Beyond regulatory compliance: evaluating radiology artificial intelligence applications in deployment.

The implementation of artificial intelligence (AI) applications in routine practice, following regulatory approval, is currently limited by practical concerns around reliability, accountability, trust, safety, and governance, in addition to factors such as cost-effectiveness and institutional information technology support. When a technology is new and relatively untested in a field, professional confidence is lacking and there is a sense of the need to go above the baseline level of validation and compliance. In this article, we propose an approach that goes beyond standard regulatory compliance for AI apps that are approved for marketing, including independent benchmarking in the lab as well as clinical audit in practice, with the aims of increasing trust and preventing harm.

The time is now: making the case for a UK registry of deployment of radiology artificial intelligence applications.

Artificial intelligence (AI)-based healthcare applications (apps) are rapidly evolving, and radiology is a target specialty for their implementation. In this paper, we put the case for a national deployment registry to track the spread of AI apps into clinical use in radiology in the UK. By gathering data on the specific locations, purposes, and people associated with AI app deployment, such a registry would provide greater transparency on their spread in the radiology field. In combination with other regulatory and audit mechanisms, it would provide radiologists and patients with greater confidence and trust in AI apps. At the same time, coordination of this information would reduce costs for the National Health Service (NHS) by preventing duplication of piloting activities. This commentary discusses the need for a UK-wide registry for such apps, its benefits and risks, and critical success factors for its establishment. We conclude by noting that a critical window of opportunity has opened up for the development of a deployment registry, before the current pattern of localised clusters of activity turns into the widespread proliferation of AI apps across clinical practice.

Fully automated deep-learning section-based muscle segmentation from CT images for sarcopenia assessment.

AIM: To develop a fully automated deep-learning-based approach to measure muscle area for assessing sarcopenia on standard-of-care computed tomography (CT) of the abdomen without any case exclusion criteria, for opportunistic screening for frailty. MATERIALS AND METHODS: This ethically approved retrospective study used publicly available and institutional unselected abdominal CT images (n=1,070 training, n=31 testing). The method consisted of two sequential steps: section detection from CT volume followed by muscle segmentation on single-section. Both stages used fully convolutional neural networks (FCNN), based on a UNet-like architecture. Input data consisted of CT volumes with a variety of fields of view, section thicknesses, occlusions, artefacts, and anatomical variations. Output consisted of segmented muscle area on a CT section at the L3 vertebral level. The muscle was segmented into erector spinae, psoas, and rectus abdominus muscle groups. Output was tested against expert manual segmentation. RESULTS: Threefold cross-validation was used to evaluate the model. Section detection cross-validation error was 1.41 ± 5.02 (in sections). Segmentation cross-validation Dice overlaps were 0.97 ± 0.02, 0.95 ± 0.04, and 0.94 ± 0.04 for erector spinae, psoas, and rectus abdominus, respectively, and 0.96 ± 0.02 for the combined muscle area, with R2 = 0.95/0.98 for muscle attenuation/area in 28/31 hold-out test cases. No statistical difference was found between the automated output and a second annotator. Fully automated processing took <1 second per CT examination. CONCLUSIONS: A FCNN pipeline accurately and efficiently automates muscle segmentation at the L3 vertebral level from unselected abdominal CT volumes, with no manual processing step. This approach is promising as a generalisable tool for opportunistic screening for frailty on standard-of-care CT.

O-RADS MRI score: analysis of misclassified cases in a prospective multicentric European cohort.

