Head-To-Head Comparison Between High- and Standard-b-Value DWI for Detecting Prostate Cancer: A Systematic Review and Meta-Analysis

Synthetic diffusion-weighted imaging in prostate cancer diagnosis: a comparison study with different B-value combinations

AIM

To evaluate the impact of different b-value combinations on synthetic diffusion-weighted imaging (sDWI) and determine the sDWI with an optimal b-value combination for prostatic cancer (PCa) diagnosis.

MATERIAL AND METHODS

A retrospective analysis of 68 patients with abnormal prostate-specific antigen (PSA) was conducted. The sDWI images with b value of 1500 s/mm2 were separately reconstructed by the following five b-value combinations: b=0, 200s/mm2 (sDWI0-200); b=600, 800s/mm2 (sDWI600-800); b=0, 600s/mm2 (sDWI0-600); b=200, 800s/mm2 sDWI200-800); b=0, 800s/mm2 (sDWI0-800). Quantitative analysis was performed on the acquired DWI (aDWI) images with b=1500s/mm2 (aDWI1500) and all sDWI images. These six image groups were scored in five aspects for image quality and further reviewed by two radiologists via six protocols: Protocol Ⅰ, T2WI+sDWI0-200; Protocol Ⅱ, T2WI+sDWI600-800; Protocol Ⅲ, T2WI+sDWI0-600; Protocol Ⅳ, T2WI+sDWI200-800; Protocol Ⅴ, T2WI+sDWI0-800; Protocol Ⅵ, T2WI+aDWI1500. The corresponding diagnostic efficacies for PCa were evaluated using receiver operating characteristic (ROC) curves.

RESULTS

Contrast ratio values of all sDWI images were higher than those of aDWI1500 images. Contrast-to-noise ratio values of sDWI0-200 and sDWI600-800 images were lower than those of the rest sDWI images. All subjective quality scores of sDWI0-600, sDWI200-800, and sDWI0-800 were significantly higher than other groups except for background signal suppression. The area under the curve (AUC) of Protocol Ⅲ, Ⅳ, Ⅴ, and Ⅵ was significantly larger than those of other protocols.

CONCLUSION

Different b-value combinations impact the image quality and diagnostic accuracy of sDWI for PCa detection. The combination of b≤200s/mm2 and b≥600s/mm2 revealed to be optimal.

Ultra-high b-value DWI in rectal cancer: image quality assessment and regional lymph node prediction based on radiomics

OBJECTIVES

This study aims to evaluate image quality and regional lymph node metastasis (LNM) in patients with rectal cancer (RC) on multi-b-value diffusion-weighted imaging (DWI).

METHODS

This retrospective study included 199 patients with RC who had undergone multi-b-value DWI. Subjective (five-point Likert scale) and objective assessments of quality images were performed on DWIb1000, DWIb2000, and DWIb3000. Patients were randomly divided into a training (n = 140) or validation cohort (n = 59). Radiomics features were extracted within the whole volume tumor on ADC maps (b = 0, 1000 s/mm2), DWIb1000, DWIb2000, and DWIb3000, respectively. Five prediction models based on selected features were developed using logistic regression analysis. The performance of radiomics models was evaluated with a receiver operating characteristic curve, calibration, and decision curve analysis (DCA).

RESULTS

The mean signal intensity of the tumor (SItumor), signal-to-noise ratio (SNR), and artifact and anatomic differentiability score gradually were decreased as the b-value increased. However, the contrast-to-noise (CNR) on DWIb2000 was superior to those of DWIb1000 and DWIb3000 (4.58 ± 0.86, 3.82 ± 0.77, 4.18 ± 0.84, p < 0.001, respectively). The overall image quality score of DWIb2000 was higher than that of DWIb3000 (p < 0.001) and showed no significant difference between DWIb1000 and DWIb2000 (p = 0.059). The area under curve (AUC) value of the radiomics model based on DWIb2000 (0.728) was higher than conventional ADC maps (0.690), DWIb1000 (0.699), and DWIb3000 (0.707), but inferior to multi-b-value DWI (0.739) in predicting LNM.

CONCLUSION

DWIb2000 provides better lesion conspicuity and LNM prediction than DWIb1000 and DWIb3000 in RC.

CLINICAL RELEVANCE STATEMENT

DWIb2000 offers satisfactory visualization of lesions. Radiomics features based on DWIb2000 can be applied for preoperatively predicting regional lymph node metastasis in rectal cancer, thereby benefiting the stratified treatment strategy.

KEY POINTS

Lymph node staging is required to determine the best treatment plan for rectal cancer. DWIb2000 provides superior contrast-to-noise ratio and lesion conspicuity and its derived radiomics best predict lymph node metastasis. DWIb2000 may be recommended as the optimal b-value in rectal MRI protocol.

Unveiling the diagnostic potential of diffusion kurtosis imaging and intravoxel incoherent motion for detecting and characterizing prostate cancer: a meta-analysis

PURPOSE

This study aims to assess the diagnostic capabilities of Diffusion Kurtosis Imaging (DKI) and Intravoxel Incoherent Motion (IVIM) in prostate cancer (PCa) detection and characterization.

MATERIALS

A comprehensive search was conducted across PubMed, Scopus, Web of Science, and the Cochrane Library for articles published up to September 10, 2023, that evaluated the diagnostic efficacy of MD, MK, Dt, f, and Dp parameters. Data were pooled using a bivariate mixed-effects regression model and analyzed with R software.

RESULTS

In total, 27 studies were included. The analysis revealed distinct diagnostic efficacies for DKI and IVIM. In the overall model, sensitivity and specificity were 0.807 and 0.797, respectively, with prospective studies showing higher specificity (0.858, p = 0.024). The detection model yielded increased sensitivity (0.845) and specificity (0.812), with DKI outperforming IVIM in both metrics (sensitivity: 0.87, p = 0.043; specificity: 0.837, p = 0.26); MD had high sensitivity (0.88) and specificity (0.82), while MK's specificity was significantly higher (0.854, p = 0.04); Dp's sensitivity was significantly lower (0.64, p = 0.016). In characterization, sensitivity and specificity were 0.708 and 0.735, respectively, with no significant differences between DKI and IVIM or Gleason Scores; MK had higher sensitivity (0.78, p = 0.039), and f's sensitivity was significantly lower (0.51, p = 0.019).

