of Aggressive Prostate Cancer in the Peripheral Zone: A Multiple Imager Study

Purpose To assess the intermanufacturer variability of quantitative models in discriminating cancers with a Gleason score of at least 7 among peripheral zone (PZ) lesions seen at 3-T multiparametric magnetic resonance (MR) imaging. Materials and Methods An institutional review board-approved prospective database of 257 patients who gave written consent and underwent T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced imaging before prostatectomy was retrospectively reviewed. It contained outlined lesions found to be suspicious for malignancy by two independent radiologists and classified as malignant or benign after correlation with prostatectomy whole-mount specimens. One hundred six patients who underwent imaging with 3-T MR systems from two manufacturers were selected (data set A, n = 72; data set B, n = 34). Eleven parameters were calculated in PZ lesions: normalized T2-weighted signal intensity, skewness and kurtosis of T2-weighted signal intensity, T2 value, wash-in rate, washout rate, time to peak (TTP), mean apparent diffusion coefficient (ADC), 10th percentile of the ADC, and skewness and kurtosis of the histogram of the ADC values. Parameters were selected on the basis of their specificity for a sensitivity of 0.95 in diagnosing cancers with a Gleason score of at least 7, and the area under the receiver operating characteristic curve (AUC) for the models was calculated. Results The model of the 10th percentile of the ADC with TTP yielded the highest AUC in both data sets. In data set A, the AUC was 0.90 (95% confidence interval [CI]: 0.85, 0.95) or 0.89 (95% CI: 0.82, 0.94) when it was trained in data set A or B, respectively. In data set B, the AUC was 0.84 (95% CI: 0.74, 0.94) or 0.86 (95% CI: 0.76, 0.95) when it was trained in data set A or B, respectively. No third variable added significantly independent information in any data set. Conclusion The model of the 10th percentile of the ADC with TTP yielded accurate results in discriminating cancers with a Gleason score of at least 7 among PZ lesions at 3 T in data from two manufacturers. (©) RSNA, 2016 Online supplemental material is available for this article.

Whole-Body Diffusion-Weighted Imaging in Chronic Recurrent Multifocal Osteomyelitis in Children

OBJECTIVE

Chronic recurrent multifocal osteomyelitis/ chronic non-bacterial osteomyelitis (CRMO/ CNO) is a rare auto-inflammatory disease and typically manifests in terms of musculoskeletal pain. Because of a high frequency of musculoskeletal disorders in children/ adolescents, it can be quite challenging to distinguish CRMO/ CNO from nonspecific musculoskeletal pain or from malignancies. The purpose of this study was to evaluate the visibility of CRMO lesions in a whole-body diffusion-weighted imaging (WB-DWI) technique and its potential clinical value to better characterize MR-visible lesions.

MATERIAL AND METHODS

Whole-body imaging at 3T was performed in 16 patients (average: 13 years) with confirmed CRMO. The protocol included 2D Short Tau Inversion Recovery (STIR) imaging in coronal and axial orientation as well as diffusion-weighted imaging in axial orientation. Visibility of lesions in DWI and STIR was evaluated by two readers in consensus. The apparent diffusion coefficient (ADC) was measured for every lesion and corresponding reference locations.

RESULTS

A total of 33 lesions (on average 2 per patient) visible in STIR and DWI images (b = 800 s/mm2 and ADC maps) were included, predominantly located in the long bones. With a mean value of 1283 mm2/s in lesions, the ADC was significantly higher than in corresponding reference regions (782 mm2/s). By calculating the ratio (lesion to reference), 82% of all lesions showed a relative signal increase of 10% or higher and 76% (25 lesions) showed a signal increase of more than 15%. The median relative signal increase was 69%.

CONCLUSION

This study shows that WB-DWI can be reliably performed in children at 3T and predominantly, the ADC values were substantially elevated in CRMO lesions. WB-DWI in conjunction with clinical data is seen as a promising technique to distinguish benign inflammatory processes (in terms of increased ADC values) from particular malignancies.

