Variability in quantitative diffusion-weighted MR imaging (DWI) across different scanners and imaging sites: is there a potential consensus that can help reducing the limits of expected bias?

Image quality of whole-body diffusion MR images comparing deep-learning accelerated and conventional sequences

OBJECTIVES

To compare the image quality of deep learning accelerated whole-body (WB) with conventional diffusion sequences.

METHODS

Fifty consecutive patients with bone marrow cancer underwent WB-MRI. Two experts compared axial b900 s/mm2 and the corresponding maximum intensity projections (MIP) of deep resolve boost (DRB) accelerated diffusion-weighted imaging (DWI) sequences (time of acquisition: 6:42 min) against conventional sequences (time of acquisition: 14 min). Readers assessed paired images for noise, artefacts, signal fat suppression, and lesion conspicuity using Likert scales, also expressing their overall subjective preference. Signal-to-noise and contrast-to-noise ratios (SNR and CNR) and the apparent diffusion coefficient (ADC) values of normal tissues and cancer lesions were statistically compared.

RESULTS

Overall, radiologists preferred either axial DRB b900 and/or corresponding MIP images in almost 80% of the patients, particularly in patients with a high body-mass index (BMI > 25 kg/m2). In qualitative assessments, axial DRB images were preferred (preferred/strongly preferred) in 56-100% of cases, whereas DRB MIP images were favoured in 52-96% of cases. DRB-SNR/CNR was higher in all normal tissues (p < 0.05). For cancer lesions, the DRB-SNR was higher (p < 0.001), but the CNR was not different. DRB-ADC values were significantly higher for the brain and psoas muscles, but not for cancer lesions (mean difference: + 53 µm2/s). Inter-class correlation coefficient analysis showed good to excellent agreement (95% CI 0.75-0.93).

CONCLUSION

DRB sequences produce higher-quality axial DWI, resulting in improved MIPs and significantly reduced acquisition times. However, differences in the ADC values of normal tissues need to be considered.

CLINICAL RELEVANCE STATEMENT

Deep learning accelerated diffusion sequences produce high-quality axial images and MIP at reduced acquisition times. This advancement could enable the increased adoption of Whole Body-MRI for the evaluation of patients with bone marrow cancer.

KEY POINTS

Deep learning reconstruction enables a more than 50% reduction in acquisition time for WB diffusion sequences. DRB images were preferred by radiologists in almost 80% of cases due to fewer artefacts, improved background signal suppression, higher signal-to-noise ratio, and increased lesion conspicuity in patients with higher body mass index. Cancer lesion diffusivity from DRB images was not different from conventional sequences.

CT/MRI technical pitfalls for diagnosis and treatment response assessment using LI-RADS and how to optimize

Hepatocellular carcinoma (HCC), the most common primary liver cancer, is a significant global health burden. Accurate imaging is crucial for diagnosis and treatment response assessment, often eliminating the need for biopsy. The Liver Imaging Reporting and Data System (LI-RADS) standardizes the interpretation and reporting of liver imaging for diagnosis and treatment response assessment, categorizing observations using defined categories that are based on the probability of malignancy or post-treatment tumor viability. Optimized imaging protocols are essential for accurate visualization and characterization of liver findings by LI-RADS. Common technical pitfalls, such as suboptimal postcontrast phase timing, and MRI-specific challenges like subtraction misregistration artifacts, can significantly reduce image quality and diagnostic accuracy. The use of hepatobiliary contrast agents introduces additional challenges including arterial phase degradation and suboptimal uptake in advanced cirrhosis. This review provides radiologists with comprehensive insights into the technical aspects of liver imaging for LI-RADS. We discuss common pitfalls encountered in routine clinical practice and offer practical solutions to optimize imaging techniques. We also highlight technical advances in liver imaging, including multi-arterial MR acquisition and compressed sensing. By understanding and addressing these technical aspects, radiologists can improve accuracy and confidence in the diagnosis and treatment response assessment for hepatocellular carcinoma.

Reduction of ADC bias in diffusion MRI with deep learning-based acceleration: A phantom validation study at 3.0 T

PURPOSE

Further acceleration of DWI in diagnostic radiology is desired but challenging mainly due to low SNR in high b-value images and associated bias in quantitative ADC values. Deep learning-based reconstruction and denoising may provide a solution to address this challenge.

METHODS

The effects of SNR reduction on ADC bias and variability were investigated using a commercial diffusion phantom and numerical simulations. In the phantom, performance of different reconstruction methods, including conventional parallel (SENSE) imaging, compressed sensing (C-SENSE), and compressed SENSE acceleration with an artificial intelligence deep learning-based technique (C-SENSE AI), was compared at different acceleration factors and flip angles using ROI-based analysis. ADC bias was assessed by Lin's Concordance correlation coefficient (CCC) followed by bootstrapping to calculate confidence intervals (CI). ADC random measurement error (RME) was assessed by the mean coefficient of variation (CV¯) and non-parametric statistical tests.

RESULTS

The simulations predicted increasingly negative bias and loss of precision towards lower SNR. These effects were confirmed in phantom measurements of increasing acceleration, for which CCC decreased from 0.947 to 0.279 and CV¯ increased from 0.043 to 0.439, and of decreasing flip angle, for which CCC decreased from 0.990 to 0.063 and CV¯ increased from 0.037 to 0.508. At high acceleration and low flip angle, C-SENSE AI reconstruction yielded best denoised ADC maps. For the lowest investigated flip angle, CCC = {0.630, 0.771 and 0.987} and CV¯={0.508, 0.426 and 0.254} were obtained for {SENSE, C-SENSE, C-SENSE AI}, the improvement by C-SENSE AI being significant as compared to the other methods (CV: p = 0.033 for C-SENSE AI vs. C-SENSE and p < 0.001 for C-SENSE AI vs. SENSE; CCC: non-overlapping CI between reconstruction methods). For the highest investigated acceleration factor, CCC = {0.479,0.926,0.960} and CV¯={0.519,0.119,0.118} were found, confirming the reduction of bias and RME by C-SENSE AI as compared to C-SENSE (by trend) and to SENSE (CV: p < 0.001; CCC: non-overlapping CI).

CONCLUSION

ADC bias and random measurement error in DWI at low SNR, typically associated with scan acceleration, can be effectively reduced by deep-learning based C-SENSE AI reconstruction.

Detecting Muscle Invasion of Bladder Cancer: An Application of Diffusion Kurtosis Imaging Ratio and Vesical Imaging-Reporting and Data System

BACKGROUND

Independent factors are needed to supplement vesical imaging-reporting and data system (VI-RADS) to improve its ability to identify muscle invasive bladder cancer (MIBC).

PURPOSE

To assess the correlation between MIBC and diffusion kurtosis imaging (DKI) ratio, VI-RADS, and other factors (such as tumor location).

STUDY TYPE

Retrospective.

POPULATION

Sixty-eight patients (50 males and 18 females; age: 70.1 ± 9.5 years) with bladder urothelial carcinoma.

FIELD STRENGTH/SEQUENCE

1.5 T, conventional diffusion-weighted imaging (DWI), and DKI (single shot echo-planar sequence).

ASSESSMENT

Three radiologists independently measured the diffusion parameters of each bladder cancer (BCa) and obturator internus, including the mean apparent diffusion coefficient (ADCmean), mean kurtosis (MK), and mean diffusion (MD). And the ratio of diffusion parameters between BCa and obturator internus was calculated (diffusion parameter ratio = bladder cancer:obturator internus). Based on the VI-RADS, the target lesions were independently scored. Furthermore, the actual tumor-wall contact length (ACTCL) and absolute tumor-wall contact length (ABTCL) were measured.

STATISTICAL TESTS

Multicollinearity among independent variables was evaluated using the variance inflation factor (VIF). Multivariable logistic regression analysis was used to determine the independent risk factors of MIBC. The receiver operating characteristic curve was used to evaluate the efficacy of each variable in detecting MIBC. The DeLong test was used to compare the area under the curve (AUC). A P < 0.05 was considered statistically significant.

RESULTS

MKratio (median: 0.62) and VI-RADS were independent risk factors for MIBC. AUCs for MKratio, VI-RADS, and MKratio combined with VI-RADS in assessing MIBC were 0.895, 0.871, and 0.973, respectively. MKratio combined with VI-RADS was more effective in diagnosing MIBC than VI-RADS alone.

DATA CONCLUSIONS

MKratio has potential to assist the assessment of MIBC. MKratio can be used as a supplement to VI-RADS for detecting MIBC.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 2.

Multiparametric MRI for characterization of the tumour microenvironment

Our understanding of tumour biology has evolved over the past decades and cancer is now viewed as a complex ecosystem with interactions between various cellular and non-cellular components within the tumour microenvironment (TME) at multiple scales. However, morphological imaging remains the mainstay of tumour staging and assessment of response to therapy, and the characterization of the TME with non-invasive imaging has not yet entered routine clinical practice. By combining multiple MRI sequences, each providing different but complementary information about the TME, multiparametric MRI (mpMRI) enables non-invasive assessment of molecular and cellular features within the TME, including their spatial and temporal heterogeneity. With an increasing number of advanced MRI techniques bridging the gap between preclinical and clinical applications, mpMRI could ultimately guide the selection of treatment approaches, precisely tailored to each individual patient, tumour and therapeutic modality. In this Review, we describe the evolving role of mpMRI in the non-invasive characterization of the TME, outline its applications for cancer detection, staging and assessment of response to therapy, and discuss considerations and challenges for its use in future medical applications, including personalized integrated diagnostics.

