Diffusion-weighted magnetic resonance imaging in cancer: Reported apparent diffusion coefficients, in-vitro and in-vivo reproducibility

Comparative evaluation of imaging methods for prognosis assessment in esophageal squamous cell carcinoma: focus on diffusion-weighted magnetic resonance imaging, computed tomography and esophagography

Objective

To identify the most sensitive imaging examination method to evaluate the prognosis of esophageal squamous cell carcinoma (ESCC).

Materials and methods

Thirty patients with esophageal squamous cell carcinoma (ESCC) participated in the study and underwent chemoradiotherapy (CRT). They were divided into two groups based on their survival status: the survival group and non-survival group. The diagnostic tests were utilized to determine the most effective imaging examination method for assessing the prognosis.

Results

1. There were no significant differences in tumor length shown on esophagography or computed tomography (CT) or the maximal esophageal wall thickness shown on CT at the specified time points between the two groups. 2. The tumor length on diffusion-weighted imaging (DWI) in the survival group was significantly lower than in the non-survival group at the end of the sixth week of treatment (P=0.001). The area under the ROC curve was 0.840 (P=0.002), and the diagnostic efficiency was moderately accurate. 3. The apparent diffusion coefficient (ADC) values of the survival group were significantly higher than those in the non-survival group at the end of the fourth week and sixth week of treatment (both P<0.001). Areas under the curve were 0.866 and 0.970, with P values of 0.001 and <0.001 and good diagnostic accuracy. Cox regression analyses indicated the ADC at the end of the sixth week of treatment was an independent risk factor.

Conclusions

Compared with esophagography and CT, DW-MRI has certain advantages in predicting the prognosis of ESCC.

Variabilities in apparent diffusion coefficient (ADC) measurements of the spleen and the paraspinal muscle: A single center large cohort study

Purpose

Evaluation of the variabilities in apparent diffusion coefficient (ADC) measurements of the spleen (ADC_spleen) and the paraspinal muscles (ADC_muscle) to identify the reference organ for normalizing the ADC from the abdominal diffusion weighted imaging (DWI).

Methods

Two MRI scanners, with 314 abdominal exams on the GE and 929 on the Siemens system, were used for MRI examinations including DWI (b-values, 50 and 800 s/mm²). For a subset of 73 exams on the Siemens system a second exam was conducted. Four regions of interest (ROIs) in each exam were placed to measure the ADC_spleen and the bilateral ADC_muscle. ADC variability between patients (on each scanner separately), ADC variability due to ROI placement between the two ROIs in each organ, and variability in the subset between the first and second exams were assessed.

Results

The ADC_spleen was more scattered and variable than the ADC_muscle in the comparability (n = 929 and 314 for two MRI scanners, respectively) and repeatability (n = 73) datasets. The Bland-Altmann bias and limits of agreement (LoAs) for the ADC_spleen (ICC, 0.47; CV, 0.070) and ADC_muscle (ICC, 0.67; CV, 0.023) in the repeatability datasets (n = 73) were -0.1 (-25.7%-25.6%) and -0.3 (-8.8%-8.1%), respectively. For the Siemens system, the Bland-Altmann bias and LoAs for the ADC_spleen (ICC, 0.72; CV, 0.061) and ADC_muscle (ICC, 0.53; CV, 0.030) in the comparability datasets (n = 929) were 2.1 (-20.0%-24.2%) and 0.7 (-10.0%-11.4%), respectively. Similar findings have been found in the GE system (n = 314). The CVs for the ADC_muscle measurements were lower than those of the ADC_spleen both in the repeatability and the comparability analyses (all p < 0.001).

Conclusion

Paraspinal muscles demonstrate better reference characteristics than the spleen in estimating ADC variability of abdominal DWI.

Diffusion-weighted MRI with deep learning for visualizing treatment results of MR-guided HIFU ablation of uterine fibroids

OBJECTIVES

No method is available to determine the non-perfused volume (NPV) repeatedly during magnetic resonance-guided high-intensity focused ultrasound (MR-HIFU) ablations of uterine fibroids, as repeated acquisition of contrast-enhanced T1-weighted (CE-T1w) scans is inhibited by safety concerns. The objective of this study was to develop and test a deep learning-based method for translation of diffusion-weighted imaging (DWI) into synthetic CE-T1w scans, for monitoring MR-HIFU treatment progression.

METHODS

The algorithm was retrospectively trained and validated on data from 33 and 20 patients respectively who underwent an MR-HIFU treatment of uterine fibroids between June 2017 and January 2019. Postablation synthetic CE-T1w images were generated by a deep learning network trained on paired DWI and reference CE-T1w scans acquired during the treatment procedure. Quantitative analysis included calculation of the Dice coefficient of NPVs delineated on synthetic and reference CE-T1w scans. Four MR-HIFU radiologists assessed the outcome of MR-HIFU treatments and NPV ratio based on the synthetic and reference CE-T1w scans.

RESULTS

Dice coefficient of NPVs was 71% (± 22%). The mean difference in NPV ratio was 1.4% (± 22%) and not statistically significant (p = 0.79). Absolute agreement of the radiologists on technical treatment success on synthetic and reference CE-T1w scans was 83%. NPV ratio estimations on synthetic and reference CE-T1w scans were not significantly different (p = 0.27).

CONCLUSIONS

Deep learning-based synthetic CE-T1w scans derived from intraprocedural DWI allow gadolinium-free visualization of the predicted NPV, and can potentially be used for repeated gadolinium-free monitoring of treatment progression during MR-HIFU therapy for uterine fibroids.

KEY POINTS

• Synthetic CE-T1w scans can be derived from diffusion-weighted imaging using deep learning. • Synthetic CE-T1w scans may be used for visualization of the NPV without using a contrast agent directly after MR-HIFU ablations of uterine fibroids.

Diffusion-Weighted Imaging of the Abdomen: Correction for Gradient Nonlinearity Bias in Apparent Diffusion Coefficient

BACKGROUND

Gradient nonlinearity (GNL) introduces spatial nonuniformity bias in apparent diffusion coefficient (ADC) measurements, especially at large offsets from the magnet isocenter.

PURPOSE

To investigate the effects of GNL in abdominal ADC measurements and to develop an ADC bias correction procedure.

STUDY TYPE

Retrospective.

PHANTOM/POPULATION

Two homemade ultrapure water phantoms/25 patients with histologically confirmed pancreatic ductal adenocarcinoma (PDAC).

FIELD STRENGTH/SEQUENCE

A 3.0 T/diffusion-weighted imaging (DWI) with single-shot echo-planar imaging sequence.

ASSESSMENT

ADC bias was computed in the three orthogonal directions at different offset locations. The spatial-dependent correctors of ADC bias were generated from the ADCs of phantom 1. The ADCs were estimated before and after corrections for the phantom 1 with both the proposed approach and the theoretical GNL correction method. For the patients, ADCs were measured in abdominal tissues including left and right liver lobes, PDAC, spleen, bilateral kidneys, and bilateral paraspinal muscles.

