External validation of a risk model predicting failure of salvage focal ablation for prostate cancer.

OBJECTIVES: To externally validate a published model predicting failure within 2 years after salvage focal ablation in men with localised radiorecurrent prostate cancer using a prospective, UK multicentre dataset. PATIENTS AND METHODS: Patients with biopsy-confirmed ≤T3bN0M0 cancer after previous external beam radiotherapy or brachytherapy were included from the FOcal RECurrent Assessment and Salvage Treatment (FORECAST) trial (NCT01883128; 2014-2018; six centres), and from the high-intensity focussed ultrasound (HIFU) Evaluation and Assessment of Treatment (HEAT) and International Cryotherapy Evaluation (ICE) UK-based registries (2006-2022; nine centres). Eligible patients underwent either salvage focal HIFU or cryotherapy, with the choice based predominantly on anatomical factors. Per the original multivariable Cox regression model, the predicted outcome was a composite failure outcome. Model performance was assessed at 2 years post-salvage with discrimination (concordance index [C-index]), calibration (calibration curve and slope), and decision curve analysis. For the latter, two clinically-reasonable risk threshold ranges of 0.14-0.52 and 0.26-0.36 were considered, corresponding to previously published pooled 2-year recurrence-free survival rates for salvage local treatments. RESULTS: A total of 168 patients were included, of whom 84/168 (50%) experienced the primary outcome in all follow-ups, and 72/168 (43%) within 2 years. The C-index was 0.65 (95% confidence interval 0.58-0.71). On graphical inspection, there was close agreement between predicted and observed failure. The calibration slope was 1.01. In decision curve analysis, there was incremental net benefit vs a 'treat all' strategy at risk thresholds of ≥0.23. The net benefit was therefore higher across the majority of the 0.14-0.52 risk threshold range, and all of the 0.26-0.36 range. CONCLUSION: In external validation using prospective, multicentre data, this model demonstrated modest discrimination but good calibration and clinical utility for predicting failure of salvage focal ablation within 2 years. This model could be reasonably used to improve selection of appropriate treatment candidates for salvage focal ablation, and its use should be considered when discussing salvage options with patients. Further validation in larger, international cohorts with longer follow-up is recommended.

Utility of diffusion MRI characteristics of cervical lymph nodes as disease classifier between patients with head and neck squamous cell carcinoma and healthy volunteers.

Diffusion MRI characteristics assessed by apparent diffusion coefficient (ADC) histogram analysis in head and neck squamous cell carcinoma (HNSCC) have been reported as helpful in classifying tumours based on diffusion characteristics. There is little reported on HNSCC lymph nodes classification by diffusion characteristics. The aim of this study was to determine whether pretreatment nodal microstructural diffusion MRI characteristics can classify diseased nodes of patients with HNSCC from normal nodes of healthy volunteers. Seventy-nine patients with histologically confirmed HNSCC prior to chemoradiotherapy, and eight healthy volunteers, underwent diffusion-weighted (DW) MRI at a 1.5-T MR scanner. Two radiologists contoured lymph nodes on DW (b = 300 s/m2 ) images. ADC, distributed diffusion coefficient (DDC) and alpha (α) values were calculated by monoexponential and stretched exponential models. Histogram analysis metrics of drawn volume were compared between patients and volunteers using a Mann-Whitney test. The classification performance of each metric between the normal and diseased nodes was determined by receiver operating characteristic (ROC) analysis. Intraclass correlation coefficients determined interobserver reproducibility of each metric based on differently drawn ROIs by two radiologists. Sixty cancerous and 40 normal nodes were analysed. ADC histogram analysis revealed significant differences between patients and volunteers (p ≤0.0001 to 0.0046), presenting ADC distributions that were more skewed (1.49 for patients, 1.03 for volunteers; p = 0.0114) and 'peaked' (6.82 for patients, 4.20 for volunteers; p = 0.0021) in patients. Maximum ADC values exhibited the highest area under the curve ([AUC] 0.892). Significant differences were revealed between patients and volunteers for DDC and α value histogram metrics (p ≤0.0001 to 0.0044); the highest AUC were exhibited by maximum DDC (0.772) and the 25th percentile α value (0.761). Interobserver repeatability was excellent for mean ADC (ICC = 0.88) and the 25th percentile α value (ICC = 0.78), but poor for all other metrics. These results suggest that pretreatment microstructural diffusion MRI characteristics in lymph nodes, assessed by ADC and α value histogram analysis, can identify nodal disease.