Abdominal Diffusion-Weighted MRI With Simultaneous Multi-Slice Acquisition: Agreement and Reproducibility of Apparent Diffusion Coefficients Measurements

BACKGROUND

Simultaneous multi-slice diffusion-weighted imaging (SMS-DWI) can shorten acquisition time in abdominal imaging.

PURPOSE

To investigate the agreement and reproducibility of apparent diffusion coefficient (ADC) from abdominal SMS-DWI acquired with different vendors and different breathing schemes.

STUDY TYPE

Prospective.

SUBJECTS

Twenty volunteers and 10 patients.

FIELD STRENGTH/SEQUENCE

3.0 T, SMS-DWI with a diffusion-weighted echo-planar imaging sequence.

ASSESSMENT

SMS-DWI was acquired using breath-hold and free-breathing techniques in scanners from two vendors, yielding four scans in each participant. Average ADC values were measured in the liver, pancreas, spleen, and both kidneys. Non-normalized ADC and ADCs normalized to the spleen were compared between vendors and breathing schemes.

STATISTICAL TESTS

Paired t-test or Wilcoxon signed rank test; intraclass correlation coefficient (ICC); Bland-Altman method; coefficient of variation (CV) analysis; significance level: P < 0.05.

RESULTS

Non-normalized ADCs from the four SMS-DWI scans did not differ significantly in the spleen (P = 0.262, 0.330, 0.166, 0.122), right kidney (P = 0.167, 0.538, 0.957, 0.086), and left kidney (P = 0.182, 0.281, 0.504, 0.405), but there were significant differences in the liver and pancreas. For normalized ADCs, there were no significant differences in the liver (P = 0.315, 0.915, 0.198, 0.799), spleen (P = 0.815, 0.689, 0.347, 0.423), pancreas (P = 0.165, 0.336, 0.304, 0.584), right kidney (P = 0.165, 0.336, 0.304, 0.584), and left kidney (P = 0.496, 0.304, 0.443, 0.371). Inter-reader agreements of non-normalized ADCs were good to excellent (ICCs ranged from 0.861 to 0.983), and agreement and reproducibility were good to excellent depending on anatomic location (CVs ranged from 3.55% to 13.98%). Overall CVs for abdominal ADCs from the four scans were 6.25%, 7.62%, 7.08, and 7.60%.

DATA CONCLUSION

The normalized ADCs from abdominal SMS-DWI may be comparable between different vendors and breathing schemes, showing good agreement and reproducibility. ADC changes above approximately 8% may potentially be considered as a reliable quantitative biomarker to assess disease or treatment-related changes.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Differentiating Uterine Sarcoma From Atypical Leiomyoma on Preoperative Magnetic Resonance Imaging Using Logistic Regression Classifier: Added Value of Diffusion-Weighted Imaging-Based Quantitative Parameters

OBJECTIVE

To evaluate the added value of diffusion-weighted imaging (DWI)-based quantitative parameters to distinguish uterine sarcomas from atypical leiomyomas on preoperative magnetic resonance imaging (MRI).

MATERIALS AND METHODS

A total of 138 patients (age, 43.7 ± 10.3 years) with uterine sarcoma (n = 44) and atypical leiomyoma (n = 94) were retrospectively collected from four institutions. The cohort was randomly divided into training (84/138, 60.0%) and validation (54/138, 40.0%) sets. Two independent readers evaluated six qualitative MRI features and two DWI-based quantitative parameters for each index tumor. Multivariable logistic regression was used to identify the relevant qualitative MRI features. Diagnostic classifiers based on qualitative MRI features alone and in combination with DWI-based quantitative parameters were developed using a logistic regression algorithm. The diagnostic performance of the classifiers was evaluated using a cross-table analysis and calculation of the area under the receiver operating characteristic curve (AUC).

RESULTS

Mean apparent diffusion coefficient value of uterine sarcoma was lower than that of atypical leiomyoma (mean ± standard deviation, 0.94 ± 0.30 10-3 mm²/s vs. 1.23 ± 0.25 10-3 mm²/s; P < 0.001), and the relative contrast ratio was higher in the uterine sarcoma (8.16 ± 2.94 vs. 4.19 ± 2.66; P < 0.001). Selected qualitative MRI features included ill-defined margin (adjusted odds ratio [aOR], 17.9; 95% confidence interval [CI], 1.41-503, P = 0.040), intratumoral hemorrhage (aOR, 27.3; 95% CI, 3.74-596, P = 0.006), and absence of T2 dark area (aOR, 83.5; 95% CI, 12.4-1916, P < 0.001). The classifier that combined qualitative MRI features and DWI-based quantitative parameters showed significantly better performance than without DWI-based parameters in the validation set (AUC, 0.92 vs. 0.78; P < 0.001).

CONCLUSION

The addition of DWI-based quantitative parameters to qualitative MRI features improved the diagnostic performance of the logistic regression classifier in differentiating uterine sarcomas from atypical leiomyomas on preoperative MRI.

Advancing noninvasive glioma classification with diffusion radiomics: Exploring the impact of signal intensity normalization

Background

This study investigates the influence of diffusion-weighted Magnetic Resonance Imaging (DWI-MRI) on radiomic-based prediction of glioma types according to molecular status and assesses the impact of DWI intensity normalization on model generalizability.

Methods

Radiomic features, compliant with image biomarker standardization initiative standards, were extracted from preoperative MRI of 549 patients with diffuse glioma, known IDH, and 1p19q-status. Anatomical sequences (T1, T1c, T2, FLAIR) underwent N4-Bias Field Correction (N4) and WhiteStripe normalization (N4/WS). Apparent diffusion coefficient (ADC) maps were normalized using N4 or N4/z-score. Nine machine-learning algorithms were trained for multiclass prediction of glioma types (IDH-mutant 1p/19q codeleted, IDH-mutant 1p/19q non-codeleted, IDH-wild type). Four approaches were compared: Anatomical, anatomical + ADC naive, anatomical + ADC N4, and anatomical + ADC N4/z-score. The University of California San Francisco (UCSF)-glioma dataset (n = 409) was used for external validation.

Results

Naïve-Bayes algorithms yielded overall the best performance on the internal test set. Adding ADC radiomics significantly improved AUC from 0.79 to 0.86 (P = .011) for the IDH-wild-type subgroup, but not for the other 2 glioma subgroups (P > .05). In the external UCSF dataset, the addition of ADC radiomics yielded a significantly higher AUC for the IDH-wild-type subgroup (P ≤ .001): 0.80 (N4/WS anatomical alone), 0.81 (anatomical + ADC naive), 0.81 (anatomical + ADC N4), and 0.88 (anatomical + ADC N4/z-score) as well as for the IDH-mutant 1p/19q non-codeleted subgroup (P < .012 each).

Conclusions

ADC radiomics can enhance the performance of conventional MRI-based radiomic models, particularly for IDH-wild-type glioma. The benefit of intensity normalization of ADC maps depends on the type and context of the used data.

Advances in application of novel magnetic resonance imaging technologies in liver disease diagnosis

Liver disease is a major health concern globally, with high morbidity and mor-tality rates. Precise diagnosis and assessment are vital for guiding treatment approaches, predicting outcomes, and improving patient prognosis. Magnetic resonance imaging (MRI) is a non-invasive diagnostic technique that has been widely used for detecting liver disease. Recent advancements in MRI technology, such as diffusion weighted imaging, intravoxel incoherent motion, magnetic resonance elastography, chemical exchange saturation transfer, magnetic resonance spectroscopy, hyperpolarized MR, contrast-enhanced MRI, and ra-diomics, have significantly improved the accuracy and effectiveness of liver disease diagnosis. This review aims to discuss the progress in new MRI technologies for liver diagnosis. By summarizing current research findings, we aim to provide a comprehensive reference for researchers and clinicians to optimize the use of MRI in liver disease diagnosis and improve patient prognosis.

MRI Apparent Diffusion Coefficient (ADC) as a Biomarker of Tumour Response: Imaging-Pathology Correlation in Patients with Hepatic Metastases from Colorectal Cancer (EORTC 1423)

Background: Tumour apparent diffusion coefficient (ADC) from diffusion-weighted magnetic resonance imaging (MRI) is a putative pharmacodynamic/response biomarker but the relationship between drug-induced effects on the ADC and on the underlying pathology has not been adequately defined. Hypothesis: Changes in ADC during early chemotherapy reflect underlying histological markers of tumour response as measured by tumour regression grade (TRG). Methods: Twenty-six patients were enrolled in the study. Baseline, 14 days, and pre-surgery MRI were performed per study protocol. Surgical resection was performed in 23 of the enrolled patients; imaging-pathological correlation was obtained from 39 lesions from 21 patients. Results: There was no evidence of correlation between TRG and ADC changes at day 14 (study primary endpoint), and no significant correlation with other ADC metrics. In scans acquired one week prior to surgery, there was no significant correlation between ADC metrics and percentage of viable tumour, percentage necrosis, percentage fibrosis, or Ki67 index. Conclusions: Our hypothesis was not supported by the data. The lack of meaningful correlation between change in ADC and TRG is a robust finding which is not explained by variability or small sample size. Change in ADC is not a proxy for TRG in metastatic colorectal cancer.

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.

