PROMISE Registry: A prostate cancer registry of outcomes and germline mutations for improved survival and treatment effectiveness.

BACKGROUND: Recently approved treatments and updates to genetic testing recommendations for prostate cancer have created a need for correlated analyses of patient outcomes data via germline genetic mutation status. Genetic registries address these gaps by identifying candidates for recently approved targeted treatments, expanding clinical trial data examining specific gene mutations, and understanding effects of targeted treatments in the real-world setting. METHODS: The PROMISE Registry is a 20-year (5-year recruitment, 15-year follow-up), US-wide, prospective genetic registry for prostate cancer patients. Five thousand patients will be screened through an online at-home germline testing to identify and enroll 500 patients with germline mutations, including: pathogenic or likely pathogenic variants and variants of uncertain significance in genes of interest. Patients will be followed for 15 years and clinical data with real time patient reported outcomes will be collected. Eligible patients will enter long-term follow-up (6-month PRO surveys and medical record retrieval). As a virtual study with patient self-enrollment, the PROMISE Registry may fill gaps in genetics services in underserved areas and for patients within sufficient insurance coverage. RESULTS: The PROMISE Registry opened in May 2021. 2114 patients have enrolled to date across 48 US states and 23 recruiting sites. 202 patients have met criteria for long-term follow-up. PROMISE is on target with the study's goal of 5000 patients screened and 500 patients eligible for long-term follow-up by 2026. CONCLUSIONS: The PROMISE Registry is a novel, prospective, germline registry that will collect long-term patient outcomes data to address current gaps in understanding resulting from recently FDA-approved treatments and updates to genetic testing recommendations for prostate cancer. Through inclusion of a broad nationwide sample, including underserved patients and those unaffiliated with major academic centers, the PROMISE Registry aims to provide access to germline genetic testing and to collect data to understand disease characteristics and treatment responses across the disease spectrum for prostate cancer with rare germline genetic variants.

Evaluation of simultaneous multi-slice acquisition with advanced processing for free-breathing diffusion-weighted imaging in patients with liver metastasis.

OBJECTIVES: Diffusion-weighted imaging (DWI) with simultaneous multi-slice (SMS) acquisition and advanced processing can accelerate acquisition time and improve MR image quality. This study evaluated the image quality and apparent diffusion coefficient (ADC) measurements of free-breathing DWI acquired from patients with liver metastases using a prototype SMS-DWI acquisition (with/without an advanced processing option) and conventional DWI. METHODS: Four DWI schemes were compared in a pilot 5-patient cohort; three DWI schemes were further assessed in a 24-patient cohort. Two readers scored image quality of all b-value images and ADC maps across the three methods. ADC measurements were performed, for all three methods, in left and right liver parenchyma, spleen, and liver metastases. The Friedman non-parametric test (post-hoc Wilcoxon test with Bonferroni correction) was used to compare image quality scoring; t-test was used for ADC comparisons. RESULTS: SMS-DWI was faster (by 24%) than conventional DWI. Both readers scored the SMS-DWI with advanced processing as having the best image quality for highest b-value images (b750) and ADC maps; Cohen's kappa inter-reader agreement was 0.6 for b750 image and 0.56 for ADC maps. The prototype SMS-DWI sequence with advanced processing allowed a better visualization of the left lobe of the liver. ADC measured in liver parenchyma, spleen, and liver metastases using the SMS-DWI with advanced processing option showed lower values than those derived from the SMS-DWI method alone (t-test, p < 0.0001; p < 0.0001; p = 0.002). CONCLUSIONS: Free-breathing SMS-DWI with advanced processing was faster and demonstrated better image quality versus a conventional DWI protocol in liver patients. CLINICAL RELEVANCE STATEMENT: Free-breathing simultaneous multi-slice- diffusion-weighted imaging (DWI) with advanced processing was faster and demonstrated better image quality versus a conventional DWI protocol in liver patients. KEY POINTS: • Diffusion-weighted imaging (DWI) with simultaneous multi-slice (SMS) can accelerate acquisition time and improve image quality. • Apparent diffusion coefficients (ADC) measured in liver parenchyma, spleen, and liver metastases using the simultaneous multi-slice DWI with advanced processing were significantly lower than those derived from the simultaneous multi-slice DWI method alone. • Simultaneous multi-slice DWI sequence with inline advanced processing was faster and demonstrated better image quality in liver patients.