CONCLUSION

In summary, the study underscores DKI's enhanced diagnostic accuracy over IVIM in detecting PCa, with MK standing out for its precision. Conversely, Dp and f lag in diagnostic performance. Despite these promising results, the study highlights the imperative for standardized protocols and study designs to achieve reliable and consistent outcomes.

Deep learning-based prediction of tumor aggressiveness in RCC using multiparametric MRI: a pilot study

OBJECTIVE

To investigate the value of multiparametric magnetic resonance imaging (MRI) as a non-invasive method to predict the aggressiveness of renal cell carcinoma (RCC) by developing a convolutional neural network (CNN) model and fusing it with clinical characteristics.

METHODS

Multiparametric abdominal MRI was performed on 47 pathologically confirmed RCC patients between 2019 and 2023. Preoperative MRI was performed on all patients to assess their clinical characteristics. The CNN model was developed and validated to assess the predictive value of b value images, combined b value images, apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and their parametric maps for RCC aggressiveness. The least absolute shrinkage and selection operator (LASSO) regression was used to identify clinical features highly correlated with RCC aggressiveness. These clinical features were combined with selected b values to develop a fusion model. All models were evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

A total of 47 patients (mean age, 56.17 ± 1.70 years; 37 men, 10 women) were evaluated. LASSO regression identified renal sinus/perirenal fat invasion, tumor stage, and tumor size as the most significant clinical features. The combined b values of b = 0,1000 achieved an area under the curve (AUC) of 0.642 (95% CI: 0.623-0.661), and b = 0,100,1000 achieved an AUC of 0.657 (95% CI: 0.647-0.667). The fusion model combining clinical features with b = 0,1000 yielded the highest performance with an AUC of 0.861 (95% CI: 0.667-0.992), demonstrating superior predictive accuracy compared to the other models.

CONCLUSION

Deep learning using a CNN fusion model, integrating multiple b value images and clinical features, could effectively promote the preoperative prediction of tumor aggressiveness in RCC patients.

Diffusion MRI in prostate cancer with ultra-strong whole-body gradients

Diffusion-weighted MRI (dMRI) is universally recommended for the detection and classification of prostate cancer (PCa), with PI-RADS recommendations to acquire b-values of ≥1.4 ms/μm2. However, clinical dMRI suffers from a low signal-to-noise ratio (SNR) as the consequence of prolonged echo times (TEs) attributable to the limited gradient power in the range of 40-80 mT/m. To overcome this, MRI systems with strong gradients have been designed but so far have mainly been applied in the brain. The aim of this work was to assess the feasibility, data quality, SNR and contrast-to-noise ratio (CNR) of measurements in PCa with a 300 mT/m whole-body system. A cohort of men without and with diagnosed PCa were imaged on a research-only 3T Connectom Siemens MRI system equipped with a gradient amplitude of 300 mT/m. dMRI at high b-values were acquired using high gradient amplitudes and compared with gradient capabilities mimicking clinical systems. Data artefacts typically amplified with stronger gradients were assessed and their correction evaluated. The SNR gains and lesion-to-healthy tissue CNR were statistically tested investigating the effect of protocol and b-value. The diagnostic quality of the images for different dMRI protocols was assessed by an experienced radiologist using a 5-point Likert scale and an adapted PI-QUAL scoring system. The strong gradients for prostate dMRI allowed a significant gain in SNR per unit time compared with clinical gradients. Furthermore, a 1.6-2.1-fold increase in CNR was observed. Despite the more pronounced artefacts typically associated with strong gradients, a satisfactory correction could be achieved. Smoother and less biased parameter maps were obtained with protocols at shorter TEs. The results of this study show that dMRI in PCa with a whole-body 300-mT/m scanner is feasible without a report of physiological effects, SNR and CNR can be improved compared with lower gradient strengths, and artefacts do not negate the benefits of strong gradients and can be ameliorated. This assessment provides the first essential step towards unveiling the full potential of cutting-edge scanners, now increasingly becoming available, to advance early detection and diagnostic precision.

Comparison of continuous-time random walk and fractional order calculus models in characterizing breast lesions using histogram analysis

OBJECTIVE

To compare the diagnostic performance of different mathematical models for DWI and explore whether parameters reflecting spatial and temporal heterogeneity can demonstrate better diagnostic accuracy than the diffusion coefficient parameter in distinguishing benign and malignant breast lesions, using whole-tumor histogram analysis.

METHODS

This retrospective study was approved by the institutional ethics committee and included 104 malignant and 42 benign cases. All patients underwent breast magnetic resonance imaging (MRI) with a 3.0 T MR scanner using the simultaneous multi-slice (SMS) readout-segment ed echo-planar imaging (rs-EPI). Histogram metrics of Mono- apparent diffusion coefficient (ADC), CTRW, and FROC-derived parameters were compared between benign and malignant breast lesions, and the diagnostic performance of each diffusion parameter was evaluated. Statistical analysis was performed using Mann-Whitney U test and receiver operating characteristic (ROC) curve.

RESULTS

The DFROC-median exhibited the highest AUC for distinguishing benign and malignant breast lesions (AUC = 0.965). The temporal heterogeneity parameter αCTRW-median generated a statistically higher AUC compared to the spatial heterogeneity parameter βCTRW-median (AUC = 0.850 and 0.741, respectively; p = 0.047). Finally, the combination of median values of CTRW parameters displayed a slightly higher AUC than that of FROC parameters, with no significant difference however (AUC = 0.971 and 0.965, respectively; p = 0.172).

CONCLUSIONS

The diffusion coefficient parameter exhibited superior diagnostic performance in distinguishing breast lesions when compared to the temporal and spatial heterogeneity parameters.