Correction to: Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists.

The original version of this article, published on 11 June 2019, unfortunately contained a mistake. The following correction has therefore been made in the original: In section "Multiparametric MRI review," the readers mentioned in the first sentence were partly incorrect.

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

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

Simplified Luminal Water Imaging for the Detection of Prostate Cancer From Multiecho T2 MR Images.

BACKGROUND: Luminal water imaging (LWI) suffers less from imaging artifacts than the diffusion-weighted imaging used in multiparametric MRI of the prostate. LWI obtains multicompartment tissue information from a multiecho T2 dataset. PURPOSE: To compare a simplified LWI technique with apparent diffusion coefficient (ADC) in classifying lesions based on groupings of PI-RADS v2 scores. Secondary aims were to investigate whether LWI differentiates between histologically confirmed tumor and normal tissue as effectively as ADC, and whether LWI is correlated with the multicompartment parameters of the vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) diffusion model. STUDY TYPE: A subset of a larger prospective study. POPULATION: In all, 65 male patients aged 49-79 were scanned. FIELD STRENGTH/SEQUENCE: A 32-echo T2 and a six b-value diffusion sequence (0, 90, 500, 1500, 2000, 3000 s/mm2 ) at 3T. ASSESSMENT: Regions of interest were placed by a board-certified radiologist in areas of lesion and benign tissue and given PI-RADS v2 scores. STATISTICAL TESTS: Receiver operating characteristic and logistic regression analyses were performed. RESULTS: LWI classifies tissue as PI-RADS 1,2 or PI-RADS 3,4,5 with an area under curve (AUC) value of 0.779, compared with 0.764 for ADC. LWI differentiated histologically confirmed malignant from nonmalignant tissue with AUC, sensitivity, and specificity values of 0.81, 75%, and 87%, compared with 0.75, 83%, and 67% for ADC. The microstructural basis of the LWI technique is further suggested by the correspondence with the VERDICT diffusion-based microstructural imaging technique, with α, A1 , A2 , and LWF showing significant correlations. DATA CONCLUSION: LWI alone can predict PI-RADS v2 score groupings and detect histologically confirmed tumors with an ability similar to ADC alone without the limitations of diffusion-weighted MRI. This is important, given that ADC has an advantage in these tests as it already informs PI-RADS v2 scoring. LWI also provides multicompartment information that has an explicit biophysical interpretation, unlike ADC. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:910-917.

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

BACKGROUND: Chemical exchange saturation transfer (CEST) can potentially support cancer imaging with metabolically derived information. Multiparametric prostate MRI has improved diagnosis but may benefit from additional information to reduce the need for biopsies. PURPOSE: To optimize an acquisition and postprocessing protocol for 3.0 T multipool CEST analysis of prostate data and evaluate the repeatability of the technique. STUDY TYPE: Prospective. SUBJECTS: Five healthy volunteers (age range: 24-47 years; median age: 28 years) underwent two sessions (interval range: 7-27 days; median interval: 20 days) and two biopsy-proven prostate cancer patients were evaluated once. Patient 1 (71 years) had a Gleason 3 + 4 transition zone (TZ) tumor and patient 2 (55 years) had a Gleason 4 + 3 peripheral zone (PZ) tumor. FIELD STRENGTH: 3.0 T. Sequences run: T2 -weighted turbo-spin-echo (TSE); diffusion-weighted imaging; CEST; WASABI (for B0 determination). ASSESSMENT: Saturation, readout, and fit-model parameters were optimized to maximize in vivo amide and nuclear Overhauser effect (NOE) signals. Repeatability (intrasession and intersession) was evaluated in healthy volunteers. Subsequently, preliminary evaluation of signal differences was made in patients. Regions of interest were drawn by two post-FRCR board-certified readers, both with over 5 years of experience in multiparametric prostate MRI. STATISTICAL TESTS: Repeatability was assessed using Bland-Altman analysis, coefficient of variation (CV), and 95% limits of agreement (LOA). Statistical significance of CEST contrast was calculated using a nonparametric Mann-Whitney U-test. RESULTS: The optimized saturation scheme was found to be 60 sinc-Gaussian pulses with 40 msec pulse duration, at 50% duty-cycle with continuous-wave pulse equivalent B1 power (B1CWPE ) of 0.92 µT. The magnetization transfer (MT) contribution to the fit-model was centered at -1.27 ppm. Intersession coefficients of variation (CVs) of the amide, NOE, and magnetization transfer (MT) and asymmetric magnetization transfer ratio (MTRasym ) signals of 25%, 23%, 18%, and 200%, respectively, were observed. Fit-metric and MTRasym CVs agreed between readers to within 4 and 10 percentage points, respectively. DATA CONCLUSION: Signal differences of 0.03-0.10 (17-43%) detectable depending upon pool, with MT the most repeatable (signal difference of 17-22% detectable). LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1238-1250.