A CT-based radiomics classification model for the prediction of histological type and tumour grade in retroperitoneal sarcoma (RADSARC-R): a retrospective multicohort analysis.

BACKGROUND: Retroperitoneal sarcomas are tumours with a poor prognosis. Upfront characterisation of the tumour is difficult, and under-grading is common. Radiomics has the potential to non-invasively characterise the so-called radiological phenotype of tumours. We aimed to develop and independently validate a CT-based radiomics classification model for the prediction of histological type and grade in retroperitoneal leiomyosarcoma and liposarcoma. METHODS: A retrospective discovery cohort was collated at our centre (Royal Marsden Hospital, London, UK) and an independent validation cohort comprising patients recruited in the phase 3 STRASS study of neoadjuvant radiotherapy in retroperitoneal sarcoma. Patients aged older than 18 years with confirmed primary leiomyosarcoma or liposarcoma proceeding to surgical resection with available contrast-enhanced CT scans were included. Using the discovery dataset, a CT-based radiomics workflow was developed, including manual delineation, sub-segmentation, feature extraction, and predictive model building. Separate probabilistic classifiers for the prediction of histological type and low versus intermediate or high grade tumour types were built and tested. Independent validation was then performed. The primary objective of the study was to develop radiomic classification models for the prediction of retroperitoneal leiomyosarcoma and liposarcoma type and histological grade. FINDINGS: 170 patients recruited between Oct 30, 2016, and Dec 23, 2020, were eligible in the discovery cohort and 89 patients recruited between Jan 18, 2012, and April 10, 2017, were eligible in the validation cohort. In the discovery cohort, the median age was 63 years (range 27-89), with 83 (49%) female and 87 (51%) male patients. In the validation cohort, median age was 59 years (range 33-77), with 46 (52%) female and 43 (48%) male patients. The highest performing model for the prediction of histological type had an area under the receiver operator curve (AUROC) of 0·928 on validation, based on a feature set of radiomics and approximate radiomic volume fraction. The highest performing model for the prediction of histological grade had an AUROC of 0·882 on validation, based on a radiomics feature set. INTERPRETATION: Our validated radiomics model can predict the histological type and grade of retroperitoneal sarcomas with excellent performance. This could have important implications for improving diagnosis and risk stratification in retroperitoneal sarcomas. FUNDING: Wellcome Trust, European Organisation for Research and Treatment of Cancer-Soft Tissue and Bone Sarcoma Group, the National Institutes for Health, and the National Institute for Health and Care Research Biomedical Research Centre at The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research.

Radiomic applications in upper gastrointestinal cancer surgery.

INTRODUCTION: Cross-sectional imaging plays an integral role in the management of upper gastrointestinal (UGI) cancer, from initial diagnosis and staging to determining appropriate treatment strategies. Subjective imaging interpretation has known limitations. The field of radiomics has evolved to extract quantitative data from medical imaging and relate these to biological processes. The key concept behind radiomics is that the high-throughput analysis of quantitative imaging features can provide predictive or prognostic information, with the goal of providing individualised care. OBJECTIVE: Radiomic studies have shown promising utility in upper gastrointestinal oncology, highlighting a potential role in determining stage of disease and degree of tumour differentiation and predicting recurrence-free survival. This narrative review aims to provide an insight into the concepts underpinning radiomics, as well as its potential applications for guiding treatment and surgical decision-making in upper gastrointestinal malignancy. CONCLUSION: Outcomes from studies to date have been promising; however, further standardisation and collaboration are required. Large prospective studies with external validation and evaluation of radiomic integration into clinical pathways are needed. Future research should now focus on translating the promising utility of radiomics into meaningful patient outcomes.

Radiomics in Oncology: A Practical Guide.