Enhanced Image Processing Using Complex Averaging in Diffusion-Weighted Imaging of the Prostate: The Impact on Image Quality and Lesion Detectability

Diffusion-weighted images of the prostate can suffer from a "hazy" background in low signal-intensity areas. We hypothesize that enhanced image processing (EIP) using complex averaging reduces artifacts, noise, and distortion in conventionally acquired diffusion-weighted images and synthesized high b-value images, thus leading to higher image quality and better detection of potentially malignant lesions. Conventional DWI trace images with a b-value of 1000 s/mm² (b1000), calculated images with a b-value of 2000 s/mm² (cb2000), and ADC maps of 3T multiparametric prostate MRIs in 53 patients (age 68.8 ± 10 years) were retrospectively evaluated. Standard images were compared to images using EIP. In the standard images, 36 lesions were detected in the peripheral zone and 20 in the transition zone. In 13 patients, EIP led to the detection of 8 additional lesions and the upgrading of 6 lesions; 6 of these patients were diagnosed with prostate carcinoma Gleason 7 or 8. EIP improved qualitative ratings for overall image quality and lesion detectability. Artifacts were significantly reduced in the cb2000 images. Quantitative measurements for lesion detectability expressed as an SI ratio were significantly improved. EIP using complex averaging led to image quality improvements in acquired and synthesized DWI, potentially resulting in elevated diagnostic accuracy and management changes.

Ultra-high b-Value DWI in predicting progression risk of locally advanced rectal cancer: a comparative study with routine DWI

BACKGROUND

The prognosis prediction of locally advanced rectal cancer (LARC) was important to individualized treatment, we aimed to investigate the performance of ultra-high b-value DWI (UHBV-DWI) in progression risk prediction of LARC and compare with routine DWI.

METHODS

This retrospective study collected patients with rectal cancer from 2016 to 2019. Routine DWI (b = 0, 1000 s/mm²) and UHBV-DWI (b = 0, 1700 ~ 3500 s/mm²) were processed with mono-exponential model to generate ADC and ADCuh, respectively. The performance of the ADCuh was compared with ADC in 3-year progression free survival (PFS) assessment using time-dependent ROC and Kaplan-Meier curve. Prognosis model was constructed with ADCuh, ADC and clinicopathologic factors using multivariate COX proportional hazard regression analysis. The prognosis model was assessed with time-dependent ROC, decision curve analysis (DCA) and calibration curve.

RESULTS

A total of 112 patients with LARC (TNM-stage II-III) were evaluated. ADCuh performed better than ADC for 3-year PFS assessment (AUC = 0.754 and 0.586, respectively). Multivariate COX analysis showed that ADCuh and ADC were independent factors for 3-year PFS (P < 0.05). Prognostic model 3 (TNM-stage + extramural venous invasion (EMVI) + ADCuh) was superior than model 2 (TNM-stage + EMVI + ADC) and model 1 (TNM-stage + EMVI) for 3-year PFS prediction (AUC = 0.805, 0.719 and 0.688, respectively). DCA showed that model 3 had higher net benefit than model 2 and model 1. Calibration curve demonstrated better agreement of model 1 than model 2 and model 1.

CONCLUSIONS

ADCuh from UHBV-DWI performed better than ADC from routine DWI in predicting prognosis of LARC. The model based on combination of ADCuh, TNM-stage and EMVI could help to indicate progression risk before treatment.

Tuning the optimal diffusion-weighted MRI parameters on a 0.35-T MR-Linac for clinical implementation: A phantom study

PURPOSE

This study aims to assess the quality of a new diffusion-weighted imaging (DWI) sequence implemented on an MR-Linac MRIdian system, evaluating and optimizing the acquisition parameters to explore the possibility of clinically implementing a DWI acquisition protocol in a 0.35-T MR-Linac.

MATERIALS AND METHODS

All the performed analyses have been carried out on two types of phantoms: a homogeneous 24-cm diameter polymethylmethacrylate (PMMA) sphere (SP) and a homemade phantom (HMP) constating in a PMMA cylinder filled with distilled water with empty sockets into which five cylindrical vials filled with five different concentrations of methylcellulose water solutions have been inserted. SP was used to evaluate the dependence of diffusion gradient inhomogeneity artifacts on gantry position. Four diffusion sequences with b-values of 500 s/mm2 and 3 averages have been acquired: three with diffusion gradients in the three main directions (phase direction, read direction, slice direction) and one with the diffusion gradients switched off. The dependence of diffusion image uniformity and SNR on the number of averages in the MR sequences was also investigated to determine the optimal number of averages. Finally, the ADC values of HMP have been computed and then compared between images acquired in the scanners at 0.35 and 1.5 T.

RESULTS

In order to acquire high-quality artifact-free DWI images, the "slice" gradient direction has been identified to be the optimal one and 0° to be the best gradient angle. Both the SNR ratio and the uniformity increase with the number of averages. A threshold value of 80 for SNR and 85% for uniformity was adopted to choose the best number of averages. By making a compromise between time and quality and limiting the number of b-values, it is possible to reduce the acquisition time to 78 s. The Passing-Bablok test showed that the two methods, with 0.35 and 1.5 T scanners, led to similar results.

CONCLUSION

The quality of the DWI has been accurately evaluated in relation to different sequence parameters, and optimal parameters have been identified to select a clinical protocol for the acquisition of ADC maps sustainable in the workflow of a hybrid radiotherapy system with a 0.35-T MRI scanner.

Amide Proton Transfer Could Provide More Accurate Lesion Characterization in the Transition Zone of the Prostate

BACKGROUND

There is an overlap comparing transition zone prostate cancer (TZ PCa) and benign prostatic hyperplasia (BPH) on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI), creating additional challenges for assessment of TZ tumors on MRI.

PURPOSE

To evaluate whether amide proton transfer-weighted (APTw) imaging provides new diagnostic ideas for TZ PCa.

STUDY TYPE

Prospective.

POPULATION

A total of 51 TZ PCa patients (age, 49-89), 44 stromal BPH (age, 57-92), and 45 glandular BPH patients (age, 56-92).

FIELD STRENGTH/SEQUENCE

A 3 T; T2WI turbo spin echo (TSE), quantitative T2*-weighted imaging, DWI echo planar imaging, 3D APTw TSE.

ASSESSMENT

Differences in APTw, apparent diffusion coefficient (ADC), and T2* among three lesions were compared by one-way analysis of variance (ANOVA). Regions of interest were drawn by two radiologists (X.Q.Z. and X.Y.Q., with 21 and 15 years of experience, respectively).

STATISTICAL TESTS

Multivariable logistic regression analyses; ANOVA with post hoc testing; receiver operator characteristic curve analysis; Delong test. Significance level: P < 0.05.

