Multiparametric Magnetic Resonance Imaging of the Prostate: Repeatability of Volume and Apparent Diffusion Coefficient Quantification

The QIBA Profile for Diffusion-Weighted MRI: Apparent Diffusion Coefficient as a Quantitative Imaging Biomarker

The apparent diffusion coefficient (ADC) provides a quantitative measure of water mobility that can be used to probe alterations in tissue microstructure due to disease or treatment. Establishment of the accepted level of variance in ADC measurements for each clinical application is critical for its successful implementation. The Diffusion-Weighted Imaging Biomarker Committee of the Quantitative Imaging Biomarkers Alliance (QIBA) has recently advanced the ADC Profile from the consensus to clinically feasible stage for the brain, liver, prostate, and breast. This profile distills multiple studies on ADC repeatability and describes detailed procedures to achieve stated performance claims on an observed ADC change within acceptable confidence limits. In addition to reviewing the current ADC Profile claims, this report has used recent literature to develop proposed updates for establishing metrology benchmarks for mean lesion ADC change that account for measurement variance. Specifically, changes in mean ADC exceeding 8% for brain lesions, 27% for liver lesions, 27% for prostate lesions, and 15% for breast lesions are claimed to represent true changes with 95% confidence. This report also discusses the development of the ADC Profile, highlighting its various stages, and describes the workflow essential to achieving a standardized implementation of advanced quantitative diffusion-weighted MRI in the clinic. The presented QIBA ADC Profile guidelines should enable successful clinical application of ADC as a quantitative imaging biomarker and ensure reproducible ADC measurements that can be used to confidently evaluate longitudinal changes and treatment response for individual patients.

Phantoms for Quantitative Body MRI: a review and discussion of the phantom value

In this paper, we review the value of phantoms for body MRI in the context of their uses for quantitative MRI methods research, clinical trials, and clinical imaging. Certain uses of phantoms are common throughout the body MRI community, including measuring bias, assessing reproducibility, and training. In addition to these uses, phantoms in body MRI methods research are used for novel methods development and the design of motion compensation and mitigation techniques. For clinical trials, phantoms are an essential part of quality management strategies, facilitating the conduct of ethically sound, reliable, and regulatorily compliant clinical research of both novel MRI methods and therapeutic agents. In the clinic, phantoms are used for development of protocols, mitigation of cost, quality control, and radiotherapy. We briefly review phantoms developed for quantitative body MRI, and finally, we review open questions regarding the most effective use of a phantom for body MRI.

Radiomics from multisite MRI and clinical data to predict clinically significant prostate cancer

BACKGROUND

Magnetic resonance imaging (MRI) is useful in the diagnosis of clinically significant prostate cancer (csPCa). MRI-derived radiomics may support the diagnosis of csPCa.

PURPOSE

To investigate whether adding radiomics from biparametric MRI to predictive models based on clinical and MRI parameters improves the prediction of csPCa in a multisite-multivendor setting.

MATERIAL AND METHODS

Clinical information (PSA, PSA density, prostate volume, and age), MRI reviews (PI-RADS 2.1), and radiomics (histogram and texture features) were retrieved from prospectively included patients examined at different radiology departments and with different MRI systems, followed by MRI-ultrasound fusion guided biopsies of lesions PI-RADS 3-5. Predictive logistic regression models of csPCa (Gleason score ≥7) for the peripheral (PZ) and transition zone (TZ), including clinical data and PI-RADS only, and combined with radiomics, were built and compared using receiver operating characteristic (ROC) curves.

RESULTS

In total, 456 lesions in 350 patients were analyzed. In PZ and TZ, PI-RADS 4-5 and PSA density, and age in PZ, were independent predictors of csPCa in models without radiomics. In models including radiomics, PI-RADS 4-5, PSA density, age, and ADC energy were independent predictors in PZ, and PI-RADS 5, PSA density and ADC mean in TZ. Comparison of areas under the ROC curve (AUC) for the models without radiomics (PZ: AUC = 0.82, TZ: AUC = 0.80) versus with radiomics (PZ: AUC = 0.82, TZ: AUC = 0.82) showed no significant differences (PZ: P = 0.366; TZ: P = 0.171).

CONCLUSION

PSA density and PI-RADS are potent predictors of csPCa. Radiomics do not add significant information to our multisite-multivendor dataset.

A Technique to Enable Efficient Adaptive Radiation Therapy: Automated Contouring of Prostate and Adjacent Organs

Purpose

The purpose of this work was to investigate the use of a segmentation approach that could potentially improve the speed and reproducibility of contouring during magnetic resonance-guided adaptive radiation therapy.

Methods and Materials

The segmentation algorithm was based on a hybrid deep neural network and graph optimization approach that also allows rapid user intervention (Deep layered optimal graph image segmentation of multiple objects and surfaces [LOGISMOS] + just enough interaction [JEI]). A total of 115 magnetic resonance-data sets were used for training and quantitative assessment. Expert segmentations were used as the independent standard for the prostate, seminal vesicles, bladder, rectum, and femoral heads for all 115 data sets. In addition, 3 independent radiation oncologists contoured the prostate, seminal vesicles, and rectum for a subset of patients such that the interobserver variability could be quantified. Consensus contours were then generated from these independent contours using a simultaneous truth and performance level estimation approach, and the deviation of Deep LOGISMOS + JEI contours to the consensus contours was evaluated and compared with the interobserver variability.

Results

The absolute accuracy of Deep LOGISMOS + JEI generated contours was evaluated using median absolute surface-to-surface distance which ranged from a minimum of 0.20 mm for the bladder to a maximum of 0.93 mm for the prostate compared with the independent standard across all data sets. The median relative surface-to-surface distance was less than 0.17 mm for all organs, indicating that the Deep LOGISMOS + JEI algorithm did not exhibit a systematic under- or oversegmentation. Interobserver variability testing yielded a mean absolute surface-to-surface distance of 0.93, 1.04, and 0.81 mm for the prostate, seminal vesicles, and rectum, respectively. In comparison, the deviation of Deep LOGISMOS + JEI from consensus simultaneous truth and performance level estimation contours was 0.57, 0.64, and 0.55 mm for the same organs. On average, the Deep LOGISMOS algorithm took less than 26 seconds for contour segmentation.

Conclusions

Deep LOGISMOS + JEI segmentation efficiently generated clinically acceptable prostate and normal tissue contours, potentially limiting the need for time intensive manual contouring with each fraction.

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.

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.

Stability of Multi-Parametric Prostate MRI Radiomic Features to Variations in Segmentation

Stability analysis remains a fundamental step in developing a successful imaging biomarker to personalize oncological strategies. This study proposes an in silico contour generation method for simulating segmentation variations to identify stable radiomic features. Ground-truth annotation provided for the whole prostate gland on the multi-parametric MRI sequences (T2w, ADC, and SUB-DCE) were perturbed to mimic segmentation differences observed among human annotators. In total, we generated 15 synthetic contours for a given image-segmentation pair. One thousand two hundred twenty-four unfiltered/filtered radiomic features were extracted applying Pyradiomics, followed by stability assessment using ICC(1,1). Stable features identified in the internal population were then compared with an external population to discover and report robust features. Finally, we also investigated the impact of a wide range of filtering strategies on the stability of features. The percentage of unfiltered (filtered) features that remained robust subjected to segmentation variations were T2w-36% (81%), ADC-36% (94%), and SUB-43% (93%). Our findings suggest that segmentation variations can significantly impact radiomic feature stability but can be mitigated by including pre-filtering strategies as part of the feature extraction pipeline.

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

OBJECTIVE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

PI-RADS 3 score: A retrospective experience of clinically significant prostate cancer detection

Rationale and objectives

The study aims to propose an optimal workflow in patients with a PI-RADS 3 (PR-3) assessment category (AC) through determining the timing and type of pathology interrogation used for the detection of clinically significant prostate cancer (csPCa) in these men based upon a 5-year retrospective review in a large academic medical center.

Materials and methods

This United States Health Insurance Probability and Accountability Act (HIPAA)-compliant, institutional review board-approved retrospective study included men without prior csPCa diagnosis who received PR-3 AC on magnetic resonance (MR) imaging (MRI). Subsequent incidence and time to csPCa diagnosis and number/type of prostate interventions was recorded. Categorical data were compared using Fisher's exact test and continuous data using ANOVA omnibus F-test.

Results

Our cohort of 3238 men identified 332 who received PR-3 as their highest AC on MRI, 240 (72.3%) of whom had pathology follow-up within 5 years. csPCa was detected in 76/240 (32%) and non-csPCa in 109/240 (45%) within 9.0 ± 10.6 months. Using a non-targeted trans-rectal ultrasound biopsy as the initial approach (n = 55), another diagnostic procedure was required to diagnose csPCa in 42/55 (76.4%) of men, compared with 3/21(14.3%) men with an initial MR targeted-biopsy approach (n = 21); (p < 0.0001). Those with csPCa had higher median serum prostate-specific antigen (PSA) and PSA density, and lower median prostate volume (p < 0.003) compared with non-csPCa/no PCa.

Conclusion

Most patients with PR-3 AC underwent prostate pathology exams within 5 years, 32% of whom were found to have csPCa within 1 year of MRI, most often with a higher PSA density and a prior non-csPCa diagnosis. Addition of a targeted biopsy approach initially reduced the need for a second biopsy to reach a for csPCa diagnosis. Thus, a combination of systematic and targeted biopsy is advised in men with PR-3 and a co-existing abnormal PSA and PSA density.

Novel radiomic analysis on bi-parametric MRI for characterizing differences between MR non-visible and visible clinically significant prostate cancer

Background

around one third of clinically significant prostate cancer (CsPCa) foci are reported to be MRI non-visible (MRI─).