Radiomics refers to the extraction of mineable data from medical imaging and has been applied within oncology to improve diagnosis, prognostication, and clinical decision support, with the goal of delivering precision medicine. The authors provide a practical approach for successfully implementing a radiomic workflow from planning and conceptualization through manuscript writing. Applications in oncology typically are either classification tasks that involve computing the probability of a sample belonging to a category, such as benign versus malignant, or prediction of clinical events with a time-to-event analysis, such as overall survival. The radiomic workflow is multidisciplinary, involving radiologists and data and imaging scientists, and follows a stepwise process involving tumor segmentation, image preprocessing, feature extraction, model development, and validation. Images are curated and processed before segmentation, which can be performed on tumors, tumor subregions, or peritumoral zones. Extracted features typically describe the distribution of signal intensities and spatial relationship of pixels within a region of interest. To improve model performance and reduce overfitting, redundant and nonreproducible features are removed. Validation is essential to estimate model performance in new data and can be performed iteratively on samples of the dataset (cross-validation) or on a separate hold-out dataset by using internal or external data. A variety of noncommercial and commercial radiomic software applications can be used. Guidelines and artificial intelligence checklists are useful when planning and writing up radiomic studies. Although interest in the field continues to grow, radiologists should be familiar with potential pitfalls to ensure that meaningful conclusions can be drawn. Online supplemental material is available for this article. Published under a CC BY 4.0 license.

The Effects of Ecological Traits on the Rate of Molecular Evolution in Ray-Finned Fishes: A Multivariable Approach.

Myriad environmental and biological traits have been investigated for their roles in influencing the rate of molecular evolution across various taxonomic groups. However, most studies have focused on a single trait, while controlling for additional factors in an informal way, generally by excluding taxa. This study utilized a dataset of cytochrome c oxidase subunit I (COI) barcode sequences from over 7000 ray-finned fish species to test the effects of 27 traits on molecular evolutionary rates. Environmental traits such as temperature were considered, as were traits associated with effective population size including body size and age at maturity. It was hypothesized that these traits would demonstrate significant correlations with substitution rate in a multivariable analysis due to their associations with mutation and fixation rates, respectively. A bioinformatics pipeline was developed to assemble and analyze sequence data retrieved from the Barcode of Life Data System (BOLD) and trait data obtained from FishBase. For use in phylogenetic regression analyses, a maximum likelihood tree was constructed from the COI sequence data using a multi-gene backbone constraint tree covering 71% of the species. A variable selection method that included both single- and multivariable analyses was used to identify traits that contribute to rate heterogeneity estimated from different codon positions. Our analyses revealed that molecular rates associated most significantly with latitude, body size, and habitat type. Overall, this study presents a novel and systematic approach for integrative data assembly and variable selection methodology in a phylogenetic framework.

A clinical-radiomic model for improved prognostication of surgical candidates with colorectal liver metastases.

BACKGROUND AND OBJECTIVES: Colorectal cancer with liver metastases is potentially curable with surgical resection however clinical prognostic factors can insufficiently stratify patients. This study aims to assess whether radiomic features are prognostic and can inform clinical decision making. METHODS: This single-site retrospective study included 102 patients who underwent colorectal liver metastases resection with preoperative computed tomography (CT), magnetic resonance imaging (MRI) with gadoxetic acid (EOB) and clinical covariates. A lasso-regularized multivariate Cox proportional hazards model was applied to 114 features (10 clinical, 104 radiomic) to determine association with disease-free survival (DFS). A prognostic index was derived using the significant Cox regression coefficients and their corresponding input features and a threshold was determined to classify patients into high- and low-risk groups, and DFS compared using log-rank tests. RESULTS: Four covariates were significantly associated with DFS; bilobar disease (hazard ratio [HR]= 1.56; P = .0043), complete pathological response (HR= 0.67; P = .025), minimum pixel value (HR= 1.66; P = .00016), and small area emphasis (HR= 0.62; P = .0013) from the EOB-MRI data. Radiomic CT features were not prognostic. The prognostic index strongly stratified high- and low-risk prognostic groups (HR = 0.31; P = .00068). CONCLUSION: Radiomic MRI features provided meaningful prognostic information above clinical covariates alone. This merits further validation for potential clinical implementation to inform management.