RESULTS

APTw among TZ PCa, stromal BPH, and glandular BPH (3.48% ± 0.83% vs. 2.76% ± 0.49% vs. 2.72% ± 0.45%, respectively) were significantly different except between stromal BPH and glandular BPH (P > 0.99). Significant differences were found in ADC (TZ PCa 0.76 ± 0.16 × 10^-3  mm² /sec vs. stromal BPH 0.91 ± 0.14 × 10^-3  mm² /sec vs. glandular BPH 1.08 ± 0.18 × 10^-3  mm² /sec) among three lesions. APTw (OR = 12.18, 11.80, respectively) and 1/ADC (OR = 703.87, 181.11, respectively) were independent predictors of TZ PCa from BPH and stromal BPH. The combination of APTw and ADC had better diagnostic performance in the identification of TZ PCa from BPH and stromal BPH.

DATA CONCLUSION

APTw imaging has the potential to be of added value to ADC in differentiating TZ PCa from BPH and stromal BPH.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Risk Stratification and Artificial Intelligence in Early Magnetic Resonance Imaging-based Detection of Prostate Cancer

Magnetic resonance imaging (MRI) has transformed the diagnostic pathway for prostate cancer and now plays an upfront role before prostate biopsies. If a suspicious lesion is found on MRI, the subsequent biopsy can be targeted. A sharp increase is expected in the number of men who will undergo prostate MRI. The challenge is to provide good image quality and diagnostic accuracy while meeting the demands of the expected higher workload. A possible solution to this challenge is to include a suitable risk stratification tool before imaging. Other solutions, such as smarter and shorter MRI protocols, need to be explored. For most of these solutions, artificial intelligence (AI) can play an important role. AI applications have the potential to improve the diagnostic quality of the prostate MRI pathway and speed up the work. PATIENT SUMMARY: The use of prostate magnetic resonance imaging (MRI) for diagnosis of prostate cancer is increasing. Risk stratification of patients before imaging and the use of shorter scan protocols can help in managing MRI resources. Artificial intelligence can also play a role in automating some tasks.

Canadian Association of Radiologists Prostate MRI White Paper

Prostate cancer is the most common malignancy and the third most common cause of death in Canadian men. In light of evolving diagnostic pathways for prostate cancer and the increased use of MRI, which now includes its use in men prior to biopsy, the Canadian Association of Radiologists established a Prostate MRI Working Group to produce a white paper to provide recommendations on establishing and maintaining a Prostate MRI Programme in the context of the Canadian healthcare system. The recommendations, which are based on available scientific evidence and/or expert consensus, are intended to maintain quality in image acquisition, interpretation, reporting and targeted biopsy to ensure optimal patient care. The paper covers technique, reporting, quality assurance and targeted biopsy considerations and includes appendices detailing suggested reporting templates, quality assessment tools and sample image acquisition protocols relevant to the Canadian healthcare context.

PI-RADS: multiparametric MRI in prostate cancer

Multiparametric MRI of the prostate gland has become the initial evaluation of biopsy naïve men with a clinical suspicion for prostate cancer. PI-RADS 2.1 is a joint initiative and framework for prostate MRI acquisition and reporting, which aims to standardize technique and interpretation across centers. Building upon experience accrued following the introduction of PI-RADS 2.0, version 2.1 provides key updates and important clarifications, although it is intended to be an active document, which continues to be updated. Continued advances in our understanding of prostate cancer and progress in imaging technology will undoubtedly shape future iterations of the reporting system.

Prostate cancer in PI-RADS scores 1 and 2 version 2.1: a comparison to previous PI-RADS versions

PURPOSE

To evaluate the validity of PI-RADS categories 1 and 2 version 2.1 (V2.1) as predictors of the absence of carcinoma and to reevaluate lesions that were analysed as suspicious prior to PI-RADS or according to PI-RADS versions 1 and 2 and classified as PI-RADS 1 or 2 in V2.1.

METHODS

Retrospective evaluation of 1170 multiparametric MRIs performed at one academic teaching hospital (2012-2019). Study cohort comprised 188 men that achieved PI-RADS scores 1 or 2 (V2.1) and underwent systematic and targeted biopsy, split into one group with suspect findings in the original reports that were created prior to PI-RADS or with version 1 and 2, and another group with unremarkable reports. Differences in presence of prostate cancer and PSA density were assessed by Chi-square and Fisher's exact test, and the negative predictive value (NPV) for both groups was conducted.

RESULTS

The NPV for clinically significant carcinoma (csCa) was 89.1% for 55 men with suspect findings in the original report and 93.2% for 133 men with negative MRI. There was no difference between the groups regarding the detection of csCa (p = 0.103). PSA density was significantly higher in the group with suspect original reports (p = 0.015).

CONCLUSION

A PI-RADS score 1 or 2 appears less likely to miss existing prostate cancer, although a small amount of csCa can be overlooked. In case of clinical suspicion or elevated PSA density and PI-RADS score 1 or 2, an individual decision has to be taken if biopsy is necessary or if monitoring is sufficient.

Detection of High-Grade Prostate Cancer With a Super High B-value (4000 s/mm2) in Diffusion-Weighted Imaging Sequences by Magnetic Resonance Imaging

Introduction: High-grade adenocarcinoma of the prostate tends to have denser glandular structures and a prominent desmoplastic reaction, which could be detected by magnetic resonance imaging (MRI) with a super-high b-value in diffusion-weighted imaging (DWI) sequence, to differentiate it from low-grade carcinomas.

Objective: To evaluate the diagnostic validity of the diffusion sequence with values ​​of b4000 s/mm2 for the diagnosis of high-grade prostate cancer (Gleason score ≥ 7).

Materials and methods: It is a retrospective analytical study of male patients who have undergone a prostate biopsy and count with a prostate MRI with a DWI sequence of a super-high b-value (4000 s/mm2).

Results: The sensitivity of the diffusion sequence with b4000 s/mm2 values ​​to classify as positive for prostate cancer was 57.14% as compared to biopsy. The specificity of the diffusion sequence with b4000 s/mm2 values ​​classifying patients with prostate carcinoma as negative was 84.62%. The probability that the diffusion sequence with b4000 s/mm2 values ​​classifies patients with prostate cancer was 80%. The probability that the diffusion sequence with b4000 s/mm2 values ​​does not classify patients with prostate cancer was 64.71%. The proportion of patients adequately classified with prostate cancer using the diffusion sequence with b4000 s/mm2 values ​​was 70.37%.

