Comparison Between Gleason Score and Apparent Diffusion Coefficient Obtained from Diffusion-Weighted Imaging of Prostate Cancer Patients

Discrimination between clinical significant and insignificant prostate cancer with apparent diffusion coefficient - a systematic review and meta analysis

Background

Prostate MRI has become a corner stone in diagnosis of prostate cancer (PC). Diffusion weighted imaging and the apparent diffusion coefficient (ADC) can be used to reflect tumor microstructure. The present analysis sought to compare ADC values of clinically insignificant with clinical significant PC based upon a large patient sample.

Methods

MEDLINE library and SCOPUS databases were screened for the associations between ADC and Gleason score (GS) in PC up to May 2019. The primary endpoint of the systematic review was the ADC value of PC groups according to Gleason score. In total 26 studies were suitable for the analysis and included into the present study. The included studies comprised a total of 1633 lesions.

Results

Clinically significant PCs (GS ≥ 7) were diagnosed in 1078 cases (66.0%) and insignificant PCs (GS 5 and 6) in 555 cases (34.0%). The pooled mean ADC value derived from monoexponenantially fitted ADC_mean of the clinically significant PC was 0.86 × 10^− 3 mm²/s [95% CI 0.83–0.90] and the pooled mean value of insignificant PC was 1.1 × 10^− 3 mm²/s [95% CI 1.03–1.18]. Clinical significant PC showed lower ADC values compared to non-significant PC. The pooled ADC values of clinically insignificant PCs were no lower than 0.75 × 10^− 3 mm²/s.

Conclusions

We evaluated the published literature comparing clinical insignificant with clinically prostate cancer in regard of the Apparent diffusion coefficient values derived from magnetic resonance imaging. We identified that the clinically insignificant prostate cancer have lower ADC values than clinically significant, which may aid in tumor noninvasive tumor characterization in clinical routine.

Radiological semantics discriminate clinically significant grade prostate cancer

Background

Identification of imaging traits to discriminate clinically significant prostate cancer is challenging due to the multi focal nature of the disease. The difficulty in obtaining a consensus by the Prostate Imaging and Data Systems (PI-RADS) scores coupled with disagreements in interpreting multi-parametric Magnetic Resonance Imaging (mpMRI) has resulted in increased variability in reporting findings and evaluating the utility of this imaging modality in detecting clinically significant prostate cancer. This study assess the ability of radiological traits (semantics) observed on multi-parametric Magnetic Resonance images (mpMRI) to discriminate clinically significant prostate cancer.

Methods

We obtained multi-parametric MRI studies from 103 prostate cancer patients with 167 targeted biopsies from a single institution. The study was approved by our Institutional Review Board (IRB) for retrospective analysis. The biopsy location had been identified and marked by a clinical radiologist for targeted biopsy based on initial study interpretation. Using the target locations, two study radiologists independently re-evaluated the scans and scored 16 semantic traits on a point scale (up to 5 levels) based on mpMRI images. The semantic traits describe size, shape, and border characteristics of the prostate lesion, as well as presence of disease around lymph nodes (lymphadenopathy). We built a linear classifier model on these semantic traits and related to pathological outcome to identify clinically significant tumors (Gleason Score ≥ 7). The discriminatory ability of the predictors was tested using cross validation method randomly repeated and ensemble values were reported. We then compared the performance of semantic predictors with the PI-RADS predictors.

Results

We found several semantic features individually discriminated high grade Gleason score (ADC-intensity, Homogeneity, early-enhancement, T2-intensity and extraprostatic extention), these univariate predictors had an average area under the receiver operator characteristics (AUROC) ranging from 0.54 to 0.68. Multivariable semantic predictors with three features (ADC-intensity; T2-intensity, enhancement homogenicity) had an average AUROC of 0.7 [0.43, 0.94]. The PI-RADS based predictor had average AUROC of 0.6 [0.47, 0.75].

Conclusion

We find semantics traits are related to pathological findings with relatively higher reproducibility between radiologists. Multivariable predictors formed on these traits shows higher discriminatory ability compared to PI-RADS scores.

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

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

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.