Probing structure of normal and malignant prostate tissue before and after radiation therapy with luminal water fraction and diffusion-weighted MRI.

BACKGROUND: Interpretation of diffusion in conjunction with T2 -weighted MRI is essential for assessing prostate cancer; however, the combination of apparent diffusion coefficient (ADC) with quantitative T2 mapping remains unexplored. PURPOSE: To document the T2 components and ADC of untreated and irradiated nonmalignant prostate tissue as a measure of their glandular luminal and cellular compartments and to compare values with those of tumor. STUDY TYPE: Prospective. POPULATION: Twenty-four men with prostate cancer (14 untreated; 10 with biochemical recurrence following radiation therapy). FIELD STRENGTH/SEQUENCES: Endorectal 3 T MRI including a 32-echo gradient echo and spin echo (GRASE) and an 8 b-value diffusion-weighted sequence. ASSESSMENT: Regions of interest were drawn on ADC maps and T2 -weighted images around focal lesions in areas of biopsy-positive prostate cancer and in nonmalignant areas of untreated and irradiated peripheral zone (PZ), and untreated transitional zone (TZ). Multiecho T2 data were fitted with mono-/biexponential decay and nonnegative least squares functions. The luminal water fraction (LWF) was derived. STATISTICAL TESTS: The preference between mono- and biexponential decay was assessed using the Bayesian information criterion. Differences in fitted parameters between tissue types were compared (paired t-test within groups, Kruskal-Wallis and Wilcoxon rank-sum test between groups) and correlations between ADC and T2 components assessed (Spearman rank correlation test). RESULTS: LWF in tumor (0.09) was significantly lower than in PZ or TZ (0.27 and 0.18, P < 0.01, respectively), but tumor values were comparable to nonmalignant irradiated prostate (0.08). The short T2 relaxation rate was lower in tumor than in nonmalignant untreated or irradiated tissue (significant compared with TZ, P = 0.01). There was a strong correlation between LWF and ADC in normal untreated tissue (r = 0.88, P < 0.001). This relationship was absent in nonmalignant irradiated prostrate (r = -0.35, P = 0.42) and in tumor (r = -0.04, P = 0.88). DATA CONCLUSION: T2 components in conjunction with ADC can be used to characterize untreated and irradiated nonmalignant prostate and tumor. LWF is most useful at discriminating tumor in the untreated prostate. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:619-627.

Is molecular evolution faster in the tropics?

The evolutionary speed hypothesis (ESH) suggests that molecular evolutionary rates are higher among species inhabiting warmer environments. Previously, the ESH has been investigated using small numbers of latitudinally-separated sister lineages; in animals, these studies typically focused on subsets of Chordata and yielded mixed support for the ESH. This study analyzed public DNA barcode sequences from the cytochrome c oxidase subunit I (COI) gene for six of the largest animal phyla (Arthropoda, Chordata, Mollusca, Annelida, Echinodermata, and Cnidaria) and paired latitudinally-separated taxa together informatically. Of 8037 lineage pairs, just over half (51.6%) displayed a higher molecular rate in the lineage inhabiting latitudes closer to the equator, while the remainder (48.4%) displayed a higher rate in the higher-latitude lineage. To date, this study represents the most comprehensive analysis of latitude-related molecular rate differences across animals. While a statistically-significant pattern was detected from our large sample size, our findings suggest that the EHS may not serve as a strong universal mechanism underlying the latitudinal diversity gradient and that COI molecular clocks may generally be applied across latitudes. This study also highlights the merits of using automation to analyze large DNA barcode datasets.

DNA barcode data reveal biogeographic trends in Arctic non-biting midges.