Conclusions: The study shows that using the diffusion sequence with values of b4000 s/mm2 is an optimal value that serves as a tool to be able to decant those high-risk carcinomas with those of low risk; however, it is not a definitive method of diagnosis that could replace the performance of a biopsy. Since the study sample was limited, these results cannot be interpreted as reliable for diagnosing high-grade prostate cancer and should encourage future studies on a larger scale population to obtain significant evidence for a non-invasive diagnostic tool with a better cost-benefit for the patient.

Diagnostic value of thyroid micronodules with high b-value diffusion weighted imaging: Comparative study with high-resolution ultrasound

PURPOSE

This study aims to compare the diagnostic performance of two imaging methods for thyroid nodules ≤1.0 cm and reduce unnecessary overdiagnosis.

METHODS

A retrospective study was conducted on 80 patients with pathologically confirmed solitary thyroid micronodules underwent both high-resolution ultrasound (HRUS) and High b-value (2000 s/mm²) diffusion weighted imaging (DWI). Intra- and interobserver agreement (Intraclass correlation coefficient) was followed by Kruskal-Wallis test to detect whether the quantitative apparent diffusion coefficient (ADC) and thyroid nodule subgroups were related. Cohen's kappa analysis was applied to assess the interobserver consistency of DWI and HRUS characteristics. The receiver operating characteristic curves were adopted for evaluating the diagnostic performance of thyroid malignancy. The sensitivity, specificity, and accuracy of the two imaging methods were compared using the McNemar's test and Kappa test.

RESULTS

A total of 80 patients were included, consisting of 43 malignant and 37 benign micronodules. The sensitivity, specificity and accuracy of DWI combined with rADC (ADCmin to ADCn ratio) for the diagnosis of thyroid micronodules were 83.7%, 89.2% and 86.3%, respectively. The area under the curve (AUC) was 0.91 (95% confidence interval [CI]: 0.84-0.97). The sensitivity, specificity and accuracy of HRUS diagnosis were 100%, 62.16% and 82.5%, respectively.

CONCLUSION

High b-value DWI is superior to HRUS for evaluating the diagnostic performance of solid thyroid micronodules. DWI and its ADC quantitative analysis could be added to the evaluation of thyroid micronodules to improve the specificity of diagnosis, reduce overdiagnosis and avoid unnecessary biopsies or surgeries.

Robotic Transrectal Computed Tomographic Ultrasound with Artificial Neural Network Analysis: First Validation and Comparison with MRI-Guided Biopsies and Radical Prostatectomy

INTRODUCTION

There is still a lack of availability of high-quality multiparametric magnetic resonance imaging (mpMRI) interpreted by experienced uro-radiologists to rule out clinically significant PC (csPC). Consequently, we developed a new imaging method based on computed tomographic ultrasound (US) supported by artificial neural network analysis (ANNA).

METHODS

Two hundred and two consecutive patients with visible mpMRI lesions were scanned and recorded by robotic CT-US during mpMRI-TRUS biopsy. Only significant index lesions (ISUP ≥2) verified by whole-mount pathology were retrospectively analyzed. Their visibility was reevaluated by 2 blinded investigators by grayscale US and ANNA.

RESULTS

In the cohort, csPC was detected in 105 cases (52%) by mpMRI-TRUS biopsy. Whole-mount histology was available in 44 cases (36%). In this subgroup, mean PSA level was 8.6 ng/mL, mean prostate volume was 33 cm3, and mean tumor volume was 0.5 cm3. Median PI-RADS and ISUP of index lesions were 4 and 3, respectively. Index lesions were visible in grayscale US and ANNA in 25 cases (57%) and 30 cases (68%), respectively. Combining CT-US-ANNA, we detected index lesions in 35 patients (80%).

CONCLUSIONS

The first results of multiparametric CT-US-ANNA imaging showed promising detection rates in patients with csPC. US imaging with ANNA has the potential to complement PC diagnosis.

Diagnosis of transition zone prostate cancer by multiparametric MRI: added value of MR spectroscopic imaging with sLASER volume selection

Background

Current multiparametric MRI (mp-MRI) in routine clinical practice has poor-to-moderate diagnostic performance for transition zone prostate cancer. The aim of this study was to evaluate the potential diagnostic performance of novel ¹H magnetic resonance spectroscopic imaging (MRSI) using a semi-localized adiabatic selective refocusing (sLASER) sequence with gradient offset independent adiabaticity (GOIA) pulses in addition to the routine mp-MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and quantitative dynamic contrast enhancement (DCE) for transition zone prostate cancer detection, localization and grading.

Methods

Forty-one transition zone prostate cancer patients underwent mp-MRI with an external phased-array coil. Normal and cancer regions were delineated by two radiologists and divided into low-risk, intermediate-risk, and high-risk categories based on TRUS guided biopsy results. Support vector machine models were built using different clinically applicable combinations of T2WI, DWI, DCE, and MRSI. The diagnostic performance of each model in cancer detection was evaluated using the area under curve (AUC) of the receiver operating characteristic diagram. Then accuracy, sensitivity and specificity of each model were calculated. Furthermore, the correlation of mp-MRI parameters with low-risk, intermediate-risk and high-risk cancers were calculated using the Spearman correlation coefficient.

Results

The addition of MRSI to T2WI + DWI and T2WI + DWI + DCE improved the accuracy, sensitivity and specificity for cancer detection. The best performance was achieved with T2WI + DWI + MRSI where the addition of MRSI improved the AUC, accuracy, sensitivity and specificity from 0.86 to 0.99, 0.83 to 0.96, 0.80 to 0.95, and 0.85 to 0.97 respectively. The (choline + spermine + creatine)/citrate ratio of MRSI showed the highest correlation with cancer risk groups (r = 0.64, p < 0.01).

Conclusion

The inclusion of GOIA-sLASER MRSI into conventional mp-MRI significantly improves the diagnostic accuracy of the detection and aggressiveness assessment of transition zone prostate cancer.