Objective

To quantify the differences between MR visible (MRI+) and MRI^─ CsPCa using intra- and peri-lesional radiomic features on bi-parametric MRI (bpMRI).

Methods

This retrospective and multi-institutional study comprised 164 patients with pre-biopsy 3T prostate multi-parametric MRI from 2014 to 2017. The MRI^─ CsPCa referred to lesions with PI-RADS v2 score < 3 but ISUP grade group > 1. Three experienced radiologists were involved in annotating lesions and PI-RADS assignment. The validation set (D_v) comprised 52 patients from a single institution, the remaining 112 patients were used for training (D_t). 200 radiomic features were extracted from intra-lesional and peri-lesional regions on bpMRI.Logistic regression with least absolute shrinkage and selection operator (LASSO) and 10-fold cross-validation was applied on D_t to identify radiomic features associated with MRI^─ and MRI⁺ CsPCa to generate corresponding risk scores R^MRI─ and R^MRI+. R_bpMRI was further generated by integrating R^MRI─ and R^MRI+. Statistical significance was determined using the Wilcoxon signed-rank test.

Results

Both intra-lesional and peri-lesional bpMRI Haralick and CoLlAGe radiomic features were significantly associated with MRI^─ CsPCa (p < 0.05). Intra-lesional ADC Haralick and CoLlAGe radiomic features were significantly different among MRI^─ and MRI⁺ CsPCa (p < 0.05). R_bpMRI yielded the highest AUC of 0.82 (95 % CI 0.72-0.91) compared to AUCs of R^MRI+ 0.76 (95 % CI 0.63-0.89), and PI-RADS 0.58 (95 % CI 0.50-0.72) on D_v. R_bpMRI correctly reclassified 10 out of 14 MRI^─ CsPCa on D_v.

Conclusion

Our preliminary results demonstrated that both intra-lesional and peri-lesional bpMRI radiomic features were significantly associated with MRI^─ CsPCa. These features could assist in CsPCa identification on bpMRI.

Test-retest repeatability of ADC in prostate using the multi b-Value VERDICT acquisition

PURPOSE

VERDICT (Vascular, Extracellular, Restricted Diffusion for Cytometry in Tumours) MRI is a multi b-value, variable diffusion time DWI sequence that allows generation of ADC maps from different b-value and diffusion time combinations. The aim was to assess precision of prostate ADC measurements from varying b-value combinations using VERDICT and determine which protocol provides the most repeatable ADC.

MATERIALS AND METHODS

Forty-one men (median age: 67.7 years) from a prior prospective VERDICT study (April 2016-October 2017) were analysed retrospectively. Men who were suspected of prostate cancer and scanned twice using VERDICT were included. ADC maps were formed using 5b-value combinations and the within-subject standard deviations (wSD) were calculated per ADC map. Three anatomical locations were analysed per subject: normal TZ (transition zone), normal PZ (peripheral zone), and index lesions. Repeated measures ANOVAs showed which b-value range had the lowest wSD, Spearman correlation and generalized linear model regression analysis determined whether wSD was related to ADC magnitude and ROI size.

RESULTS

The mean lesion ADC for b0b1500 had the lowest wSD in most zones (0.18-0.58x10-4 mm2/s). The wSD was unaffected by ADC magnitude (Lesion: p = 0.064, TZ: p = 0.368, PZ: p = 0.072) and lesion Likert score (p = 0.95). wSD showed a decrease with ROI size pooled over zones (p = 0.019, adjusted regression coefficient = -1.6x10-3, larger ROIs for TZ versus PZ versus lesions). ADC maps formed with a maximum b-value of 500 s/mm2 had the largest wSDs (1.90-10.24x10-4 mm2/s).

CONCLUSION

ADC maps generated from b0b1500 have better repeatability in normal TZ, normal PZ, and index lesions.

A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning

This survey aims to identify commonly used methods, datasets, future trends, knowledge gaps, constraints, and limitations in the field to provide an overview of current solutions used in medical image analysis in parallel with the rapid developments in transfer learning (TL). Unlike previous studies, this survey grouped the last five years of current studies for the period between January 2017 and February 2021 according to different anatomical regions and detailed the modality, medical task, TL method, source data, target data, and public or private datasets used in medical imaging. Also, it provides readers with detailed information on technical challenges, opportunities, and future research trends. In this way, an overview of recent developments is provided to help researchers to select the most effective and efficient methods and access widely used and publicly available medical datasets, research gaps, and limitations of the available literature.

A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer

Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER- classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers.

Cross-Modality Image Registration Using a Training-Time Privileged Third Modality

In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additional modality that is different to those being registered. As an example, we focus on aligning intra-subject multiparametric Magnetic Resonance (mpMR) images, between T2-weighted (T2w) scans and diffusion-weighted scans with high b-value (DWI [Formula: see text]). For the application of localising tumours in mpMR images, diffusion scans with zero b-value (DWI [Formula: see text]) are considered easier to register to T2w due to the availability of corresponding features. We propose a learning from privileged modality algorithm, using a training-only imaging modality DWI [Formula: see text], to support the challenging multi-modality registration problems. We present experimental results based on 369 sets of 3D multiparametric MRI images from 356 prostate cancer patients and report, with statistical significance, a lowered median target registration error of 4.34 mm, when registering the holdout DWI [Formula: see text] and T2w image pairs, compared with that of 7.96 mm before registration. Results also show that the proposed learning-based registration networks enabled efficient registration with comparable or better accuracy, compared with a classical iterative algorithm and other tested learning-based methods with/without the additional modality. These compared algorithms also failed to produce any significantly improved alignment between DWI [Formula: see text] and T2w in this challenging application.

Repeatability and Reproducibility Assessment of the Apparent Diffusion Coefficient in the Prostate: A Trial of the ECOG-ACRIN Research Group (ACRIN 6701)

BACKGROUND

Uncertainty regarding the reproducibility of the apparent diffusion coefficient (ADC) hampers the use of quantitative diffusion-weighted imaging (DWI) in evaluation of the prostate with magnetic resonance imaging MRI. The quantitative imaging biomarkers alliance (QIBA) profile for quantitative DWI claims a within-subject coefficient of variation (wCV) for prostate lesion ADC of 0.17. Improved understanding of ADC reproducibility would aid the use of quantitative diffusion in prostate MRI evaluation.

PURPOSE

Evaluation of the repeatability (same-day) and reproducibility (multi-day) of whole-prostate and focal-lesion ADC assessment in a multi-site setting.

STUDY TYPE

Prospective multi-institutional.

SUBJECTS

Twenty-nine males, ages 53 to 80 (median 63) years, following diagnosis of prostate cancer, 10 with focal lesions.

FIELD STRENGTH/SEQUENCE

3T, single-shot spin-echo diffusion-weighted echo-planar sequence with four b-values.

ASSESSMENT

Sites qualified for the study using an ice-water phantom with known ADC. Readers performed DWI analyses at visit 1 ("V1") and visit 2 ("V2," 2-14 days after V1), where V2 comprised scans before ("V2pre") and after ("V2post") a "coffee-break" interval with subject removal and repositioning. A single reader segmented the whole prostate. Two readers separately placed region-of-interests for focal lesions.

STATISTICAL TESTS

Reproducibility and repeatability coefficients for whole prostate and focal lesions derived from median pixel ADC. We estimated the wCV and 95% confidence interval using a variance stabilizing transformation and assessed interreader reliability of focal lesion ADC using the intraclass correlation coefficient (ICC).

RESULTS

The ADC biases from b0 -b600 and b0 -b800 phantom scans averaged 1.32% and 1.44%, respectively; mean b-value dependence was 0.188%. Repeatability and reproducibility of whole prostate median pixel ADC both yielded wCVs of 0.033 (N = 29). In 10 subjects with an evaluable focal lesion, the individual reader wCVs were 0.148 and 0.074 (repeatability) and 0.137 and 0.078 (reproducibility). All time points demonstrated good to excellent interreader reliability for focal lesion ADC (ICCV1  = 0.89; ICCV2pre  = 0.76; ICCV2post  = 0.94).

DATA CONCLUSION

This study met the QIBA claim for prostate ADC. Test-retest repeatability and multi-day reproducibility were largely equivalent. Interreader reliability for focal lesion ADC was high across time points.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 2 TOC CATEGORY: Pelvis.

Statistical considerations for repeatability and reproducibility of quantitative imaging biomarkers

Quantitative imaging biomarkers (QIBs) are increasingly used in clinical studies. Because many QIBs are derived through multiple steps in image data acquisition and data analysis, QIB measurements can produce large variabilities, posing a significant challenge in translating QIBs into clinical trials, and ultimately, clinical practice. Both repeatability and reproducibility constitute the reliability of a QIB measurement. In this article, we review the statistical aspects of repeatability and reproducibility of QIB measurements by introducing methods and metrics for assessments of QIB repeatability and reproducibility and illustrating the impact of QIB measurement error on sample size and statistical power calculations, as well as predictive performance with a QIB as a predictive biomarker.