Ratio of Tumor to Normal Prostate Tissue Apparent Diffusion Coefficient as a Method for Quantifying DWI of the Prostate

OBJECTIVE

The purpose of this study was to investigate the ability of the apparent diffusion coefficient (ADC) ratio of tumor to normal prostate tissue to overcome inherent variability based on choice of b values, with whole-mount histopathologic analysis as the reference standard for tumor identification.

MATERIALS AND METHODS

Thirty-nine patients with prostate cancer underwent 3-T MRI, including DWI with b values of 0, 150, 750, and 1000 s/mm(2). ADC maps were derived from four b value combinations. Histologically derived ROIs were defined for prostate tumor and benign prostate tissue to generate a ratio. The concordance correlation coefficient was used to evaluate agreement and reproducibility at different b values. Bland-Altman plots were used to evaluate the pattern of relative measurement difference between b value combinations. The relationship between ADC values and Gleason score was tested by Spearman rank correlation.

RESULTS

ADC values varied depending on the b value combination selected. The concordance correlation coefficient was higher for ADC ratios (0.883; 95% CI, 0.816-0.927) compared with absolute ADC values for normal tissue (0.873; 95% CI, 0.799-0.921) and tumor (0.792; 95% CI, 0.688-0.864). The ADC ratio concordance correlation coefficient for transition zone tumors was considerably higher than that for the peripheral zone in all cases. Bland-Altman analysis showed higher variation for ADC maps incorporating a b value of zero for both ratio and absolute values. There was a stronger inverse relationship to Gleason score for ADC ratios (rho, -0.354 to -0.456) compared with absolute ADC values (rho, -0.117 to -0.379).

CONCLUSION

The use of a simple ratio of prostate tumor ADC to normal tissue ADC improved the concordance between different b value combinations and could provide a more robust means of assessing restricted diffusion in the prostate.

PI-RADS version 2: what you need to know

Prostate cancer is the second most prevalent cancer in men worldwide and its incidence is expected to double by 2030. Multi-parametric magnetic resonance imaging (MRI) incorporating anatomical and functional imaging has now been validated as a means of detecting and characterising prostate tumours and can aid in risk stratification and treatment selection. The European Society of Urogenital Radiology (ESUR) in 2012 established the Prostate Imaging-Reporting and Data System (PI-RADS) guidelines aimed at standardising the acquisition, interpretation and reporting of prostate MRI. Subsequent experience and technical developments have highlighted some limitations, and a joint steering committee formed by the American College of Radiology, ESUR, and the AdMeTech Foundation have recently announced an updated version of the proposals. We summarise the main proposals of PI-RADS version 2, explore the evidence behind the recommendations, and highlight key differences for the benefit of those already familiar with the original.

Updates in advanced diffusion-weighted magnetic resonance imaging techniques in the evaluation of prostate cancer

Diffusion-weighted magnetic resonance imaging (DW-MRI) is considered part of the standard imaging protocol for the evaluation of patients with prostate cancer. It has been proven valuable as a functional tool for qualitative and quantitative analysis of prostate cancer beyond anatomical MRI sequences such as T2-weighted imaging. This review discusses ongoing controversies in DW-MRI acquisition, including the optimal number of b-values to be used for prostate DWI, and summarizes the current literature on the use of advanced DW-MRI techniques. These include intravoxel incoherent motion imaging, which better accounts for the non-mono-exponential behavior of the apparent diffusion coefficient as a function of b-value and the influence of perfusion at low b-values. Another technique is diffusion kurtosis imaging (DKI). Metrics from DKI reflect excess kurtosis of tissues, representing its deviation from Gaussian diffusion behavior. Preliminary results suggest that DKI findings may have more value than findings from conventional DW-MRI for the assessment of prostate cancer.

High and ultra-high b-value diffusion-weighted imaging in prostate cancer: a quantitative analysis

BACKGROUND

Diffusion-weighted imaging (DWI) is routinely used in magnetic resonance imaging (MRI) of prostate cancer. However, the routine use of b values higher than 1000 s/mm(2) is not clear up to present. Moreover, the complex diffusion behavior of malignant and benign prostate tissues hampers precise predictions of contrast in DWI images and apparent diffusion coefficient (ADC) maps.

PURPOSE

To quantitatively analyze DWI with different b values in prostate cancer and to identify b values best suitable for cancer detection.