Chironomid flies (non-biting midges) are among the most abundant and diverse animals in Arctic regions, but detailed analyses of species distributions and biogeographical patterns are hampered by challenging taxonomy and reliance on morphology for species-level identification. Here we take advantage of available DNA barcode data of Arctic Chironomidae in BOLD to analyse similarities in species distributions across a northern Nearctic - West Palearctic gradient. Using more than 260 000 barcodes representing 4666 BINs (Barcode Index Numbers) and 826 named species (some with interim names) from a combination of public and novel data, we show that the Greenland chironomid fauna shows affinities to both the Nearctic and the West Palearctic regions. While raw taxon counts indicate a strong Greenland - North American affinity, comparisons using Chao's dissimilarity metric support a slightly higher similarity between Greenland and West Palearctic chironomid communities. Results were relatively consistent across different definitions of species taxonomic units, including morphologically determined species, BINs, and superBINs based on a ∼4.5% threshold. While most taxa found in Greenland are shared with at least one other region, reflecting circum-Arctic dispersal, our results also reveal that Greenland harbours a small endemic biodiversity. Our exploratory study showcases how DNA barcoding efforts using standardized gene regions contribute to an understanding of broad-scale patterns in biogeography by enabling joint analysis of public DNA sequence data derived from diverse prior studies.

Characterisation of fibrosis in chemically-induced rat mammary carcinomas using multi-modal endogenous contrast MRI on a 1.5T clinical platform.

OBJECTIVES: To determine the ability of multi-parametric, endogenous contrast MRI to detect and quantify fibrosis in a chemically-induced rat model of mammary carcinoma. METHODS: Female Sprague-Dawley rats (n=18) were administered with N-methyl-N-nitrosourea; resulting mammary carcinomas underwent nine-b-value diffusion-weighted (DWI), ultrashort-echo (UTE) and magnetisation transfer (MT) magnetic resonance imaging (MRI) on a clinical 1.5T platform, and associated quantitative MR parameters were calculated. Excised tumours were histologically assessed for degree of necrosis, collagen, hypoxia and microvessel density. Significance level adjusted for multiple comparisons was p=0.0125. RESULTS: Significant correlations were found between MT parameters and degree of picrosirius red staining (r > 0.85, p < 0.0002 for ka and δ, r < -0.75, p < 0.001 for T1 and T1s, Pearson), indicating that MT is sensitive to collagen content in mammary carcinoma. Picrosirius red also correlated with the DWI parameter fD* (r=0.801, p=0.0004) and conventional gradient-echo T2* (r=-0.660, p=0.0055). Percentage necrosis correlated moderately with ultrashort/conventional-echo signal ratio (r=0.620, p=0.0105). Pimonidazole adduct (hypoxia) and CD31 (microvessel density) staining did not correlate with any MR parameter assessed. CONCLUSIONS: Magnetisation transfer MRI successfully detects collagen content in mammary carcinoma, supporting inclusion of MT imaging to identify fibrosis, a prognostic marker, in clinical breast MRI examinations. KEY POINTS: • Magnetisation transfer imaging is sensitive to collagen content in mammary carcinoma. • Magnetisation transfer imaging to detect fibrosis in mammary carcinoma fibrosis is feasible. • IVIM diffusion does not correlate with microvessel density in preclinical mammary carcinoma.

Extracranial Soft-Tissue Tumors: Repeatability of Apparent Diffusion Coefficient Estimates from Diffusion-weighted MR Imaging.

Purpose To assess the repeatability of apparent diffusion coefficient (ADC) estimates in extracranial soft-tissue diffusion-weighted magnetic resonance imaging across a wide range of imaging protocols and patient populations. Materials and Methods Nine prospective patient studies and one prospective volunteer study, performed between 2006 and 2016 with research ethics committee approval and written informed consent from each subject, were included in this single-institution study. A total of 141 tumors and healthy organs were imaged twice (interval between repeated examinations, 45 minutes to 10 days, depending the on study) to assess the repeatability of median and mean ADC estimates. The Levene test was used to determine whether ADC repeatability differed between studies. The Pearson linear correlation coefficient was used to assess correlation between coefficient of variation (CoV) and the year the study started, study size, and volumes of tumors and healthy organs. The repeatability of ADC estimates from small, medium, and large tumors and healthy organs was assessed irrespective of study, and the Levene test was used to determine whether ADC repeatability differed between these groups. Results CoV aggregated across all studies was 4.1% (range for each study, 1.7%-6.5%). No correlation was observed between CoV and the year the study started or study size. CoV was weakly correlated with volume (r = -0.5, P = .1). Repeatability was significantly different between small, medium, and large tumors (P < .05), with the lowest CoV (2.6%) for large tumors. There was a significant difference in repeatability between studies-a difference that did not persist after the study with the largest tumors was excluded. Conclusion ADC is a robust imaging metric with excellent repeatability in extracranial soft tissues across a wide range of tumor sites, sizes, patient populations, and imaging protocol variations. Online supplemental material is available for this article.