Scan Time Reduction in Intravoxel Incoherent Motion Diffusion-Weighted Imaging and Diffusion Kurtosis Imaging of the Abdominal Organs: Using a Simultaneous Multislice Technique With Different Acceleration Factors

OBJECTIVE

To investigate the feasibility of quantitative intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) analyses in the upper abdominal organs by simultaneous multislice diffusion-weighted imaging (SMS-DWI).

SUBJECTS AND METHODS

In this prospective study, a total of 32 participants underwent conventional DWI (C-DWI) and SMS-DWI sequences with acceleration factors of 2 and 3 (SMS2-DWI and SMS3-DWI, respectively) in the upper abdomen with multiple b-values (0, 10, 20, 50, 80, 100, 150, 200, 500, 800, 1000, 1500, and 2000 seconds/mm2) on a 3 T system (MAGNETOM Prisma; Siemens Healthcare, Erlangen, Germany). Image quality and quantitatively measurements of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), mean kurtosis (MK), and mean apparent diffusivity (MD) for the liver, pancreas, kidney cortex and medulla, spleen, and erector spine muscle were compared between the 3 sequences.

RESULTS

The acquisition times for C-DWI, SMS2-DWI, and SMS3-DWI were 10 minutes 57 seconds, 5 minutes 9 seconds, and 3 minutes 54 seconds. For image quality parameters, C-DWI and SMS2-DWI yielded better results than SMS3-DWI (P < 0.05). SMS2-DWI had equivalent IVIM and DKI parameters compared with that of C-DWI (P > 0.05). No statistically significant differences in the ADC, D, f, and MD values between the 3 sequences (P > 0.05) were observed. The D* and MK values of the liver (P = 0.005 and P = 0.012) and pancreas (P = 0.019) between SMS3-DWI and C-DWI were significantly different.

CONCLUSIONS

SMS2-DWI can substantially reduce the scan time while maintaining equivalent IVIM and DKI parameters in the abdominal organs compared with C-DWI.

Can high b-value 3.0 T biparametric MRI with the Simplified Prostate Image Reporting and Data System (S-PI-RADS) be used in biopsy-naïve men?

OBJECTIVE

To analyze the clinical value of high b-value 3.0 T biparametric magnetic resonance with the Simplified Prostate Image Reporting and Data System (S-PI-RADS) in biopsy-naïve men.

METHODS

A retrospective analysis of the data of 224 patients who underwent prostate biopsy (cognitive fusion targeted biopsy combined with systematic biopsy) after a high b-value 3.0 T magnetic resonance examination at Haikou Hospital from July 2018 to July 2020 was performed. Two radiologists performed multi-parameter magnetic resonance imaging (mp-MRI) with the prostate imaging report and data system version 2 (PI-RADS v2) and biparametric magnetic resonance imaging (bp-MRI) with the simplified prostate image reporting and data system (S-PI-RADS). The detection efficacy of the two regimens was evaluated by classifying prostate cancer (PCa) and clinically significant prostate cancer (csPCa) according to pathology, and the statistical significance of the differences between the two regimens was determined by Z-test.

RESULTS

The area under the receiver operating curve (AUC) values of mp-MRI based on PI-RADS v2 and bp-MRI based on S-PI-RADS to detect PCa were 0.905 and 0.892, respectively, while the AUC values for the detection of csPCa were 0.919 and 0.906, respectively. There was no statistically significant difference between the two tests (Z values were 0.909 and 1.145, p > 0.05).

CONCLUSION

There was no significant difference in the detection efficacy of high b-value bp-MRI based on the S-PI-RADS score for prostate cancer and clinically significant prostate cancer compared with the standard PI-RADS v2 score with mp-MRI protocols, which can be applied clinically.

Prostate cancer GTV delineation with biparametric MRI and 68Ga-PSMA-PET: comparison of expert contours and semi-automated methods

OBJECTIVE

The optimal method for delineation of dominant intraprostatic lesions (DIL) for targeted radiotherapy dose escalation is unclear. This study evaluated interobserver and intermodality variability of delineations on biparametric MRI (bpMRI), consisting of T₂ weighted (T₂W) and diffusion-weighted (DWI) sequences, and ⁶⁸Ga-PSMA-PET/CT; and compared manually delineated GTV contours with semi-automated segmentations based on quantitative thresholding of intraprostatic apparent diffusion coefficient (ADC) and standardised uptake values (SUV).

METHODS

16 patients who had bpMRI and PSMA-PET scanning performed prior to any treatment were eligible for inclusion. Four observers (two radiation oncologists, two radiologists) manually delineated the DIL on: (1) bpMRI (GTV_MRI), (2) PSMA-PET (GTV_PSMA) and (3) co-registered bpMRI/PSMA-PET (GTV_Fused) in separate sittings. Interobserver, intermodality and semi-automated comparisons were evaluated against consensus Simultaneous Truth and Performance Level Estimation (STAPLE) volumes, created from the relevant manual delineations of all observers with equal weighting. Comparisons included the Dice Similarity Coefficient (DSC), mean distance to agreement (MDA) and other metrics.

RESULTS

Interobserver agreement was significantly higher (p < 0.05) for GTV_PSMA (DSC: 0.822, MDA: 1.12 mm) and GTV_Fused (DSC: 0.787, MDA: 1.34 mm) than for GTV_MRI (DSC: 0.705, MDA 2.44 mm). Intermodality agreement between GTV_MRI and GTV_PSMA was low (DSC: 0.440, MDA: 4.64 mm). Agreement between semi-automated volumes and consensus GTV was low for MRI (DSC: 0.370, MDA: 8.16 mm) and significantly higher for PSMA-PET (0.571, MDA: 4.45 mm, p < 0.05).

CONCLUSION

⁶⁸Ga-PSMA-PET appears to improve interobserver consistency of DIL localisation vs bpMRI and may be more viable for simple quantitative delineation approaches; however, more sophisticated approaches to semi-automatic delineation factoring for patient- and disease-related heterogeneity are likely required.

ADVANCES IN KNOWLEDGE

This is the first study to evaluate the interobserver variability of prostate GTV delineations with co-registered bpMRI and ⁶⁸Ga-PSMA-PET.