STATISTICAL TEST

Friedman tests and Wilcoxon tests.

RESULTS

The ADC bias measured by phantom 1 was 9.7% and 12.6% higher in the right-left and anterior-posterior directions and 9.2% lower in the superior-inferior direction at the 150 mm offsets from the magnetic isocenter. The corrected vs. the uncorrected ADCs measurements (median: 2.20 × 10^-3  mm² /sec for both the proposed method and the theoretical GNL method vs. 2.31 × 10^-3  mm² /sec, respectively) and their relative ADC errors (0.014, 0.016, and 0.054, respectively) were lower in the phantom 1. The relative ADC errors substantially decreased after correction in the phantom 2 (median: 0.048 and -0.008, respectively). The ADCs of all the abdominal tissues were lower after correction except for the left liver lobes (P = 0.13).

DATA CONCLUSION

GNL bias in abdominal ADC can be measured by a DWI phantom. The proposed correction procedure was successfully applied for the bias correction in abdominal ADC.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 1.

Estimation of colon cancer grade and metastatic lymph node involvement using DWI/ADC sequences

BACKGROUND

The potential benefit of neoadjuvant chemotherapy (NAC) in colon cancer is under evaluation. There is a need to improve preoperative non-invasive diagnostics using techniques that provide more accurate staging information in assessing patient eligibility for NAC.

PURPOSE

To investigate the link between the tumor grade (pathohistological confirmed) and the N status (corresponding to lymph node involvement) with apparent diffusion coefficient (ADC) values.

MATERIAL AND METHODS

A total of 17 patients planned for surgical resection had a biopsy confirming colon carcinoma and participated in the study. Abdominal magnetic resonance imaging with diffusion-weighted imaging/ADC sequence was recorded before surgery. The tumor and all visible lymph nodes were manually delineated directly on a grayscale ADC map for every single slice and detected to access the total tumor and summarized lymph node volume. The mean ADC value was further calculated for the mean tumor and mean lymph node values.

RESULTS

Low-grade tumors had a mean ADC equivalent to 1225 ± 170×10^-6 mm²/s, and the coefficient of high-grade tumors was 1444 ± 69×10^-6 mm²/s. The group of patients with positive lymph nodes in operative tissue samples (N+) exhibited lower mean ADC values (1023 ± 142×10^-6 mm²/s) as opposed to the group without metastatic lymph nodes (N-) with ADC values of 1260 ± 231×10^-6 mm²/s.

CONCLUSION

The mean whole-tumor ADC is associated with the histological tumor grade, and the mean ADC value of whole-volume abdominal lymph nodes could assume real nodal infiltration.

Quantitative imaging of uterine cancers with diffusion-weighted MRI and 18-fluorodeoxyglucose PET/CT

Imaging plays an important role in the diagnosis and treatment of women with uterine cervical and endometrial cancers. Quantitative imaging, through MRI, PET/CT, and hybrid PET/MRI, allows for characterization of primary tumors beyond anatomic and qualitative descriptors. MRI diffusion-weighted imaging (DWI) yields an apparent diffusion coefficient (ADC), which can be applied in both the pre-and post-treatment assessment of uterine tumors. PET/CT assesses metabolic activity, and measurement of tumor standardized uptake value (SUV) is a useful metric in the staging of uterine malignancies. Hybrid PET/MRI is an emerging modality that combines the soft tissue contrast of MRI with the molecular imaging capability of PET. This review provides an overview of these quantitative imaging modalities, and their current and potential roles in the assessment of uterine cervical and cancer.

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.

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

PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Diagnostic Performance of Diffusion Kurtosis Imaging for Benign and Malignant Breast Lesions: A Systematic Review and Meta-Analysis

Purpose

Magnetic resonance imaging (MRI) has a high sensitivity for differentiating between malignant and non-malignant breast lesions but is sometimes limited due to its low specificity. Here, we performed a meta-analysis to evaluate the diagnostic performance of mean kurtosis (MK) and mean diffusivity (MD) values in magnetic resonance diffusion kurtosis imaging (DKI) for benign and malignant breast lesions.

Methods

Original articles on relevant topics, published from 2010 to 2019, in PubMed, EMBASE, and WanFang databases were systematically reviewed. According to the purpose of the study and the characteristics of DKI reported, the diagnostic performances of MK and MD were evaluated, and meta-regression was conducted to explore the source of heterogeneity.

Results

Fourteen studies involving 1,099 (451 benign and 648 malignant) lesions were analyzed. The pooled sensitivity, pooled specificity, positive likelihood ratio, and negative likelihood ratio for MD were 0.84 (95% confidence interval (CI), 0.81-0.87), 0.83 (95% CI, 0.79-0.86), 4.44 (95% CI, 3.54-5.57), and 0.18 (95% CI, 0.13-0.26), while those for MK were 0.89 (95% CI, 0.86-0.91), 0.86 (95% CI, 0.82-0.89), 5.72 (95% CI, 4.26-7.69), and 0.13 (95% CI, 0.09-0.19), respectively. The overall area under the curve (AUC) was 0.91 for MD and 0.95 for MK.

Conclusions

Analysis of the data from 14 studies showed that MK had a higher pooled sensitivity, pooled specificity, and diagnostic performance for differentiating between breast lesions, compared with MD.

The value of diffusion-weighted imaging in diagnosing gallbladder malignancy: performance of a new parameter

AIM

To assess the value of the ratio of signal intensities at high and low b-values (b800/b0 ratio) during diffusion-weighted imaging (DWI) for gallbladder cancer diagnosis.

MATERIALS AND METHODS

All patients presenting with suspicious gallbladder lesions between January 2011 and December 2016 who underwent DWI and histopathological diagnoses of the lesions were assessed.

RESULTS

Thirty-two patients (24 men, eight women) were identified. Eighteen patients had benign gallbladder lesions while 14 had malignant lesions. The mean apparent diffusion coefficient (ADC) value was 1.62 (±0.57)×10^-3 mm²/s for benign cases and 1.27 (±0.39)×10^-3 mm²/s for malignant cases; this difference was not significant (p=0.0773). The mean b800/b0 ratio was 0.31 (±0.19) for benign cases and 0.48 (±0.13) for malignant cases; this difference was significant (p=0.007). The ROC curve for b800/b0 had an AUC of 0.782 (95% confidence interval [CI]: 0.616-0.947) with a sensitivity and specificity of 85.7% and 72.2%, respectively, at a cut-off point of 0.33.

CONCLUSION

The b800/b0 ratio can help differentiate benign and malignant gallbladder lesions and may be more reliable than ADC values in quantitative DWI assessments.