Multiparametric whole-body 3.0-T MRI in newly diagnosed intermediate- and high-risk prostate cancer: diagnostic accuracy and interobserver agreement for nodal and metastatic staging.

OBJECTIVES: To determine the diagnostic accuracy and interobserver concordance of whole-body (WB)-MRI, vs. 99mTc bone scintigraphy (BS) and 18fluoro-ethyl-choline (18F-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) ± 18F-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 18F-choline PET/CT. The mean sensitivity and specificity of WB-MRI, 18F-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 18F-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.

Multi-parametric MRI zone-specific diagnostic model performance compared with experienced radiologists for detection of prostate cancer.

OBJECTIVES: Compare the performance of zone-specific multi-parametric-MRI (mp-MRI) diagnostic models in prostate cancer detection with experienced radiologists. METHODS: A single-centre, IRB approved, prospective STARD compliant 3 T MRI test dataset of 203 patients was generated to test validity and generalisability of previously reported 1.5 T mp-MRI diagnostic models. All patients included within the test dataset underwent 3 T mp-MRI, comprising T2, diffusion-weighted and dynamic contrast-enhanced imaging followed by transperineal template ± targeted index lesion biopsy. Separate diagnostic models (transition zone (TZ) and peripheral zone (PZ)) were applied to respective zones. Sensitivity/specificity and the area under the receiver operating characteristic curve (ROC-AUC) were calculated for the two zone-specific models. Two radiologists (A and B) independently Likert scored test 3 T mp-MRI dataset, allowing ROC analysis for each radiologist for each prostate zone. RESULTS: Diagnostic models applied to the test dataset demonstrated a ROC-AUC = 0.74 (95% CI 0.67-0.81) in the PZ and 0.68 (95% CI 0.61-0.75) in the TZ. Radiologist A/B had a ROC-AUC = 0.78/0.74 in the PZ and 0.69/0.69 in the TZ. Radiologists A and B each scored 51 patients in the PZ and 41 and 45 patients respectively in the TZ as Likert 3. The PZ model demonstrated a ROC-AUC = 0.65/0.67 for the patients Likert scored as indeterminate by radiologist A/B respectively, whereas the TZ model demonstrated a ROC-AUC = 0.74/0.69. CONCLUSION: Zone-specific mp-MRI diagnostic models demonstrate generalisability between 1.5 and 3 T mp-MRI protocols and show similar classification performance to experienced radiologists for prostate cancer detection. Results also indicate the ability of diagnostic models to classify cases with an indeterminate radiologist score. KEY POINTS: • MRI diagnostic models had similar performance to experienced radiologists for classification of prostate cancer. • MRI diagnostic models may help radiologists classify tumour in patients with indeterminate Likert 3 scores.

Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists.

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.

Localising occult prostate cancer metastasis with advanced imaging techniques (LOCATE trial): a prospective cohort, observational diagnostic accuracy trial investigating whole-body magnetic resonance imaging in radio-recurrent prostate cancer.