Individualised profiling of white matter organisation in moderate-to-severe traumatic brain injury patients

BACKGROUND AND PURPOSE

Approximately 65% of moderate-to-severe traumatic brain injury (m-sTBI) patients present with poor long-term behavioural outcomes, which can significantly impair activities of daily living. Numerous diffusion-weighted MRI studies have linked these poor outcomes to decreased white matter integrity of several commissural tracts, association fibres and projection fibres in the brain. However, most studies have focused on group-based analyses, which are unable to deal with the substantial between-patient heterogeneity in m-sTBI. As a result, there is increasing interest and need in conducting individualised neuroimaging analyses.

MATERIALS AND METHODS

Here, we generated a detailed subject-specific characterisation of microstructural organisation of white matter tracts in 5 chronic patients with m-sTBI (29 - 49y, 2 females), presented as a proof-of-concept. We developed an imaging analysis framework using fixel-based analysis and TractLearn to determine whether the values of fibre density of white matter tracts at the individual patient level deviate from the healthy control group (n = 12, 8F, M_age = 35.7y, age range 25 - 64y).

RESULTS

Our individualised analysis revealed unique white matter profiles, confirming the heterogenous nature of m-sTBI and the need of individualised profiles to properly characterise the extent of injury. Future studies incorporating clinical data, as well as utilising larger reference samples and examining the test-retest reliability of the fixel-wise metrics are warranted.

CONCLUSIONS

Individualised profiles may assist clinicians in tracking recovery and planning personalised training programs for chronic m-sTBI patients, which is necessary to achieve optimal behavioural outcomes and improved quality of life.

Machine learning-based radiomics model to predict benign and malignant PI-RADS v2.1 category 3 lesions: a retrospective multi-center study

Purpose

To develop machine learning-based radiomics models derive from different MRI sequences for distinction between benign and malignant PI-RADS 3 lesions before intervention, and to cross-institution validate the generalization ability of the models.

Methods

The pre-biopsy MRI datas of 463 patients classified as PI-RADS 3 lesions were collected from 4 medical institutions retrospectively. 2347 radiomics features were extracted from the VOI of T2WI, DWI and ADC images. The ANOVA feature ranking method and support vector machine classifier were used to construct 3 single-sequence models and 1 integrated model combined with the features of three sequences. All the models were established in the training set and independently verified in the internal test and external validation set. The AUC was used to compared the predictive performance of PSAD with each model. Hosmer–lemeshow test was used to evaluate the degree of fitting between prediction probability and pathological results. Non-inferiority test was used to check generalization performance of the integrated model.

Results

The difference of PSAD between PCa and benign lesions was statistically significant (P = 0.006), with the mean AUC of 0.701 for predicting clinically significant prostate cancer (internal test AUC = 0.709 vs. external validation AUC = 0.692, P = 0.013) and 0.630 for predicting all cancer (internal test AUC = 0.637 vs. external validation AUC = 0.623, P = 0.036). T2WI-model with the mean AUC of 0.717 for predicting csPCa (internal test AUC = 0.738 vs. external validation AUC = 0.695, P = 0.264) and 0.634 for predicting all cancer (internal test AUC = 0.678 vs. external validation AUC = 0.589, P = 0.547). DWI-model with the mean AUC of 0.658 for predicting csPCa (internal test AUC = 0.635 vs. external validation AUC = 0.681, P = 0.086) and 0.655 for predicting all cancer (internal test AUC = 0.712 vs. external validation AUC = 0.598, P = 0.437). ADC-model with the mean AUC of 0.746 for predicting csPCa (internal test AUC = 0.767 vs. external validation AUC = 0.724, P = 0.269) and 0.645 for predicting all cancer (internal test AUC = 0.650 vs. external validation AUC = 0.640, P = 0.848). Integrated model with the mean AUC of 0.803 for predicting csPCa (internal test AUC = 0.804 vs. external validation AUC = 0.801, P = 0.019) and 0.778 for predicting all cancer (internal test AUC = 0.801 vs. external validation AUC = 0.754, P = 0.047).

Conclusions

The radiomics model based on machine learning has the potential to be a non-invasive tool to distinguish cancerous, noncancerous and csPCa in PI-RADS 3 lesions, and has relatively high generalization ability between different date set.

Effects of fitting methods, high b-values and image quality on diffusion and perfusion quantification and reproducibility in the calf

PURPOSES

The study aimed to optimize diffusion-weighted imaging (DWI) image acquisition and analysis protocols in calf muscles by investigating the effects of different model-fitting methods, image quality, and use of high b-value and constraints on parameters of interest (POIs). The optimized modeling methods were used to select the optimal combinations of b-values, which will allow shorter acquisition time while achieving the same reliability as that obtained using 16 b-values.

METHODS

Test-retest baseline and high-quality DWI images of ten healthy volunteers were acquired on a 3T MR scanner, using 16 b-values, including a high b-value of 1200 s/mm², and structural T1-weighted images for calf muscle delineation. Three and six different fitting methods were used to derive ADC from monoexponential (ME) model and D_d, f_p, and D_p from intravoxel incoherent motion (IVIM) model, with or without the high b-value. The optimized ME and IVIM models were then used to determine the optimal combinations of b-values, obtainable with the least number of b-values, using the selection criteria of coefficient of variance (CV) ≤10% for all POIs.

RESULTS

The find minimum multivariate algorithm was more flexible and yielded smaller fitting errors. The 2-steps fitting method, with fixed D_d, performed the best for IVIM model. The inclusion of high b-value reduced outliers, while constraints improved 2-steps fitting only.

CONCLUSIONS

The optimal numbers of b-values for ME and IVIM models were nine and six b-values respectively. Test-retest reliability analyses showed that only ADC and D_d were reliable for calf diffusion evaluation, with CVs of 7.22% and 4.09%.

Assessing the variability and correlation between SUV and ADC parameters of head and neck cancers derived from simultaneous PET/MRI: A single-center study

OBJECTIVE

Intratumoral heterogeneity is associated with poor outcomes in head and neck cancer (HNC) patients owing to chemoradiotherapy resistance. [¹⁸ F]-FDG positron emission tomography (PET) / Magnetic Resonance Imaging (MRI) provides spatial information about tumor mass, allowing intratumor heterogeneity assessment through histogram analysis. However, variability in quantitative PET/MRI parameter measurements could influence their reliability in assessing patient prognosis. Therefore, to use standardized uptake value (SUV) and apparent diffusion coefficient (ADC) parameters for assessing tumor response, this study aimed to measure SUV and ADC's variability and assess their relationship in HNC.

METHODS

First, ADC variability was measured in an in-house diffusion phantom and in five healthy volunteers. The SUV variability was only measured with the NEMA phantom using a clinical imaging protocol. Furthermore, simultaneous PET/MRI data of 11 HNC patients were retrospectively collected from the National Cyclotron and PET center in Chulabhorn Hospital. Tumor contours were manually drawn from PET images by an experienced nuclear medicine radiologist before tumor volume segmentation. Next, SUV and ADC's histogram were used to extract statistic variables of ADC and SUV: mean, median, min, max, skewness, kurtosis, and 5^th , 10^th , 25^th , 50^th , 75^th , 90^th , and 95^th percentiles. Finally, the correlation between the statistic variables of ADC and SUV, as well as Metabolic Tumor volume and Total Lesion Glycolysis parameters was assessed using Pearson's correlation.

RESULTS

This pilot study showed that both parameters' maximum coefficient of variation was 13.9% and 9.8% in the phantom and in vivo, respectively. Furthermore, we found a strong and negative correlation between SUV_max and ADV_med (r = -0.75, P = 0.01).

CONCLUSION

The SUV and ADC obtained by simultaneous PET/MRI can be potentially used as an imaging biomarker for assessing intratumoral heterogeneity in patients with HNC. The low variability and relationship between SUV and ADC could allow multimodal prediction of tumor response in future studies.

Optional MRI sequences for LI-RADS: why, what, and how?

Hepatocellular carcinoma (HCC) is the most common primary malignant tumor of the liver worldwide. Noninvasive diagnosis of HCC is possible based on imaging features, without the need for tissue diagnosis. Liver Imaging Reporting and Data System (LI-RADS) CT/MRI diagnostic algorithm allows for standardized radiological interpretation and reporting of imaging studies for patients at high risk for HCC. Diagnostic categories of LR-1 to LR-5 designate each liver observation to reflect the probability of overall malignancy, HCC, or benignity based on imaging features, where LR-5 category has > 95% probability of HCC. Optimal imaging protocol and scanning technique as described by the technical recommendations for LI-RADS are essential for the depiction of features to accurately characterize liver observations. The LI-RADS MRI technical guidelines recommend the minimum required sequences of T1-weighted out-of-phase and in-phase Imaging, T2-weighted Imaging, and multiphase T1-weighted Imaging. Additional sequences, including diffusion-weighted imaging, subtraction imaging, and the hepatobiliary phase when using gadobenate dimeglumine as contrast, improve diagnostic confidence, but are not required by the guidelines. These optional sequences can help differentiate true lesions from pseudolesions, detect additional observations, identify parenchymal observations when other sequences are suboptimal, and improve observations conspicuity. This manuscript reviews the optional sequences, the advantages they offer, and discusses technical optimization of these sequences to obtain the highest image quality and to avoid common artifacts.