Transformational Role of Medical Imaging in (Radiation) Oncology

Simple Summary

Onboard, imaging techniques have brought about a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables us to better visualize where to deliver lethal doses of radiation and thus allows the shrinking of necessary geometric margins leading to reduced toxicities. Alongside improvements in treatment delivery, advances in medical imaging have also allowed us to better define the volumes we wish to target. The development of imaging techniques that can capture aspects of the tumor’s biology before, during and after therapy is transforming how treatment can be delivered. Technological changes have further made these biological imaging techniques available in real-time providing the opportunity to monitor a patient’s response to treatment closely and often before any volume changes are visible on conventional radiological images. Here we discuss the development of robust quantitative imaging biomarkers and how they can personalize therapy towards meaningful clinical endpoints.

Abstract

Onboard, real-time, imaging techniques, from the original megavoltage planar imaging devices, to the emerging combined MRI-Linear Accelerators, have brought a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables lethal doses of radiation to be delivered to target volumes with progressively more accuracy and thus allows shrinking of necessary geometric margins, leading to reduced toxicities. Alongside these improvements in treatment delivery, advances in medical imaging, e.g., PET, and MRI, have also allowed target volumes themselves to be better defined. The development of functional and molecular imaging is now driving a conceptually larger step transformation to both better understand the cancer target and disease to be treated, as well as how tumors respond to treatment. A biological description of the tumor microenvironment is now accepted as an essential component of how to personalize and adapt treatment. This applies not only to radiation oncology but extends widely in cancer management from surgical oncology planning and interventional radiology, to evaluation of targeted drug delivery efficacy in medical oncology/immunotherapy. Here, we will discuss the role and requirements of functional and metabolic imaging techniques in the context of brain tumors and metastases to reliably provide multi-parametric imaging biomarkers of the tumor microenvironment.

Diffusion-weighted MRI of the liver: challenges and some solutions for the quantification of apparent diffusion coefficient and intravoxel incoherent motion

Diffusion-weighted imaging (DWI) is sensitive to the mobility of water molecule at cellular and macromolecular level, much smaller than the spatial resolution of the images. It is commonly based on single shot echo-planar imaging sequence with the addition of motion-probing gradient pulses and fat suppression. DWI is increasingly incorporated into routine body magnetic resonance imaging protocols. However, the liver is particularly affected by physiological motions such as respiration; the left liver is also affected by cardiac motion artifacts and susceptibility artefact due to contents in the stomach. Intravoxel incoherent motion (IVIM) DWI data analysis requires high-quality data acquisition using multiple b-values and confidence in the measurements at low b-values. This article reviews the technical developments of DWI and its applications in the liver. Challenges and some solutions for the quantification of apparent diffusion coefficient and intravoxel incoherent motion are discussed. Currently, acquisition protocols vary between research groups; patient preparation and data post-processing are not standardized. Increased standardization, both in data acquisition and in image analysis, is imperative so to allow generation of reliable DW-MRI biomarker measures that are broadly applicable.

Repeatability and reproducibility of ADC measurements: a prospective multicenter whole-body-MRI study

OBJECTIVES

Multicenter oncology trials increasingly include MRI examinations with apparent diffusion coefficient (ADC) quantification for lesion characterization and follow-up. However, the repeatability and reproducibility (R&R) limits above which a true change in ADC can be considered relevant are poorly defined. This study assessed these limits in a standardized whole-body (WB)-MRI protocol.

METHODS

A prospective, multicenter study was performed at three centers equipped with the same 3.0-T scanners to test a WB-MRI protocol including diffusion-weighted imaging (DWI). Eight healthy volunteers per center were enrolled to undergo test and retest examinations in the same center and a third examination in another center. ADC variability was assessed in multiple organs by two readers using two-way mixed ANOVA, Bland-Altman plots, coefficient of variation (CoV), and the upper limit of the 95% CI on repeatability (RC) and reproducibility (RDC) coefficients.

RESULTS

CoV of ADC was not influenced by other factors (center, reader) than the organ. Based on the upper limit of the 95% CI on RC and RDC (from both readers), a change in ADC in an individual patient must be superior to 12% (cerebrum white matter), 16% (paraspinal muscle), 22% (renal cortex), 26% (central and peripheral zones of the prostate), 29% (renal medulla), 35% (liver), 45% (spleen), 50% (posterior iliac crest), 66% (L5 vertebra), 68% (femur), and 94% (acetabulum) to be significant.

CONCLUSIONS

This study proposes R&R limits above which ADC changes can be considered as a reliable quantitative endpoint to assess disease or treatment-related changes in the tissue microstructure in the setting of multicenter WB-MRI trials.

KEY POINTS

• The present study showed the range of R&R of ADC in WB-MRI that may be achieved in a multicenter framework when a standardized protocol is deployed. • R&R was not influenced by the site of acquisition of DW images. • Clinically significant changes in ADC measured in a multicenter WB-MRI protocol performed with the same type of MRI scanner must be superior to 12% (cerebrum white matter), 16% (paraspinal muscle), 22% (renal cortex), 26% (central zone and peripheral zone of prostate), 29% (renal medulla), 35% (liver), 45% (spleen), 50% (posterior iliac crest), 66% (L5 vertebra), 68% (femur), and 94% (acetabulum) to be detected with a 95% confidence level.

Distortion-free 3D diffusion imaging of the prostate using a multishot diffusion-prepared phase-cycled acquisition and dictionary matching

PURPOSE

To achieve three-dimensional (3D) distortion-free apparent diffusion coefficient (ADC) maps for prostate imaging using a multishot diffusion prepared-gradient echo (msDP-GRE) sequence and ADC dictionary matching.

METHODS

The msDP-GRE sequence is combined with a 3D Cartesian, centric k-space trajectory with center oversampling. Oversampled k-space center averaging and phase cycling are used to address motion- and eddy current-induced magnitude corruption. Extended-phase-graph (EPG) simulations and ADC dictionary matching are used to compensate for T₁ effects. To shorten the acquisition time, each volume is undersampled by a factor of two and reconstructed using iterative sensitivity encoding. The proposed approach is characterized using simulations and validated in a kiwifruit phantom, comparing the msDP-GRE ADC maps obtained using both standard monoexponential fitting and dictionary matching with the clinical standard single-shot diffusion weighted-echo planar imaging (ssDW-EPI) ADC. Initial in vivo feasibility is tested in three healthy subjects, and geometric distortion is compared with anatomical T₂ -weighted-turbo spin echo.

RESULTS

In the kiwifruit phantom experiment, the signal magnitude could be recovered using k-space center averaging and phase cycling. No statistically significant difference was observed in the ADC values estimated using msDP-GRE with dictionary matching and clinical standard DW-EPI (P < .05). The in vivo prostate msDP-GRE scans were free of geometric distortion caused by off-resonance susceptibility, and the ADC values in the prostate were in agreement with values found in the published literature.

CONCLUSION

Nondistorted 3D ADC maps of the prostate can be achieved using a msDP sequence and dictionary matching.

Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer

BACKGROUND AND PURPOSE

A wide variation of MRI systems is a challenge in multicenter imaging biomarker studies as it adds variation in quantitative MRI values. The aim of this study was to design and test a quality assurance (QA) framework based on phantom measurements, for the quantitative MRI protocols of a multicenter imaging biomarker trial of locally advanced cervical cancer.

MATERIALS AND METHODS

Fifteen institutes participated (five 1.5 T and ten 3 T scanners). Each institute optimized protocols for T2, diffusion-weighted imaging, T1, and dynamic contrast-enhanced (DCE-)MRI according to system possibilities, institutional preferences and study-specific constraints. Calibration phantoms with known values were used for validation. Benchmark protocols, similar on all systems, were used to investigate whether differences resulted from variations in institutional protocols or from system variations. Bias, repeatability (%RC), and reproducibility (%RDC) were determined. Ratios were used for T2 and T1 values.

RESULTS

The institutional protocols showed a range in bias of 0.88-0.98 for T2 (median %RC = 1%; %RDC = 12%), -0.007 to 0.029 × 10^-3 mm²/s for the apparent diffusion coefficient (median %RC = 3%; %RDC = 18%), and 0.39-1.29 for T1 (median %RC = 1%; %RDC = 33%). For DCE a nonlinear vendor-specific relation was observed between measured and true concentrations with magnitude data, whereas the relation was linear when phase data was used.

CONCLUSION

We designed a QA framework for quantitative MRI protocols and demonstrated for a multicenter trial for cervical cancer that measurement of consistent T2 and apparent diffusion coefficient values is feasible despite protocol differences. For DCE-MRI and T1 mapping with the variable flip angle method, this was more challenging.

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.

Diagnostic test accuracy of ADC values for identification of clear cell renal cell carcinoma: systematic review and meta-analysis

OBJECTIVES

To perform a systematic review on apparent diffusion coefficient (ADC) values of renal tumor subtypes and meta-analysis on the diagnostic performance of ADC for differentiation of localized clear cell renal cell carcinoma (ccRCC) from other renal tumor types.

METHODS

Medline, Embase, and the Cochrane Library databases were searched for studies published until May 1, 2019, that reported ADC values of renal tumors. Methodological quality was evaluated. For the meta-analysis on diagnostic test accuracy of ADC for differentiation of ccRCC from other renal lesions, we applied a bivariate random-effects model and compared two subgroups of ADC measurement with vs. without cystic and necrotic areas.

RESULTS

We included 48 studies (2588 lesions) in the systematic review and 13 studies (1126 lesions) in the meta-analysis. There was no significant difference in ADC of renal parenchyma using b values of 0-800 vs. 0-1000 (p = 0.08). ADC measured on selected portions (sADC) excluding cystic and necrotic areas differed significantly from whole-lesion ADC (wADC) (p = 0.002). Compared to ccRCC, minimal-fat angiomyolipoma, papillary RCC, and chromophobe RCC showed significantly lower sADC while oncocytoma exhibited higher sADC. Summary estimates of sensitivity and specificity to differentiate ccRCC from other tumors were 80% (95% CI, 0.76-0.88) and 78% (95% CI, 0.64-0.89), respectively, for sADC and 77% (95% CI, 0.59-0.90) and 77% (95% CI, 0.69-0.86) for wADC. sADC offered a higher area under the receiver operating characteristic curve than wADC (0.852 vs. 0.785, p = 0.02).

CONCLUSIONS

ADC values of kidney tumors that exclude cystic or necrotic areas more accurately differentiate ccRCC from other renal tumor types than whole-lesion ADC values.

KEY POINTS

• Selective ADC of renal tumors, excluding cystic and necrotic areas, provides better discriminatory ability than whole-lesion ADC to differentiate clear cell RCC from other renal lesions, with area under the receiver operating characteristic curve (AUC) of 0.852 vs. 0.785, respectively (p = 0.02). • Selective ADC of renal masses provides moderate sensitivity and specificity of 80% and 78%, respectively, for differentiation of clear cell renal cell carcinoma (RCC) from papillary RCC, chromophobe RCC, oncocytoma, and minimal-fat angiomyolipoma. • Selective ADC excluding cystic and necrotic areas are preferable to whole-lesion ADC as an additional tool to multiphasic MRI to differentiate clear cell RCC from other renal lesions whether the highest b value is 800 or 1000.

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

Purpose

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

Results

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

Conclusion

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

Assessment of Stability and Discrimination Capacity of Radiomic Features on Apparent Diffusion Coefficient Images

The objectives of the study are to develop a new way to assess stability and discrimination capacity of radiomic features without the need of test-retest or multiple delineations and to use information obtained to perform a preliminary feature selection. Apparent diffusion coefficient (ADC) maps were computed from diffusion-weighted magnetic resonance images (DW-MRI) of two groups of patients: 18 with soft tissue sarcomas (STS) and 18 with oropharyngeal cancers (OPC). Sixty-nine radiomic features were computed, using three different histogram discretizations (16, 32, and 64 bins). Geometrical transformations (translations) of increasing entity were applied to the regions of interest (ROIs), and the intra-class correlation coefficient (ICC) was used to compare the features computed on the original and modified ROIs. The distribution of ICC values for minimal and maximal entity translations (ICC₁₀ and ICC₁₀₀, respectively) was used to adjust thresholds of ICC (ICC_min and ICC_max) used to discriminate between good, unstable (ICC₁₀ < ICC_min), and non-discriminative features (ICC₁₀₀ > ICC_max). Fifty-four and 59 radiomic features passed the stability-based selection for all the three histogram discretizations for the OPC and STS datasets, respectively. The excluded features were similar across the different histogram discretizations (Jaccard’s index 0.77 ± 0.13 and 0.9 ± 0.1 for OPC and STS, respectively) but different between datasets (Jaccard’s index 0.19 ± 0.02). The results suggest that the observed radiomic features are mainly stable and discriminative, but the stability depends on the region of the body under observation. The method provides a way to assess stability without the need of test-retest or multiple delineations.