OBJECTIVE: To retrospectively review the causes of categorization errors using O-RADS-MRI score and to determine the presumptive causes of these misclassifications. METHODS: EURAD database was retrospectively queried to identify misclassified lesions. In this cohort, 1194 evaluable patients with 1502 pelvic masses (277 malignant / 1225 benign lesions) underwent standardized MRI to characterize adnexal masses with histology or 2 years' follow-up as a reference standard. An expert radiologist reviewed cases with two junior radiologists and lesions termed misclassified if malignant lesion was scored ≤ 3, a benign lesion was scored ≥ 4, the site of origin was incorrect, or a non-adnexal mass was incorrectly categorized as benign or malignant. RESULTS: There were 139 / 1502 (9.2%) misclassified masses in 116 women including 109 adnexal and 30 non-adnexal masses. False-negative cases corresponded to 16 borderline or invasive malignant adnexal masses rated score ≤ 3 (16 / 139, 11.5%). False-positive cases corresponded to 88 benign masses were rated score 4 (67 / 139, 48.2%) or 5 (18 / 139,12.9%) or considered suspicious non-adnexal lesions (3 / 139, 2.2%). Misclassifications were only due to origin error in 12 adnexal masses (8 benign, 4 malignant) (8.6%, 12 / 139) and 23 non-adnexal masses (18 benign, 5 malignant,16.5%, 23 / 139) perceived respectively as non-adnexal and adnexal masses. Interpretive error (n = 104), failure to recognize technical insufficient exams (n = 9), and perceptual errors (n = 4) were found. Most interpretive was due to misinterpretation of solid tissue or incorrect assignment of mass origin. Eighty-four out of 139 cases were correctly reclassified by the readers with strict adherence to the score rules. CONCLUSION: Most errors were due to misinterpretation of solid tissue or incorrect assignment of mass origin. KEY POINTS: • Prospective assignment of O-RADS-MRI score resulted in misclassification of 9.25% of sonographically indeterminate pelvic masses. • Most errors were interpretive (74.8%) due to misinterpretation of solid tissue as defined by the lexicon or incorrect assignment of mass origin. • Pelvic inflammatory disease is a common source of misclassification (8.9%) (12 / 139).

Machine learning in whole-body MRI: experiences and challenges from an applied study using multicentre data.

Machine learning is now being increasingly employed in radiology to assist with tasks such as automatic lesion detection, segmentation, and characterisation. We are currently involved in an National Institute of Health Research (NIHR)-funded project, which aims to develop machine learning methods to improve the diagnostic performance and reduce the radiology reading time of whole-body magnetic resonance imaging (MRI) scans, in patients being staged for cancer (MALIBO study). We describe here the main challenges we have encountered during the course of this project. Data quality and uniformity are the two most important data traits to be considered in clinical trials incorporating machine learning. Robust data pre-processing and machine learning pipelines have been employed in MALIBO, a task facilitated by the now freely available machine learning libraries and toolboxes. Another important consideration for achieving the desired clinical outcome in MALIBO, was to effectively host the resulting machine learning output, along with the clinical images, for reading in a clinical environment. Finally, a range of legal, ethical, and clinical acceptance issues should be considered when attempting to incorporate computer-assisting tools into clinical practice. The road from translating computational methods into potentially useful clinical tools involves an analytical, stepwise adaptation approach, as well as engagement of a multidisciplinary team.

Pre-operative MRI staging of endometrial cancer in a multicentre cancer network: can we match single centre study results?

OBJECTIVES: To evaluate the staging accuracy of magnetic resonance imaging (MRI) for endometrial cancer in daily practice over a 3-year period at a tertiary referral centre receiving scans from a large number of hospitals with varying protocols. To compare these daily practice results to published data from single-centre studies. METHODS: After ethical approval, MRI staging records for 270 studies from nine network and three centre hospitals were retrospectively collected and compared with final operative histopathology. The International Federation of Gynaecology and Obstetrics (FIGO) stage, depth of invasion assessment and cervical stromal invasion were analysed and reasons for discrepancies reviewed. RESULTS: MRI-based complete FIGO stage was fully concordant with histopathology in 65.6%. MRI accuracy for depth of myometrial invasion and cervical stromal invasion was 73.3% and 89.3% respectively. Our results did not match the high accuracy previously reported in studies based on single centres. CONCLUSIONS: Published MRI staging accuracy from small single-centre studies were not replicated in a tertiary referral centre receiving scans with heterogeneous protocols over a 3-year period. These results highlight the challenges faced in daily practice and may reflect achievable and realistic MRI staging accuracies in large rapid throughput referral networks. Adherence to standardised high-quality protocols may help to improve future results. KEY POINTS: • Three-year MRI-staging accuracy for endometrial cancer in a multicentre cancer network • Daily practice MRI-staging accuracy did not meet results of single-centre studies • Large scale cancer network MRI-staging accuracies should be further evaluated • Treatment recommendations should be based on achievable MRI-staging accuracies.

MRI measurement of residual cervical length after radical trachelectomy for cervical cancer and the risk of adverse pregnancy outcomes: a blinded imaging analysis.