BACKGROUND: Accurate whole-body staging following biochemical relapse in prostate cancer is vital in determining the optimum disease management. Current imaging guidelines recommend various imaging platforms such as computed tomography (CT), Technetium 99 m (99mTc) bone scan and 18F-choline and recently 68Ga-PSMA positron emission tomography (PET) for the evaluation of the extent of disease. Such approach requires multiple hospital attendances and can be time and resource intensive. Recently, whole-body magnetic resonance imaging (WB-MRI) has been used in a single visit scanning session for several malignancies, including prostate cancer, with promising results, providing similar accuracy compared to the combined conventional imaging techniques. The LOCATE trial aims to investigate the application of WB-MRI for re-staging of patients with biochemical relapse (BCR) following external beam radiotherapy and brachytherapy in patients with prostate cancer. METHODS/DESIGN: The LOCATE trial is a prospective cohort, multi-centre, non-randomised, diagnostic accuracy study comparing WB-MRI and conventional imaging. Eligible patients will undergo WB-MRI in addition to conventional imaging investigations at the time of BCR and will be asked to attend a second WB-MRI exam, 12-months following the initial scan. WB-MRI results will be compared to an enhanced reference standard comprising all the initial, follow-up imaging and non-imaging investigations. The diagnostic performance (sensitivity and specificity analysis) of WB-MRI for re-staging of BCR will be investigated against the enhanced reference standard on a per-patient basis. An economic analysis of WB-MRI compared to conventional imaging pathways will be performed to inform the cost-effectiveness of the WB-MRI imaging pathway. Additionally, an exploratory sub-study will be performed on blood samples and exosome-derived human epidermal growth factor receptor (HER) dimer measurements will be taken to investigate its significance in this cohort. DISCUSSION: The LOCATE trial will compare WB-MRI versus the conventional imaging pathway including its cost-effectiveness, therefore informing the most accurate and efficient imaging pathway. TRIAL REGISTRATION: LOCATE trial was registered on ClinicalTrial.gov on 18th of October 2016 with registration reference number NCT02935816.

"Textural analysis of multiparametric MRI detects transition zone prostate cancer".

OBJECTIVES: To evaluate multiparametric-MRI (mpMRI) derived histogram textural-analysis parameters for detection of transition zone (TZ) prostatic tumour. METHODS: Sixty-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. RESULTS: ADC 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). CONCLUSION: Textural 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.

Logistic regression model for diagnosis of transition zone prostate cancer on multi-parametric MRI.

OBJECTIVES: We aimed to develop logistic regression (LR) models for classifying prostate cancer within the transition zone on multi-parametric magnetic resonance imaging (mp-MRI). METHODS: One 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. RESULTS: Twenty-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. CONCLUSIONS: LR 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.

Zone-specific logistic regression models improve classification of prostate cancer on multi-parametric MRI.

OBJECTIVES: To assess the interchangeability of zone-specific (peripheral-zone (PZ) and transition-zone (TZ)) multiparametric-MRI (mp-MRI) logistic-regression (LR) models for classification of prostate cancer. METHODS: Two hundred and thirty-one patients (70 TZ training-cohort; 76 PZ training-cohort; 85 TZ temporal validation-cohort) underwent mp-MRI and transperineal-template-prostate-mapping biopsy. PZ and TZ uni/multi-variate mp-MRI LR-models for classification of significant cancer (any cancer-core-length (CCL) with Gleason > 3 + 3 or any grade with CCL ≥ 4 mm) were derived from the respective cohorts and validated within the same zone by leave-one-out analysis. Inter-zonal performance was tested by applying TZ models to the PZ training-cohort and vice-versa. Classification performance of TZ models for TZ cancer was further assessed in the TZ validation-cohort. ROC area-under-curve (ROC-AUC) analysis was used to compare models. RESULTS: The univariate parameters with the best classification performance were the normalised T2 signal (T2nSI) within the TZ (ROC-AUC = 0.77) and normalized early contrast-enhanced T1 signal (DCE-nSI) within the PZ (ROC-AUC = 0.79). Performance was not significantly improved by bi-variate/tri-variate modelling. PZ models that contained DCE-nSI performed poorly in classification of TZ cancer. The TZ model based solely on maximum-enhancement poorly classified PZ cancer. CONCLUSION: LR-models dependent on DCE-MRI parameters alone are not interchangable between prostatic zones; however, models based exclusively on T2 and/or ADC are more robust for inter-zonal application. KEY POINTS: • The ADC and T2-nSI of benign/cancer PZ are higher than benign/cancer TZ. • DCE parameters are significantly different between benign PZ and TZ, but not between cancerous PZ and TZ. • Diagnostic models containing contrast enhancement parameters have reduced performance when applied across zones.