Bitter melon juice-intake modulates glucose metabolism and lactate efflux in tumors in its efficacy against pancreatic cancer

The established role of bitter melon juice (BMJ), a natural product, in activating master metabolic regulator adenosine monophosphate-activated protein kinase in pancreatic cancer (PanC) cells served as a basis for pursuing deeper investigation into the underlying metabolic alterations leading to BMJ efficacy in PanC. We investigated the comparative metabolic profiles of PanC cells with differential KRAS mutational status on BMJ exposure. Specifically, we employed nuclear magnetic resonance (NMR) metabolomics and in vivo imaging platforms to understand the relevance of altered metabolism in PanC management by BMJ. Multinuclear NMR metabolomics was performed, as a function of time, post-BMJ treatment followed by partial least square discriminant analysis assessments on the quantitative metabolic data sets to visualize the treatment group clustering; altered glucose uptake, lactate export and energy state were identified as the key components responsible for cell death induction. We next employed PANC1 xenograft model for assessing in vivo BMJ efficacy against PanC. Positron emission tomography ([18FDG]-PET) and magnetic resonance imaging on PANC1 tumor-bearing animals reiterated the in vitro results, with BMJ-associated significant changes in tumor volumes, tumor cellularity and glucose uptake. Additional studies in BMJ-treated PanC cells and xenografts displayed a strong decrease in the expression of glucose and lactate transporters GLUT1 and MCT4, respectively, supporting their role in metabolic changes by BMJ. Collectively, these results highlight BMJ-induced modification in PanC metabolomics phenotype and establish primarily lactate efflux and glucose metabolism, specifically GLUT1 and MCT4 transporters, as the potential metabolic targets underlying BMJ efficacy in PanC.

Detection of clinically signifi cant prostate cancer with PIRADS v2 scores, PSA density, and ADC values in regions with and without mpMRI visible lesions

PURPOSE

To determine if PSAD, PSADtz, and ADC values improve the accuracy of PI-RADS v2 and identify men whose concurrent systematic biopsy detects clinically significant cancer on areas without mpMRI visible lesions.

MATERIALS AND METHODS

Single reference-center, cross-sectional, retrospective study of consecutive men with suspected or known low to intermediate-risk prostate cancer who underwent 3T mpMRI and TRUS-MRI fusion biopsy from 07/15/2014 to 02/17/2018. Cluster-corrected logistic regression analyses were utilized to predict clinically significant prostate cancer (Gleason score ≥3+4) at targeted mpMRI lesions and on systematic biopsy.

RESULTS

538 men (median age=66 years, median PSA=7.0ng/mL) with 780mpMRI lesions were included. Clinically significant disease was diagnosed in 371 men. PI-RADS v2 scores of 3, 4, and 5 were clinically significant cancer in 8.0% (16/201), 22.8% (90/395), and 59.2% (109/184). ADC values, PSAD, and PI-RADS v2 scores were independent predictors of clinically significant cancer in targeted lesions (OR 2.25-8.78; P values <0.05; AUROC 0.84, 95% CI 0.81-0.87). Increases in PSAD were also associated with upgrade on systematic biopsy (OR 2.39-2.48; P values <0.05; AUROC 0.69, 95% CI 0.64-0.73).

CONCLUSIONS

ADC values and PSAD improve characterization of PI-RADS v2 score 4 or 5 lesions. Upgraded on systematic biopsy is slightly more likely with PSAD ≥0.15 and multiple small PI-RADS v2 score 3 or 4 lesions.

DCE-MRI-Derived Volume Transfer Constant (Ktrans) and DWI Apparent Diffusion Coefficient as Predictive Markers of Short- and Long-Term Efficacy of Chemoradiotherapy in Patients With Esophageal Cancer

This study aimed to evaluate both the short- and long-term efficacies of chemoradiotherapy in relation to the treatment of esophageal cancer . This was achieved through the use of dynamic contrast-enhanced magnetic resonance imaging–derived volume transfer constant and diffusion weighted imaging–derived apparent diffusion coefficient . Patients with esophageal cancer were assigned into the sensitive and resistant groups based on respective efficacies in chemoradiotherapy. Dynamic contrast-enhanced magnetic resonance imaging and diffusion weighted imaging were used to measure volume transfer constant and apparent diffusion coefficient, while computed tomography was used to calculate tumor size reduction rate. Pearson correlation analyses were conducted to analyze correlation between volume transfer constant, apparent diffusion coefficient, and the tumor size reduction rate. Receiver operating characteristic curve was constructed to analyze the short-term efficacy of volume transfer constant and apparent diffusion coefficient, while Kaplan-Meier curve was employed for survival rate analysis. Cox proportional hazard model was used for the risk factors for prognosis of patients with esophageal cancer. Our results indicated reduced levels of volume transfer constant, while increased levels were observed in ADC_min, ADC_mean, and ADC_max following chemoradiotherapy. A negative correlation was determined between ADC_min, ADC_mean, and ADC_max, as well as in the tumor size reduction rate prior to chemoradiotherapy, whereas a positive correlation was uncovered postchemoradiotherapy. Volume transfer constant was positively correlated with tumor size reduction rate both before and after chemoradiotherapy. The 5-year survival rate of patients with esophageal cancer having high ADC_min, ADC_mean, and ADC_max and volume transfer constant before chemoradiotherapy was greater than those with respectively lower values. According to the Cox proportional hazard model, ADC_mean, clinical stage, degree of differentiation, and tumor stage were all confirmed as being independent risk factors in regard to the prognosis of patients with EC. The findings of this study provide evidence suggesting that volume transfer constant and apparent diffusion coefficient as being tools allowing for the evaluation of both the short- and long-term efficacies of chemoradiotherapy esophageal cancer treatment.

Diffusion-weighted magnetic resonance imaging using a preclinical 1 T PET/MRI in healthy and tumor-bearing rats

Background

Hybrid positron emission tomography and magnetic resonance imaging (PET/MRI) scanners are increasingly used for both clinical and preclinical imaging. Especially functional MRI sequences such as diffusion-weighted imaging (DWI) are of great interest as they provide information on a molecular level, thus, can be used as surrogate biomarkers. Due to technical restrictions, MR sequences need to be adapted for each system to perform reliable imaging. There is, to our knowledge, no suitable DWI protocol for 1 Tesla PET/MRI scanners. We aimed to establish such DWI protocol with focus on the choice of b values, suitable for longitudinal monitoring of tumor characteristics in a rat liver tumor model.

Material and methods

DWI was first performed in 18 healthy rat livers using the scanner-dependent maximum of 4 b values (0, 100, 200, 300 s/mm²). Apparent diffusion coefficients (ADC) were calculated from different b value combinations and compared to the reference measurement with four b values. T2-weighted MRI and optimized DWI with best agreement between accuracy, scanning time, and system performance stability were used to monitor orthotopic hepatocellular carcinomas (HCC) in five rats of which three underwent additional 2-deoxy-2-(¹⁸F)fluoro-d-glucose(FDG)-PET imaging. ADCs were calculated for the tumor and the surrounding liver parenchyma and verified by histopathological analysis.

Results

Compared to the reference measurements, the combination b = 0, 200, 300 s/mm² showed the highest correlation coefficient (r_s = 0.92) and agreement while reducing the acquisition time. However, measurements with less than four b values yielded significantly higher ADCs (p < 0.001). When monitoring the HCC, an expected drop of the ADC was observed over time. These findings were paralleled by FDG-PET showing both an increase in tumor size and uptake heterogeneity. Interestingly, surrounding liver parenchyma also showed a change in ADC values revealing varying levels of inflammation by immunohistochemistry.