Repeatability and reproducibility of magnetic resonance imaging-based radiomic features in rectal cancer

Purpose: Radiomics of magnetic resonance images (MRIs) in rectal cancer can non-invasively characterize tumor heterogeneity with potential to discover new imaging biomarkers. However, for radiomics to be reliable, the imaging features measured must be stable and reproducible. The aim of this study is to quantify the repeatability and reproducibility of MRI-based radiomic features in rectal cancer. Approach: An MRI radiomics phantom was used to measure the longitudinal repeatability of radiomic features and the impact of post-processing changes related to image resolution and noise. Repeatability measurements in rectal cancers were also quantified in a cohort of 10 patients with test-retest imaging among two observers. Results: We found that many radiomic features, particularly from texture classes, were highly sensitive to changes in image resolution and noise. About 49% of features had coefficient of variations ≤ 10 % in longitudinal phantom measurements. About 75% of radiomic features in in vivo test-retest measurements had an intraclass correlation coefficient of ≥ 0.8 . We saw excellent interobserver agreement with mean Dice similarity coefficient of 0.95 ± 0.04 for test and retest scans. Conclusions: The results of this study show that even when using a consistent imaging protocol many radiomic features were unstable. Therefore, caution must be taken when selecting features for potential imaging biomarkers.

Analysis of Apparent Diffusion Coefficient Value and Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameters of Prostate Cancer Patients after Diagnosis and Treatment with Magnetic Resonance Imaging

This research was aimed at exploring the changes in the apparent diffusion coefficient (ADC) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters of prostate cancer (PCa) patients. Sixty PCa patients from the hospital were recruited as the research object, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans were performed to determine the shape, scope, and enhancement characteristics of prostate lesions and their relationship with surrounding tissues. The quantitative parameters of ADC and DCE-MRI were measured. There were 4 patients (6.67%) with a Gleason score of 6 and 15 patients (25%) with a 4 + 3 score. The ADC with Gleason = 6 is 0.81 ± 0.08 × 10⁻³ s/mm², the ADC with Gleason = 3 + 4 is 0.74 ± 0.07 × 10⁻³ s/mm², the ADC with Gleason = 4 + 3 is 0.73 ± 0.05 × 10⁻³ s/mm², the ADC with Gleason = 9 is 0.65 ± 0.06 × 10⁻³ s/mm², and the ADC with Gleason = 10 is 0.59 ± 0.07 × 10⁻³ s/mm². As the Gleason score increased, the ADC decreased and the permeation parameter transfer constant increased. When the ADC was combined with the permeability parameter transfer constant, the AUC of Gleason = 6 points and Gleason = 7 points was greatly different (P < 0.05). The 95% CI of the ADC combined permeability parameter transport constant when Gleason = 6 points and Gleason = 7 points was 0.898-0.934, the sensitivity was 75.4%, and the specificity was 86.2%. The ADC value was negatively correlated with Gleason score. The ADC value combined with VTC value has good diagnostic performance in evaluating the invasion of PCa, which is very important for making treatment plan and evaluating prognosis.

Serial changes in tumour measurements and apparent diffusion coefficients in prostate cancer patients on active surveillance with and without histopathological progression

OBJECTIVE

To analyse serial changes in MRI-derived tumour measurements and apparent diffusion coefficient (ADC) values in prostate cancer (PCa) patients on active surveillance (AS) with and without histopathological disease progression.

METHODS

This study included AS patients with biopsy-proven PCa with a minimum of two consecutive MR examinations and at least one repeat targeted biopsy. Tumour volumes, largest axial two-dimensional (2D) surface areas, and maximum diameters were measured on T2 weighted images (T2WI). ADC values were derived from the whole lesions, 2D areas, and small-volume regions of interest (ROIs) where tumours were most conspicuous. Areas under the ROC curve (AUCs) were calculated for combinations of T2WI and ADC parameters with optimal specificity and sensitivity.

RESULTS

60 patients (30 progressors and 30 non-progressors) were included. In progressors, T2WI-derived tumour volume, 2D surface area, and maximum tumour diameter had a median increase of +99.5%,+55.3%, and +21.7% compared to +29.2%,+8.1%, and +6.9% in non-progressors (p < 0.005 for all). Follow-up whole-volume and small-volume ROIs ADC values were significantly reduced in progressors (-11.7% and -9.5%) compared to non-progressors (-6.1% and -1.6%) (p < 0.05 for both). The combined AUC of a relative increase in maximum tumour diameter by 20% and reduction in small-volume ADC by 10% was 0.67.

CONCLUSION

AS patients show significant differences in tumour measurements and ADC values between those with and without histopathological disease progression.

ADVANCES IN KNOWLEDGE

This paper proposes specific clinical cut-offs for T2WI-derived maximum tumour diameter (+20%) and small-volume ADC (-10%) to predict histopathological PCa progression on AS and supplement subjective serial MRI assessment.

Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability

Background

Radiomic features extracted from breast MRI have potential for diagnostic, prognostic, and predictive purposes. However, before they can be used as biomarkers in clinical decision support systems, features need to be repeatable and reproducible.

Objective

Identify repeatable radiomics features within breast tissue on prospectively collected MRI exams through multiple test–retest measurements.

Study Type

Prospective.

Population

11 healthy female volunteers.

Field Strength/Sequence

1.5 T; MRI exams, comprising T2‐weighted turbo spin‐echo (T2W) sequence, native T1‐weighted turbo gradient‐echo (T1W) sequence, diffusion‐weighted imaging (DWI) sequence using b‐values 0/150/800, and corresponding derived ADC maps.

Assessment

18 MRI exams (three test–retest settings, repeated on 2 days) per healthy volunteer were examined on an identical scanner using a fixed clinical breast protocol. For each scan, 91 features were extracted from the 3D manually segmented right breast using Pyradiomics, before and after image preprocessing. Image preprocessing consisted of 1) bias field correction (BFC); 2) z‐score normalization with and without BFC; 3) grayscale discretization using 32 and 64 bins with and without BFC; and 4) z‐score normalization + grayscale discretization using 32 and 64 bins with and without BFC.

Statistical Tests

Features' repeatability was assessed using concordance correlation coefficient(CCC) for each pair, i.e. each MRI was compared to each of the remaining 17 MRI with a cut‐off value of CCC > 0.90.

Results

Images without preprocessing produced the highest number of repeatable features for both T1W sequence and ADC maps with 15 of 91 (16.5%) and 8 of 91 (8.8%) repeatable features, respectively. Preprocessed images produced between 4 of 91 (4.4%) and 14 of 91 (15.4%), and 6 of 91 (6.6%) and 7 of 91 (7.7%) repeatable features, respectively for T1W and ADC maps. Z‐score normalization produced highest number of repeatable features, 26 of 91 (28.6%) in T2W sequences, in these images, no preprocessing produced 11 of 91 (12.1%) repeatable features.

Data Conclusion

Radiomic features extracted from T1W, T2W sequences and ADC maps from breast MRI exams showed a varying number of repeatable features, depending on the sequence. Effects of different preprocessing procedures on repeatability of features were different for each sequence.

Level of Evidence

2

Technical Efficacy Stage

1

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.

Differentiating prostate cancer from benign prostatic hyperplasia using whole-lesion histogram and texture analysis of diffusion- and T2-weighted imaging

Background

To explore the usefulness of analyzing histograms and textures of apparent diffusion coefficient (ADC) maps and T2-weighted (T2W) images to differentiate prostatic cancer (PCa) from benign prostatic hyperplasia (BPH) using histopathology as the reference.

Methods

Ninety patients with PCa and 112 patients with BPH were included in this retrospective study. Differences in whole-lesion histograms and texture parameters of ADC maps and T2W images between PCa and BPH patients were evaluated using the independent samples t-test. The diagnostic performance of ADC maps and T2W images in being able to differentiate PCa from BPH was assessed using receiver operating characteristic (ROC) curves.

Results 

The mean, median, 5^th, and 95^th percentiles of ADC values in images from PCa patients were significantly lower than those from BPH patients (p < 0.05). Significant differences were observed in the means, standard deviations, medians, kurtosis, skewness, and 5^th percentile values of T2W image between PCa and BPH patients (p < 0.05). The ADC_5th showed the largest AUC (0.906) with a sensitivity of 83.3 % and specificity of 89.3 %. The diagnostic performance of the T2W image histogram and texture analysis was moderate and had the largest AUC of 0.634 for T2W_Kurtosis with a sensitivity and specificity of 48.9% and 79.5 %, respectively. The diagnostic performance of the combined ADC_5th & T2W_Kurtosis parameters was also similar to that of the ADC_5th & ADC_{Diff−Variance}.

Conclusions

Histogram and texture parameters derived from the ADC maps and T2W images for entire prostatic lesions could be used as imaging biomarkers to differentiate PCa and BPH biologic characteristics, however, histogram parameters outperformed texture parameters in the diagnostic performance.

Impact of different phased-array coils on the quality of prostate magnetic resonance images

•

Image quality is similar for different body phased-array receive coil setups.

•

An 18-channel body phased-array receive coil setup achieved good image quality.

•

60-channel body phased-array receive coil setup slightly improves SNR in T2W images.

Purpose

To evaluate the influence of body phased-array (BPA) receive coil setups on signal-to-noise ratio (SNR) and image quality (IQ) in prostate MRI.

Methods

This prospective study evaluated axial T2-weighted images (T2W-TSE) and DWI of the prostate in ten healthy volunteers with 18-channel (18CH), 30-channel and 60-channel (60CH) BPA receive coil setups. SNR and ADC values were assessed in the peripheral and transition zones (TZ). Two radiologists rated IQ features. Differences in qualitative and quantitative image features between BPA receive coil setups were compared. After correction for multiple comparisons, p-values <0.004 for quantitative and p-values <0.017 for qualitative image analysis were considered statistically significant.

Results

Significantly higher SNR was found in T2W-TSE images in the TZ using 60CH BPA compared to 18CH BPA coil setups (15.20 ± 4.22 vs. 7.68 ± 2.37; p = 0.001). There were no significant differences between all other quantitative (T2W-TSE, p = 0.007−0.308; DWI, p = 0.024−0.574) and qualitative image features (T2W-TSE, p = 0.083–1.0; DWI, p = 0.046–1.0).

