SummaryBackgroundMagnetic resonance enterography (MRE) and ultrasound are used to image Crohn's disease, but their comparative accuracy for assessing disease extent and activity is not known with certainty. Therefore, we did a multicentre trial to address this issue.MethodsWe recruited patients from eight UK hospitals. Eligible patients were 16 years or older, with newly diagnosed Crohn's disease or with established disease and suspected relapse. Consecutive patients had MRE and ultrasound in addition to standard investigations. Discrepancy between MRE and ultrasound for the presence of small bowel disease triggered an additional investigation, if not already available. The primary outcome was difference in per-patient sensitivity for small bowel disease extent (correct identification and segmental localisation) against a construct reference standard (panel diagnosis). This trial is registered with the International Standard Randomised Controlled Trial, number ISRCTN03982913, and has been completed.Findings284 patients completed the trial (133 in the newly diagnosed group, 151 in the relapse group). Based on the reference standard, 233 (82%) patients had small bowel Crohn's disease. The sensitivity of MRE for small bowel disease extent (80% [95% CI 72–86]) and presence (97% [91–99]) were significantly greater than that of ultrasound (70% [62–78] for disease extent, 92% [84–96] for disease presence); a 10% (95% CI 1–18; p=0·027) difference for extent, and 5% (1–9; p=0·025) difference for presence. The specificity of MRE for small bowel disease extent (95% [85–98]) was significantly greater than that of ultrasound (81% [64–91]); a difference of 14% (1–27; p=0·039). The specificity for small bowel disease presence was 96% (95% CI 86–99) with MRE and 84% (65–94) with ultrasound (difference 12% [0–25]; p=0·054). There were no serious adverse events.InterpretationBoth MRE and ultrasound have high sensitivity for detecting small bowel disease presence and both are valid first-line investigations, and viable alternatives to ileocolonoscopy. However, in a national health service setting, MRE is generally the preferred radiological investigation when available because its sensitivity and specificity exceed ultrasound significantly.FundingNational Institute of Health and Research Health Technology Assessment.

The aim of this systematic review was to determine whether ultrasound (US)/US procedural simulation leads to improvement in US competence, particularly in the clinical setting. The electronic databases MEDLINE, EMBASE, CINAHL, ERIC, and OVID were searched for relevant published articles between 1950 and April 2011. Fourteen articles of an initial 371 articles met the inclusion criteria. The eligible studies differed in terms of the study population, sample size, study design, US simulator used, and measured outcomes. Most of the studies demonstrated acquisition of knowledge and skills with suggestions of correlation with simulation training and improved performance in the same simulated environment. There is little compelling evidence based on published studies at present to support the widespread adoption of simulation-based medical education to improve clinical US competence.

Summary Background Whole-body magnetic resonance imaging (WB-MRI) could be an alternative to multi-modality staging of non-small-cell lung cancer (NSCLC), but its diagnostic accuracy, effect on staging times, number of tests needed, cost, and effect on treatment decisions are unknown. We aimed to prospectively compare the diagnostic accuracy and efficiency of WB-MRI-based staging pathways with standard pathways in NSCLC. Methods The Streamline L trial was a prospective, multicentre trial done in 16 hospitals in England. Eligible patients were 18 years or older, with newly diagnosed NSCLC that was potentially radically treatable on diagnostic chest CT (defined as stage IIIb or less). Exclusion criteria were severe systemic disease, pregnancy, contraindications to MRI, or histologies other than NSCLC. Patients underwent WB-MRI, the result of which was withheld until standard staging investigations were complete and the first treatment decision made. The multidisciplinary team recorded its treatment decision based on standard investigations, then on the WB-MRI staging pathway (WB-MRI plus additional tests generated), and finally on all tests. The primary outcome was difference in per-patient sensitivity for metastases between standard and WB-MRI staging pathways against a consensus reference standard at 12 months, in the per-protocol population. Secondary outcomes were difference in per-patient specificity for metastatic disease detection between standard and WB-MRI staging pathways, differences in treatment decisions, staging efficiency (time taken, test number, and costs) and per-organ sensitivity and specificity for metastases and per-patient agreement for local T and N stage. This trial is registered with the International Standard Randomised Controlled Trial registry, number ISRCTN50436483, and is complete. Findings Between Feb 26, 2013, and Sept 5, 2016, 976 patients were screened for eligibility. 353 patients were recruited, 187 of whom completed the trial; 52 (28%) had metastasis at baseline. Pathway sensitivity was 50% (95% CI 37–63) for WB-MRI and 54% (41–67) for standard pathways, a difference of 4% (−7 to 15, p=0·73). No adverse events related to imaging were reported. Specificity did not differ between WB-MRI (93% [88–96]) and standard pathways (95% [91–98], p=0·45). Agreement with the multidisciplinary team's final treatment decision was 98% for WB-MRI and 99% for the standard pathway. Time to complete staging was shorter for WB-MRI (13 days [12–14]) than for the standard pathway (19 days [17–21]); a 6-day (4–8) difference. The number of tests required was similar WB-MRI (one [1–1]) and standard pathways (one [1–2]). Mean per-patient costs were £317 (273–361) for WBI-MRI and £620 (574–666) for standard pathways. Interpretation WB-MRI staging pathways have similar accuracy to standard pathways, and reduce the staging time and costs. Funding UK National Institute...