Conclusion

We established a respiratory-gated DWI protocol for a preclinical 1 T PET/MRI scanner allowing to monitor growth-related changes in ADC values of orthotopic HCC liver tumors. By monitoring the changes in tumor ADCs over time, different cellular stages were described. However, each study needs to adapt the protocol further according to their question to generate best possible results.

Electronic supplementary material

The online version of this article (10.1186/s13550-019-0489-6) contains supplementary material, which is available to authorized users.

Machine learning improves classification of preclinical models of pancreatic cancer with chemical exchange saturation transfer MRI

PURPOSE

We sought to assess whether machine learning-based classification approaches can improve the classification of pancreatic tumor models relative to more simplistic analysis methods, using T1 relaxation, CEST, and DCE MRI.

METHODS

The T1 relaxation time constants, % CEST at five saturation frequencies, and vascular permeability constants from DCE MRI were measured from Hs 766 T, MIA PaCa-2, and SU.86.86 pancreatic tumor models. We used each of these measurements as predictors for machine learning classifier algorithms. We also used principal component analysis to reduce the dimensionality of entire CEST spectra and DCE signal evolutions, which were then analyzed using classification methods.

RESULTS

The T1 relaxation time constants, % CEST amplitudes at specific saturation frequencies, and the relative Ktrans and kep values from DCE MRI could not classify all three tumor types. However, the area under the curve from DCE signal evolutions could classify each tumor type. Principal component analysis was used to analyze the entire CEST spectrum and DCE signal evolutions, which predicted the correct tumor model with 87.5% and 85.1% accuracy, respectively.

CONCLUSIONS

Machine learning applied to the entire CEST spectrum improved the classification of the three tumor models, relative to classifications that used % CEST values at single saturation frequencies. A similar improvement was not attained with machine learning applied to T1 relaxation times or DCE signal evolutions, relative to more simplistic analysis methods.

Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials

Physiological properties of tumors can be measured both in vivo and noninvasively by diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging. Although these techniques have been used for more than two decades to study tumor diffusion, perfusion, and/or permeability, the methods and studies on how to reduce measurement error and bias in the derived imaging metrics is still lacking in the literature. This is of paramount importance because the objective is to translate these quantitative imaging biomarkers (QIBs) into clinical trials, and ultimately in clinical practice. Standardization of the image acquisition using appropriate phantoms is the first step from a technical performance standpoint. The next step is to assess whether the imaging metrics have clinical value and meet the requirements for being a QIB as defined by the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA). The goal and mission of QIBA and the National Cancer Institute Quantitative Imaging Network (QIN) initiatives are to provide technical performance standards (QIBA profiles) and QIN tools for producing reliable QIBs for use in the clinical imaging community. Some of QIBA's development of quantitative diffusion-weighted imaging and dynamic contrast-enhanced QIB profiles has been hampered by the lack of literature for repeatability and reproducibility of the derived QIBs. The available research on this topic is scant and is not in sync with improvements or upgrades in MRI technology over the years. This review focuses on the need for QIBs in oncology applications and emphasizes the importance of the assessment of their reproducibility and repeatability. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;49:e101-e121.

Imaging biomarkers in oncology: Basics and application to MRI

Cancer remains a global killer alongside cardiovascular disease. A better understanding of cancer biology has transformed its management with an increasing emphasis on a personalized approach, so-called "precision cancer medicine." Imaging has a key role to play in the management of cancer patients. Imaging biomarkers that objectively inform on tumor biology, the tumor environment, and tumor changes in response to an intervention complement genomic and molecular diagnostics. In this review we describe the key principles for imaging biomarker development and discuss the current status with respect to magnetic resonance imaging (MRI).

LEVEL OF EVIDENCE

5 TECHNICAL EFFICACY: Stage 5 J. Magn. Reson. Imaging 2018;48:13-26.

National implementation of multi-parametric magnetic resonance imaging for prostate cancer detection - recommendations from a UK consensus meeting

OBJECTIVES

To identify areas of agreement and disagreement in the implementation of multi-parametric magnetic resonance imaging (mpMRI) of the prostate in the diagnostic pathway.

MATERIALS AND METHODS

Fifteen UK experts in prostate mpMRI and/or prostate cancer management across the UK (involving nine NHS centres to provide for geographical spread) participated in a consensus meeting following the Research and Development Corporation and University of California-Los Angeles (UCLA-RAND) Appropriateness Method, and were moderated by an independent chair. The experts considered 354 items pertaining to who can request an mpMRI, prostate mpMRI protocol, reporting guidelines, training, quality assurance (QA) and patient management based on mpMRI levels of suspicion for cancer. Each item was rated for agreement on a 9-point scale. A panel median score of ≥7 constituted 'agreement' for an item; for an item to reach 'consensus', a panel majority scoring was required.

RESULTS

Consensus was reached on 59% of items (208/354); these were used to provide recommendations for the implementation of prostate mpMRI in the UK. Key findings include prostate mpMRI requests should be made in consultation with the urological team; mpMRI scanners should undergo QA checks to guarantee consistently high diagnostic quality scans; scans should only be reported by trained and experienced radiologists to ensure that men with unsuspicious prostate mpMRI might consider avoiding an immediate biopsy.

CONCLUSIONS

Our consensus statements demonstrate a set of criteria that are required for the practical dissemination of consistently high-quality prostate mpMRI as a diagnostic test before biopsy in men at risk.

Evaluation of Six Diffusion-weighted MRI Models for Assessing Effects of Neoadjuvant Chemoradiation in Pancreatic Cancer Patients

PURPOSE

To compare 6 diffusion-weighted imaging (DWI) MRI models for response evaluation in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

DWI images were acquired at 3T for b = 0-600 s/mm² in fourteen patients with advanced PDAC during 2 separate pretreatment sessions and 9 patients with (borderline) resectable PDAC pre and post neoadjuvant chemoradiation. Data was fitted with a mono-exponential (ADC), double mono-exponential to b = 0 and 100 s/mm² (ADC_fast), and b = 100 and 600 s/mm² (ADC_slow), IVIM model with D* free (D, f, D*) and fixed (D, f), tri-exponent (D, f₁, f₂), and stretched exponent model (DDC, α). Goodness of fit (adjusted R²), tumor to normal tissue contrast, repeatability (coefficient of variation), and parameter correlations (Spearman's rho) were assessed for the repeated measures. Treatment induced changes were assessed and compared to the repeatability.

RESULTS

The mono-exponential model had the lowest goodness of fit in both tumor (R² = 0.94) and normal-appearing pancreas (R² = 0.88). Tumour to normal tissue contrast was higher for the 'non-diffusion' parameters (ADC_fast, f, D*, f₁, f₂, α), with better repeatability for the diffusion parameters (ADC, ADC_slow, D, DDC). Diffusion parameters were strongly correlated between the models (rho ≥0.81) and showed a general treatment associated increase. All models were able to identify individual treatment effects, showing a change greater than the repeatability in 5 out of 9 patients for at least one of the parameters.