Conclusion

60CH BPA receive coil setup showed marginal SNR improvement in T2W-TSE images. Good IQ could be achieved with 18CH BPA coil setups.

Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions

Despite the key-role of the Prostate Imaging and Reporting and Data System (PI-RADS) in the diagnosis and characterization of prostate cancer (PCa), this system remains to be affected by several limitations, primarily associated with the interpretation of equivocal PI-RADS 3 lesions and with the debated role of Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI), which is only used to upgrade peripheral PI-RADS category 3 lesions to PI-RADS category 4 if enhancement is focal. We aimed at investigating the usefulness of radiomics for detection of PCa lesions (Gleason Score ≥ 6) in PI-RADS 3 lesions and in peripheral PI-RADS 3 upgraded to PI-RADS 4 lesions (upPI-RADS 4). Multiparametric MRI (mpMRI) data of patients who underwent prostatic mpMRI between April 2013 and September 2018 were retrospectively evaluated. Biopsy results were used as gold standard. PI-RADS 3 and PI-RADS 4 lesions were re-scored according to the PI-RADS v2.1 before and after DCE-MRI evaluation. Radiomic features were extracted from T2-weighted MRI (T2), Apparent diffusion Coefficient (ADC) map and DCE-MRI subtracted images using PyRadiomics. Feature selection was performed using Wilcoxon-ranksum test and Minimum Redundancy Maximum Relevance (mRMR). Predictive models were constructed for PCa detection in PI-RADS 3 and upPI-RADS 4 lesions using at each step an imbalance-adjusted bootstrap resampling (IABR) on 1000 samples. 41 PI-RADS 3 and 32 upPI-RADS 4 lesions were analyzed. Among 293 radiomic features, the top selected features derived from T2 and ADC. For PI-RADS 3 stratification, second order model showed higher performances (Area Under the Receiver Operating Characteristic Curve-AUC- = 80%), while for upPI-RADS 4 stratification, first order model showed higher performances respect to superior order models (AUC = 89%). Our results support the significant role of T2 and ADC radiomic features for PCa detection in lesions scored as PI-RADS 3 and upPI-RADS 4. Radiomics models showed high diagnostic efficacy in classify PI-RADS 3 and upPI-RADS 4 lesions, outperforming PI-RADS v2.1 performance.

Quantitative MRI: Defining repeatability, reproducibility and accuracy for prostate cancer imaging biomarker development

INTRODUCTION

Quantitative MRI (qMRI) parameters have been increasingly used to develop predictive models to accurately monitor treatment response in prostate cancer after radiotherapy. To reliably detect changes in signal due to treatment response, predictive models require qMRI parameters with high repeatability and reproducibility. The purpose of this study was to measure qMRI parameter uncertainties in both commercial and in-house developed phantoms to guide the development of robust predictive models for monitoring treatment response.

MATERIALS AND METHODS

ADC, T1, and R2* values were acquired across three 3 T scanners with a prostate-specific qMRI protocol using the NIST/ISMRM system phantom, RSNA/NIST diffusion phantom, and an in-house phantom. A B1 field map was acquired to correct for flip angle inhomogeneity in T1 maps. All sequences were repeated in each scan to assess within-session repeatability. Weekly scans were acquired on one scanner for three months with the in-house phantom. Between-session repeatability was measured with test-retest scans 6-months apart on all scanners with all phantoms. Accuracy, defined as percentage deviation from reference value for ADC and T1, was evaluated using the system and diffusion phantoms. Repeatability and reproducibility coefficients of variation (%CV) were calculated for all qMRI parameters on all phantoms.

RESULTS

Overall, repeatability CV of ADC was <2.40%, reproducibility CV was <3.98%, and accuracy ranged between -8.0% to 2.7% across all scanners. Applying B1 correction on T1 measurements significantly improved the repeatability and reproducibility (p<0.05) but increased error in accuracy (p<0.001). Repeatability and reproducibility of R2* was <4.5% and <7.3% respectively in the system phantom across all scanners.

CONCLUSION

Repeatability, reproducibility, and accuracy in qMRI parameters from a prostate-specific protocol was estimated using both commercial and in-house phantoms. Results from this work will be used to identify robust qMRI parameters for use in the development of predictive models to longitudinally monitor treatment response for prostate cancer in current and future clinical trials.

Multiparametric MRI as a Biomarker of Response to Neoadjuvant Therapy for Localized Prostate Cancer-A Pilot Study

RATIONALE AND OBJECTIVES

To explore a role for multiparametric MRI (mpMRI) as a biomarker of response to neoadjuvant androgen deprivation therapy (ADT) for prostate cancer (PCa).

MATERIALS AND METHODS

This prospective study was approved by the institutional review board and was HIPAA compliant. Eight patients with localized PCa had a baseline mpMRI, repeated after 6-months of ADT, followed by prostatectomy. mpMRI indices were extracted from tumor and normal regions of interest (TROI/NROI). Residual cancer burden (RCB) was measured on mpMRI and on the prostatectomy specimen. Paired t-tests compared TROI/NROI mpMRI indices and pre/post-treatment TROI mpMRI indices. Spearman's rank tested for correlations between MRI/pathology-based RCB, and between pathological RCB and mpMRI indices.

RESULTS

At baseline, TROI apparent diffusion coefficient (ADC) was lower and dynamic contrast enhanced (DCE) metrics were higher, compared to NROI (ADC: 806 ± 137 × 10^-6 vs. 1277 ± 213 × 10^-6 mm²/sec, p = 0.0005; K^trans: 0.346 ± 0.16 vs. 0.144 ± 0.06 min^-1, p = 0.002; AUC₉₀: 0.213 ± 0.08 vs. 0.11 ± 0.03, p = 0.002). Post-treatment, there was no change in TROI ADC, but a decrease in TROI K^trans (0.346 ± 0.16 to 0.188 ± 0.08 min^-1; p = 0.02) and AUC₉₀ (0.213 ± 0.08 to 0.13 ± 0.06; p = 0.02). Tumor volume decreased with ADT. There was no difference between mpMRI-based and pathology-based RCB, which positively correlated (⍴ = 0.74-0.81, p < 0.05). Pathology-based RCB positively correlated with post-treatment DCE metrics (⍴ = 0.76-0.70, p < 0.05) and negatively with ADC (⍴ = -0.79, p = 0.03).

CONCLUSION

Given the heterogeneity of PCa, an individualized approach to ADT may maximize potential benefit. This pilot study suggests that mpMRI may serve as a biomarker of ADT response and as a surrogate for RCB at prostatectomy.

Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence

Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.

Intrascanner Reproducibility of an SPM-based Head MR-based Attenuation Correction Method

Recently, an exhaustive examination of 11 state of the art MR-based attenuation correction (AC) concluded that there are currently a few methods showing similar results compared to the gold-standard, CT-based AC. While the study presented a thorough portfolio of metrics to quantify accuracy (bias) and quality, it lacked one of the most important metrics to quantify robustness that is critical for its clinical applicability: intrascanner reproducibility (repeatability). In this work, we provide for the first time a study of the repeatability of one of the outperforming brain MR-based AC methods: the SPM-based pseudo-CT approach. 22 subjects undergoing 3 ¹⁸F-FDG PET/MRI visits within 2 months were retrospectively analyzed in this study. Pseudo-CT mu-maps were obtained from the coregistered MR images for all 3 visits and the PET data from visit 1 was reconstructed using all three mu-maps. Relative changes (RC), Intraclass correlation coefficient (ICC), Reproducibility coefficient (RDC_95%) and Bland-Altman Limits of Agreement (LoA) were used to measure repeatability. Voxel-based and ROI-based results showed that absolute RC for the reconstructed PET images are within ~2%. The brain cortex and the cerebellum were the regions with the largest variability (~3%). The differences across visits were not statistically significant (p=0.90). In conclusion this study shows for the first time the repeatability of the SPM-based pseudo-CT approach for brain MR-AC. These results, in addition to the ease of implementation and the quality and robustness previously demonstrated, confer this SPM-based method an ideal candidate for routine brain PET/MRI research and clinical studies.

Repeatability of Quantitative Imaging Features in Prostate Magnetic Resonance Imaging

Background: Multiparametric magnetic resonance imaging (mpMRI) has emerged as a non-invasive modality to diagnose and monitor prostate cancer. Quantitative metrics on the regions of abnormality have shown to be useful descriptors to discriminate clinically significant cancers. In this study, we evaluate the reproducibility of quantitative imaging features using repeated mpMRI on the same patients. Methods: We retrospectively obtained the deidentified records of patients, who underwent two mpMRI scans within 2 weeks of the first baseline scan. The patient records were obtained as deidentified data (including imaging), obtained through the TCIA (The Cancer Imaging Archive) repository and analyzed in our institution with an institutional review board-approved Health Insurance Portability and Accountability Act-compliant retrospective study protocol. Indicated biopsied regions were used as a marker for our study radiologists to delineate the regions of interest. We extracted 307 quantitative features in each mpMRI modality [T2-weighted MR sequence image (T2w) and apparent diffusion coefficient (ADC) with b values of 0 and 1,400 mm/s2] across the two sequential scans. Concordance correlation coefficients (CCCs) were computed on the features extracted from sequential scans. Redundant features were removed by computing the coefficient of determination (R 2) among them and replaced with a feature that had the highest dynamic range within intercorrelated groups. Results: We have assessed the reproducibility of quantitative imaging features among sequential scans and found that habitat region characterization improves repeatability in ADC maps. There were 19 T2w features and two ADC features in radiologist drawn regions (native raw image), compared to 18 T2w and 15 ADC features in habitat regions (sphere), which were reproducible (CCC ≥0.65) and non-redundant (R 2 ≥ 0.99). We also found that z-transformation of the images prior to feature extraction reduced the number of reproducible features with no detrimental effect. Conclusion: We have shown that there are quantitative imaging features that are reproducible across sequential prostate mpMRI acquisition at a preset level of filters. We also found that a habitat approach improves feature repeatability in ADC. A validated set of reproducible image features in mpMRI will allow us to develop clinically useful disease risk stratification, enabling the possibility of using imaging as a surrogate to invasive biopsies.