Relationship between Gleason score and apparent diffusion coefficients of diffusion-weighted magnetic resonance imaging in prostate cancer patients

INTRODUCTION

We assessed the correlation between the apparent diffusion coefficient (ADC) and pathological Gleason score (GS) of prostate cancer patients.

METHODS

A total of 125 patients who underwent multiparametric magnetic resonance imaging before radical prostatectomy for prostate cancer were included in this study. ADC values were compared with different GS. We used receiver operating characteristic analysis and determined the ADC cutoff value to differentiate tumours with a GS of 6 from those with a GS ≥7.

RESULTS

We identified 34 patients (27.2%) with a GS of 6; 33 patients (26.4%) with a GS of 7; 22 patients (17.6%) with a GS of 8; and 36 patients (28.8%) with a GS of ≥9. The mean ADC value for disease with a GS of 6 was 0.914 ± 0.161 ×10^-3 mm²/s; GS of 7: 0.741 ± 0.164 ×10^-3 mm²/s; GS of 8: 0.679 ± 0.130 ×10^-3 mm²/s; and GS of ≥9: 0.593 ± 0.089 ×10^-3 mm²/s. An ADC value of 0.830 ×10^-3mm²/s was the best cutoff value to identify prostate cancer with a GS of 6.

CONCLUSIONS

We observed an inverse relationship between GS and ADC value. Moreover, a cutoff ADC value may help differentiate disease with a GS of 6 from disease with a GS ≥7.

A model describing diffusion in prostate cancer

PURPOSE

Quantitative diffusion MRI has frequently been studied as a means of grading prostate cancer. Interpretation of results is complicated by the nature of prostate tissue, which consists of four distinct compartments: vascular, ductal lumen, epithelium, and stroma. Current diffusion measurements are an ill-defined weighted average of these compartments. In this study, prostate diffusion is analyzed in terms of a model that takes explicit account of tissue compartmentalization, exchange effects, and the non-Gaussian behavior of tissue diffusion.

METHOD

The model assumes that exchange between the cellular (ie, stromal plus epithelial) and the vascular and ductal compartments is slow. Ductal and cellular diffusion characteristics are estimated by Monte Carlo simulation and a two-compartment exchange model, respectively. Vascular pseudodiffusion is represented by an additional signal at b = 0. Most model parameters are obtained either from published data or by comparing model predictions with the published results from 41 studies. Model prediction error is estimated using 10-fold cross-validation.

RESULTS

Agreement between model predictions and published results is good. The model satisfactorily explains the variability of ADC estimates found in the literature.

CONCLUSION

A reliable model that predicts the diffusion behavior of benign and cancerous prostate tissue of different Gleason scores has been developed. Magn Reson Med 78:316-326, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Correlation between apparent diffusion coefficients and HER2 status in gastric cancers: pilot study

Background

To evaluate whether apparent diffusion coefficient (ADC) value of gastric cancer obtained from diffusion weighted imaging (DWI) correlates with the HER2 status.

Methods

Forty-five patients, who had been diagnosed with gastric cancer through biopsy, were enrolled in this IRB-approved study. Each patient underwent a DWI (b values: 0 and 1,000 sec/mm²) prior to surgery (curative gastrectomy or palliative resection). Postoperative microscopic findings, HER2 status by immunohistochemical analysis and fluorescence in situ hybridization (FISH) were obtained. HER2 status was compared among gastric cancers with various histopathological features using the chi square test. The ADC values of gastric cancers with positive and negative HER2 were compared using the student t test.

Results

A weak yet significant correlation was observed between the mean ADC values and HER2 status (r = 0.312, P = 0.037) and scores (r = 0.419, P = 0.004). The mean ADC value of HER2-positive gastric cancers was significantly higher than those of HER2-negative tumors (1.211 vs. 0.984 mm²/s, P = 0.020). The minimal ADC value of HER2-positive gastric cancers was significantly higher than those of HER2-negative tumors (1.105 vs. 0.905 × 10⁻³ mm²/s, P = 0.036).

Conclusions

In this pilot study, we have demonstrated that the ADC values of gastric cancer correlate with the HER2 status. Future research is warranted to see if DWI can predict HER2 status and help in tailoring therapy for gastric cancer.