Directional and inter-acquisition variability in diffusion-weighted imaging and editing for restricted diffusion

PURPOSE

To evaluate and quantify inter-directional and inter-acquisition variation in diffusion-weighted imaging (DWI) and emphasize signals that report restricted diffusion to enhance cancer conspicuity, while reducing the effects of local microscopic motion and magnetic field fluctuations.

METHODS

Ten patients with biopsy-proven prostate cancer were studied under an Institutional Review Board-approved protocol. Individual acquisitions of DWI signal intensities were reconstructed to calculate inter-acquisition distributions and their statistics, which were compared for healthy versus cancer tissue. A method was proposed to detect and filter the acquisitions affected by motion-induced signal loss. First, signals that reflect restricted diffusion were separated from the acquisitions that suffer from signal loss, likely due to microscopic motion, by imposing a cutoff value. Furthermore, corrected apparent diffusion coefficient maps were calculated by employing a weighted sum of the multiple acquisitions, instead of conventional averaging. These weights were calculated by applying a soft-max function to the set of acquisitions per-voxel, making the analysis immune to acquisitions with significant signal loss, even if the number of such acquisitions is high.

RESULTS

Inter-acquisition variation is much larger than the Rician noise variance, local spatial variations, and the estimates of diffusion anisotropy based on the current data, as well as the published values of anisotropy. The proposed method increases the contrast for cancers and yields a sensitivity of98 . 8 % $$ 98.8\% $$ with a false positive rate of3 . 9 % $$ 3.9\% $$ .

CONCLUSION

Motion-induced signal loss makes conventional signal-averaging suboptimal and can obscure signals from areas with restricted diffusion. Filtering or weighting individual acquisitions prior to image analysis can overcome this problem.

Diffusion-weighted imaging as an imaging biomarker for assessing survival of patients with intrahepatic mass-forming cholangiocarcinoma

BACKGROUND

Mass-forming cholangiocarcinoma is the most common form of intrahepatic cholangiocarcinoma and is associated with a worse prognosis. This study aimed to assess the role of diffusion-weighted imaging and other imaging features as prognostic markers to predict the survival of patients with intrahepatic mass-forming cholangiocarcinoma (IMCC).

MATERIALS AND METHODS

The study included patients with pathologically proven IMCC from January 2011 to January 2018. Two radiologists retrospectively reviewed various imaging findings and manually estimated the area of diffusion restriction. Patients were grouped according to their restriction area into (group 1) restriction ≥ 1/3 of the tumor and (group 2) restriction < 1/3 of the tumor. Statistical analysis was performed to assess the relationship between various imaging features and patients' survival.

RESULTS

Seventy-three patients were included in the study. IMCC patients with tumor size ≥ 5 cm had increased intrahepatic- and peritoneal metastases (p = 039 and p = 0.001 for reader 1 and p = 0.048 and p = 0.057 for reader 2). There was no significant relationship between the diffusion restriction area and tumor size, enhancement pattern, vascular involvement, lymph node metastasis, peritoneal- and distant metastasis. The number of deaths was significantly higher in patients with group 2 restriction (63.6% for group 1 vs. 96.6% for group 2; p = 0.001 for reader 1)(68.2% for group 1 vs. 89.7%% for group 2; p = 0.030 for reader 2). Patients with group 2 restriction had shorter 1- and 3-year survival rates and lower median survival time. Multivariable survival analysis showed two independent prognostic factors relating to poor survival outcomes: peritoneal metastasis (p = 0.04 for reader 1 and p = 0.041 for reader 2) and diffusion restriction < 1/3 (p = 0.011 for reader 1 and p = 0.042 for reader 2). Lymph node metastasis and intrahepatic metastasis were associated with shorter survival in the univariate analysis. However, these factors were non-significant in the multivariate analysis.

CONCLUSION

Restriction diffusion of less than 1/3 and peritoneal metastasis were associated with shorter overall survival of IMCC patients. Other features that might correlate with the outcome are suspicious lymph nodes and multifocal lesions.

Positron emission tomography/magnetic resonance imaging (PET/MRI) vs. gastroscopy: Can it improve detection of extranodal marginal zone lymphomas of the stomach following H. pylori treatment?

INTRODUCTION

The stomach is the most common site of origin for extranodal marginal zone B-cell lymphoma of the mucosa-associated lymphoid tissue (MALT lymphoma). Antibiotic eradication of Helicobacter pylori (H. pylori) is the standard first-line treatment, with response assessment being performed by histological evaluation of multiple gastric biopsies.

AREAS COVERED

The objective of this review is to provide an update on results obtained using noninvasive methods, including magnetic resonance imaging (MRI), positron emission tomography combined with computed tomography (PET/CT), and most recently, PET/MRI for the assessment of disease extent and response to treatment in patients with gastric MALT lymphoma.

EXPERT OPINION

While CT is the officially recommended imaging technique, few studies in small cohorts have suggested that diffusion-weighted MRI shows higher sensitivity, also relative to 18 F-FDG PET/CT, for both gastric and nongastric MALT lymphomas. A recent prospective study using PET/MRI with the novel CXCR4-targeting radiotracer 68 Ga-Pentixafor suggested that, for patients with gastric MALT lymphoma after H. pylori eradication, this imaging technique may provide excellent accuracy (97%) for assessment of residual or recurrent disease. Although recent studies on CXCR4-targeting PET and to some extent also diffusion-weighted MRI are promising, there is insufficient evidence to suggest a change in clinical practice.

Impact of measurement method on interobserver variability of apparent diffusion coefficient of lesions in prostate MRI

Purpose

To compare the inter-observer variability of apparent diffusion coefficient (ADC) values of prostate lesions measured by 2D-region of interest (ROI) with and without specific measurement instruction.

Methods

Forty lesions in 40 patients who underwent prostate MR followed by targeted prostate biopsy were evaluated. A multi-reader study (10 readers) was performed to assess the agreement of ADC values between 2D-ROI without specific instruction and 2D-ROI with specific instruction to place a 9-pixel size 2D-ROI covering the lowest ADC area. The computer script generated multiple overlapping 9-pixel 2D-ROIs within a 3D-ROI encompassing the entire lesion placed by a single reader. The lowest mean ADC values from each 2D-small-ROI were used as reference values. Inter-observer agreement was assessed using the Bland-Altman plot. Intraclass correlation coefficient (ICC) was assessed between ADC values measured by 10 readers and the computer-calculated reference values.

Results

Ten lesions were benign, 6 were Gleason score 6 prostate carcinoma (PCa), and 24 were clinically significant PCa. The mean±SD ADC reference value by 9-pixel-ROI was 733 ± 186 (10⁻⁶ mm²/s). The 95% limits of agreement of ADC values among readers were better with specific instruction (±112) than those without (±205). ICC between reader-measured ADC values and computer-calculated reference values ranged from 0.736–0.949 with specific instruction and 0.349–0.919 without specific instruction.

Conclusion

Interobserver agreement of ADC values can be improved by indicating a measurement method (use of a specific ROI size covering the lowest ADC area).

Whole-tumour apparent diffusion coefficient (ADC) histogram analysis to identify MYCN-amplification in neuroblastomas: preliminary results

OBJECTIVES

To determine the role of apparent diffusion coefficient (ADC) histogram analysis in the identification of MYCN-amplification status in neuroblastomas.

METHODS

We retrospectively evaluated imaging records from 62 patients with neuroblastomas (median age: 15 months (interquartile range (IQR): 7-24 months); 38 females) who underwent magnetic resonance imaging at our institution before the initiation of any therapy or biopsy. Fourteen patients had MYCN-amplified (MYCNA) neuroblastoma. Histogram parameters of ADC maps from the entire tumour was obtained from the baseline images and the normalised images. The Mann-Whitney U test was used to compare the absolute and normalised histogram parameters amongst neuroblastomas with and without MYCN-amplification. Receiver operating characteristic (ROC) curves and area under the curves (AUC) were generated for the statistically significant histogram parameters. Cut-offs obtained from the ROC curves were evaluated on an external validation set (n-15, MYCNA-6, F-7, age 24 months (10-60)). A logistic regression model was trained to predict MYCNA by combining statistically significant histogram parameters and was evaluated on the validation set.

RESULTS

MYCN-amplified neuroblastomas had statistically significant higher maximum ADC and lower minimum ADC than non-amplified neuroblastomas. They also demonstrated higher entropy, variance, energy, and lower uniformity than non-amplified neoplasms (p > 0.05). Energy, entropy, and maximum ADC had AUC of 0.85, 0.79, and 0.82, respectively.

CONCLUSIONS

Whole tumour ADC histogram analysis of neuroblastomas can differentiate between tumours with and without MYCN-amplification. These parameters can help identify areas for targeted biopsies or can be used to predict subtypes of these high-risk tumours before biopsy results are available.

KEY POINTS

• MYCN-amplification significantly affects treatment decisions in neuroblastomas. • MYCN-amplified neuroblastomas had significantly different ADC histogram metrics as compared to tumours without amplification. • ADC histogram metrics can be used to predict MYCN-amplification status based on imaging.

Reproducibility of diffusion tensor image analysis along the perivascular space (DTI-ALPS) for evaluating interstitial fluid diffusivity and glymphatic function: CHanges in Alps index on Multiple conditiON acquIsition eXperiment (CHAMONIX) study

PURPOSE

The diffusion tensor image analysis along the perivascular space (DTI-ALPS) method was developed to evaluate the brain's glymphatic function or interstitial fluid dynamics. This study aimed to evaluate the reproducibility of the DTI-ALPS method and the effect of modifications in the imaging method and data evaluation.