CONCLUSIONS

Individual treatment evaluation is possible with all investigated DWI models, with treatment associated changes exceeding the repeatability. The double monoexponential fit with ADC_fast and ADC_slow is able to discriminate between non-diffusion and diffusion related effects, is measured fast and can be performed on most commercial scanners, making it an attractive alternative for the more advanced multiparametric models in radiotherapy treatment evaluation.

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

OBJECTIVES

The aim of this study was to evaluate the repeatability of a region of interest (ROI) volume and mean apparent diffusion coefficient (ADC) in standard-of-care 3 T multiparametric magnetic resonance imaging (mpMRI) of the prostate obtained with the use of endorectal coil.

MATERIALS AND METHODS

This prospective study was Health Insurance Portability and Accountability Act compliant, with institutional review board approval and written informed consent. Men with confirmed or suspected treatment-naive prostate cancer scheduled for mpMRI were offered a repeat mpMRI within 2 weeks. Regions of interest corresponding to the whole prostate gland, the entire peripheral zone (PZ), normal PZ, and suspected tumor ROI (tROI) on axial T2-weighted, dynamic contrast-enhanced subtract, and ADC images were annotated and assessed using Prostate Imaging Reporting and Data System (PI-RADS) v2. Repeatability of the ROI volume for each of the analyzed image types and mean ROI ADC was summarized with repeatability coefficient (RC) and RC%.

RESULTS

A total of 189 subjects were approached to participate in the study. Of 40 patients that gave initial agreement, 15 men underwent 2 mpMRI examinations and completed the study. Peripheral zone tROIs were identified in 11 subjects. Tumor ROI volume was less than 0.5 mL in 8 of 11 subjects. PI-RADS categories were identical between baseline-repeat studies in 11/15 subjects and differed by 1 point in 4/15. Peripheral zone tROI volume RC (RC%) was 233 mm (71%) on axial T2-weighted, 422 mm (112%) on ADC, and 488 mm (119%) on dynamic contrast-enhanced subtract. Apparent diffusion coefficient ROI mean RC (RC%) were 447 × 10 mm/s (42%) in PZ tROI and 471 × 10 mm/s (30%) in normal PZ. Significant difference in repeatability of the tROI volume across series was observed (P < 0.005). The mean ADC RC% was lower than volume RC% for tROI ADC (P < 0.05).

CONCLUSIONS

PI-RADS v2 overall assessment was highly repeatable. Multiparametric magnetic resonance imaging sequences differ in volume measurement repeatability. The mean tROI ADC is more repeatable compared with tROI volume in ADC. Repeatability of prostate ADC is comparable with that in other abdominal organs.

Apparent diffusion coefficient for prediction of parametrial invasion in cervical cancer: a critical evaluation based on stratification to a Likert scale using T2-weighted imaging

PURPOSE

To evaluate the value of apparent diffusion coefficient (ADC) for determining parametrial invasion (PMI) in cervical cancer, by stratifying them into subgroups based on a Likert scale using T2-weighted imaging (T2WI).

MATERIALS AND METHODS

This retrospective study included 87 patients with FIGO stage IA2-IIB cervical cancer who underwent preoperative MRI followed by radical hysterectomy. Radiological PMI was assessed on T2WI using a six-point Likert scale and ADC values of the tumors were measured. MRI findings were compared between patients with and without PMI. Differences in ADC according to the Likert scale were also assessed.

RESULTS

19 (21.8%) patients had pathological PMI. The prevalence of PMI was significantly associated with Likert scale (P < 0.001). ADC values significantly differed according to Likert scale (P < 0.001). However, only tumors with a Likert score of 0 (MRI-invisible) had significantly greater ADC than others (P < 0.001) while no significant difference was observed among tumors with Likert scores of 1-5 (P = 0.070-0.889). Patients with PMI had significantly lower ADC values than those without PMI (P = 0.034). However, no significant difference was seen between patients with and without PMI within each Likert score group (P = 0.180-0.857).

CONCLUSION

T2WI-based Likert score for radiological PMI and ADC values of the tumor were significantly associated with pathological PMI. However, the apparent association seen between ADC values and PMI may be due to contribution of high ADC values of MRI-invisible tumors rather than reflecting their relationship.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Local staging of sigmoid colon cancer using MRI

Background

An accurate radiological staging of colon cancer is crucial to select patients who may benefit from neoadjuvant chemotherapy.

Purpose

To evaluate the diagnostic accuracy of preoperative magnetic resonance imaging (MRI) in identifying locally advanced sigmoid colon cancer, poor prognostic factors, and the inter-observer variation of the tumor apparent diffusion coefficient (ADC) values of diffusion-weighted imaging (DWI).

Material and Methods

Using 1.5 T MRI with high resolution T2-weighted (T2W) imaging, DWI, and no contrast enhancement, 35 patients with sigmoid colon cancer were assessed. T-stage, N-stage, extramural vascular invasion (EMVI), and ADC values of the tumors were assessed and blindly compared by two observers using postoperative histopathological examination as the gold standard. Early tumors were defined as T1 to T3ab, and advanced tumors as T3cd or T4.

Results

The accuracy of the two radiologists in staging early versus advanced tumors, N-stage, and identification of EMVI was 94%/89%, 60%/66%, and 77%/60% with an inter-observer agreement of к = 0.86 (95% confidence interval [CI] = 0.67–1.00), к = 0.64 (95% CI = 0.39–0.90), and к = 0.52 (95% CI = 0.23–0.80). All the measured mean ADC values were below 1.0 × 10⁻³ mm²/s with an intra-class correlation coefficient in T3cd–T4 tumors of 0.85.

Conclusion

Preoperative MRI can identify locally advanced sigmoid colon cancer and has potential as the imaging of choice to select patients for neoadjuvant chemotherapy. Initial experience with ADC measurement was achieved with an excellent inter-observer agreement in advanced tumors.

Performance of a fast and high-resolution multi-echo spin-echo sequence for prostate T2 mapping across multiple systems

PURPOSE

To evaluate the performance of a multi-echo spin-echo sequence with k-t undersampling scheme (k-t T₂ ) in prostate cancer.

METHODS

Phantom experiments were performed at five systems to estimate the bias, short-term repeatability, and reproducibility across all systems expressed with the within-subject coefficient of variation (wCV). Monthly measurements were performed on two systems for long-term repeatability estimation. To evaluate clinical repeatability, two T₂ maps (voxel size 0.8 × 0.8 × 3 mm³ ; 5 min) were acquired at separate visits on one system for 13 prostate cancer patients. Repeatability was assessed per patient in relation to spatial resolution. T₂ values were compared for tumor, peripheral zone, and transition zone.