MATERIAL AND METHODS

Forty-one patients with histologically proven prostate cancer were examined with high resolution T2-weighted imaging and DWI at 3 Tesla. Five different b values (0, 800, 1000, 1500, 2000 s/mm(2)) were applied. ADC values of tumors and reference areas were measured on ADC maps derived from different pairs of b values. Furthermore, signal intensities of DW images of tumors and reference areas were measured. For analysis, contrast ratios of ADC values and signal intensities of DW images were calculated and compared.

RESULTS

No significant differences were found between contrast ratios measured on ADC maps of all analyzed b value pairs (P = 0.43). Contrast ratios calculated from signal intensities of DW images were highest at b values of 1500 and 2000 s/mm(2) and differed significantly from contrast ratios at b values of 800 and 1000 s/mm(2) (P < 0.01).

CONCLUSION

Whereas contrast in ADC maps does not significantly change with different b values, contrast ratios of DW images are significantly higher at b-values of 1500 and 2000 s/mm(2) in comparison to b values of 800 and 1000 s/mm(2). Therefore, diagnostic performance of DWI in prostate cancer might be increased by application of b values higher than 1000 s/mm(2).

Diffusion-weighted imaging of prostate cancer: effect of b-value distribution on repeatability and cancer characterization

PURPOSE

To evaluate the effect of b-value distribution on the repeatability and Gleason score (GS) prediction of prostate cancer (PCa).

METHODS

Fifty PCa patients underwent two repeated 3T diffusion-weighted imaging (DWI) examinations using 12 b values in the range from 0 to 2000s/mm(2) and diffusion time of 20.3ms. Mean signal intensities of regions of interest, placed in PCa using whole mount prostatectomy sections as the reference, were fitted using monoexponential, kurtosis, stretched exponential, and biexponential models. In total, 4083 different b-value combinations consisting of 2 to 12 b values were evaluated. Repeatability was assessed by intraclass correlation coefficient, ICC(3,1), and coefficient of repeatability (CoR). Areas under receiver operating characteristic curve (AUCs) for PCa characterization were estimated while the correlation of the fitted values with GS groups (3+3, 3+4, >3+4) was evaluated by using the Spearman correlation coefficient (ρ).

RESULTS

The parameters of monoexponential, kurtosis, and stretched exponential models estimated using only 4-5, 5-7, 5-7 b values, respectively, had similar ICC(3,1), CoR, AUC, and ρ values as the parameters estimated using all 12 b values. Optimized b-value distributions demonstrated improved ICC(3,1) and CoR values but failed to improve AUC and ρ values. The parameters of biexponential model demonstrated the worst repeatability and diagnostic performance.

CONCLUSION

B-value distribution influences mainly the repeatability of DWI-derived parameters rather than the diagnostic performance.

Advances in diffusion-weighted imaging

Diffusion-weighted imaging (DWI) has become a routine component of clinical MR imaging. Its unique soft tissue contrast mechanism exploits differences in the motion of water molecules in vivo at a biologically meaningful scale. The clinical potential of DWI in lesion detection, characterization, and response assessment has been explored. This review briefly covers basic principles of DWI and introduces advances, specifically for abdominopelvic organs.

Apparent diffusion coefficient values of diffusion-weighted imaging for distinguishing focal pulmonary lesions and characterizing the subtype of lung cancer: a meta-analysis

OBJECTIVES

The potential performance of apparent diffusion coefficient (ADC) values for distinguishing malignant and benign pulmonary lesions, further characterizing the subtype of lung cancer was assessed.

METHODS

PubMed, EMBASE, Cochrane Library, EBSCO, and three Chinese databases were searched to identify eligible studies on diffusion-weighted imaging (DWI) of focal pulmonary lesions. ADC values of malignant and benign lesions were extracted by lesion type and statistically pooled based on a linear mixed model. Further analysis for subtype of lung cancer was also performed. The methodological quality was assessed using the quality assessment of diagnostic accuracy studies tool.