OBJECTIVE: To determine the association between the residual cervix measured on postoperative MRI after radical vaginal trachelectomy (RVT) and adverse obstetrical outcomes. DESIGN: Observational study. SETTING: Referral Cancer centre. POPULATION: Women who conceived after RVT for cervical cancer at the Royal Marsden Hospital, London, between 1995 and 2015. METHODS: Postoperative MRI scans were analysed by three researchers. The agreement between researchers was assessed by Pearson's correlation coefficient and Bland-Altman plot. Patients were divided into two groups (<10 and ≥10 mm residual cervix) for the analysis of adverse obstetrical outcomes. MAIN OUTCOME MEASURES: Late miscarriage, premature delivery, premature rupture of membranes (PROM) and chorioamnionitis. RESULTS: Thirty-one MRI scans were available; 29 of these women had a pregnancy that progressed beyond the first trimester. There was a strong reproducibility of the measurement of residual cervix (P < 0.001). Nineteen women (65.5%) had <10 mm residual cervix and 10 (34.5%) had ≥10 mm. Among women with <10 mm residual cervix, seven (36.8%) experienced PROM and ten (66.7%) had a preterm birth; No women with ≥10 mm residual cervix had PROM and two (22.2%) had a preterm birth (P = 0.028 and P = 0.035, respectively). Overall, there were nine (16.7%) first-trimester miscarriages, six (11.1%) late fetal losses, 12 (31.6%) preterm births and 36 (66.7%) live births. After a mean follow up of 78.1 months, 36 women were disease-free and one woman had died. CONCLUSIONS: MRI measurements of the residual cervix are reproducible between observers. The incidence of PROM and premature delivery is higher when the residual cervix after RVT is <10 mm. TWEETABLE ABSTRACT: The risk of prematurity after RVT can be predicted from measurements of residual cervical length on postoperative MRI scan.

Session 1: The evolution and development of the multidisciplinary team approach: USA, European and UK experiences - what can we do better?

The process of determining the best treatments that should be offered to patients with newly diagnosed colon and rectal cancer remains highly variable around the world. The aim of this expert review was to agree the key elements of good quality preoperative treatment decision making.

Histogram analysis of apparent diffusion coefficient from whole-body diffusion-weighted MRI to predict early response to chemotherapy in patients with metastatic colorectal cancer: preliminary results.

AIM: To evaluate apparent diffusion coefficient (ADC) histogram analysis parameters, acquired from whole-body diffusion-weighted magnetic resonance imaging (DW-MRI), as very early predictors of response to chemotherapy in patients with metastatic colorectal cancer (mCRC). MATERIALS AND METHODS: This was a single-institution prospective study, approved by the West Midlands-South Birmingham research ethics committee. All patients gave fully informed consent prior to imaging. Sixteen patients with histologically confirmed mCRC were enrolled to the study and 11 were successfully scanned with whole-body DW-MRI before (baseline) and 10.8±2.7 days after commencing chemotherapy (follow-up). Therapy response was assessed by RECIST 1.1. Mean ADC and histogram parameters (skewness, kurtosis, 25th, 50th, and 75th percentiles) were compared between progressors and non-progressors at baseline and follow-up. Receiver operating characteristics (ROC) analysis was performed for the statistically significant parameters. Data from metastases were also compared to normative tissue data acquired from healthy volunteers. RESULTS: Three patients had progressive disease (progressors) and eight had partial response/stable disease (non-progressors). Mean, 25th, 50th, and 75th percentiles were significantly lower for progressors at baseline (p=0.012, 0.012, 0.012 and 0.025 respectively) with areas under the ROC curves (AUC)=0.58, 0.50, 0.58 and 0.63, respectively. Skewness and kurtosis were significantly lower for non-progressors at follow-up (p=0.001 and 0.003 respectively) with AUC=0.67 and 0.79 respectively. CONCLUSION: ADC histogram analysis shows potential in discriminating progressive from non-progressive disease in patients with mCRC, who underwent whole-body DW-MRI. The technique can potentially be tested as a response assessment methodology in larger trials.

European society of urogenital radiology (ESUR) guidelines: MR imaging of pelvic endometriosis.