Quantitative Imaging Informatics for Cancer Research

PURPOSE

We summarize Quantitative Imaging Informatics for Cancer Research (QIICR; U24 CA180918), one of the first projects funded by the National Cancer Institute (NCI) Informatics Technology for Cancer Research program.

METHODS

QIICR was motivated by the 3 use cases from the NCI Quantitative Imaging Network. 3D Slicer was selected as the platform for implementation of open-source quantitative imaging (QI) tools. Digital Imaging and Communications in Medicine (DICOM) was chosen for standardization of QI analysis outputs. Support of improved integration with community repositories focused on The Cancer Imaging Archive (TCIA). Priorities included improved capabilities of the standard, toolkits and tools, reference datasets, collaborations, and training and outreach.

RESULTS

Fourteen new tools to support head and neck cancer, glioblastoma, and prostate cancer QI research were introduced and downloaded over 100,000 times. DICOM was amended, with over 40 correction proposals addressing QI needs. Reference implementations of the standard in a popular toolkit and standalone tools were introduced. Eight datasets exemplifying the application of the standard and tools were contributed. An open demonstration/connectathon was organized, attracting the participation of academic groups and commercial vendors. Integration of tools with TCIA was improved by implementing programmatic communication interface and by refining best practices for QI analysis results curation.

CONCLUSION

Tools, capabilities of the DICOM standard, and datasets we introduced found adoption and utility within the cancer imaging community. A collaborative approach is critical to addressing challenges in imaging informatics at the national and international levels. Numerous challenges remain in establishing and maintaining the infrastructure of analysis tools and standardized datasets for the imaging community. Ideas and technology developed by the QIICR project are contributing to the NCI Imaging Data Commons currently being developed.

Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation

Accurate segmentation of the prostate from magnetic resonance (MR) images provides useful information for prostate cancer diagnosis and treatment. However, automated prostate segmentation from 3D MR images faces several challenges. The lack of clear edge between the prostate and other anatomical structures makes it challenging to accurately extract the boundaries. The complex background texture and large variation in size, shape and intensity distribution of the prostate itself make segmentation even further complicated. Recently, as deep learning, especially convolutional neural networks (CNNs), emerging as the best performed methods for medical image segmentation, the difficulty in obtaining large number of annotated medical images for training CNNs has become much more pronounced than ever. Since large-scale dataset is one of the critical components for the success of deep learning, lack of sufficient training data makes it difficult to fully train complex CNNs. To tackle the above challenges, in this paper, we propose a boundary-weighted domain adaptive neural network (BOWDA-Net). To make the network more sensitive to the boundaries during segmentation, a boundary-weighted segmentation loss is proposed. Furthermore, an advanced boundary-weighted transfer leaning approach is introduced to address the problem of small medical imaging datasets. We evaluate our proposed model on three different MR prostate datasets. The experimental results demonstrate that the proposed model is more sensitive to object boundaries and outperformed other state-of-the-art methods.

Are superior cervical sympathetic ganglia avid on whole body 68Ga-PSMA-11 PET/magnetic resonance?: a comprehensive morphologic and molecular assessment in patients with prostate cancer

OBJECTIVES

Recent reports warn against erroneous mistaking of celiac and stellate sympathetic ganglia for metastatic lymph nodes on multimodal prostate-specific membrane antigen (PSMA)-ligand PET imaging. The aim was to check the intensity of Ga-PSMA-11 uptake and magnetic resonance (MR) features of superior cervical ganglia (SCG) on PET/MR imaging.

METHODS

In 89 patients 106 SCG were reliably identified on Ga-PSMA-11 PET/MR. For each SCG, qualitative assessment (visual subjective avidity, diffusion restriction, shape, and the presence of central hypointensity) and quantitative measurements [dimensions, maximal standardized uptake value (SUVmax), mean apparent diffusion coefficient (ADC)] were performed.

RESULTS

Mean SUVmax in SCG amounted to 1.88 ± 0.63 (range: 0.87-4.42), with considerable metabolic activity (SUVmax ≥ 2) in 37.7% of SCG; mean thickness was 3.18 ± 1.08 mm. In subjective visual evaluation, SCG avidity was classified as mistakable or potentially mistakable with underlying malignancy in 32.1% of cases. Mean ADC values amounted 1749.83 ± 428.83 × 10mm/s. In visual assessment, 74.5% of ganglia showed moderate to high diffusion restriction. An oval or longitudinal shape on transverse MR plane was presented by 59.4% of SCG. The central hypointensity was detected on MR T2-weighted images only in 10.4% of SCG.

CONCLUSION

SCG, similar to other sympathetic ganglia, show Ga-PSMA-11 uptake. SCG avidity may be of significance, especially in view of frequently occurring SCG oval or longitudinal shape, and moderate to high diffusion restriction in visual assessment, potentially suggesting malignancy on transverse MR plane. Diagnostic imaging specialists and clinicians should be aware of the above.

A Single-Arm, Multicenter Validation Study of Prostate Cancer Localization and Aggressiveness With a Quantitative Multiparametric Magnetic Resonance Imaging Approach

OBJECTIVES

The aims of this study were to assess the discriminative performance of quantitative multiparametric magnetic resonance imaging (mpMRI) between prostate cancer and noncancer tissues and between tumor grade groups (GGs) in a multicenter, single-vendor study, and to investigate to what extent site-specific differences affect variations in mpMRI parameters.

MATERIALS AND METHODS

Fifty patients with biopsy-proven prostate cancer from 5 institutions underwent a standardized preoperative mpMRI protocol. Based on the evaluation of whole-mount histopathology sections, regions of interest were placed on axial T2-weighed MRI scans in cancer and noncancer peripheral zone (PZ) and transition zone (TZ) tissue. Regions of interest were transferred to functional parameter maps, and quantitative parameters were extracted. Across-center variations in noncancer tissues, differences between tissues, and the relation to cancer grade groups were assessed using linear mixed-effects models and receiver operating characteristic analyses.

RESULTS

Variations in quantitative parameters were low across institutes (mean [maximum] proportion of total variance in PZ and TZ, 4% [14%] and 8% [46%], respectively). Cancer and noncancer tissues were best separated using the diffusion-weighted imaging-derived apparent diffusion coefficient, both in PZ and TZ (mean [95% confidence interval] areas under the receiver operating characteristic curve [AUCs]; 0.93 [0.89-0.96] and 0.86 [0.75-0.94]), followed by MR spectroscopic imaging and dynamic contrast-enhanced-derived parameters. Parameters from all imaging methods correlated significantly with tumor grade group in PZ tumors. In discriminating GG1 PZ tumors from higher GGs, the highest AUC was obtained with apparent diffusion coefficient (0.74 [0.57-0.90], P < 0.001). The best separation of GG1-2 from GG3-5 PZ tumors was with a logistic regression model of a combination of functional parameters (mean AUC, 0.89 [0.78-0.98]).

CONCLUSIONS

Standardized data acquisition and postprocessing protocols in prostate mpMRI at 3 T produce equivalent quantitative results across patients from multiple institutions and achieve similar discrimination between cancer and noncancer tissues and cancer grade groups as in previously reported single-center studies.

Molecular imaging of the prostate: Comparing total sodium concentration quantification in prostate cancer and normal tissue using dedicated 13 C and 23 Na endorectal coils

BACKGROUND

There has been recent interest in nonproton MRI including hyperpolarized carbon-13 (13 C) imaging. Prostate cancer has been shown to have a higher tissue sodium concentration (TSC) than normal tissue. Sodium (23 Na) and 13 C nuclei have a frequency difference of only 1.66 MHz at 3T, potentially enabling 23 Na imaging with a 13 C-tuned coil and maximizing the metabolic information obtained from a single study.

PURPOSE

To compare TSC measurements from a 13 C-tuned endorectal coil to those quantified with a dedicated 23 Na-tuned coil.

STUDY TYPE

Prospective.

POPULATION

Eight patients with biopsy-proven, intermediate/high risk prostate cancer imaged prior to prostatectomy.

SEQUENCE

3T MRI with separate dual-tuned 1 H/23 Na and 1 H/13 C endorectal receive coils to quantify TSC.

ASSESSMENT

Regions-of-interest for TSC quantification were defined for normal peripheral zone (PZ), normal transition zone (TZ), and tumor, with reference to histopathology maps.

STATISTICAL TESTS

Two-sided Wilcoxon rank sum with additional measures of correlation, coefficient of variation, and Bland-Altman plots to assess for between-test differences.

RESULTS

Mean TSC for normal PZ and TZ were 39.2 and 33.9 mM, respectively, with the 23 Na coil and 40.1 and 36.3 mM, respectively, with the 13 C coil (P = 0.22 and P = 0.11 for the intercoil comparison, respectively). For tumor tissue, there was no statistical difference between the overall mean tumor TSC measured with the 23 Na coil (41.8 mM) and with the 13 C coil (46.6 mM; P = 0.38). Bland-Altman plots showed good repeatability for tumor TSC measurements between coils, with a reproducibility coefficient of 9 mM; the coefficient of variation between the coils was 12%. The Pearson correlation coefficient for TSC between coils for all measurements was r = 0.71 (r2 = 0.51), indicating a strong positive linear relationship. The mean TSC within PZ tumors was significantly higher compared with normal PZ for both the 23 Na coil (45.4 mM; P = 0.02) and the 13 C coil (49.4 mM; P = 0.002).