Objectives To determine the diagnostic accuracy and interobserver concordance of whole-body (WB)-MRI, vs. 99m Tc bone scintigraphy (BS) and 18 fluoro-ethyl-choline ( 18 F-choline) PET/CT for the primary staging of intermediate/high-risk prostate cancer. Methods An institutional review board approved prospective cohort study carried out between July 2012 and November 2015, whereby 56 men prospectively underwent 3.0-T multiparametric (mp)-WB-MRI in addition to BS (all patients) ± 18 F-choline PET/CT (33 patients). MRI comprised pre- and post-contrast modified Dixon (mDixon), T2-weighted (T2W) imaging, and diffusion-weighted imaging (DWI). Patients underwent follow-up mp-WB-MRI at 1 year to derive the reference standard. WB-MRIs were reviewed by two radiologists applying a 6-point scale and a locked sequential read (LSR) paradigm for the suspicion of nodal (N) and metastatic disease (M1a and M1b). Results The mean sensitivity/specificity of WB-MRI for N1 disease was 1.00/0.96 respectively, compared with 1.00/0.82 for 18 F-choline PET/CT. The mean sensitivity and specificity of WB-MRI, 18 F-choline PET/CT, and BS were 0.90/0.88, 0.80/0.92, and 0.60/1.00 for M1b disease. ROC-AUC did not show statistically significant improvement for each component of the LSR; mean ROC-AUC 0.92, 0.94, and 0.93 ( p < 0.05) for mDixon + DWI, + T2WI, and + contrast respectively. WB-MRI had an interobserver concordance ( κ ) of 0.79, 0.68, and 0.58 for N1, M1a, and M1b diseases respectively. Conclusions WB-MRI provides high levels of diagnostic accuracy for both nodal and metastatic bone disease, with higher levels of sensitivity than BS for metastatic disease, and similar performance to 18 F-choline PET/CT. T2 and post-contrast mDixon had no significant additive value above a protocol comprising mDixon and DWI alone. Key Points • A whole-body MRI protocol comprising unenhanced mDixon and diffusion-weighted imaging provides high levels of diagnostic accuracy for the primary staging of intermediate- and high-risk prostate cancer. • The diagnostic accuracy of whole-body MRI is much higher than that of bone scintigraphy, as currently recommended for clinical use. • Staging using WB-MRI, rather than bone scintigraphy, could result in better patient stratification and treatment delivery than is currently provided to patients worldwide. Electronic supplementary material The online version of this article (10.1007/s00330-018-5813-4) contains supplementary material, which is available to authorized users.

espite the merits of the apparent diffusion coefficient (ADC), reporting quantitative ADC values is not a routine part of clinical practice. This is partially due to lack of biologic specificity (1). Recently, our group presented the feasibility of Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumors (VERDICT) MRI as a quantitative microstructural imaging tool for prostate cancer (2). VERDICT combines a diffusion-weighted MRI acquisition with a mathematical model and assigns the diffusion-weighted MRI signal to three principal components: (a) intracellular water, (b) water in the extracellular extravascular space, and (c) water in the microvasculature. Because the fraction of each of these compartments differs between each Gleason grade (3), we hypothesized that

ObjectivesTo evaluate multiparametric-MRI (mpMRI) derived histogram textural-analysis parameters for detection of transition zone (TZ) prostatic tumour.MethodsSixty-seven consecutive men with suspected prostate cancer underwent 1.5T mpMRI prior to template-mapping-biopsy (TPM). Twenty-six men had ‘significant’ TZ tumour. Two radiologists in consensus matched TPM to the single axial slice best depicting tumour, or largest TZ diameter for those with benign histology, to define single-slice whole TZ-regions-of-interest (ROIs). Textural-parameter differences between single-slice whole TZ-ROI containing significant tumour versus benign/insignificant tumour were analysed using Mann Whitney U test. Diagnostic accuracy was assessed by receiver operating characteristic area under curve (ROC-AUC) analysis cross-validated with leave-one-out (LOO) analysis.ResultsADC kurtosis was significantly lower (p < 0.001) in TZ containing significant tumour with ROC-AUC 0.80 (LOO-AUC 0.78); the difference became non-significant following exclusion of significant tumour from single-slice whole TZ-ROI (p = 0.23). T1-entropy was significantly lower (p = 0.004) in TZ containing significant tumour with ROC-AUC 0.70 (LOO-AUC 0.66) and was unaffected by excluding significant tumour from TZ-ROI (p = 0.004). Combining these parameters yielded ROC-AUC 0.86 (LOO-AUC 0.83).ConclusionTextural features of the whole prostate TZ can discriminate significant prostatic cancer through reduced kurtosis of the ADC-histogram where significant tumour is included in TZ-ROI and reduced T1 entropy independent of tumour inclusion.Key Points• MR textural features of prostate transition zone may discriminate significant prostatic cancer. • Transition zone (TZ) containing significant tumour demonstrates a less peaked ADC histogram. • TZ containing significant tumour reveals higher post-contrast T1-weighted homogeneity. • The utility of MR texture analysis in prostate cancer merits further investigation.