Endometriosis is a common gynaecological condition of unknown aetiology that primarily affects women of reproductive age. The accepted first-line imaging modality is pelvic ultrasound. However, magnetic resonance imaging (MRI) is increasingly performed as an additional investigation in complex cases and for surgical planning. There is currently no international consensus regarding patient preparation, MRI protocols or reporting criteria. Our aim was to develop clinical guidelines for MRI evaluation of pelvic endometriosis based on literature evidence and consensus expert opinion. This work was performed by a group of radiologists from the European Society of Urogenital Radiology (ESUR), experts in gynaecological imaging and a gynaecologist expert in methodology. The group discussed indications for MRI, technical requirements, patient preparation, MRI protocols and criteria for the diagnosis of pelvic endometriosis on MRI. The expert panel proposed a final recommendation for each criterion using Oxford Centre for Evidence Based Medicine (OCEBM) 2011 levels of evidence. KEY POINTS: • This report provides guidelines for MRI in endometriosis. • Minimal and optimal MRI acquisition protocols are provided. • Recommendations are proposed for patient preparation, best MRI sequences and reporting criteria.

Correlation of pre-operative CT findings with surgical & histological tumor dissemination patterns at cytoreduction for primary advanced and relapsed epithelial ovarian cancer: A retrospective evaluation.

OBJECTIVES: Computed tomography (CT) is an essential part of preoperative planning prior to cytoreductive surgery for primary and relapsed epithelial ovarian cancer (EOC). Our aim is to correlate pre-operative CT results with intraoperative surgical and histopathological findings at debulking surgery. METHODS: We performed a systematic comparison of intraoperative tumor dissemination patterns and surgical resections with preoperative CT assessments of infiltrative disease at key resection sites, in women who underwent multivisceral debulking surgery due to EOC between January 2013 and December 2014 at a tertiary referral center. The key sites were defined as follows: diaphragmatic involvement(DI), splenic disease (SI), large (LBI) and small (SBI) bowel involvement, rectal involvement (RI), porta hepatis involvement (PHI), mesenteric disease (MI) and lymph node involvement (LNI). RESULTS: A total of 155 patients, mostly with FIGO stage IIIC disease (65%) were evaluated (primary=105, relapsed=50). Total macroscopic cytoreduction rates were: 89%. Pre-operative CT findings displayed high specificity across all tumor sites apart from the retroperitoneal lymph node status, with a specificity of 65%. The ability however of the CT to accurately identify sites affected by invasive disease was relatively low with the following sensitivities as relating to final histology: 32% (DI), 26% (SI), 46% (LBI), 44% (SBI), 39% (RI), 57% (PHI), 31% (MI), 63% (LNI). CONCLUSION: Pre-operative CT imaging shows high specificity but low sensitivity in detecting tumor involvement at key sites in ovarian cancer surgery. CT findings alone should not be used for surgical decision making.

Diffusion-weighted and multiphase contrast-enhanced MRI as surrogate markers of response to neoadjuvant sunitinib in metastatic renal cell carcinoma.

BACKGROUND: Current imaging criteria for categorising disease response in metastatic renal cell carcinoma (mRCC) correlate poorly with overall survival (OS) in patients on anti-angiogenic therapies. We prospectively assess diffusion-weighted and multiphase contrast-enhanced (MCE) MR imaging (MRI) as markers of outcome. METHODS: Treatment-naive mRCC patients on a phase II trial using sunitinib completed an MRI substudy. Whole-tumour apparent diffusion coefficient (ADC) maps and histograms were generated, and mean ADC and AUC(low) (proportion of the tumour with ADC values lying below the 25th percentile of the ADC histogram) recorded. On MCE-MRI, regions of interest were drawn around the most avidly enhancing components to analyse enhancement parameters. Baseline (n=26) and treatment-related changes in surviving patients (n=20) were correlated with OS. Imaged metastases were also analysed. RESULTS: Forty-seven per cent of the patients showed significant changes in whole-tumour mean ADC following therapy, but there was no correlation with outcome. Patients with a high baseline AUC(low) and greater-than-median AUC(low) increase had reduced OS (HR=3.67 (95% confidence interval (CI)=1.23-10.9), P=0.012 and HR=3.72 (95% CI=0.98-14.21), P=0.038, respectively). There was no correlation between MCE-MRI parameters and OS. Twenty-eight metastases were analysed and showed positive correlation with primary tumour mean ADC for individual patients (r=0.607; P<0.001). CONCLUSION: Primary RCC ADC histogram analysis shows dynamic changes with sunitinib. Patients in whom the tumour ADC histogram demonstrated high baseline AUC(low) or a greater-than-median increase in AUC(low) with treatment had reduced OS.

MRI appearances of benign uterine disease.