MATERIALS AND METHODS

Seven healthy volunteers were enrolled in this study. Image acquisition was performed for this test-retest study using a fixed imaging sequence and modified imaging methods which included the placement of region of interest (ROI), imaging plane, head position, averaging, number of motion-proving gradients, echo time (TE), and a different scanner. The ALPS-index values were evaluated for the change of conditions listed above.

RESULTS

This test-retest study by a fixed imaging sequence showed very high reproducibility (intraclass coefficient = 0.828) for the ALPS-index value. The bilateral ROI placement showed higher reproducibility. The number of averaging and the difference of the scanner did not influence the ALPS-index values. However, modification of the imaging plane and head position impaired reproducibility, and the number of motion-proving gradients affected the ALPS-index value. The ALPS-index values from 12-axis DTI and 3-axis diffusion-weighted image (DWI) showed good correlation (r = 0.86). Also, a shorter TE resulted in a larger value of the ALPS-index.

CONCLUSION

ALPS index was robust under the fixed imaging method even when different scanners were used. ALPS index was influenced by the imaging plane, the number of motion-proving gradient axes, and TE in the imaging sequence. These factors should be uniformed in the planning ALPS method studies. The possibility to develop a 3-axis DWI-ALPS method using three axes of the motion-proving gradient was also suggested.

Changes in apparent diffusion coefficient and pathological response in colorectal liver metastases after preoperative chemotherapy

BACKGROUND

The pathological response to preoperative chemotherapy of colorectal liver metastases (CRLMs) is predictive of long-term prognosis after liver resection. Accurate preoperative assessment of chemotherapy response could enable treatment optimization.

PURPOSE

To investigate whether changes in lesion-apparent diffusion coefficient (ADC) measured with diffusion-weighted magnetic resonance imaging (MRI) can be used to assess pathological treatment response in patients with CRLMs undergoing preoperative chemotherapy.

MATERIAL AND METHODS

Patients who underwent liver resection for CRLMs after preoperative chemotherapy between January 2011 and December 2019 were retrospectively included if they had undergone MRI before and after preoperative chemotherapy on the same 1.5-T MRI scanner with diffusion-weighted imaging with b-values 50, 400, and 800 s/mm². The pathological chemotherapy response was assessed using the tumor regression grade (TRG) by AJCC/CAP. Lesions were divided into two groups: pathological responding (TRG 0-2) and non-responding (TRG 3). The change in lesion ADC after preoperative chemotherapy was compared between responding and non-responding lesions.

RESULTS

A total of 27 patients with 49 CRLMs were included, and 24/49 lesions showed a pathological chemotherapy response. After chemotherapy, ADC increased in both pathological responding (pretreatment ADC: 1.26 [95% confidence interval (CI)=1.06-1.37] vs. post-treatment ADC: 1.33 [95% CI=1.13-1.56] × 10^-3 mm²/s; P = 0.026) and non-responding lesions (1.12 [95% CI=0.980-1.21] vs. 1.20 [95% CI=1.09-1.43] × 10^-3 mm²/s; P = 0.018). There was no difference in median relative difference in ADC after chemotherapy between pathological responding and non-responding lesions (15.8 [95% CI=1.42-26.3] vs. 7.17 [95% CI=-4.31 to 31.2]%; P = 0.795).

CONCLUSION

Changes in CRLM ADCs did not differ between pathological responding and non-responding lesions.

Proton Density Fat Fraction Spine MRI for Differentiation of Erosive Vertebral Endplate Degeneration and Infectious Spondylitis

Vertebral Modic type 1 (MT1) degeneration may mimic infectious disease on conventional spine magnetic resonance imaging (MRI), potentially leading to additional costly and invasive investigations. This study evaluated the diagnostic performance of the proton density fat fraction (PDFF) for distinguishing MT1 degenerative endplate changes from infectious spondylitis. A total of 31 and 22 patients with equivocal diagnosis of MT1 degeneration and infectious spondylitis, respectively, were retrospectively enrolled in this IRB-approved retrospective study and examined with a chemical-shift encoding (CSE)-based water-fat 3D six-echo modified Dixon sequence in addition to routine clinical spine MRI. Diagnostic reference standard was established according to histopathology or clinical and imaging follow-up. Intravertebral PDFF [%] and PDFFratio (i.e., vertebral endplate PDFF/normal vertebrae PDFF) were calculated voxel-wise within the single most prominent edematous bone marrow lesion per patient and examined for differences between MT1 degeneration and infectious spondylitis. Mean PDFF and PDFFratio of infectious spondylitis were significantly lower compared to MT1 degenerative changes (mean PDFF, 4.28 ± 3.12% vs. 35.29 ± 17.15% [p < 0.001]; PDFFratio, 0.09 ± 0.06 vs. 0.67 ± 0.37 [p < 0.001]). The areas under the curve (AUC) and diagnostic accuracies were 0.977 (p < 0.001) and 98.1% (cut-off at 12.9%) for PDFF and 0.971 (p < 0.001) and 98.1% (cut-off at 0.27) for PDFFratio. Our data suggest that quantitative evaluation of vertebral PDFF can provide a high diagnostic accuracy for differentiating erosive MT1 endplate changes from infectious spondylitis.

Early Response to Chemotherapy in Malignant Pleural Mesothelioma Evaluated Using Diffusion-Weighted Magnetic Resonance Imaging: Initial Observations

Introduction

We compared the magnetic resonance imaging total tumor volume (TTV) and median apparent diffusion coefficient (ADC) of malignant pleural mesothelioma (MPM) before and at 4 weeks after chemotherapy, to evaluate whether these are potential early markers of treatment response.

Methods

Diffusion-weighted magnetic resonance imaging was performed in 23 patients with MPM before and after 4 weeks of chemotherapy. The TTV was measured by semiautomatic segmentation (GrowCut) and transferred onto ADC maps to record the median ADC. Test-retest repeatability of TTV and ADC was evaluated in eight patients. TTV and median ADC changes were compared between responders and nonresponders, defined using modified Response Evaluation Criteria In Solid Tumors on computed tomography (CT) at 12 weeks after treatment. TTV and median ADC were also correlated with CT size measurement and disease survival.

Results

The test-retest 95% limits of agreement for TTV were -13.9% to 16.2% and for median ADC -1.2% to 3.3%. A significant increase in median ADC in responders was observed at 4 weeks after treatment (p = 0.02). Correlation was found between CT tumor size change at 12 weeks and median ADC changes at 4 weeks post-treatment (r = -0.560, p = 0.006). An increase in median ADC greater than 5.1% at 4 weeks has 100% sensitivity and 90% specificity for responders (area under the curve = 0.933, p < 0.001). There was also moderate correlation between median tumor ADC at baseline and overall survival (r = 0.45, p = 0.03).

Conclusions

Diffusion-weighted magnetic resonance imaging measurements of TTV and median ADC in MPM have good measurement repeatability. Increase in ADC at 4 weeks post-treatment has the potential to be an early response biomarker.

Utility of machine learning of apparent diffusion coefficient (ADC) and T2-weighted (T2W) radiomic features in PI-RADS version 2.1 category 3 lesions to predict prostate cancer diagnosis

PURPOSE

To evaluate if machine learning (ML) of radiomic features extracted from apparent diffusion coefficient (ADC) and T2-weighted (T2W) MRI can predict prostate cancer (PCa) diagnosis in Prostate Imaging-Reporting and Data System (PI-RADS) version 2.1 category 3 lesions.

METHODS

This multi-institutional review board-approved retrospective case-control study evaluated 158 men with 160 PI-RADS category 3 lesions (79 peripheral zone, 81 transition zone) diagnosed at 3-Tesla MRI with histopathology diagnosis by MRI-TRUS-guided targeted biopsy. A blinded radiologist confirmed PI-RADS v2.1 score and segmented lesions on axial T2W and ADC images using 3D Slicer, extracting radiomic features with an open-source software (Pyradiomics). Diagnostic accuracy for (1) any PCa and (2) clinically significant (CS; International Society of Urogenital Pathology Grade Group ≥ 2) PCa was assessed using XGBoost with tenfold cross -validation.

RESULTS

From 160 PI-RADS 3 lesions, there were 50.0% (80/160) PCa, including 36.3% (29/80) CS-PCa (63.8% [51/80] ISUP 1, 23.8% [19/80] ISUP 2, 8.8% [7/80] ISUP 3, 3.8% [3/80] ISUP 4). The remaining 50.0% (80/160) lesions were benign. ML of all radiomic features from T2W and ADC achieved area under receiver operating characteristic curve (AUC) for diagnosis of (1) CS-PCa 0.547 (95% Confidence Intervals 0.510-0.584) for T2W and 0.684 (CI 0.652-0.715) for ADC and (2) any PCa 0.608 (CI 0.579-0.636) for T2W and 0.642 (CI 0.614-0.0.670) for ADC.

CONCLUSION

Our results indicate ML of radiomic features extracted from T2W and ADC achieved at best moderate accuracy for determining which PI-RADS category 3 lesions represent PCa.