Diffusion-weighted magnetic resonance imaging in patients with prostate cancer treated with radiotherapy

AIMS

To evaluate the utility of a multiparametric 3T magnetic resonance imaging (MRI) study using diffusion-weighted images (DWI) for the assessment of prostate cancer before and after radiotherapy (RT).

METHODS

A total of 34 patients, who received a histologic diagnosis of prostate adenocarcinoma, underwent MRI examination before and after local RT for the assessment of response to treatment. Apparent diffusion coefficient (ADC) values were calculated and compared.

RESULTS

Before RT, DWI shows pathologic restriction of signal, while after RT pathologic restriction of signal was reduced or disappeared. The ADC values were significantly increased after therapy (p&lt;0.05).

CONCLUSIONS

The use of DWI with ADC measurements may be an imaging biomarker in the assessment of prostate cancer.

Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models

BACKGROUND

Prostate cancer is the most common form of cancer and the second leading cause of cancer death in North America. Auto-detection of prostate cancer can play a major role in early detection of prostate cancer, which has a significant impact on patient survival rates. While multi-parametric magnetic resonance imaging (MP-MRI) has shown promise in diagnosis of prostate cancer, the existing auto-detection algorithms do not take advantage of abundance of data available in MP-MRI to improve detection accuracy. The goal of this research was to design a radiomics-based auto-detection method for prostate cancer via utilizing MP-MRI data.

METHODS

In this work, we present new MP-MRI texture feature models for radiomics-driven detection of prostate cancer. In addition to commonly used non-invasive imaging sequences in conventional MP-MRI, namely T2-weighted MRI (T2w) and diffusion-weighted imaging (DWI), our proposed MP-MRI texture feature models incorporate computed high-b DWI (CHB-DWI) and a new diffusion imaging modality called correlated diffusion imaging (CDI). Moreover, the proposed texture feature models incorporate features from individual b-value images. A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models. We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models.

RESULTS

The performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets. The proposed MP-MRI texture feature models outperformed the conventional model (i.e., T2w+DWI) with regard to cancer detection accuracy.

CONCLUSIONS

Comprehensive texture feature models were developed for improved radiomics-driven detection of prostate cancer using MP-MRI. Using a comprehensive set of texture features and a feature selection method, optimal texture feature models were constructed that improved the prostate cancer auto-detection significantly compared to conventional MP-MRI texture feature models.

Diffusion weighted imaging and apparent diffusion coefficient in 3 tesla magnetic resonance imaging of breast lesions

OBJECTIVE

To evaluate the utility of diffusion-weighted-imaging (DWI) and apparent-diffusion-coefficient (ADC) in a 3T magnetic-resonance-imaging (MRI) study of breast cancer. In particular, the study aims to classify ADC-values according to histology either for benign or malignant lesions.

METHODS

110 Breast MRI with MRI-DWI sequences and quantitative evaluation of the ADC were retrospectively reviewed. Results obtained with MRI-DWI and with biopsy were analyzed and ADC values were compared to histological results.

RESULTS

MRI showed a 95.5% sensitivity and a 83.7% specificity. The mean ADC values of benign and malignant lesions were 2.06 ± 0.19 and 1.03 ± 0.07 mm(2)/s, respectively (p < .05).

CONCLUSIONS

DWI and ADC-values could help distinguishing malignant and benign breast masses.

Gastric cancer: The role of diffusion weighted imaging in the preoperative staging

PURPOSE

Evaluate the role of diffusion-weighted-imaging (DWI) in the diagnosis and staging of gastric carcinoma.

MATERIALS AND METHODS

A total of 31 patients with gastric adenocarcinoma, which underwent preoperative staging with 3Tesla Magnetic Resonance Imaging (MRI), were enrolled. Two radiologists evaluated the tumor staging in DWI. Results were compared to postsurgical pathologic findings.

RESULTS

The T factor accuracy of conventional MRI and DWI was 73% and 80% respectively; while the N staging accuracy of conventional MRI and DWI was 80% and 93%, respectively.

CONCLUSION

DWI and apparent diffusion coefficient (ADC) values showed to be useful in preoperative staging of gastric cancer.