68Ga-PSMA-11 PET/CT combining ADC value of MRI in the diagnosis of naive prostate cancer: Perspective of radiologist

Ga-PSMA-11 positron emission computed tomography /computed tomography (PET/CT) is more sensitive than magnetic resonance imaging (MRI) in detecting prostate cancer (PCa). We evaluated the value of Ga-PSMA-11 PET/CT with MRI in treatment-naive PCa.This retrospective study was approved by the hospital ethics committee. The MRI and Ga-PSMA-11 PET/CT imaging data of 63 cases of highly suspected PCa were enrolled in this study. The SUVmax and apparent diffusion coefficient (ADC), and their ratio, were assessed as diagnostic markers to distinguish PCa from benign disease.There were 107 prostate lesions detected in 63 cases. Forty cases with 64 malignant primary lesions were confirmed PCa, whereas 23 cases had 43 benign lesions. PSMA-avid lesions correlated with hypointense signal on ADC maps and hyperintense signal on diffusion-weighted imaging. The ADC of PCa was lower than that of benign lesions, and SUVmax and SUVmax/ADC of PCa was higher than that of benign lesions (P < .01). ADC had significant negative correlation with Gleason score (GS) and SUVmax, SUVmax, and SUVmax/ADC positively correlated with GS. From ROC analysis, we established cutoff values of ADC, SUVmax, and SUVmax/ADC at 1.02 × 10mm/s, 11.72, and 12.35, respectively, to differentiate PCa from benign lesions. The sensitivity, specificity, and AUC were 90.6%, 58.1%, and 0.816 for ADC, 67.2%, 97.7%, and 0.905 for SUVmax, and 81.2%, 88.4%, and 0.929 for SUVmax/ADC, respectively.Ga-PSMA-11 PET/CT combined with MRI offers higher diagnostic efficacy in the detection of PCa than either modality alone.

Supervised risk predictor of central gland lesions in prostate cancer using 1 H MR spectroscopic imaging with gradient offset-independent adiabaticity pulses

BACKGROUND

Due to the histological heterogeneity of the central gland, accurate detection of central gland prostate cancer remains a challenge.

PURPOSE

To evaluate the efficacy of in vivo 3D ¹ H MR spectroscopic imaging (3D ¹ H MRSI) with a semi-localized adiabatic selective refocusing (sLASER) sequence and gradient-modulated offset-independent adiabatic (GOIA) pulses for detection of central gland prostate cancer. Additionally four risk models were developed to differentiate 1) normal vs. cancer, 2) low- vs. high-risk cancer, 3) low- vs. intermediate-risk cancer, and 4) intermediate- vs. high-risk cancer voxels.

STUDY TYPE

Prospective.

SUBJECTS

Thirty-six patients with biopsy-proven central gland prostate cancer.

FIELD STRENGTH/SEQUENCE

3T MRI / 3D ¹ H MRSI using GOIA-sLASER.

ASSESSMENT

Cancer and normal regions of interest (ROIs) were selected by an experienced radiologist and ¹ H MRSI voxels were placed within the ROIs to calculate seven metabolite signal ratios. Voxels were split into two subsets, 80% for model training and 20% for testing.

STATISTICAL TESTS

Four support vector machine (SVM) models were built using the training dataset. The accuracy, sensitivity, and specificity for each model were calculated for the testing dataset.

RESULTS

High-quality MR spectra were obtained for the whole central gland of the prostate. The normal vs. cancer diagnostic model achieved the highest predictive performance with an accuracy, sensitivity, and specificity of 96.2%, 95.8%, and 93.1%, respectively. The accuracy, sensitivity, and specificity of the low- vs. high-risk cancer and low- vs. intermediate-risk cancer models were 82.5%, 89.2%, 70.2%, and 73.0%, 84.7%, 60.8%, respectively. The intermediate- vs. high-risk cancer model yielded an accuracy, sensitivity, and specificity lower than 55%.

DATA CONCLUSION

The GOIA-sLASER sequence with an external phased-array coil allows for fast assessment of central gland prostate cancer. The classification offers a promising diagnostic tool for discriminating normal vs. cancer, low- vs. high-risk cancer, and low- vs. intermediate-risk cancer.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1926-1936.

Predictive role of PI-RADSv2 and ADC parameters in differentiating Gleason pattern 3 + 4 and 4 + 3 prostate cancer

PURPOSE

To compare the predictive roles of qualitative (PI-RADSv2) and quantitative assessment (ADC metrics), in differentiating Gleason pattern (GP) 3 + 4 from the more aggressive GP 4 + 3 prostate cancer (PCa) using radical prostatectomy (RP) specimen as the reference standard.

METHODS

We retrospectively identified treatment-naïve peripheral (PZ) and transitional zone (TZ) Gleason Score 7 PCa patients who underwent multiparametric 3T prostate MRI (DWI with b value of 0,1400 and where unavailable, 0,500) and subsequent RP from 2011 to 2015. For each lesion identified on MRI, a PI-RADSv2 score was assigned by a radiologist blinded to pathology data. A PI-RADSv2 score ≤ 3 was defined as "low risk," a PI-RADSv2 score ≥ 4 as "high risk" for clinically significant PCa. Mean tumor ADC (ADCT), ADC of adjacent normal tissue (ADCN), and ADCratio (ADCT/ADCN) were calculated. Stepwise regression analysis using tumor location, ADCT and ADCratio, b value, low vs. high PI-RADSv2 score was performed to differentiate GP 3 + 4 from 4 + 3.

RESULTS

119 out of 645 cases initially identified met eligibility requirements. 76 lesions were GP 3 + 4, 43 were 4 + 3. ADCratio was significantly different between the two GP groups (p = 0.001). PI-RADSv2 score ("low" vs. "high") was not significantly different between the two GP groups (p = 0.17). Regression analysis selected ADCT (p = 0.03) and ADCratio (p = 0.0007) as best predictors to differentiate GP 4 + 3 from 3 + 4. Estimated sensitivity, specificity, and accuracy of the predictive model in differentiating GP 4 + 3 from 3 + 4 were 37, 82, and 66%, respectively.

CONCLUSIONS

ADC metrics could differentiate GP 3 + 4 from 4 + 3 PCa with high specificity and moderate accuracy while PI-RADSv2, did not differentiate between these patterns.