DATA CONCLUSION

We demonstrated the feasibility of using a carbon-tuned coil to quantify TSC, enabling dual metabolic information from a single coil. This approach could make the acquisition of both 23 Na-MRI and 13 C-MRI feasible in a single clinical imaging session.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:90-97.

The roles of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen concentrations in predicting prostate cancer and high-risk prostate cancer

Prostate biopsies are frequently performed to screen for prostate cancer (PCa) with complications such as infections and bleeding. To reduce unnecessary biopsies, here we designed an improved predictive model of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen (PSA) concentrations for diagnosing PCa and risk stratification. Multiparametric MRI administered to 422 consecutive patients before initial transrectal ultrasonography-guided 13-core prostate biopsies from January 2012 to March 2018 at Fujian Medical University Union Hospital. Univariate and multivariate logistic regression analyses and determination of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was performed to evaluate and integrate the predictors of PCa and high-risk prostate cancer (HR-PCa). The detection rates of PCa was 43.84% (185/422). And the detection rates of HR-PCa was 71.35% (132/185) in PCa patients. Multivariate analysis revealed that prostate volume(PV), PSA density(PSAD), transitional zone volume(TZV), PSA density of the transitional zone(PSADTZ), and MR were independent predictors of PCa and HR-PCa. PSA, peripheral zone volume(PZV) and PSA density of the peripheral zone(PSADPZ) were independent predictors of PCa but not HR-PCa. The AUC of our best predictive model including PSA + PV + PSAD + MR + TZV or PSA + PV + PSAD + MR + PZV was 0.906 for PCa. The AUC of the best predictive model of PV + PSAD + MR + TZV was 0.893 for HR-PCa. In conclusion, our results will likely improve the detection rate of prostate cancer, avoiding unnecessary prostate biopsies, and for evaluating risk stratification.

Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer

PURPOSE

To evaluate repeatability of prostate DWI-derived radiomics and machine learning methods for prostate cancer (PCa) characterization.

METHODS

A total of 112 patients with diagnosed PCa underwent 2 prostate MRI examinations (Scan1 and Scan2) performed on the same day. DWI was performed using 12 b-values (0-2000 s/mm² ), post-processed using kurtosis function, and PCa areas were annotated using whole mount prostatectomy sections. A total of 1694 radiomic features including Sobel, Kirch, Gradient, Zernike Moments, Gabor, Haralick, CoLIAGe, Haar wavelet coefficients, 3D analogue to Laws features, 2D contours, and corner detectors were calculated. Radiomics and 4 feature pruning methods (area under the receiver operator characteristic curve, maximum relevance minimum redundancy, Spearman's ρ, Wilcoxon rank-sum) were evaluated in terms of Scan1-Scan2 repeatability using intraclass correlation coefficient (ICC)(3,1). Classification performance for clinically significant and insignificant PCa with Gleason grade groups 1 versus >1 was evaluated by area under the receiver operator characteristic curve in unseen random 30% data split.

RESULTS

The ICC(3,1) values for conventional radiomics and feature pruning methods were in the range of 0.28-0.90. The machine learning classifications varied between Scan1 and Scan2 with % of same class labels between Scan1 and Scan2 in the range of 61-81%. Surface-to-volume ratio and corner detector-based features were among the most represented features with high repeatability, ICC(3,1) >0.75, consistently high ranking using all 4 feature pruning methods, and classification performance with area under the receiver operator characteristic curve >0.70.

CONCLUSION

Surface-to-volume ratio and corner detectors for prostate DWI led to good classification of unseen data and performed similarly in Scan1 and Scan2 in contrast to multiple conventional radiomic features.

Optimization and repeatability of multipool chemical exchange saturation transfer MRI of the prostate at 3.0 T

BACKGROUND

Chemical exchange saturation transfer (CEST) can potentially support cancer imaging with metabolically derived information. Multiparametric prostate MRI has improved diagnosis but may benefit from additional information to reduce the need for biopsies.

PURPOSE

To optimize an acquisition and postprocessing protocol for 3.0 T multipool CEST analysis of prostate data and evaluate the repeatability of the technique.

STUDY TYPE

Prospective.

SUBJECTS

Five healthy volunteers (age range: 24-47 years; median age: 28 years) underwent two sessions (interval range: 7-27 days; median interval: 20 days) and two biopsy-proven prostate cancer patients were evaluated once. Patient 1 (71 years) had a Gleason 3 + 4 transition zone (TZ) tumor and patient 2 (55 years) had a Gleason 4 + 3 peripheral zone (PZ) tumor.

FIELD STRENGTH

3.0 T. Sequences run: T2 -weighted turbo-spin-echo (TSE); diffusion-weighted imaging; CEST; WASABI (for B0 determination).

ASSESSMENT

Saturation, readout, and fit-model parameters were optimized to maximize in vivo amide and nuclear Overhauser effect (NOE) signals. Repeatability (intrasession and intersession) was evaluated in healthy volunteers. Subsequently, preliminary evaluation of signal differences was made in patients. Regions of interest were drawn by two post-FRCR board-certified readers, both with over 5 years of experience in multiparametric prostate MRI.

STATISTICAL TESTS

Repeatability was assessed using Bland-Altman analysis, coefficient of variation (CV), and 95% limits of agreement (LOA). Statistical significance of CEST contrast was calculated using a nonparametric Mann-Whitney U-test.

RESULTS

The optimized saturation scheme was found to be 60 sinc-Gaussian pulses with 40 msec pulse duration, at 50% duty-cycle with continuous-wave pulse equivalent B1 power (B1CWPE ) of 0.92 μT. The magnetization transfer (MT) contribution to the fit-model was centered at -1.27 ppm. Intersession coefficients of variation (CVs) of the amide, NOE, and magnetization transfer (MT) and asymmetric magnetization transfer ratio (MTRasym ) signals of 25%, 23%, 18%, and 200%, respectively, were observed. Fit-metric and MTRasym CVs agreed between readers to within 4 and 10 percentage points, respectively.

DATA CONCLUSION

Signal differences of 0.03-0.10 (17-43%) detectable depending upon pool, with MT the most repeatable (signal difference of 17-22% detectable).

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1238-1250.

Selection of Fitting Model and Arterial Input Function for Repeatability in Dynamic Contrast-Enhanced Prostate MRI

RATIONALE AND OBJECTIVES

Analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging is notable for the variability of calculated parameters. The purpose of this study was to evaluate the level of measurement variability and error/variability due to modeling in DCE magnetic resonance imaging parameters.

MATERIALS AND METHODS

Two prostate DCE scans were performed on 11 treatment-naïve patients with suspected or confirmed prostate peripheral zone cancer within an interval of less than two weeks. Tumor-suspicious and normal-appearing regions of interest (ROI) in the prostate peripheral zone were segmented. Different Tofts-Kety based models and different arterial input functions, with and without bolus arrival time (BAT) correction, were used to extract pharmacokinetic parameters. The percent repeatability coefficient (%RC) of fitted model parameters K_trans, v_e, and k_ep was calculated. Paired t-tests comparing parameters in tumor-suspicious ROIs and in normal-appearing tissue evaluated each parameter's sensitivity to pathology.

RESULTS

Although goodness-of-fit criteria favored the four-parameter extended Tofts-Kety model with the BAT correction included, the simplest two-parameter Tofts-Kety model overall yielded the best repeatability scores. The best %RC in the tumor-suspicious ROI was 63% for k_ep, 28% for v_e, and 83% for K_trans . The best p values for discrimination between tissues were p <10^-5 for k_ep and K_trans, and p = 0.11 for v_e. Addition of the BAT correction to the models did not improve repeatability.

CONCLUSION

The parameter k_ep, using an arterial input functions directly measured from blood signals, was more repeatable than K_trans. Both K_trans and k_ep values were highly discriminatory between healthy and diseased tissues in all cases. The parameter v_e had high repeatability but could not distinguish the two tissue types.

Repeatability of Multiparametric Prostate MRI Radiomics Features

In this study we assessed the repeatability of radiomics features on small prostate tumors using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI). The premise of radiomics is that quantitative image-based features can serve as biomarkers for detecting and characterizing disease. For such biomarkers to be useful, repeatability is a basic requirement, meaning its value must remain stable between two scans, if the conditions remain stable. We investigated repeatability of radiomics features under various preprocessing and extraction configurations including various image normalization schemes, different image pre-filtering, and different bin widths for image discretization. Although we found many radiomics features and preprocessing combinations with high repeatability (Intraclass Correlation Coefficient > 0.85), our results indicate that overall the repeatability is highly sensitive to the processing parameters. Neither image normalization, using a variety of approaches, nor the use of pre-filtering options resulted in consistent improvements in repeatability. We urge caution when interpreting radiomics features and advise paying close attention to the processing configuration details of reported results. Furthermore, we advocate reporting all processing details in radiomics studies and strongly recommend the use of open source implementations.