Separation of type and grade in cervical tumours using non-mono-exponential models of diffusion-weighted MRI.

OBJECTIVES: Assessment of empirical diffusion-weighted MRI (DW-MRI) models in cervical tumours to investigate whether fitted parameters distinguish between types and grades of tumours. METHODS: Forty-two patients (24 squamous cell carcinomas, 14 well/moderately differentiated, 10 poorly differentiated; 15 adenocarcinomas, 13 well/moderately differentiated, two poorly differentiated; three rare types) were imaged at 3 T using nine b-values (0 to 800 s mm-2). Mono-exponential, stretched exponential, kurtosis, statistical, and bi-exponential models were fitted. Model preference was assessed using Bayesian Information Criterion analysis. Differences in fitted parameters between tumour types/grades and correlation between fitted parameters were assessed using two-way analysis of variance and Pearson's linear correlation coefficient, respectively. RESULTS: Non-mono-exponential models were preferred by 83 % of tumours with bi-exponential and stretched exponential models preferred by the largest numbers of tumours. Apparent diffusion coefficient (ADC) and diffusion coefficients from non-mono-exponential models were significantly lower in poorly differentiated tumours than well/moderately differentiated tumours. α (stretched exponential), K (kurtosis), f and D* (bi-exponential) were significantly different between tumour types. Strong correlation was observed between ADC and diffusion coefficients from other models. CONCLUSIONS: Non-mono-exponential models were preferred to the mono-exponential model in DW-MRI data from cervical tumours. Parameters of non-mono-exponential models showed significant differences between types and grades of tumours. KEY POINTS: • Non-mono-exponential DW-MRI models are preferred in the majority of cervical tumours. • Poorly differentiated cervical tumours exhibit lower diffusion coefficients than well/moderately differentiated tumours. • Non-mono-exponential model parameters α, K, f, and D* differ between tumour types. • Micro-structural features are likely to affect parameters in non-mono-exponential models differently.

Repeatability of derived parameters from histograms following non-Gaussian diffusion modelling of diffusion-weighted imaging in a paediatric oncological cohort.

OBJECTIVES: To examine repeatability of parameters derived from non-Gaussian diffusion models in data acquired in children with solid tumours. METHODS: Paediatric patients (<16 years, n = 17) were scanned twice, 24 h apart, using DWI (6 b-values, 0-1000 mm-2 s) at 1.5 T in a prospective study. Tumour ROIs were drawn (3 slices) and all data fitted using IVIM, stretched exponential, and kurtosis models; percentage coefficients of variation (CV) calculated for each parameter at all ROI histogram centiles, including the medians. RESULTS: The values for ADC, D, DDCα, α, and DDCK gave CV < 10 % down to the 5th centile, with sharp CV increases below 5th and above 95th centile. K, f, and D* showed increased CV (>30 %) over the histogram. ADC, D, DDCα, and DDCK were strongly correlated (ρ > 0.9), DDCα and α were not correlated (ρ = 0.083). CONCLUSION: Perfusion- and kurtosis-related parameters displayed larger, more variable CV across the histogram, indicating observed clinical changes outside of D/DDC in these models should be interpreted with caution. Centiles below 5th for all parameters show high CV and are unreliable as diffusion metrics. The stretched exponential model behaved well for both DDCα and α, making it a strong candidate for modelling multiple-b-value diffusion imaging data. KEY POINTS: • ADC has good repeatability as low 5th centile of the histogram distribution. • High CV was observed for all parameters at extremes of histogram. • Parameters from the stretched exponential model showed low coefficients of variation. • The median ADC, D, DDC α , and DDC K are highly correlated and repeatable. • Perfusion/kurtosis parameters showed high CV variations across their histogram distributions.

Feasibility and applicability of diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging in routine assessments of children with high-grade gliomas.

Diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) have been used as imaging biomarkers in adults with high-grade gliomas (HGGs). We incorporated free-breathing DW-MRI and DCE-MRI, at a single time point, in the routine follow-up of five children (median age 9 years, range 8-15) with histologically confirmed HGG within a prospective imaging study. It was feasible to incorporate DW-MRI and DCE-MRI in routine assessments of children with HGG. DW and DCE parameters were repeatable in paediatric HGG. Higher median ADC100-1000 significantly correlated with longer survival in our sample.

Lung volume reproducibility under ABC control and self-sustained breath-holding.

An Active Breathing Coordinator (ABC) can be employed to induce breath-holds during CT imaging and radiotherapy of lung, breast and liver cancer, and recently during lung cancer MRI. The apparatus measures and controls respiratory volume, hence subject lung volume reproducibility is its principal measure of effectiveness. To assess ABC control quality, the intra-session reproducibility of ABC-induced lung volumes was evaluated and compared with that reached by applying the clinical standard of operator-guided self-sustained breath-holds on healthy volunteers during MRI. Inter-session reproducibility was investigated by repeating ABC-controlled breath-holds on a second visit. Additionally, lung volume agreement with ABC devices used with different imaging modalities in the same institution (MR, CT), or for a breast trial treatment, was assessed. Lung volumes were derived from three-dimensional (3D) T1-weighted MRI datasets by three observers employing semiautomatic lung delineation on a radiotherapy treatment planning system. Inter-observer variability was less than 6% of the delineated lung volumes. Lung volume agreement between the different conditions over all subjects was investigated using descriptive statistics. The ABC equipment dedicated for MR application exhibited good intra-session and inter-session lung volume reproducibility (1.8% and 3% lung volume variability on average, respectively). MR-assessed lung volumes were similar using different ABC equipment dedicated to MR, CT, or breast radiotherapy. Overall, lung volumes controlled by the same or different ABC devices agreed better than with self-controlled breath-holds, as suggested by the average ABC variation of 1.8% of the measured lung volumes (99 mL), compared to the 4.1% (226 mL) variability observed on average with self-sustained breath-holding.

Distortion correction of echo-planar diffusion-weighted images of uterine cervix.

PURPOSE: To investigate the clinical utility of the reverse gradient algorithm in correcting distortions in diffusion-weighted images of the cervix and for increasing diagnostic performance. MATERIALS AND METHODS: Forty-one patients ages 25-72 years (mean 40 ± 11 years) with suspected or early stage cervical cancer were imaged at 3T using an endovaginal coil. T2 -weighted (W) and diffusion-weighted images with right and left phase-encode gradient directions were obtained coronal to the cervix (b = 0, 100, 300, 500, 800 s mm(-2) ). Differences in angle of the endocervical canal to the x-axis between T2 W and right-gradient, left-gradient, and corrected images were measured. Uncorrected and corrected images were assessed for diagnostic performance when viewed together with T2 W images by two independent observers against subsequent histology. RESULTS: The angles of the endocervical canal relative to the x-axis were significantly different between the T2 W images and the right-gradient images (P = 0.007), approached significance for left-gradient images (P = 0.055), and were not significantly different after correction (P = 0.95). Corrected images enabled a definitive diagnosis in 34% (n = 14) of patients classified as equivocal on uncorrected images. Tumor volume in this subset was 0.18 ± 0.44 cm(3) (mean ± SD; sensitivity of detection 100% [8/8], specificity 50% [3/6] for an experienced observer). Correction did not improve diagnostic performance for the less-experienced observer. CONCLUSION: Distortion-corrected diffusion-weighted images improved correspondence with T2 W images and diagnostic performance in a third of cases.

Assessment of renal function using intravoxel incoherent motion diffusion-weighted imaging and dynamic contrast-enhanced MRI.