Objective The purpose of this study was: To test whether machine learning classifiers for transition zone (TZ) and peripheral zone (PZ) can correctly classify prostate tumors into those with/without a Gleason 4 component, and to compare the performance of the best performing classifiers against the opinion of three board-certified radiologists. Methods A retrospective analysis of prospectively acquired data was performed at a single center between 2012 and 2015. Inclusion criteria were (i) 3-T mp-MRI compliant with international guidelines, (ii) Likert ≥ 3/5 lesion, (iii) transperineal template ± targeted index lesion biopsy confirming cancer ≥ Gleason 3 + 3. Index lesions from 164 men were analyzed (119 PZ, 45 TZ). Quantitative MRI and clinical features were used and zone-specific machine learning classifiers were constructed. Models were validated using a fivefold cross-validation and a temporally separated patient cohort. Classifier performance was compared against the opinion of three board-certified radiologists. Results The best PZ classifier trained with prostate-specific antigen density, apparent diffusion coefficient (ADC), and maximum enhancement (ME) on DCE-MRI obtained a ROC area under the curve (AUC) of 0.83 following fivefold cross-validation. Diagnostic sensitivity at 50% threshold of specificity was higher for the best PZ model (0.93) when compared with the mean sensitivity of the three radiologists (0.72). The best TZ model used ADC and ME to obtain an AUC of 0.75 following fivefold cross-validation. This achieved higher diagnostic sensitivity at 50% threshold of specificity (0.88) than the mean sensitivity of the three radiologists (0.82). Conclusions Machine learning classifiers predict Gleason pattern 4 in prostate tumors better than radiologists. Key Points • Predictive models developed from quantitative multiparametric magnetic resonance imaging regarding the characterization of prostate cancer grade should be zone-specific. • Classifiers trained differently for peripheral and transition zone can predict a Gleason 4 component with a higher performance than the subjective opinion of experienced radiologists. • Classifiers would be particularly useful in the context of active surveillance, whereby decisions regarding whether to biopsy are necessitated.

ObjectivesWe aimed to develop logistic regression (LR) models for classifying prostate cancer within the transition zone on multi-parametric magnetic resonance imaging (mp-MRI).MethodsOne hundred and fifty-five patients (training cohort, 70 patients; temporal validation cohort, 85 patients) underwent mp-MRI and transperineal-template-prostate-mapping (TPM) biopsy. Positive cores were classified by cancer definitions: (1) any-cancer; (2) definition-1 [≥Gleason 4 + 3 or ≥ 6 mm cancer core length (CCL)] [high risk significant]; and (3) definition-2 (≥Gleason 3 + 4 or ≥ 4 mm CCL) cancer [intermediate–high risk significant]. For each, logistic-regression mp-MRI models were derived from the training cohort and validated internally and with the temporal cohort. Sensitivity/specificity and the area under the receiver operating characteristic (ROC-AUC) curve were calculated. LR model performance was compared to radiologists’ performance.ResultsTwenty-eight of 70 patients from the training cohort, and 25/85 patients from the temporal validation cohort had significant cancer on TPM. The ROC-AUC of the LR model for classification of cancer was 0.73/0.67 at internal/temporal validation. The radiologist A/B ROC-AUC was 0.65/0.74 (temporal cohort). For patients scored by radiologists as Prostate Imaging Reporting and Data System (Pi-RADS) score 3, sensitivity/specificity of radiologist A ‘best guess’ and LR model was 0.14/0.54 and 0.71/0.61, respectively; and radiologist B ‘best guess’ and LR model was 0.40/0.34 and 0.50/0.76, respectively.ConclusionsLR models can improve classification of Pi-RADS score 3 lesions similar to experienced radiologists.Key Points• MRI helps find prostate cancer in the anterior of the gland• Logistic regression models based on mp-MRI can classify prostate cancer• Computers can help confirm cancer in areas doctors are uncertain about