Influence of diffusion weighted imaging and contrast enhanced T1 sequences on the diagnostic accuracy of magnetic resonance enterography for Crohn's disease.

OBJECTIVES: To evaluate the additional diagnostic benefit of diffusion weighted imaging (DWI) and contrast enhanced (CE) images during MR enterography (MRE) of Crohn's disease. METHODS: Datasets from 73 patients (mean age 32; 40 male) (28 new-diagnosis, 45 relapsed) were read independently by two radiologists selected from a pool of 13. Radiologists interpreted datasets using three sequential sequence blocks: (1) T2 weighted and steady state free precession gradient echo (SSFP) images alone (T2^); (2) T2 weighted and SSFP images with DWI (T2 + DWI^) and; (3) T2 weighted images, SSFP, DWI and post-contrast enhanced (CE) T1 images (T2 + DWI + CE^), documenting presence, location, and activity of small bowel disease. For each sequence block, sensitivity and specificity (readers combined) was calculated against an outcome-based construct reference standard. RESULTS: 59/73 patients had small bowel disease. Per-patient sensitivity for disease detection was essentially identical (80 % [95 % CI 72, 86], 81 % [73,87], and 79 % [71,86] for T2^, T2 + DWI^and T2 + DWI + CE^respectively). Specificity was identical (82 % [64 to 92]). Per patient sensitivity for disease extent was 56 % (47,65), 56 % (47,65) and 52 % (43 to 61) respectively, and specificity was 82 % (64 to 92) for all blocks. Sensitivity for active disease was 97 % (90,99), 97 % (90,99) and 98 % (92,99), and specificity was also comparable between all sequence combination reads. Results were consistent across segments and newly diagnosed/relapse patients. CONCLUSION: There is no additional diagnostic benefit of adding either DWI or CE to T2 FSE and SSFP sequences for evaluating small bowel Crohn's disease, suggesting MRE protocols can be simplified safely.

The Role of Multiparametric MRI and MRI-targeted Biopsy in the Diagnosis of Radiorecurrent Prostate Cancer: An Analysis from the FORECAST Trial.