Widespread effects of dMRI data quality on diffusion measures in children

Diffusion magnetic resonance imaging (dMRI) datasets are susceptible to several confounding factors related to data quality, which is especially true in studies involving young children. With the recent trend of large‐scale multicenter studies, it is more critical to be aware of the varied impacts of data quality on measures of interest. Here, we investigated data quality and its effect on different diffusion measures using a multicenter dataset. dMRI data were obtained from 691 participants (5–17 years of age) from six different centers. Six data quality metrics—contrast to noise ratio, outlier slices, and motion (absolute, relative, translation, and rotational)—and four diffusion measures—fractional anisotropy, mean diffusivity, tract density, and length—were computed for each of 36 major fiber tracts for all participants. The results indicated that four out of six data quality metrics (all except absolute and translation motion) differed significantly between centers. Associations between these data quality metrics and the diffusion measures differed significantly across the tracts and centers. Moreover, these effects remained significant after applying recently proposed harmonization algorithms that purport to remove unwanted between‐site variation in diffusion data. These results demonstrate the widespread impact of dMRI data quality on diffusion measures. These tracts and measures have been routinely associated with individual differences as well as group‐wide differences between neurotypical populations and individuals with neurological or developmental disorders. Accordingly, for analyses of individual differences or group effects (particularly in multisite dataset), we encourage the inclusion of data quality metrics in dMRI analysis.

Current Applications and Future Development of Magnetic Resonance Fingerprinting in Diagnosis, Characterization, and Response Monitoring in Cancer

Magnetic resonance imaging (MRI) has enabled non-invasive cancer diagnosis, monitoring, and management in common clinical settings. However, inadequate quantitative analyses in MRI continue to limit its full potential and these often have an impact on clinicians' judgments. Magnetic resonance fingerprinting (MRF) has recently been introduced to acquire multiple quantitative parameters simultaneously in a reasonable timeframe. Initial retrospective studies have demonstrated the feasibility of using MRF for various cancer characterizations. Further trials with larger cohorts are still needed to explore the repeatability and reproducibility of the data acquired by MRF. At the moment, technical difficulties such as undesirable processing time or lack of motion robustness are limiting further implementations of MRF in clinical oncology. This review summarises the latest findings and technology developments for the use of MRF in cancer management and suggests possible future implications of MRF in characterizing tumour heterogeneity and response assessment.

The role of magnetic resonance imaging in active surveillance of prostate cancer

Active surveillance (AS) is an important strategy to avoid overtreatment of prostate cancer (PCa) and has become the standard of care for low-risk patients. The role of magnetic resonance imaging (MRI) in AS has expanded due to its ability to risk stratify patients with suspected or known PCa, and MRI has become an integral part of the AS protocols at various institutions. A negative pre-biopsy MRI result is associated with a very high negative predictive value for a Gleason score ≥ 3+4. A positive MRI result in men who are otherwise eligible for AS has been shown to be associated with the presence of high-grade PCa and therefore with ineligibility. In addition, MRI can be used to guide and determine the timing of per-protocol biopsy during AS. However, there are several MRI-related issues that remain unresolved, including the lack of a consensus and guidelines; concerns about gadolinium deposition in various tissues; and increased demand for higher efficiency and productivity. Similarly, the need for the combined use of targeted and systematic sampling is still a matter of debate when lesions are visible on MRI. Here, we review the current AS guidelines, as well as the accepted roles of MRI in patient selection and monitoring, the potential uses of MRI that are still in question, and the limitations of the method.

Improvement of PI-RADS-dependent prostate cancer classification by quantitative image assessment using radiomics or mean ADC

Background Currently, interpretation of prostate MRI is performed qualitatively. Quantitative assessment of the mean apparent diffusion coefficient (mADC) is promising to improve diagnostic accuracy while radiomic machine learning (RML) allows to probe complex parameter spaces to identify the most promising multi-parametric models. We have previously developed quantitative RML and ADC classifiers for prediction of clinically significant prostate cancer (sPC) from prostate MRI, however these have not been combined with radiologist PI-RADS assessment. Purpose To propose and evaluate diagnostic algorithms combining quantitative ADC or RML and qualitative PI-RADS assessment for prediction of sPC. Methods and population The previously published quantitative models (RML and mADC) were utilized to construct four algorithms: 1) Down(ADC) and 2) Down(RML): clinically detected PI-RADS positive prostate lesions (defined as either PI-RADS≥3 or ≥4) were downgraded to MRI negative upon negative quantitative assessment; and 3) Up(ADC) and 4) Up(RML): MRI-negative lesions were upgraded to MRI-positive upon positive assessment of quantitative parameters. Analyses were performed at the individual lesion level and the patient level in 133 consecutive patients with suspicion for clinically significant prostate cancer (sPC, International Society of Urological Pathology (ISUP) grade group≥2), the test set subcohort of a previously published patient population. McNemar test was used to compare differences in sensitivity, specificity and accuracy. Differences between lesions of different prostate zones were assessed using ANOVA. Reduction in false positive assessments was assessed as ratios. Results Compared to clinical assessment at the PI-RADS≥4 cut-off alone, algorithms Down(ADC/RML) improved specificity from 43% to 65% (p = 0.001)/62% (p = 0.003), while sensitivity did not change significantly at 89% compared to 87% (p = 1.0)/89% (unchanged) on the patient level. Reduction of false positive lesions was 50% [26/52] in the PZ and 53% [15/28] in the TZ. Algorithms Up(ADC/RML) led, on a patient basis, to an unfavorable loss of specificity from 43% to 30% (p = 0.039)/32% (p = 0.106), with insignificant increase of sensitivity from 89% to 96%/96% (both p = 1.0). Compared to clinical assessment at the PI-RADS≥3 cut-off alone, similar results were observed for Down(ADC) with significantly increased specificity from 2% to 23% (p < 0.001) and unchanged sensitivity on the lesion level; patient level specificity increased only non-significantly. Conclusion Downgrading PI-RADS≥3 and ≥ 4 lesions based on quantitative mADC measurements or RML classifiers can increase diagnostic accuracy by enhancing specificity and preserving sensitivity for detection of sPC and reduce false positives.

The prominent value of apparent diffusion coefficient in assessing high-risk factors and prognosis for patients with endometrial carcinoma before treatment

BACKGROUND

To date, there are no consensus methods to evaluate the high-risk factors and prognosis for managing the personalized treatment schedule of patients with endometrial carcinoma (EC) before treatment. Apparent diffusion coefficient (ADC) is regarded as a kind of technique to assess heterogeneity of malignant tumor.

PURPOSE

To explore the role of ADC value in assessing the high-risk factors and prognosis of EC.

MATERIAL AND METHODS

A retrospective analysis was made on 185 patients with EC who underwent 1.5-T magnetic resonance imaging (MRI). Mean ADC (mADC), minimum ADC (minADC), and maximum ADC (maxADC) were measured and compared in different groups.

RESULTS

Among the 185 patients with EC, the mADC and maxADC values in those with high-risk factors (type 2, deep myometrial invasion, and lymph node metastasis) were significantly lower than in those without. According to receiver operating characteristic (ROC) curve analysis, the areas under the curve (AUC) were significant for mADC, minADC, and maxADC predicting high-risk factors. Furthermore, the AUCs were significant for mADC and maxADC predicting lymph node metastasis but were not significant for minADC. Patients with lower mADC were associated with worse overall survival and disease-free survival; the opposite was true for patients with higher mADC.

CONCLUSION

Our study showed that ADC values could be applied to assess the high-risk factors of EC before treatment and might significantly relate to the prognosis of EC. It might contribute to managing initial individualized treatment schedule and improve outcome in patients with EC.

Utility of multiparametric pre-operative magnetic resonance imaging in differentiation of chordoid meningioma from the other histopathological subtypes of meningioma-a retrospective study

PURPOSE

To determine the magnetic resonance imaging (MRI) features which could pre-operatively differentiate chordoid meningioma (CM) from other histopathological subtypes of meningioma.

METHODS

Retrospective analysis of pre-operative MRI of cases with histopathologically confirmed diagnosis of meningioma during the last 5 years at our institute was done. T1W, T2W, FLAIR sequences, and post-contrast enhancement were evaluated on a qualitative scale. Normalized ADC ratios (nADCR) and normalized fractional anisotropy ratios (nFAR) were derived. The intratumoral susceptibility score (ITSS), presence of sunburst pattern of vasculature, bone changes, tumour-parenchyma interface, and oedema-to-tumour ratio were also determined.

RESULTS

A total of 81 lesions were analyzed out of which 15 were CM. CM showed a higher relative contrast enhancement as compared to all other subtypes except for angiomatous and microcystic meningioma. Relative signal intensity on FLAIR could differentiate CM from transitional meningioma. nFAR was found to be significantly higher in fibroblastic meningioma and significantly lower in microcystic meningiomas as compared to CM. Anaplastic meningiomas were remarkable for bone changes and an ill-defined tumour-brain interface in significantly higher proportion of cases as compared to CM. nADCR > 1.5 was found to be an independent predictor of CM with a sensitivity of 84.6%, specificity of 89.8%, positive predictive value of 64.7%, and negative predictive value of 96.4%.

CONCLUSION

Routine pre-operative MRI may be able to differentiate CM from other meningioma subtypes and a cut-off value of greater than 1.5 for nADCR could be predictive of > 50% chordoid histology of meningioma with a high sensitivity, specificity, and negative predictive value.

Evaluation of the Effect of Patient Preparation Using Castor Oil on ADC Value of Focal Liver Lesion

Purpose

To estimate the role of patient preparation using castor oil on the ADC value of focal liver lesion.