The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective

Modern radiation therapy is delivered with great precision, in part by relying on high-resolution multidimensional anatomic imaging to define targets in space and time. The development of quantitative imaging (QI) modalities capable of monitoring biologic parameters could provide deeper insight into tumor biology and facilitate more personalized clinical decision-making. The Quantitative Imaging Network (QIN) was established by the National Cancer Institute to advance and validate these QI modalities in the context of oncology clinical trials. In particular, the QIN has significant interest in the application of QI to widen the therapeutic window of radiation therapy. QI modalities have great promise in radiation oncology and will help address significant clinical needs, including finer prognostication, more specific target delineation, reduction of normal tissue toxicity, identification of radioresistant disease, and clearer interpretation of treatment response. Patient-specific QI is being incorporated into radiation treatment design in ways such as dose escalation and adaptive replanning, with the intent of improving outcomes while lessening treatment morbidities. This review discusses the current vision of the QIN, current areas of investigation, and how the QIN hopes to enhance the integration of QI into the practice of radiation oncology.

Delivering Functional Imaging on the MRI-Linac: Current Challenges and Potential Solutions

Magnetic resonance imaging (MRI) is a highly versatile imaging modality that can be used to measure features of the tumour microenvironment including cell death, proliferation, metabolism, angiogenesis, and hypoxia. Mapping and quantifying these pathophysiological features has the potential to alter the use of adaptive radiotherapy planning. Although these methods are available for use on diagnostic machines, several challenges exist for implementing these functional MRI methods on the MRI-linear accelerators (linacs). This review considers these challenges and potential solutions.

Diffusion MRI of cancer: From low to high b-values

Following its success in early detection of cerebral ischemia, diffusion-weighted imaging (DWI) has been increasingly used in cancer diagnosis and treatment evaluation. These applications are propelled by the rapid development of novel diffusion models to extract biologically valuable information from diffusion-weighted MR signals, and significant advances in MR hardware that has enabled image acquisition with high b-values. This article reviews recent technical developments and clinical applications in cancer imaging using DWI, with a special emphasis on high b-value diffusion models. The article is organized in four sections. First, we provide an overview of diffusion models that are relevant to cancer imaging. The model parameters are discussed in relation to three tissue properties-cellularity, vascularity, and microstructures. An emphasis is placed on characterization of microstructural heterogeneity, given its novelty and close relevance to cancer. Second, we illustrate diffusion MR clinical applications in each of the following three categories: 1) cancer detection and diagnosis; 2) cancer grading, staging, and classification; and 3) cancer treatment response prediction and evaluation. Third, we discuss several practical issues, including selection of image acquisition parameters, reproducibility and reliability, motion management, image distortion, etc., that are commonly encountered when applying DWI to cancer in clinical settings. Lastly, we highlight a few ongoing challenges and provide some possible future directions, particularly in the area of establishing standards via well-organized multicenter clinical trials to accelerate clinical translation of advanced DWI techniques to improving cancer care on a large scale. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:23-40.

MRI-guided localization of the dominant intraprostatic lesion and dose analysis of volumetric modulated arc therapy planning for prostate cancer

PURPOSE

Primary radiation therapy is a curative treatment option for prostate cancer. The aim of this study was to evaluate the detection of the dominant intraprostatic lesion (DIL) with magnetic resonance imaging (MRI) for radiotherapy treatment planning, the comparison with transrectal ultrasound (TRUS)-guided biopsies and the examination of the dose distribution in relation to the DIL location.

MATERIALS AND METHODS

In all, 54 patients with treatment planning MRI for primary radiotherapy of prostate cancer from 03/2015 to 03/2017 at the Universitätsklinikum Würzburg were identified. The localization of the DIL was based on MRI with T2- and diffusion-weighted imaging. After registration of the MR image sets within Pinnacle³ (Philips Radiation Oncology Systems, Fitchburg, WI, USA), the dose distribution was analyzed. The location of the DIL was compared to the pathology reports in a side-based manner.

RESULTS

The DIL mean dose (Dmean) was 77.51 ± 0.77 Gy and in 50/51 cases within the tolerance range or exceeded the prescribed dose. There was a significant difference in Dmean between ventral (n = 21) and dorsal (n = 30) DIL (77.87 ± 0.67 vs. 77.26 ± 0.77 Gy; p = 0.005). MRI-guided localization showed an accuracy and sensitivity of up to 78.8% and 82.1% for inclusion of secondary lesions, respectively.

CONCLUSION

Up to 82.1% of histologically verified intraprostatic lesions were identified in the context of MRI-guided radiotherapy treatment planning. As expected, dorsal DIL tend to be minimally underdosed in comparison to ventral DIL. Adequate dose coverage was achieved in over 98% of patients.

MRI features after prostatic artery embolization for the treatment of medium- and large-volume benign hyperplasia

PURPOSE

To assess magnetic resonance imaging (MRI) features after prostatic artery embolization (PAE) for the treatment of medium- and large-volume benign prostatic hyperplasia and to correlate prostate volume with clinical indexes.

METHODS

We retrospectively evaluated 28 patients who underwent PAE. MRI examinations of the prostate were performed to evaluate signal intensity changes and the characteristics of infarcted areas. Prostate volume and the apparent diffusion coefficient (ADC) were measured at an average of 10 days post-PAE and at 1, 3, 6, and 12 months post-PAE. Some clinical indexes were evaluated before and 12 months after PAE. The paired t test, ANOVA, and multiple linear correlation analyses were performed by using the statistical software, SPSS.

RESULTS

All patients experienced prostatic infarction. The prostate volume decreased continuously (p < 0.05). The ADC values before and after 1, 3, 6, or 12 months of embolization (b = 1000 and 2000 s/mm²) were statistically significantly different. The ADC values (b = 3000 s/mm²) were also statistically significantly different before and at each interval time after embolization (p < 0.05). Prostate volume changes correlated significantly with patient age and post-void residual urine volume (p < 0.05).

CONCLUSIONS

MRI can be used for assessing changes in signal intensity and ADC values of infarction as well as the volume of the prostate after PAE. After PAE, ultrahigh b value diffusion-weighted imaging (DWI) can show early infarction better than lower b value DWI.