RESULTS

Thirty-four articles involving 2086 patients were included. Malignant pulmonary lesions have significantly lower ADC values than benign lesions [1.21 (95% CI, 1.19-1.22) mm(2)/s vs. 1.76 (95% CI, 1.72-1.80) mm(2)/s; P < 0.05]. There is a significant difference between ADC values of small cell lung cancer and non-small cell lung cancer (P < 0.05), while the differences were not significant among histological subtypes of lung cancer. The methodological quality was relatively high, and the data points from Begg's test indicated that there was probably no obvious publication bias.

CONCLUSIONS

The ADC value is helpful for distinguishing malignant and benign pulmonary lesions and provides a promising method for differentiation of SCLC from NSCLC.

KEY POINTS

• This meta-analysis assesses the role of DWI in pulmonary lesions. • Differentiation and classification subtype of lung cancer is essential for treatment decision-making. • ADC values can help distinguish between malignant and benign lesions. • ADC values might help characterize the subtype of lung cancer.

Non-invasive estimation of prostate cancer aggressiveness using diffusion-weighted MRI and 3D proton MR spectroscopy at 3.0 T

BACKGROUND

Clinical management of prostate cancer increasingly aims to distinguish aggressive types that require immediate and radical treatment from indolent tumors that are candidates for watchful waiting. This requires reliable and reproducible parameters to effectively control potential cancer progression. Magnetic resonance imaging (MRI) may provide a non-invasive means for this purpose.

PURPOSE

To assess the value of diffusion-weighted imaging and proton MR spectroscopy for the prediction of prostate cancer (PCa) aggressiveness.

MATERIAL AND METHODS

In 39 of 64 consecutive patients who underwent endorectal 3-T MRI prior to radical prostatectomy, prostate specimens were analyzed as whole-mount step sections. Apparent diffusion coefficient (ADC), normalized ADC (nADC: tumor/healthy tissue), choline/citrate (CC), and (choline + creatine)/citrate (CCC) ratios were correlated with Gleason scores (GS) from histopathological results. The power to discriminate low (GS ≤ 6) from higher-risk (GS ≥ 7) tumors was assessed with receiver operating characteristics (area under the curve [AUC]). Resulting threshold values were used by a blinded reader to distinguish between aggressive and indolent tumors.

RESULTS

Ninety lesions (1 × GS = 5, 41 × GS = 6, 36 × GS = 7, 12 × GS = 8) were considered. nADC (AUC = 0.90) showed a higher discriminatory power than ADC (AUC = 0.79). AUC for CC and CCC were 0.73 and 0.82, respectively. Using either nADC < 0.46 or CCC > 1.3, as well as both criteria for aggressive PCa, the reader correctly identified aggressive and indolent tumors in 31 (79%), 28 (72%), and 33 of 39 patients (85%), respectively. Predictions of tumor aggressiveness from TRUS-guided biopsies were correct in 27 of 36 patients (75%).

CONCLUSION

The combination of a highly sensitive normalized ADC with a highly specific CCC was found to be well suited to prospectively estimate PCa aggressiveness with a similar diagnostic accuracy as biopsy results.

Characterization of prostate cancer using T2 mapping at 3T: a multi-scanner study

RATIONALE AND OBJECTIVES

To assess the prostate T2 value as a predictor of malignancy on two different 3T scanners.

PATIENTS AND METHODS

Eighty-three pre-prostatectomy multiparametric MRIs were retrospectively evaluated [67 obtained on a General Electric MRI (scanner 1) and 16 on a Philips MRI (scanner 2)]. After correlation with prostatectomy specimens, readers measured the T2 value of regions-of-interest categorized as "cancers", "false positive lesions", or "normal tissue".

RESULTS

On scanner 1, in PZ, cancers had significantly lower T2 values than false positive lesions (P=0.02) and normal tissue (P=2×10(-9)). Gleason≥6 cancers had similar T2 values than false positive lesions and significantly higher T2 values than Gleason≥7 cancers (P=0.009). T2 values corresponding to a 25% and 75% risk of Gleason≥7 malignancy were respectively 132 ms (95% CI: 129-135 ms) and 77 ms (95% CI: 74-81 ms). In TZ, cancers had significantly lower T2 values than normal tissue (P=0.008), but not than false positive findings. Mean T2 values measured on scanner 2 were not significantly different than those measured on scanner 1 for all tissue classes.