BACKGROUND: The role of multiparametric magnetic resonance imaging (MRI) for detecting recurrent prostate cancer after radiotherapy is unclear. OBJECTIVE: To evaluate MRI and MRI-targeted biopsies for detecting intraprostatic cancer recurrence and planning for salvage focal ablation. DESIGN, SETTING, AND PARTICIPANTS: FOcal RECurrent Assessment and Salvage Treatment (FORECAST; NCT01883128) was a prospective cohort diagnostic study that recruited 181 patients with suspected radiorecurrence at six UK centres (2014 to 2018); 144 were included here. INTERVENTION: All patients underwent MRI with 5 mm transperineal template mapping biopsies; 84 had additional MRI-targeted biopsies. MRI scans with Likert scores of 3 to 5 were deemed suspicious. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: First, the diagnostic accuracy of MRI was calculated. Second, the pathological characteristics of MRI-detected and MRI-undetected tumours were compared using the Wilcoxon rank sum test and chi-square test for trend. Third, four biopsy strategies involving an MRI-targeted biopsy alone and with systematic biopsies of one to two other quadrants were studied. Fisher's exact test was used to compare MRI-targeted biopsy alone with the best other strategy for the number of patients with missed cancer and the number of patients with cancer harbouring additional tumours in unsampled quadrants. Analyses focused primarily on detecting cancer of any grade or length. Last, eligibility for focal therapy was evaluated for men with localised (≤T3bN0M0) radiorecurrent disease. RESULTS AND LIMITATIONS: Of 144 patients, 111 (77%) had cancer detected on biopsy. MRI sensitivity and specificity at the patient level were 0.95 (95% confidence interval [CI] 0.92 to 0.99) and 0.21 (95% CI 0.07 to 0.35), respectively. At the prostate quadrant level, 258/576 (45%) quadrants had cancer detected on biopsy. Sensitivity and specificity were 0.66 (95% CI 0.59 to 0.73) and 0.54 (95% CI 0.46 to 0.62), respectively. At the quadrant level, compared with MRI-undetected tumours, MRI-detected tumours had longer maximum cancer core length (median difference 3 mm [7 vs 4 mm]; 95% CI 1 to 4 mm, p < 0.001) and a higher grade group (p = 0.002). Of the 84 men who also underwent an MRI-targeted biopsy, 73 (87%) had recurrent cancer diagnosed. Performing an MRI-targeted biopsy alone missed cancer in 5/73 patients (7%; 95% CI 3 to 15%); with additional systematic sampling of the other ipsilateral and contralateral posterior quadrants (strategy 4), 2/73 patients (3%; 95% CI 0 to 10%) would have had cancer missed (difference 4%; 95% CI -3 to 11%, p = 0.4). If an MRI-targeted biopsy alone was performed, 43/73 (59%; 95% CI 47 to 69%) patients with cancer would have harboured undetected additional tumours in unsampled quadrants. This reduced but only to 7/73 patients (10%; 95% CI 4 to 19%) with strategy 4 (difference 49%; 95% CI 36 to 62%, p < 0.0001). Of 73 patients, 43 (59%; 95% CI 47 to 69%) had localised radiorecurrent cancer suitable for a form of focal ablation. CONCLUSIONS: For patients with recurrent prostate cancer after radiotherapy, MRI and MRI-targeted biopsy, with or without perilesional sampling, will diagnose cancer in the majority where present. MRI-undetected cancers, defined as Likert scores of 1 to 2, were found to be smaller and of lower grade. However, if salvage focal ablation is planned, an MRI-targeted biopsy alone is insufficient for prostate mapping; approximately three of five patients with recurrent cancer found on an MRI-targeted biopsy alone harboured further tumours in unsampled quadrants. Systematic sampling of the whole gland should be considered in addition to an MRI-targeted biopsy to capture both MRI-detected and MRI-undetected disease. PATIENT SUMMARY: After radiotherapy, magnetic resonance imaging (MRI) is accurate for detecting recurrent prostate cancer, with missed cancer being smaller and of lower grade. Targeting a biopsy to suspicious areas on MRI results in a diagnosis of cancer in most patients. However, for every five men who have recurrent cancer, this targeted approach would miss cancers elsewhere in the prostate in three of these men. If further focal treatment of the prostate is planned, random biopsies covering the whole prostate in addition to targeted biopsies should be considered so that tumours are not missed.

Development and Evaluation of Machine Learning in Whole-Body Magnetic Resonance Imaging for Detecting Metastases in Patients With Lung or Colon Cancer: A Diagnostic Test Accuracy Study.