Patients and Methods

Retrospective case-control study over more than two years. Magnetic resonance imaging (MRI) scan, including diffusion-weighted imaging (DWI) of the upper abdomen performed for 87 cases and 71 controls in patients with focal hepatic hemangiomas. Cases were prepared using castor oil prior to the scan without identifiable unwanted effect, while controls did not receive any special preparation. Since liver hemangioma is a common lesion, it was selected and used as a sample. Apparent Diffusion Coefficient (ADC) values of focal liver lesion were calculated in cases and controls.

Results

The mean ADC value of liver hemangioma was lower in cases compared to controls; the mean ADC value was (2.21±0.39x10ˉ³mm²/s) in cases and (2.51±0.49x10ˉ³mm²/s) in controls. Left lobes were more affected by lesions; the mean ADC value of the left lobe lesions was (2.26±0.37 x10ˉ³mm²/s) and (2.86±0.43 x10ˉ³mm²/s) in cases and controls, respectively. The ADC value of lesions in the right lobe was (2.19±0.39x10ˉ³mm²/s) in cases and (2.39± 0.45x10ˉ³mm²/s) in controls. There was a significant segmental ADC variation; lesions at segments II, III, IVb, and V demonstrated illusive ADC elevation in controls.

Conclusion

There is erroneous elevation of lobar and segmental ADC value of liver hemangiomas in non prepared patients. This Potential source of error (peristalsis, partial volume, and paramagnetic gas effect of gastrointestinal tract) on hepatic lesions’ ADC value can be avoided by proper preparation using castor oil prior to MRI scanning.

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.

A Confidence Habitats Methodology in MR Quantitative Diffusion for the Classification of Neuroblastic Tumors

Simple Summary

There is growing interest in applying quantitative diffusion techniques to magnetic resonance imaging for cancer diagnosis and treatment. These measurements are used as a surrogate marker of tumor cellularity and aggressiveness, although there may be factors that introduce some bias to these approaches. Thus, we explored a novel methodology based on confidence habitats and voxel uncertainty to improve the power of the apparent diffusion coefficient to discriminate between benign and malignant neuroblastic tumor profiles in children. We were able to show this offered an improved sensitivity and negative predictive value relative to standard voxel-based methodologies.

Abstract

Background/Aim: In recent years, the apparent diffusion coefficient (ADC) has been used in many oncology applications as a surrogate marker of tumor cellularity and aggressiveness, although several factors may introduce bias when calculating this coefficient. The goal of this study was to develop a novel methodology (Fit-Cluster-Fit) based on confidence habitats that could be applied to quantitative diffusion-weighted magnetic resonance images (DWIs) to enhance the power of ADC values to discriminate between benign and malignant neuroblastic tumor profiles in children. Methods: Histogram analysis and clustering-based algorithms were applied to DWIs from 33 patients to perform tumor voxel discrimination into two classes. Voxel uncertainties were quantified and incorporated to obtain a more reproducible and meaningful estimate of ADC values within a tumor habitat. Computational experiments were performed by smearing the ADC values in order to obtain confidence maps that help identify and remove noise from low-quality voxels within high-signal clustered regions. The proposed Fit-Cluster-Fit methodology was compared with two other methods: conventional voxel-based and a cluster-based strategy. Results: The cluster-based and Fit-Cluster-Fit models successfully differentiated benign and malignant neuroblastic tumor profiles when using values from the lower ADC habitat. In particular, the best sensitivity (91%) and specificity (89%) of all the combinations and methods explored was achieved by removing uncertainties at a 70% confidence threshold, improving standard voxel-based sensitivity and negative predictive values by 4% and 10%, respectively. Conclusions: The Fit-Cluster-Fit method improves the performance of imaging biomarkers in classifying pediatric solid tumor cancers and it can probably be adapted to dynamic signal evaluation for any tumor.

Evaluation of the changes in hepatic apparent diffusion coefficient and hepatic fat fraction in healthy cats during body weight gain

OBJECTIVE

To determine the change in mean hepatic apparent diffusion coefficient (ADC) and hepatic fat fraction (HFF) during body weight gain in cats by use of MRI.

ANIMALS

12 purpose-bred adult neutered male cats.

PROCEDURES

The cats underwent general health and MRI examination at time 0 (before dietary intervention) and time 1 (after 40 weeks of being fed high-energy food ad libitum). Sequences included multiple-echo gradient-recalled echo MRI and diffusion-weighted MRI with 3 b values (0, 400, and 800 s/mm²). Variables (body weight and the HFF and ADC in selected regions of interest in the liver parenchyma) were compared between time points by Wilcoxon paired-sample tests. Relationships among variables were assessed with generalized mixed-effects models.

RESULTS

Median body weight was 4.5 and 6.5 kg, mean ± SD HFF was 3.39 ± 0.89% and 5.37 ± 1.92%, and mean ± SD hepatic ADC was 1.21 ± 0.08 × 10^-3 mm²/s and 1.01 ± 0.2 × 10^-3 mm²/s at times 0 and 1, respectively. Significant differences between time points were found for body weight, HFF, and ADC. The HFF was positively associated with body weight and ADC was negatively associated with HFF.

CONCLUSIONS AND CLINICAL RELEVANCE

Similar to findings in people, cats had decreasing hepatic ADC as HFF increased. Protons associated with fat tissue in the liver may reduce diffusivity, resulting in a lower ADC than in liver with lower HFF. Longer studies and evaluation of cats with different nutritional states are necessary to further investigate these findings.

Is the level of diffusion restriction in celiac and cervico-thoracic sympathetic ganglia helpful in their proper recognition on PSMA ligand PET/MR?

AIM

 To check if diffusion weighted imaging (DWI) might be helpful in proper recognition of celiac (CG) and cervicothoracic (CTG) sympathetic ganglia on the whole-body multimodal PSMA-ligand PET/MR imaging, in the view of their common misleading avidity on PET potentially suggestive of malignant lesions, including metastatic lymph nodes.

METHODS

 The thickness and the level of diffusion restriction was assessed qualitatively and quantitatively in 406 sympathetic ganglia (189 CTG in 101 males and 217 CG in 116 males) on DWI maps (b-value 0 and 800 s/mm2) and apparent diffusion coefficient (ADC) maps (mean ADC) of the whole-body PET/MR 68Ga-PSMA-11 PET/MR. To form a reference group of a matching ganglia size, the smallest lymph node was chosen from each patient with metastases and underwent the same procedure.

RESULTS

 Very low and low level of diffusion restriction was noted in the majority of sympathetic ganglia (81.0 % CTG, 67.3 % CG, and 73.6 % of all). In the majority (91.7 %) of metastatic lymph nodes the level of diffusion restriction was moderate to high.The mean ADC values in sympathetic ganglia were statistically significantly higher in CTG, CG and all ganglia than in metastatic lymph nodes (p < 0.001; the effect size was large).

CONCLUSIONS

 Sympathetic celiac and cervicothoracic ganglia present very low and low level of diffusion restriction in visual DWI assessment, and significantly higher than metastatic lymph nodes mean ADC values in the majority of cases, which may serve as additional factors aiding differential diagnosis on multimodal PSMA-ligand PET/MR imaging.Therefore, PSMA-ligand PET/MR appears potentially superior to PSMA-ligand PET/CT in proper identification of sympathetic ganglia.

Peritumor to tumor apparent diffusion coefficient ratio is associated with biologically more aggressive breast cancer features and correlates with the prognostication tools

PURPOSE

The apparent diffusion coefficient (ADC) is increasingly used to characterize breast cancer. The peritumor/tumor ADC ratio is suggested to be a reliable and generally applicable index. However, its overall prognostication value remains unclear. We aimed to evaluate the associations between the peritumor/tumor ADC ratio and histopathological biomarkers and published prognostic tools in patients with invasive breast cancer.

MATERIALS AND METHODS

This prospective study included 88 lesions (five bilateral) in 83 patients with primary invasive breast cancer who underwent preoperative 3.0-T magnetic resonance imaging. The lowest intratumoral mean ADC value on the slice with the largest tumor cross-sectional area was designated the tumor ADC, and the highest mean ADC value on the peritumoral breast parenchymal tissue adjacent to the tumor border was designated the peritumor ADC. The peritumor/tumor ADC ratio was then calculated. The tumor and peritumor ADC values and peritumor/tumor ADC ratios were compared with histopathological parameters using an unpaired t test, and their correlations with published prognostic tools were evaluated with Pearson's correlation coefficient.

RESULTS

The peritumor/tumor ADC ratio was significantly associated with tumor size (p<0.001), histological grade (p = 0.005), Ki-67 index (p = 0.006), axillary-lymph-node metastasis (p = 0.001), and lymphovascular invasion (p = 0.006), but was not associated with estrogen receptor status (p = 0.931), progesterone receptor status (p = 0.160), or human epidermal growth factor receptor 2 status (p = 0.259). The peritumor/tumor ADC ratio showed moderate positive correlations with the Nottingham Prognostic Index (r = 0.498, p<0.001) and mortality predicted using PREDICT (r = 0.436, p<0.001).

CONCLUSION

The peritumor/tumor ADC ratio was correlated with histopathological biomarkers in patients with invasive breast cancer, showed significant correlations with published prognostic indexes, and may provide an easily applicable imaging index for the preoperative prognostic evaluation of breast cancer.