CONCLUSION

All tested tissue classes had similar mean T2 values on both scanners. In PZ, the T2 value was a significant predictor of Gleason≥7 cancers.

Histogram analysis of apparent diffusion coefficient at 3.0 T in urinary bladder lesions: correlation with pathologic findings

RATIONALE AND OBJECTIVES

To investigate the potential value of histogram analysis of apparent diffusion coefficient (ADC) obtained at standard (700 s/mm(2)) and high (1500 s/mm(2)) b values on a 3.0-T scanner in the differentiation of bladder cancer from benign lesions and in assessing bladder tumors of different pathologic T stages and to evaluate the diagnostic performance of ADC-based histogram parameters.

MATERIALS AND METHODS

In all, 52 patients with bladder lesions, including benign lesions (n = 7) and malignant tumors (n = 45; T1 stage or less, 23; T2 stage, 7; T3 stage, 8; and T4 stage, 7), were retrospectively evaluated. Magnetic resonance examination at 3.0 T and diffusion-weighted imaging were performed. ADC maps were obtained at two b values (b = 700 and 1500 s/mm(2); ie, ADC-700 and ADC-1500). Parameters of histogram analysis included mean, kurtosis, skewness, and entropy. The correlations between these parameters and pathologic results were revealed. Receiver operating characteristic (ROC) curves were generated to determine the diagnostic value of histogram parameters.

RESULTS

Significant differences were found in mean ADC-700, mean ADC-1500, skewness ADC-1500, and kurtosis ADC-1500 between bladder cancer and benign lesions (P = .002-.032). There were also significant differences in mean ADC-700, mean ADC-1500, and kurtosis ADC-1500 among bladder tumors of different pathologic T stages (P = .000-.046). No significant differences were observed in other parameters. Mean ADC-1500 and kurtosis ADC-1500 were significantly correlated with T stage, respectively (ρ = -0.614, P < .001; ρ = 0.374, P = .011). ROC analysis showed that the combination of mean ADC-1500 and kurtosis ADC-1500 has the maximal area under the ROC curve (AUC, 0.894; P < .001) in the differentiation of benign lesions and malignant tumors, with a sensitivity of 77.78% and specificity of 100%. AUCs for differentiating low- and high-stage tumors were 0.840 for mean ADC-1500 (P < .001) and 0.696 for kurtosis ADC-1500 (P = .015).

CONCLUSIONS

Histogram analysis of ADC-1500 at 3.0 T can be useful in evaluation of bladder lesions. A combination of mean ADC-1500 and kurtosis ADC-1500 may be more beneficial in the differentiation of benign and malignant lesions. Mean ADC-1500 was the most promising parameter for differentiating low- from high-stage bladder cancer.

Optimization of b-value distribution for four mathematical models of prostate cancer diffusion-weighted imaging using b values up to 2000 s/mm(2): simulation and repeatability study

PURPOSE

To find optimal b-value distributions for monoexponential, stretched exponential, kurtosis, and biexponential models of prostate cancer (PCa) diffusion-weighted imaging (DWI) using simulations and repeated DWI examinations.

METHODS

Simulations aiming to minimize estimation accuracy error were performed. Ten PCa patients underwent in total four repeated 3-tesla DWI examinations using 12 equally spaced b values (0-2000 s/mm(2) ). Normalized mean signal intensities of regions-of-interest placed in normal tissue and PCa were fitted. In total, 210 different b-value combinations consisting of six b values, 0 and 100 s/mm(2) included in every b-value distribution, were evaluated in terms of accuracy and repeatability.

RESULTS

The simulations and in vivo DWI data suggest the optimal b-value distribution for the monoexponential model consists of four to five equally distributed b values in the range of 0 to 1200 s/mm(2) . The parameters of the stretched exponential and kurtosis models are best estimated using five to seven b values in the ranges of 300 to 700 and close to 2000 s/mm(2) , in addition to low b value. B-value distribution consisting of eight to 10 b values in the ranges of 0 to 100, 800 to 1200, and 1800 to 2000 s/mm(2) is the preferred method for estimation of the biexponential model parameters of PCa DWI.

CONCLUSION

The optimized b-value distributions demonstrated improved estimation accuracy and repeatability of DWI signal decay-derived parameters.