Comparison of single-scanner single-protocol quantitative ADC measurements to ADC ratios to detect clinically significant prostate cancer

Diagnostic performance of ADC and ADCratio in MRI-based prostate cancer assessment: A systematic review and meta-analysis

OBJECTIVES

To identify factors influencing the diagnostic performance of the quantitative imaging biomarkers ADC and ADCratio in prostate cancer (PCa) detection.

MATERIALS AND METHODS

A systematic literature search was conducted in Embase, Medline and Web of Science, for studies evaluating ADC values and ADCratio for PCa diagnosis, using the same patient cohorts and using histopathological references as ground truth. Pooled sensitivities, specificities, summary ROC curves and AUCs were calculated from constructed contingency data tables. Diagnostic performance (AUC) was quantitatively pooled using a bivariate mixed effects model. For identifying influencing factors, subgroup analysis, publication bias and heterogeneity assessment were investigated.

RESULTS

Thirteen studies, involving 1038 patients and 1441 lesions, were included. For ADC, the pooled sensitivity and specificity was 80% (95% CI: 74-85%) and 78% (95% CI: 70-85%), respectively. For ADCratio pooled sensitivity and specificity was 80% (95% CI: 74-84%) and 80% (95% CI: 71-87%). Summary ROC analysis revealed AUCs of 0.86 (95% CI: 0.83-0.89) and 0.86 (95% CI: 0.83-0.89), respectively. Meta-regression showed heterogeneity between both imaging biomarkers. Subgroup analysis showed that ADCratio improved diagnostic performance in comparison to ADC when including both peripheral and transitional zone lesions (AUC: 0.87 [95% CI: 0.84-0.90] and 0.82 [95% CI: 0.79-0.85], respectively).

CONCLUSION

Both ADC and ADCratio imaging biomarkers showed good and comparable diagnostic performance in PCa diagnosis. However, ADCratio shows better diagnostic performance than ADC in diagnosing transition zone cancers.

CLINICAL RELEVANCE STATEMENT

In quantitative MRI-based PCa diagnosis, the imaging biomarker ADCratio is useful in challenging MRI readings of lesions. Understanding the performance of quantitative imaging biomarkers better can aid diagnostic MRI protocols, enhancing the precision of PCa assessments.

KEY POINTS

MRI diffusion-weighted imaging-based ADC and ADCratio have comparable diagnostic performance in PCa assessment. In contrast to ADC, the ADCratio improves diagnostic performance, when assessing whole gland lesions. Compared to ADCratio, the ADC demonstrates enhanced diagnostic performance when evaluating peripheral zone lesions.

Integration of PSAd and multiparametric MRI to forecast biopsy outcomes in biopsy-naïve patients with PSA 4~20 ng/ml

Introduction

This study aims to investigate whether the transrectal ultrasound-guided combined biopsy (CB) improves the detection rates of prostate cancer (PCa) and clinically significant PCa (csPCa) in biopsy-naïve patients. We also aimed to compare the Prostate Imaging Reporting and Data System (PI-RADS v2.1) score, ADC values, and PSA density (PSAd) in predicting csPCa by the combined prostate biopsy.

Methods

This retrospective and single-center study included 389 biopsy-naïve patients with PSA level 4~20 ng/ml, of whom 197 underwent prebiopsy mpMRI of the prostate. The mpMRI-based scores (PI-RADS v2.1 scores and ADC values) and clinical parameters were collected and evaluated by logistic regression analyses. Multivariable models based on the mpMRI-based scores and clinical parameters were developed by the logistic regression analyses to forecast biopsy outcomes of CB in biopsy-naïve patients. The ROC curves measured by the AUC values, calibration plots, and DCA were performed to assess multivariable models.

Results

The CB can detect more csPCa compared with TRUSB (32.0% vs. 53%). The Spearman correlation revealed that Gleason scores of the prostate biopsy significantly correlated with PI-RADS scores and ADC values. The multivariate logistic regression confirmed that PI-RADS scores 4, 5, and prostate volume were important predictors of csPCa. The PI-RADS+ADC+PSAd (PAP) model had the highest AUCs of 0.913 for predicting csPCa in biopsy-naïve patients with PSA level 4~20 ng/ml. When the biopsy risk threshold of the PAP model was greater than or equal to 0.10, 51% of patients could avoid an unnecessary biopsy, and only 5% of patients with csPCa were missed.

Conclusion

The prebiopsy mpMRI and the combined prostate biopsy have a high CDR of csPCa in biopsy-naïve patients. A multivariable model based on the mpMRI-based scores and PSAd could provide a reference for clinicians in forecasting biopsy outcomes in biopsy-naïve patients with PSA 4~20 ng/ml and make a more comprehensive assessment during the decision-making of the prostate biopsy.