OBJECTIVES: Whole-body magnetic resonance imaging (WB-MRI) has been demonstrated to be efficient and cost-effective for cancer staging. The study aim was to develop a machine learning (ML) algorithm to improve radiologists' sensitivity and specificity for metastasis detection and reduce reading times. MATERIALS AND METHODS: A retrospective analysis of 438 prospectively collected WB-MRI scans from multicenter Streamline studies (February 2013-September 2016) was undertaken. Disease sites were manually labeled using Streamline reference standard. Whole-body MRI scans were randomly allocated to training and testing sets. A model for malignant lesion detection was developed based on convolutional neural networks and a 2-stage training strategy. The final algorithm generated lesion probability heat maps. Using a concurrent reader paradigm, 25 radiologists (18 experienced, 7 inexperienced in WB-/MRI) were randomly allocated WB-MRI scans with or without ML support to detect malignant lesions over 2 or 3 reading rounds. Reads were undertaken in the setting of a diagnostic radiology reading room between November 2019 and March 2020. Reading times were recorded by a scribe. Prespecified analysis included sensitivity, specificity, interobserver agreement, and reading time of radiology readers to detect metastases with or without ML support. Reader performance for detection of the primary tumor was also evaluated. RESULTS: Four hundred thirty-three evaluable WB-MRI scans were allocated to algorithm training (245) or radiology testing (50 patients with metastases, from primary 117 colon [n = 117] or lung [n = 71] cancer). Among a total 562 reads by experienced radiologists over 2 reading rounds, per-patient specificity was 86.2% (ML) and 87.7% (non-ML) (-1.5% difference; 95% confidence interval [CI], -6.4%, 3.5%; P = 0.39). Sensitivity was 66.0% (ML) and 70.0% (non-ML) (-4.0% difference; 95% CI, -13.5%, 5.5%; P = 0.344). Among 161 reads by inexperienced readers, per-patient specificity in both groups was 76.3% (0% difference; 95% CI, -15.0%, 15.0%; P = 0.613), with sensitivity of 73.3% (ML) and 60.0% (non-ML) (13.3% difference; 95% CI, -7.9%, 34.5%; P = 0.313). Per-site specificity was high (>90%) for all metastatic sites and experience levels. There was high sensitivity for the detection of primary tumors (lung cancer detection rate of 98.6% with and without ML [0.0% difference; 95% CI, -2.0%, 2.0%; P = 1.00], colon cancer detection rate of 89.0% with and 90.6% without ML [-1.7% difference; 95% CI, -5.6%, 2.2%; P = 0.65]). When combining all reads from rounds 1 and 2, reading times fell by 6.2% (95% CI, -22.8%, 10.0%) when using ML. Round 2 read-times fell by 32% (95% CI, 20.8%, 42.8%) compared with round 1. Within round 2, there was a significant decrease in read-time when using ML support, estimated as 286 seconds (or 11%) quicker ( P = 0.0281), using regression analysis to account for reader experience, read round, and tumor type. Interobserver variance suggests moderate agreement, Cohen κ = 0.64; 95% CI, 0.47, 0.81 (with ML), and Cohen κ = 0.66; 95% CI, 0.47, 0.81 (without ML). CONCLUSIONS: There was no evidence of a significant difference in per-patient sensitivity and specificity for detecting metastases or the primary tumor using concurrent ML compared with standard WB-MRI. Radiology read-times with or without ML support fell for round 2 reads compared with round 1, suggesting that readers familiarized themselves with the study reading method. During the second reading round, there was a significant reduction in reading time when using ML support.

Imaging features of therapeutic drug-induced musculoskeletal abnormalities.

Increasing use of a wide variety of therapeutic drugs with known musculoskeletal side-effect profiles necessitates a rigorous understanding and approach when evaluating imaging features suggestive of drug-induced musculoskeletal abnormalities. The etiology of such abnormalities is diverse, and the clinical and imaging manifestations may be nonspecific. The recognition of adverse effects depends, first, on the physician's vigilant review of clinical information for relevant drug history and indicative signs, and second, on the radiologist's ability to detect musculoskeletal changes consistent with known potential effects of specific drugs. Musculoskeletal abnormalities induced by therapeutic drugs may be broadly categorized as embryopathic, juvenile, or postmaturation. Embryopathic skeletal abnormalities result from the teratogenic effects of drugs administered to pregnant women (eg, thalidomide, anticonvulsants). Other therapeutic agents characteristically lead to abnormalities during postnatal skeletal maturation (eg, high-dose vitamins or prostaglandin) either because they are used exclusively in children or because they have idiosyncratic effects on immature musculoskeletal structures. Many drugs (eg, statins) may have musculoskeletal side effects that, although independent of the stage of skeletal maturation, are most often seen in adults or elderly people because they are commonly prescribed for people in these age groups. Drug-induced musculoskeletal abnormalities may be further characterized according to the predominant skeletal manifestations as osteomalacic, proliferative, or osteoporotic and according to the involvement of soft tissues as musculotendinous or chondral.

Role of simulation-based education in ultrasound practice training.

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.