Quantitative Prostate MRI

Prostate MRI is reported in clinical practice using the Prostate Imaging and Data Reporting System (PI-RADS). PI-RADS aims to standardize, as much as possible, the acquisition, interpretation, reporting, and ultimately the performance of prostate MRI. PI-RADS relies upon mainly subjective analysis of MR imaging findings, with very few incorporated quantitative features. The shortcomings of PI-RADS are mainly: low-to-moderate interobserver agreement and modest accuracy for detection of clinically significant tumors in the transition zone. The use of a more quantitative analysis of prostate MR imaging findings is therefore of interest. Quantitative MR imaging features including: tumor size and volume, tumor length of capsular contact, tumor apparent diffusion coefficient (ADC) metrics, tumor T₁ and T₂ relaxation times, tumor shape, and texture analyses have all shown value for improving characterization of observations detected on prostate MRI and for differentiating between tumors by their pathological grade and stage. Quantitative analysis may therefore improve diagnostic accuracy for detection of cancer and could be a noninvasive means to predict patient prognosis and guide management. Since quantitative analysis of prostate MRI is less dependent on an individual users' assessment, it could also improve interobserver agreement. Semi- and fully automated analysis of quantitative (radiomic) MRI features using artificial neural networks represent the next step in quantitative prostate MRI and are now being actively studied. Validation, through high-quality multicenter studies assessing diagnostic accuracy for clinically significant prostate cancer detection, in the domain of quantitative prostate MRI is needed. This article reviews advances in quantitative prostate MRI, highlighting the strengths and limitations of existing and emerging techniques, as well as discussing opportunities and challenges for evaluation of prostate MRI in clinical practice when using quantitative assessment. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

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.

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.

Whole brain apparent diffusion coefficient measurements correlate with survival in glioblastoma patients

INTRODUCTION

Glioblastoma (GBM) is the most common malignant primary brain tumor, and methods to improve the early detection of disease progression and evaluate treatment response are highly desirable. We therefore explored changes in whole-brain apparent diffusion coefficient (ADC) values with respect to survival (progression-free [PFS], overall [OS]) in a cohort of GBM patients followed at regular intervals until disease progression.

METHODS

A total of 43 subjects met inclusion criteria and were analyzed retrospectively. Histogram data were extracted from standardized whole-brain ADC maps including skewness, kurtosis, entropy, median, mode, 15th percentile (p15) and 85th percentile (p85) values, and linear regression slopes (metrics versus time) were fitted. Regression slope directionality (positive/negative) was subjected to univariate Cox regression. The final model was determined by aLASSO on metrics above threshold.

RESULTS

Skewness, kurtosis, median, p15 and p85 were all below threshold for both PFS and OS and were analyzed further. Median regression slope directionality best modeled PFS (p = 0.001; HR 3.3; 95% CI 1.6-6.7), while p85 was selected for OS (p = 0.002; HR 0.29; 95% CI 0.13-0.64).

CONCLUSIONS

Our data show tantalizing potential in the use of whole-brain ADC measurements in the follow up of GBM patients, specifically serial median ADC values which correlated with PFS, and serial p85 values which correlated with OS. Whole-brain ADC measurements are fast and easy to perform, and free of ROI-placement bias.

Non-Gaussian models of diffusion weighted imaging for detection and characterization of prostate cancer: a systematic review and meta-analysis

The importance of Diffusion Weighted Imaging (DWI) in prostate cancer (PCa) diagnosis have been widely handled in literature. In the last decade, due to the mono-exponential model limitations, several studies investigated non-Gaussian DWI models and their utility in PCa diagnosis. Since their results were often inconsistent and conflicting, we performed a systematic review of studies from 2012 examining the most commonly used Non-Gaussian DWI models for PCa detection and characterization. A meta-analysis was conducted to assess the ability of each Non-Gaussian model to detect PCa lesions and distinguish between low and intermediate/high grade lesions. Weighted mean differences and 95% confidence intervals were calculated and the heterogeneity was estimated using the I2 statistic. 29 studies were selected for the systematic review, whose results showed inconsistence and an unclear idea about the actual usefulness and the added value of the Non-Gaussian model parameters. 12 studies were considered in the meta-analyses, which showed statistical significance for several non-Gaussian parameters for PCa detection, and to a lesser extent for PCa characterization. Our findings showed that Non-Gaussian model parameters may potentially play a role in the detection and characterization of PCa but further studies are required to identify a standardized DWI acquisition protocol for PCa diagnosis.

Emerging quantitative MR imaging biomarkers in inflammatory arthritides

PURPOSE

To review quantitative magnetic resonance imaging (qMRI) methods for imaging inflammation in connective tissues and the skeleton in inflammatory arthritis. This review is designed for a broad audience including radiologists, imaging technologists, rheumatologists and other healthcare professionals.

METHODS

We discuss the use of qMRI for imaging skeletal inflammation from both technical and clinical perspectives. We consider how qMRI can be targeted to specific aspects of the pathological process in synovium, cartilage, bone, tendons and entheses. Evidence for the various techniques from studies of both adults and children with inflammatory arthritis is reviewed and critically appraised.

RESULTS

qMRI has the potential to objectively identify, characterize and quantify inflammation of the connective tissues and skeleton in both adult and pediatric patients. Measurements of tissue properties derived using qMRI methods can serve as imaging biomarkers, which are potentially more reproducible and informative than conventional MRI methods. Several qMRI methods are nearing transition into clinical practice and may inform diagnosis and treatment decisions, with the potential to improve patient outcomes.

CONCLUSIONS

qMRI enables specific assessment of inflammation in synovium, cartilage, bone, tendons and entheses, and can facilitate a more consistent, personalized approach to diagnosis, characterisation and monitoring of disease.

Do DWI and quantitative DCE perfusion MR have a prognostic value in high-grade serous ovarian cancer?

PURPOSE

To evaluate whether perfusion and diffusion parameters from staging MR in ovarian cancer (OC) patients may predict the presence of residual tumor at surgery and the progression-free survival (PFS) in 12 months.

MATERIALS AND METHODS

Patients who are from a single institution, candidate for OC to cytoreductive surgery and undergoing MR for staging purposes were included in this study. Inclusion criteria were: preoperative MR including diffusion-weighted imaging (DWI) and perfusion dynamic contrast-enhanced (DCE) sequence; cytoreductive surgery performed within a month from MR; and minimum follow-up of 12 months. Patients' characteristics including the presence of residual tumor at surgery (R0 or R1) and relapse within 12 months from surgery were recorded. DWI parameters included apparent diffusion coefficient (ADC) of the largest ovarian mass (O-ADC) and normalized ovarian ADC as a ratio between ovarian ADC and muscle ADC (M-ADC). DCE quantitative parameters included were descriptors of tumor vascular properties such as forward and backward transfer constants, plasma volume and volume of extracellular space. Statistical analysis was performed, and p values < 0.05 were considered significant.

RESULTS

Forty-nine patients were included. M-ADC showed a slightly significant association with the presence of residual tumor at surgery. None of the other functional parameters showed either difference between R0 and R1 patients or association with PFS in the first 12 months.

CONCLUSIONS

This preliminary study demonstrated a slightly significant association between normalized ovarian ADC and the presence of residual tumor at surgery. The other perfusion and diffusion parameters were not significant for the endpoints of this study.

Peritumoral ADC values in breast cancer: region of interest selection, associations with hyaluronan intensity, and prognostic significance

Objectives

We aimed to evaluate the differences in peritumoral apparent diffusion coefficient (ADC) values by four different ROI selection methods and to validate the optimal method. Furthermore, we aimed to evaluate if the peritumor-tumor ADC ratios are correlated with axillary lymph node positivity and hyaluronan accumulation.

Methods

Altogether, 22 breast cancer patients underwent 3.0-T breast MRI, histopathological evaluation, and hyaluronan assay. Paired t and Friedman tests were used to compare minimum, mean, and maximum values of tumoral and peritumoral ADC by four methods: (M1) band ROI, (M2) whole tumor surrounding ROI, (M3) clockwise multiple ROI, and (M4) visual assessment of ROI selection. Subsequently, peritumor/tumor ADC ratios were compared with hyaluronan levels and axillary lymph node status by the Mann-Whitney U test.

Results

No statistically significant differences were found among the four ROI selection methods regarding minimum, mean, or maximum values of tumoral and peritumoral ADC. Visual assessment ROI measurements represented the less time-consuming evaluation method for the peritumoral area, and with sufficient accuracy. Peritumor/tumor ADC ratios obtained by all methods except the clockwise ROI (M3) showed a positive correlation with hyaluronan content (M1, p = 0.004; M2, p = 0.012; M3, p = 0.20; M4, p = 0.025) and lymph node metastasis (M1, p = 0.001; M2, p = 0.007; M3, p = 0.22; M4, p = 0.015), which are established factors for unfavorable prognosis.

Conclusions

Our results suggest that the peritumor/tumor ADC ratio could be a readily applicable imaging index associated with axillary lymph node metastasis and extensive hyaluronan accumulation. It could be related to the biological aggressiveness of breast cancer and therefore might serve as an additional prognostic factor.

Key Points

• Out of four different ROI selection methods for peritumoral ADC evaluation, measurements based on visual assessment provided sufficient accuracy and were the less time-consuming method.

• The peritumor/tumor ADC ratio can provide an easily applicable supplementary imaging index for breast cancer assessment.

• A higher peritumor/tumor ADC ratio was associated with axillary lymph node metastasis and extensive hyaluronan accumulation and might serve as an additional prognostic factor.