VERDICT MRI for Prostate Cancer: Intracellular Volume Fraction versus Apparent Diffusion Coefficient

Background Biologic specificity of diffusion MRI in relation to prostate cancer aggressiveness may improve by examining separate components of the diffusion MRI signal. The Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumors (VERDICT) model estimates three distinct signal components and associates them to (a) intracellular water, (b) water in the extracellular extravascular space, and (c) water in the microvasculature. Purpose To evaluate the repeatability, image quality, and diagnostic utility of intracellular volume fraction (FIC) maps obtained with VERDICT prostate MRI and to compare those maps with apparent diffusion coefficient (ADC) maps for Gleason grade differentiation. Materials and Methods Seventy men (median age, 62.2 years; range, 49.5-82.0 years) suspected of having prostate cancer or undergoing active surveillance were recruited to a prospective study between April 2016 and October 2017. All men underwent multiparametric prostate and VERDICT MRI. Forty-two of the 70 men (median age, 67.7 years; range, 50.0-82.0 years) underwent two VERDICT MRI acquisitions to assess repeatability of FIC measurements obtained with VERDICT MRI. Repeatability was measured with use of intraclass correlation coefficients (ICCs). The image quality of FIC and ADC maps was independently evaluated by two board-certified radiologists. Forty-two men (median age, 64.8 years; range, 49.5-79.6 years) underwent targeted biopsy, which enabled comparison of FIC and ADC metrics in the differentiation between Gleason grades. Results VERDICT MRI FIC demonstrated ICCs of 0.87-0.95. There was no significant difference between image quality of ADC and FIC maps (score, 3.1 vs 3.3, respectively; P = .90). FIC was higher in lesions with a Gleason grade of at least 3+4 compared with benign and/or Gleason grade 3+3 lesions (mean, 0.49 ± 0.17 vs 0.31 ± 0.12, respectively; P = .002). The difference in ADC between these groups did not reach statistical significance (mean, 1.42 vs 1.16 × 10-3 mm2/sec; P = .26). Conclusion Fractional intracellular volume demonstrates high repeatability and image quality and enables better differentiation of a Gleason 4 component cancer from benign and/or Gleason 3+3 histology than apparent diffusion coefficient. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Sigmund and Rosenkrantz in this issue.

Repeatability analysis of ADC histogram metrics of the uterus

BACKGROUND

Recently, histogram analysis based on voxel-wise apparent diffusion coefficient (ADC) value distribution has been increasingly performed. However, few studies have been reported regarding its repeatability.

PURPOSE

To evaluate the repeatability of ADC histogram metrics of the uterus in clinical magnetic resonance imaging (MRI).

MATERIAL AND METHODS

Thirty-three female patients who underwent pelvic MRI including diffusion-weighted imaging (DWI) were prospectively included after providing informed consent. Two sequential DWI acquisitions with identical parameters and position were obtained. Regions of interest (ROIs) for histologically confirmed uterine lesions (five cervical and three endometrial cancers, and one endometrial hyperplasia) and normal appearing tissues (21 endometrium and 33 myometrium) were assigned on the first DWI dataset and then pasted onto the second DWI dataset. ADC histogram metrics within the ROIs were calculated and repeatability was evaluated by calculating within-subject coefficient of variance (%) (wCV (%)) and Bland-Altman plot (%).

RESULTS

ADC 10%, 25%, median, 75%, 90%, maximum, mean, and entropy showed high repeatability (wCV (%) < 7, 95% limit of agreement in Bland-Altman plot (%) < ±20), followed by ADC minimum (wCV (%) = 8.12, 95% limit of agreement in Bland-Altman plot (%) < ±30). However, ADC skewness and kurtosis showed very low repeatability in all evaluations.

CONCLUSION

ADC histogram metrics like ADC 10%, 25%, median, 75%, 90%, maximum, mean, and entropy are robust biomarkers and could be applicable to clinical use. However, ADC skewness and kurtosis lack robustness. Radiologists should keep these characteristics and limitations in mind when interpreting quantitative DWI.

An overview of publicly available patient-centered prostate cancer datasets

Prostate cancer (PCa) is the second most common cancer in men, and the second leading cause of death from cancer in men. Many studies on PCa have been carried out, each taking much time before the data is collected and ready to be analyzed. However, on the internet there is already a wide range of PCa datasets available, which could be used for data mining, predictive modelling or other purposes, reducing the need to setup new studies to collect data. In the current scientific climate, moving more and more to the analysis of "big data" and large, international, multi-site projects using a modern IT infrastructure, these datasets could be proven extremely valuable. This review presents an overview of publicly available patient-centered PCa datasets, divided into three categories (clinical, genomics and imaging) and an "overall" section to enable researchers to select a suitable dataset for analysis, without having to go through days of work to find the right data. To acquire a list of human PCa databases, scientific literature databases and academic social network sites were searched. We also used the information from other reviews. All databases in the combined list were then checked for public availability. Only databases that were either directly publicly available or available after signing a research data agreement or retrieving a free login were selected for inclusion in this review. Data should be available to commercial parties as well. This paper focuses on patient-centered data, so the genomics data section does not include gene-centered databases or pathway-centered databases. We identified 42 publicly available, patient-centered PCa datasets. Some of these consist of different smaller datasets. Some of them contain combinations of datasets from the three data domains: clinical data, imaging data and genomics data. Only one dataset contains information from all three domains. This review presents all datasets and their characteristics: number of subjects, clinical fields, imaging modalities, expression data, mutation data, biomarker measurements, etc. Despite all the attention that has been given to making this overview of publicly available databases as extensive as possible, it is very likely not complete, and will also be outdated soon. However, this review might help many PCa researchers to find suitable datasets to answer the research question with, without the need to start a new data collection project. In the coming era of big data analysis, overviews like this are becoming more and more useful.

Repeatability of diffusion-weighted MRI of the prostate using whole lesion ADC values, skew and histogram analysis

PURPOSE

To investigate the repeatability of diffusion-weighted imaging parameter including ADC-derived histogram values in prostate cancer.

METHODS

10 patients with prostate cancer were prospectively recruited to a retest cohort. 3 T diffusion-weighted MRI of the prostate was acquired consecutively with patient getting off the scanner between studies. Prostatectomy-histopathology defined tumour regions-of-interest were outlined on ADC maps and diffusion-weighted metrics including histograms were calculated. The coefficient of reproducibility (CoR) and Bland-Altman plots were used to assess repeatability.

RESULTS

10th centile, 90th centile, and median ADC showed good repeatability with mean difference ranging from -0.005 to -0.025 × 103 mm2s-1, and CoR ranging from 0.271-0.294 × 103 mm2s-1 of scan 1 mean). Two measures of heterogeneity and simplified texture, IQR and mean local range, had only moderate repeatability. IQR had a mean difference of -0.032 × 103 mm2s-1 between scans with CoR 0.181 × 103 mm2s-1 (56% of scan 1 mean). Mean local range had a mean difference -0.008 × 103 mm2s-1 between scans (37% of scan 1 mean). Bland-Altman plots showed good repeatability for test and re-test analysis for median, percentile and mean range values. All ADC values had good reliability regardless of whether the tumour border was included in quantitative analysis. ADC histogram skew had poor repeatability, CoR 0.78 × 103 mm2s-1 (373% of scan 1 mean).

CONCLUSION

10th and 90th centile ADC demonstrated sufficient repeatability for clinical use. However, more advanced measures of heterogeneity such as histogram skew, IQR, or mean local range may be limited by their repeatability.

An annotated test-retest collection of prostate multiparametric MRI

Multiparametric Magnetic Resonance Imaging (mpMRI) is widely used for characterizing prostate cancer. Standard of care use of mpMRI in clinic relies on visual interpretation of the images by an expert. mpMRI is also increasingly used as a quantitative imaging biomarker of the disease. Little is known about repeatability of such quantitative measurements, and no test-retest datasets have been available publicly to support investigation of the technical characteristics of the MRI-based quantification in the prostate. Here we present an mpMRI dataset consisting of baseline and repeat prostate MRI exams for 15 subjects, manually annotated to define regions corresponding to lesions and anatomical structures, and accompanied by region-based measurements. This dataset aims to support further investigation of the repeatability of mpMRI-derived quantitative prostate measurements, study of the robustness and reliability of the automated analysis approaches, and to support development and validation of new image analysis techniques. The manuscript can also serve as an example of the use of DICOM for standardized encoding of the image annotation and quantification results.

Diffusion-weighted imaging (DWI) in lymph node staging for prostate cancer

In patients with prostate cancer, the presence of lymph node (LN) metastases is a critical prognostic factor and is essential for treatment planning. Conventional cross-sectional imaging performs poorly for nodal staging as both computed tomography (CT) and magnetic resonance imaging (MRI) are mainly dependent on size and basic morphological criteria. Therefore, extended pelvic LN dissection (ePLND) remains the gold standard for LN staging, however, it is an invasive procedure with its own drawbacks, thus creating a need for accurate preoperative imaging test. Incorporating functional MRI by using diffusion-weighted MRI has proven superior to conventional MRI protocol by means of both qualitative and quantitative assessment. Currently, the increased diagnostic performance remains insufficient to replace ePLND and the future role of DWI may be through combination with MR lymphangiography or with novel positron emission tomography (PET) tracers. In this article, the current state of data supporting DWI in LN staging of patients with prostate cancer is discussed.