Diagnostic Accuracy of Abbreviated Bi-Parametric MRI (a-bpMRI) for Prostate Cancer Detection and Screening: A Multi-Reader Study.

(1) Background: There is currently limited evidence on the diagnostic accuracy of abbreviated biparametric MRI (a-bpMRI) protocols for prostate cancer (PCa) detection and screening. In the present study, we aim to investigate the performance of a-bpMRI among multiple readers and its potential application to an imaging-based screening setting. (2) Methods: A total of 151 men who underwent 3T multiparametric MRI (mpMRI) of the prostate and transperineal template prostate mapping biopsies were retrospectively selected. Corresponding bpMRI (multiplanar T2WI, DWI, ADC maps) and a-bpMRI (axial T2WI and b 2000 s/mm2 DWI only) dataset were derived from mpMRI. Three experienced radiologists scored a-bpMRI, standard biparametric MRI (bpMRI) and mpMRI in separate sessions. Diagnostic accuracy and interreader agreement of a-bpMRI was tested for different positivity thresholds and compared to bpMRI and mpMRI. Predictive values of a-bpMRI were computed for lower levels of PCa prevalence to simulate a screening setting. The primary definition of clinically significant PCa (csPCa) was Gleason ≥ 4 + 3, or cancer core length ≥ 6 mm. (3) Results: The median age was 62 years, the median PSA was 6.8 ng/mL, and the csPCa prevalence was 40%. Using a cut off of MRI score ≥ 3, the sensitivity and specificity of a-bpMRI were 92% and 48%, respectively. There was no significant difference in sensitivity compared to bpMRI and mpMRI. Interreader agreement of a-bpMRI was moderate (AC1 0.58). For a low prevalence of csPCa (e.g., <10%), higher cut offs (MRI score ≥ 4) yield a more favourable balance between the predictive values and positivity rate of MRI. (4) Conclusion: Abbreviated bpMRI protocols could match the diagnostic accuracy of bpMRI and mpMRI for the detection of csPCa. If a-bpMRI is used in low-prevalence settings, higher cut-offs for MRI positivity should be prioritised.

Interobserver variation in the interpretation of magnetic resonance enterography in Crohn's disease.

OBJECTIVES: To evaluate interobserver variability for diagnosis of disease presence and extent of small bowel and colonic Crohn's disease using MR enterography (MRE). METHODS: Data from the first 73 consecutive patients (mean age 32, 33F, 28 new diagnosis, 45 suspected relapse) recruited to a multicentre, prospective diagnostic accuracy trial evaluating MRE for small bowel Crohn's disease were each read independently by three (from a pool of 20) radiologists. Radiologists documented presence and segmental location of small bowel Crohn's disease and recorded morphological mural/extramural parameters for involved segments. Per patient percentage agreement for disease presence and extent were calculated against an outcome-based construct reference standard (averaged between pairs of readers). Prevalence-adjusted bias-adjusted κ (PABAK) was calculated. RESULTS: Agreement for small bowel disease presence for new diagnosis/relapsed patients was 68%(κ = 0.36)/ 78% (κ = 0.56) and 43%(κ = 0.14)/ 53% for disease extent (κ = 0.07), respectively. For disease presence, all three radiologists agreed correctly with the reference standard in 41/59 (69%) of patients with small bowel involvement, and in 8/14 (57%) cases of without small bowel disease. Agreement was highest for multisegment disease, greater than 5 cm in length, with mural thickness>6 mm, and increased mural T2 signal. Agreement for colonic disease presence was 61% (κ = 0.21 fair agreement) for new diagnosis/ 60% (κ = 0.20, slight agreement) for relapsed patients. CONCLUSION: There is a reasonable agreement between radiologists for small bowel disease presence using MRE for newly diagnosed Crohn's disease, and patients with suspected relapse, respectively. Agreement is lower for disease extent. ADVANCES IN KNOWLEDGE: There is reasonable agreement between radiologists for small bowel disease presence using MRE for newly diagnosed (68%) Crohn's disease, and patients with suspected relapse (78%). Agreement is lower for disease extent (43% new diagnosis and 53% suspected relapse).