Same-day repeatability and Between-Sequence reproducibility of Mean ADC in PI-RADS lesions

PURPOSE

This study aimed to assess repeatability after repositioning (inter-scan), intra-rater, inter-rater and inter-sequence variability of mean apparent diffusion coefficient (ADC) measurements in MRI-detected prostate lesions.

METHOD

Forty-three patients with suspicion for prostate cancer were included and received a clinical prostate bi-/multiparametric MRI examination with repeat scans of the T2-weighted and two DWI-weighted sequences (ssEPI and rsEPI). Two raters (R1 and R2) performed single-slice, 2D regions of interest (2D-ROIs) and 3D-segmentation-ROIs (3D-ROIs). Mean bias, corresponding limits of agreement (LoA), mean absolute difference, within-subject coefficient of variation (CoV) and repeatability/reproducibility coefficient (RC/RDC) were calculated. Bradley & Blackwood test was used for variance comparison. Linear mixed models (LMM) were used to account for multiple lesions per patient.

RESULTS

Inter-scan repeatability, intra-rater and inter-sequence reproducibility analysis of ADC showed no significant bias. 3D-ROIs demonstrated significantly less variability than 2D-ROIs (p < 0.01). Inter-rater comparison demonstrated small significant systematic bias of 57 × 10-6 mm2/s for 3D-ROIs (p < 0.001). Intra-rater RC, with the lowest variation, was 145 and 189 × 10-6 mm2/s for 3D- and 2D-ROIs, respectively. For 3D-ROIs of ssEPI, RCs and RDCs were 190-198 × 10-6 mm2/s for inter-scan, inter-rater and inter-sequence variation. No significant differences were found for inter-scan, inter-rater and inter-sequence variability.

CONCLUSIONS

In a single-scanner setting, single-slice ADC measurements showed considerable variation, which may be lowered using 3D-ROIs. For 3D-ROIs, we propose a cut-off of ∼ 200 × 10-6 mm2/s for differences introduced by repositioning, rater or sequence effects. The results suggest that follow-up measurements should be possible by different raters or sequences.

[Artificial intelligence (AI) in radiology? : Do we need as many radiologists in the future?]

We are in the middle of a digital revolution in medicine. This raises the question of whether subjects such as radiology, which is superficially concerned with the interpretation of images, will be particularly changed by this revolution. In particular, it should be discussed whether in the future the completion of initially simpler, then more complex image analysis tasks by computer systems may lead to a reduced need for radiologists. What distinguishes radiology in particular is its key position between advanced technology and medical care. This article discusses that not only radiology but every medical discipline will be affected by innovations due to the digital revolution, and that a redefinition of medical specialties focusing on imaging and visual interpretation makes sense and that the arrival of artificial intelligence (AI) in radiology is to be welcomed in the context of ever larger amounts of image data-to at all be able to handle the increasing amount of image data in the future at the current number of radiologists. In this respect, the balance between research and teaching in comparison to patient care is more difficult to maintain in the academic environment. AI can help improve efficiency and balance in the areas mentioned. With regard to specialist training, information technology topics are expected to be integrated into the radiological curriculum. Radiology acts as a pioneer designing the entry of AI into medicine. It is to be expected that by the time radiologists can be substantially replaced by AI, the replacement of human contributions in other medical and non-medical fields will also be well advanced.

Magnetic resonance fingerprinting in prostate cancer before and after contrast enhancement

OBJECTIVES

To assess the apparent diffusion coefficient (ADC) values and the T1 and T2 values derived from nonenhanced (NE) and contrast-enhanced (CE) magnetic resonance fingerprinting (MRF) in the prostate gland and to evaluate differences in values among prostate cancer, the normal peripheral zone (PZ) and the normal transition zone (TZ).

METHODS

Fifty-seven patients (median age, 73 years; range, 48-86) with prostate cancer who underwent multiparametric MRI including NE and CE MRF were included in this study. T1 and T2 values were extracted from NE and CE MRF, respectively. Five quantitative values (the ADC, NE T1, NE T2, CE T1 and CE T2 values) were measured in three areas: prostate cancer, PZ and TZ. We compared the values among the three areas and evaluated the differences between NE MRF and CE MRF values.

RESULTS

ADC values and MRF-derived values were significantly higher in PZ than prostate cancer or TZ (p < 0.001). TZ had a significantly lower CE T1 but significantly higher values of the other variables than prostate cancer (p < 0.001). The T1 values in all three areas and the T2 values in prostate cancer and TZ were significantly lower on CE MRF than on NE MRF (p < 0.001).

CONCLUSIONS

Quantitative analysis of NE and CE MRI can be conducted by using the MRF technique. The ADC value and the T1 and T2 values from CE MRF and NE MRF were found to be significantly different between prostate cancer and normal prostate tissue.

ADVANCES IN KNOWLEDGE

The T1 and T2 values from contrast-enhanced MR fingerprinting are significantly different between prostate cancer and normal prostate tissue.