PURPOSE: To assess the correlation between each of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) metrics in renal parenchyma with renal function, in a cohort of patients with chronic liver disease. MATERIALS AND METHODS: Thirty patients with liver disease underwent abdominal MRI at 1.5T, including a coronal respiratory-triggered IVIM-DWI sequence and a coronal 3D FLASH DCE-MRI acquisition. Diffusion signals in the renal cortex and medulla were fitted to the IVIM model to estimate the diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (PF). The apparent diffusion coefficient (ADC) was calculated using all b-values. The glomerular filtration rate (GFR), cortical and medullary renal plasma flow (RPF), mean transit times (MTT) of vascular and tubular compartments and the whole kidney, were calculated from DCE-MRI data by fitting to a three-compartment model. The estimated GFR (eGFR) was calculated from serum creatinine measured 30 ± 27 days of MRI. RESULTS: ADC, PF, and RPF were significantly higher in renal cortex vs. medulla (P < 10(-5) ). DCE-MRI GFR significantly correlated with, but underestimated, eGFR (Spearman's r/P = 0.49/0.01). IVIM-DWI parameters were not significantly correlated with eGFR. DCE-MRI GFR correlated weakly with D (cortex, r/P = 0.3/0.03; medulla r/P = 0.27/0.05) and ADC (cortex r/P = 0.28/0.04; medulla r/P = 0.34/0.01). Weak correlations were observed for pooled cortical and medullar RPF with PF (r/P = 0.32/10(-3) ) and with ADC (r/P = 0.29/0.0025). Significant negative correlations were observed for vascular MTT with cortical D* (r/P = -0.38/0.004) and D*×PF (r/P = -0.34/0.01). CONCLUSION: The weak correlations between renal IVIM and DCE-MRI perfusion parameters imply that these functional measures could be complementary. J. Magn. Reson. Imaging 2016;44:317-326.

Diffusion-weighted imaging outside the brain: Consensus statement from an ISMRM-sponsored workshop.

The significant advances in magnetic resonance imaging (MRI) hardware and software, sequence design, and postprocessing methods have made diffusion-weighted imaging (DWI) an important part of body MRI protocols and have fueled extensive research on quantitative diffusion outside the brain, particularly in the oncologic setting. In this review, we summarize the most up-to-date information on DWI acquisition and clinical applications outside the brain, as discussed in an ISMRM-sponsored symposium held in April 2015. We first introduce recent advances in acquisition, processing, and quality control; then review scientific evidence in major organ systems; and finally describe future directions. J. Magn. Reson. Imaging 2016;44:521-540.

Assessment of repeatability and treatment response in early phase clinical trials using DCE-MRI: comparison of parametric analysis using MR- and CT-derived arterial input functions.

OBJECTIVES: Pharmacokinetic (PK) modelling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data requires a reliable measure of the arterial input function (AIF) to robustly characterise tumour vascular properties. This study compared repeatability and treatment-response effects of DCE-MRI-derived PK parameters using a population-averaged AIF and three patient-specific AIFs derived from pre-bolus MRI, DCE-MRI and dynamic contrast computed tomography (DC-CT) data. METHODS: The four approaches were compared in 13 patients with abdominal metastases. Baseline repeatability [Bland-Altman statistics; coefficient of variation (CoV)], cohort percentage change and p value (paired t test) and number of patients with significant DCE-MRI parameter change post-treatment (limits of agreement) were assessed. RESULTS: Individual AIFs were obtained for all 13 patients with pre-bolus MRI and DC-CT-derived AIFs, but only 10/13 patients had AIFs measurable from DCE-MRI data. The best CoV (7.5 %) of the transfer coefficient between blood plasma and extravascular extracellular space (K (trans)) was obtained using a population-averaged AIF. All four AIF methods detected significant treatment changes: the most significant was the DC-CT-derived AIF. The population-based AIF was similar to or better than the pre-bolus and DCE-MRI-derived AIFs. CONCLUSIONS: A population-based AIF is the recommended approach for measuring cohort and individual effects since it has the best repeatability and none of the PK parameters derived using measured AIFs demonstrated an improvement in treatment sensitivity. KEY POINTS: • Pharmacokinetic modelling of DCE-MRI data requires a reliable measure of AIF. • Individual MRI-DCE-derived AIFs cannot reliably be extracted from patients. • All four AIF methods detected significant K (trans) changes after treatment. • A population-based AIF can be recommended for measuring cohort treatment responses in trials.