Optimization of ZD2 Peptide Targeted Gd(HP-DO3A) for Detection and Risk-Stratification of Prostate Cancer with MRI

The aim of this work is to optimize a peptide targeted macrocyclic MRI contrast agent for detection and risk-stratification of aggressive prostate cancer. The optimized agent was prepared using click chemistry in the presence of CuSO4 and ascorbate at room temperature. The T1 and T2 relaxivities of ZD2-N3-Gd(HP-DO3A) are 5.44 and 7.10 mM-1 s-1 at 1.4 T, and 5.53 and 7.81 mM-1 s-1 at 7 T, respectively, higher than the previously reported ZD2-Gd(HP-DO3A). The specific tumor enhancement of the agent was investigated in male nude mice bearing aggressive PC3 human prostate cancer xenografts and slow-growing LNCaP tumor xenografts. Contrast enhanced MR images were acquired using a 2D spin-echo sequence and a 3D FLASH sequence with a 7 T small animal scanner. ZD2-N3-Gd(HP-DO3A) produced robust contrast enhancement in aggressive PC3 tumors and little enhancement in slow-growing LNCaP tumors. It produced 400% and 100% CNR increases in the T1-weighted 2D spin-echo MR images and 3D FLASH images of PC3 tumors, respectively, for at least 30 min at a dose of 0.1 mmol/kg. In contrast, less than 20% CNR increase was observed in the LNCaP tumors with both sequences. The optimized targeted contrast agent has higher relaxivities and are effective to detect aggressive PC3 tumors and differentiate the aggressive cancer from the slow-growing LNCaP prostate cancer in contrast enhanced MRI. ZD2-N3-Gd(HP-DO3A) has the promise for accurate detection and risk-stratification of aggressive prostate cancer.

Can Apparent Diffusion Coefficient Values Assist PI-RADS Version 2 DWI Scoring? A Correlation Study Using the PI-RADSv2 and International Society of Urological Pathology Systems

OBJECTIVE

The purposes of this study were to assess correlation of apparent diffusion coefficient (ADC) and normalized ADC (ratio of tumor to nontumor tissue) with the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) and updated International Society of Urological Pathology (ISUP) categories and to determine how to optimally use ADC metrics for objective assistance in categorizing lesions within PI-RADSv2 guidelines.

MATERIALS AND METHODS

In this retrospective study, 100 patients (median age, 62 years; range, 44-75 years; prostate-specific antigen level, 7.18 ng/mL; range, 1.70-84.56 ng/mL) underwent 3-T multiparametric MRI of the prostate with an endorectal coil. Mean ADC was extracted from ROIs based on subsequent prostatectomy specimens. Histopathologic analysis revealed 172 lesions (113 peripheral, 59 transition zone). Two radiologists blinded to histopathologic outcome assigned PI-RADSv2 categories. Kendall tau was used to correlate ADC metrics with PI-RADSv2 and ISUP categories. ROC curves were used to assess the utility of ADC metrics in differentiating each reader's PI-RADSv2 DWI category 4 or 5 assessment in the whole prostate and by zone.

RESULTS

ADC metrics negatively correlated with ISUP category in the whole prostate (ADC, τ = -0.21, p = 0.0002; normalized ADC, τ = -0.21, p = 0.0001). Moderate negative correlation was found in expert PI-RADSv2 DWI categories (ADC, τ = -0.34; normalized ADC, τ = -0.31; each p < 0.0001) maintained across zones. In the whole prostate, AUCs of ADC and normalized ADC were 87% and 82% for predicting expert PI-RADSv2 DWI category 4 or 5. A derived optimal cutoff ADC less than 1061 and normalized ADC less than 0.65 achieved positive predictive values of 83% and 84% for correct classification of PI-RADSv2 DWI category 4 or 5 by an expert reader. Consistent relations and predictive values were found by an independent novice reader.

CONCLUSION

ADC and normalized ADC inversely correlate with PI-RADSv2 and ISUP categories and can serve as quantitative metrics to assist with assigning PI-RADSv2 DWI category 4 or 5.

Comparison of the accuracy of multiparametric magnetic resonance imaging (mpMRI) results with the final pathology findings for radical prostatectomy specimens in the detection of prostate cancer

AIMS

To assess the accuracy of multiparametric magnetic resonance imaging (mpMRI), used in conjunction with the Prostrate Imaging Reporting and Data System (PI-RADS), version 2, in the detection of prostate cancer (PCa), and to determine the extent of the efficacy of mpMRI as a screening test in biopsy-naïve patients.

METHODS

Retrospective analysis was conducted in 107 patients who underwent mpMRI prior to radical prostatectomy (RP) at a single institution. The mpMRI findings were reassessed using PI-RADS, version 2. A comparison was made between the histological findings for the RP specimens and the mpMRI results.

RESULTS

Unique histologically confirmed PCa foci (237) were identified in 107 patients. Overall, mpMRI sensitivity of 46% was found for PCa detection (110/237). The sensitivity, specificity and negative predictive value of mpMRI was 75.5%, 77.0% and 79.8%, respectively, for clinically significant cancer, and 75.7%, 77.7% and 79.5%, for pathological index tumors. A moderate and significant correlation was observed between a high PI-RADS score and a high pathological grade, tumor volume, index tumor status and clinically significant cancer status (all, P < 0.001, respectively). Pathological tumor volume was a significant predictor of PCa detection using mpMRI according to multivariate analysis. Using a cut-off value of 0.89 cc, the sensitivity and specificity of mpMRI for PCa detection were 0.87 and 0.65, respectively.

CONCLUSION

The mpMRI, used in conjunction with PI-RADS, was useful in detecting PCa and in predicting tumor aggressiveness. However, the detection of 20% of clinically significant cancer was missed using mpMRI. Thus, its inclusion in a triage test should be limited to selected biopsy-naïve patients.

Quantitative characterisation of clinically significant intra-prostatic cancer by prostate-specific membrane antigen (PSMA) expression and cell density on PSMA-11

OBJECTIVES

To quantitatively characterize clinically significant intra-prostatic cancer (IPC) by prostate-specific membrane antigen (PSMA) expression and cell density on PSMA-11 positron emission tomography/magnetic resonance (PET/MR).

METHODS

Retrospective study approved by the institutional review board with informed written consent obtained. Patients with a solitary, biopsy-proven prostate cancer, Gleason score (GS) ≥7, presenting for initial evaluation by PET/computerised tomography (PET/CT), underwent early prostate PET/MR immediately after PSMA-11 tracer injection. PET/MR [MRI-based attenuation correction (MRAC)] and PET/CT [CT-based AC (CTAC)] maximal standardised uptake value (SUVmax) and minimal and mean apparent diffusion coefficient (ADCmin, ADCmean; respectively) in normal prostatic tissue (NPT) were compared to IPC area. The relationship between SUVmax, ADCmin and ADCmean measurements was obtained.

RESULTS

Twenty-two patients (mean age 69.5±5.0 years) were included in the analysis. Forty-four prostate areas were evaluated (22 IPC and 22 NPT). Median MRAC SUVmax of NPT was significantly lower than median MRAC SUVmax of IPC (p < 0.0001). Median ADCmin and ADCmean of NPT was significantly higher than median ADCmin and ADCmean of IPC (p < 0.0001). A very good correlation was found between MRAC SUVmax with CTAC SUVmax (rho = -0.843, p < 0.0001). A good inverse relationship was found between MRAC SUVmax and CTAC SUVmax with ADCmin (rho = -0.717, p < 0.0001 and -0.740, p < 0.0001; respectively; Z = 0.22, p = 0.82, NS) and with MRAC SUVmax and ADCmean (rho = -0.737, p < 0.0001).

CONCLUSIONS

PET/MR SUVmax, ADCmin and ADCmean are distinct biomarkers able to differentiate between IPC and NPT in naïve prostate cancer patients with GS ≥ 7.

KEY POINTS

• PSMA PET/MR metrics differentiate between normal and tumoural prostatic tissue. • A multi-parametric approach combining molecular and anatomical information might direct prostate biopsy. • PSMA PET/MR metrics are warranted for radiomics analysis.

Comparison of quantitative apparent diffusion coefficient parameters with prostate imaging reporting and data system V2 assessment for detection of clinically significant peripheral zone prostate cancer

PURPOSE

To compare diagnostic performance of PI-RADSv2 with ADC parameters to identify clinically significant prostate cancer (csPC) and to determine the impact of csPC definitions on diagnostic performance of ADC and PI-RADSv2.

METHODS

We retrospectively identified treatment-naïve pathology-proven peripheral zone PC patients who underwent 3T prostate MRI, using high b-value diffusion-weighted imaging from 2011 to 2015. Using 3D slicer, areas of suspected tumor (T) and normal tissue (N) on ADC (b = 0, 1400) were outlined volumetrically. Mean ADC_T, mean ADC_N, ADC_ratio (ADC_T/ADC_N) were calculated. PI-RADSv2 was assigned. Three csPC definitions were used: (A) Gleason score (GS) ≥ 4 + 3; (B) GS ≥ 3 + 4; (C) MRI-based tumor volume >0.5 cc. Performances of ADC parameters and PI-RADSv2 in identifying csPC were measured using nonparametric comparison of receiver operating characteristic curves using the area under the curve (AUC).

RESULTS

Eighty five cases met eligibility requirements. Diagnostic performances (AUC) in identifying csPC using three definitions were: (A) ADC_T (0.83) was higher than PI-RADSv2 (0.65, p = 0.006); (B) ADC_T (0.86) was higher than ADC_ratio (0.68, p < 0.001), and PI-RADSv2 (0.70, p = 0.04); (C) PI-RADSv2 (0.73) performed better than ADC_ratio (0.56, p = 0.02). ADC_T performance was higher when csPC was defined by A or B versus C (p = 0.038 and p = 0.01, respectively). ADC_ratio performed better when csPC was defined by A versus C (p = 0.01). PI-RADSv2 performance was not affected by csPC definition.

CONCLUSIONS

When csPC was defined by GS, ADC parameters provided better csPC discrimination than PI-RADSv2, with ADC_T providing best result. When csPC was defined by MRI-calculated volume, PI-RADSv2 provided better discrimination than ADC_ratio. csPC definition did not affect PI-RADSv2 diagnostic performance.