RESULTS

Phantom measurements showed a small bias (median = -0.9 ms) and good short-term repeatability (median wCV = 0.5%). Long-term repeatability was 0.9 and 1.1% and reproducibility between systems was 1.7%. The median bias observed in patients was -1.1 ms. At voxel level, the median wCV was 15%, dropping to 4% for structures of 0.5 cm³ . The median tumor T₂ values (79 ms) were significantly lower (P < 0.001) than in the peripheral zone (149 ms), but overlapped with the transition zone (91 ms).

CONCLUSIONS

Reproducible T₂ mapping of the prostate is feasible with good spatial resolution in a clinically reasonable scan time, allowing reliable measurement of T₂ in structures as small as 0.5 cm³ . Magn Reson Med 79:1586-1594, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high-grade lesions

PURPOSE

To assess the feasibility of grading soft tissue sarcomas (STSs) using MRI features (radiomics).

MATERIALS AND METHODS

MRI (echo planar SE, 1.5T) from 19 patients with STSs and a known histological grading, were retrospectively analyzed. The apparent diffusion coefficient (ADC) maps, obtained by diffusion-weighted imaging acquisitions, were analyzed through 65 radiomic features, intensity-based (first order statistics, FOS) and texture (gray level co-occurrence matrix, GLCM; and gray level run length matrix, GLRLM) features. Feature selection (sequential forward floating search) and classification (k-nearest neighbor classifier) were performed to distinguish intermediate- from high-grade STSs. Classification was performed using the three different sub-groups of features separately as well as all the features together. The entire dataset was divided in three subsets: the training, validation and test set, containing, respectively, 60, 30, and 10% of the data.

RESULTS

Intermediate-grade lesions had a higher and less disperse ADC values compared with high-grade ones: most of FOS related to intensity are higher for the intermediate-grade STSs, while FOS related to signal variability were higher in the high grade (e.g., the feature variance is 2.6*10⁵  ± 0.9*10⁵ versus 3.3*10⁵  ± 1.6*10⁵ , P = 0.3). The GLCM features related to entropy and dissimilarity were higher in the high-grade. When performing classification, the best accuracy is obtained with a maximum of three features for each subgroup, FOS features being those leading to the best classification (validation set: FOS accuracy 0.90 ± 0.11, area under the curve [AUC] 0.85 ± 0.16; test set: FOS accuracy 0.88 ± 0.25, AUC 0.87 ± 0.34).

CONCLUSION

Good accuracy and AUC could be obtained using only few Radiomic features, belonging to the FOS class.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:829-840.

Advanced diffusion MRI and biomarkers in the central nervous system: a new approach

The introduction of diffusion-weighted sequences has revolutionized the detection and characterization of central nervous system (CNS) disease. Nevertheless, the assessment of diffusion studies of the CNS is often limited to qualitative estimation. Moreover, the pathophysiological complexity of the different entities that affect the CNS cannot always be correctly explained through classical models. The development of new models for the analysis of diffusion sequences provides numerous parameters that enable a quantitative approach to both diagnosis and prognosis as well as to monitoring the response to treatment; these parameters can be considered potential biomarkers of health and disease. In this update, we review the physical bases underlying diffusion studies and diffusion tensor imaging, advanced models for their analysis (intravoxel coherent motion and kurtosis), and the biological significance of the parameters derived.

Everyman's prostate phantom: kiwi-fruit substitute for human prostates at magnetic resonance imaging, diffusion-weighted imaging and magnetic resonance spectroscopy

OBJECTIVES

To apply an easy-to-assemble phantom substitute for human prostates in T2-weighted magnetic resonance imaging (T2WI), diffusion-weighted imaging (DWI) and 3D magnetic resonance spectroscopy (MRS).

METHODS

Kiwi fruit were fixed with gel hot and cold compress packs on two plastic nursery pots, separated by a plastic plate, and submerged in tap water inside a 1-L open-spout plastic watering can for T2WI (TR/TE 7500/101 ms), DWI (5500/61 ms, ADC b50-800 s/mm² map) and MRS (940/145 ms) at 3.0 T, with phased array surface coils. One green kiwi fruit was additionally examined with an endorectal coil. Retrospective comparison with benign peripheral zone (PZ) and transitional zone (TZ) of prostate (n = 5), Gleason 6-7a prostate cancer (n = 8) and Gleason 7b-9 prostate cancer (n = 7) validated the phantom.

RESULTS

Mean contrast between central placenta (CP) and outer pericarp (OP, 0.346-0.349) or peripheral placenta (PP, 0.364-0.393) of kiwi fruit was similar to Gleason 7b-9 prostate cancer and PZ (0.308) in T2WI. ADC values of OP and PP (1.27 ± 0.07-1.37 ± 0.08 mm²/s × 10^-3) resembled PZ and TZ (1.39 ± 0.17-1.60 ± 0.24 mm²/s × 10^-3), while CP (0.91 ± 0.14-0.99 ± 0.10 mm²/s × 10^-3) resembled Gleason 7b-9 prostate cancer (1.00 ± 0.25 mm²/s × 10^-3). MR spectra showed peaks of citrate and myo-inositol in kiwi fruit, and citrate and "choline+creatine" in prostates. The phantom worked with an endorectal coil, too.

CONCLUSIONS

The kiwi fruit phantom reproducibly showed zones similar to PZ, TZ and cancer in human prostates in T2WI and DWI and two metabolite peaks in MRS and appears suitable to compare different MR protocols, coil systems and scanners.

KEY POINTS

• Kiwi fruit appear suitable as phantoms for human prostate in MR examinations. • Kiwi fruit show zonal anatomy like human prostates in T2-weighted MRI and DWI. • MR spectroscopy reliably shows peaks in kiwi fruit (citrate/inositol) and human prostates (citrate/choline+creatine). • The kiwi fruit phantom works both with and without an endorectal coil. • EU regulation No. 543/2011 specifies physical and biochemical properties of kiwi fruit.

Restriction spectrum imaging: An evolving imaging biomarker in prostate MRI

Restriction spectrum imaging (RSI) is a novel diffusion-weighted MRI technique that uses the mathematically distinct behavior of water diffusion in separable microscopic tissue compartments to highlight key aspects of the tissue microarchitecture with high conspicuity. RSI can be acquired in less than 5 min on modern scanners using a surface coil. Multiple field gradients and high b-values in combination with postprocessing techniques allow the simultaneous resolution of length-scale and geometric information, as well as compartmental and nuclear volume fraction filtering. RSI also uses a distortion correction technique and can thus be fused to high resolution T2-weighted images for detailed localization, which improves delineation of disease extension into critical anatomic structures. In this review, we discuss the acquisition, postprocessing, and interpretation of RSI for prostate MRI. We also summarize existing data demonstrating the applicability of RSI for prostate cancer detection, in vivo characterization, localization, and targeting.

LEVEL OF EVIDENCE

5 J. Magn. Reson. Imaging 2017;45:323-336.