Non-timely clinically applicable ADC ratio in prostate mpMRI: a comparison with fusion biopsy results

PURPOSE

The purpose of the study was to assess the diagnostic accuracy of ADC ratio and to evaluate its efficacy in reducing the number of false positives in prostatic mpMRI.

MATERIALS AND METHODS

All patients who underwent an mpMRI and a targeted fusion biopsy in our institution from 2016 to 2021 were retrospectively selected. Two experienced readers (R1 and R2) independently evaluated the images, blindly to biopsy results. The radiologists assessed the ADC ratios by tracing a circular 10 mm² ROI on the biopsied lesion and on the apparently benign contralateral parenchyma. Prostate cancers were divided into non-clinically significant (nsPC, Gleason score = 6) and clinically significant (sPC, Gleason score ≥ 7). ROC analyses were performed.

RESULTS

167 patients and188 lesions were included. Concordance was 0.62 according to Cohen's K. ADC ratio showed an AUC for PCAs of 0.78 in R1 and 0.8 in R2. The AUC for sPC was 0.85 in R1 and 0.84 in R2. The 100% sensitivity cut-off for sPCs was 0.65 (specificity 25.6%) in R1 and 0.66 (specificity 27.4%) in R2. Forty-three benign or not clinically significant lesions were above the 0.65 threshold in R1; 46 were above the 0.66 cut-off in R2. This would have allowed to avoid an equal number of unnecessary biopsies at the cost of 2 nsPCs in R1 and one nsPC in R2.

CONCLUSION

In our sample, the ADC ratio was a useful and accurate tool that could potentially reduce the number of false positives in mpMRI.

A Pilot Study of Multidimensional Diffusion MRI for Assessment of Tissue Heterogeneity in Prostate Cancer

OBJECTIVES

The objectives of this exploratory study were to investigate the feasibility of multidimensional diffusion magnetic resonance imaging (MddMRI) in assessing diffusion heterogeneity at both a macroscopic and microscopic level in prostate cancer (PCa).

MATERIALS AND METHODS

Informed consent was obtained from 46 subjects who underwent 3.0-T prostate multiparametric MRI, complemented with a prototype spin echo-based MddMRI sequence in this institutional review board-approved study. Prostate cancer tumors and comparative normal tissue from each patient were contoured on both apparent diffusion coefficient and MddMRI-derived mean diffusivity (MD) maps (from which microscopic diffusion heterogeneity [MKi] and microscopic diffusion anisotropy were derived) using 3D Slicer. The discriminative ability of MddMRI-derived parameters to differentiate PCa from normal tissue was determined using the Friedman test. To determine if tumor diffusion heterogeneity is similar on macroscopic and microscopic scales, the linear association between SD of MD and mean MKi was estimated using robust regression (bisquare weighting). Hypothesis testing was 2 tailed; P values less than 0.05 were considered statistically significant.

RESULTS

All MddMRI-derived parameters could distinguish tumor from normal tissue in the fixed-effects analysis (P < 0.0001). Tumor MKi was higher (P < 0.05) compared with normal tissue (median, 0.40; interquartile range, 0.29-0.52 vs 0.20-0.18; 0.25), as was tumor microscopic diffusion anisotropy (0.55; 0.36-0.81 vs 0.20-0.15; 0.28). The MKi could not be predicted (no significant association) by SD of MD. There was a significant correlation between tumor volume and SD of MD (R2 = 0.50, slope = 0.008 μm2/ms per millimeter, P < 0.001) but not between tumor volume and MKi.

CONCLUSIONS

This explorative study demonstrates that MddMRI provides novel information on MKi and microscopic anisotropy, which differ from measures at the macroscopic level. MddMRI has the potential to characterize tumor tissue heterogeneity at different spatial scales.

[Machine learning and multiparametric MRI for early diagnosis of prostate cancer]

In the last few years, the early diagnosis of prostate cancer has continued to shift from systematic biopsies to multiparametric MRI (mpMRI)-guided/MRI-transrectal ultrasound (TRUS) fusion biopsies and guidelines are already reflecting these changes. While MRI-TRUS fusion biopsies have already resulted in significant improvements in diagnostic sensitivity and, thus, correct diagnosis of clinically significant prostate cancer (sPC), its use to avoid biopsies in certain men is still controversial. Optimal use of mpMRI requires a high degree of reader expertise due to the difficulty of image interpretation and poses the problem of training sufficient numbers of radiologists while demand is increasing. Recently, artificial intelligence (AI) has been utilized to create fully automatic analysis tools for interpretation of mpMRI of the prostate, rivaling the performance of clinical radiologist interpretation in retrospective research studies, demonstrating the promising potential of AI for diagnostic prostate MRI in the future. This article will provide an overview of machine and deep learning and its application in mpMRI of the prostate for early diagnosis of prostate cancer.