Benign uterine disease is a common entity affecting women of all ages. Ultrasound has historically been the predominant imaging method used in the evaluation of benign gynaecological disease, magnetic resonance imaging (MRI) being reserved for use in the staging of malignant uterine and cervical disease. MRI is now increasingly used in the diagnosis of benign uterine disease as well as a tool for problem-solving in cases of diagnostic dilemma. It allows detailed assessment of benign conditions, such as endometrial lesions, leiomyomas, and adenomyosis, and can be helpful in the stratification of patients to different treatment modalities, including surgical resection, uterine artery embolization, and medical therapies. In this article, we review the MRI findings in the common benign uterine diseases.

The prevalence of incidentally detected adrenal enlargement on CT.

AIM: To assess the prevalence and the department's detection rate of adrenocortical hyperplasia. MATERIALS AND METHODS: All computed tomography (CT) examinations of the adrenal glands between February and April 2011 were reviewed. The study excluded patients with known underlying cancer, abdominal trauma, or endocrine disease. The adrenal gland was deemed enlarged if its body was greater than 10 mm diameter, or a limb greater than 5 mm. RESULTS: There were a total of 564 eligible CT studies during this period. A total of 64 cases of incidental adrenal enlargement were found giving a prevalence of 11.3%. Only nine cases were reported in the contemporaneous CT report. CONCLUSION: The results of the present study show that incidental adrenal enlargement has a significant prevalence. It is often dismissed during reporting, and awareness needs to be raised in the radiological community. Equally, the clinical and biochemical significance needs to be assessed with endocrine correlation.

Guidelines for the management of gastroenteropancreatic neuroendocrine (including carcinoid) tumours (NETs).

These guidelines update previous guidance published in 2005. They have been revised by a group who are members of the UK and Ireland Neuroendocrine Tumour Society with endorsement from the clinical committees of the British Society of Gastroenterology, the Society for Endocrinology, the Association of Surgeons of Great Britain and Ireland (and its Surgical Specialty Associations), the British Society of Gastrointestinal and Abdominal Radiology and others. The authorship represents leaders of the various groups in the UK and Ireland Neuroendocrine Tumour Society, but a large amount of work has been carried out by other specialists, many of whom attended a guidelines conference in May 2009. We have attempted to represent this work in the acknowledgements section. Over the past few years, there have been advances in the management of neuroendocrine tumours, which have included clearer characterisation, more specific and therapeutically relevant diagnosis, and improved treatments. However, there remain few randomised trials in the field and the disease is uncommon, hence all evidence must be considered weak in comparison with other more common cancers.

A systematic review and meta-analysis of the diagnostic accuracy of biparametric prostate MRI for prostate cancer in men at risk.

INTRODUCTION: Multiparametric magnetic resonance imaging (mpMRI), the use of three multiple imaging sequences, typically T2-weighted, diffusion weighted (DWI) and dynamic contrast enhanced (DCE) images, has a high sensitivity and specificity for detecting significant cancer. Current guidance now recommends its use prior to biopsy. However, the impact of DCE is currently under debate regarding test accuracy. Biparametric MRI (bpMRI), using only T2 and DWI has been proposed as a viable alternative. We conducted a contemporary systematic review and meta-analysis to further examine the diagnostic performance of bpMRI in the diagnosis of any and clinically significant prostate cancer. METHODS: A systematic review of the literature from 01/01/2017 to 06/07/2019 was performed by two independent reviewers using predefined search criteria. The index test was biparametric MRI and the reference standard whole-mount prostatectomy or prostate biopsy. Quality of included studies was assessed by the QUADAS-2 tool. Statistical analysis included pooled diagnostic performance (sensitivity; specificity; AUC), meta-regression of possible covariates and head-to-head comparisons of bpMRI and mpMRI where both were performed in the same study. RESULTS: Forty-four articles were included in the analysis. The pooled sensitivity for any cancer detection was 0.84 (95% CI, 0.80-0.88), specificity 0.75 (95% CI, 0.68-0.81) for bpMRI. The summary ROC curve yielded a high AUC value (AUC = 0.86). The pooled sensitivity for clinically significant prostate cancer was 0.87 (95% CI, 0.78-0.93), specificity 0.72 (95% CI, 0.56-0.84) and the AUC value was 0.87. Meta-regression analysis revealed no difference in the pooled diagnostic estimates between bpMRI and mpMRI. CONCLUSIONS: This meta-analysis on contemporary studies shows that bpMRI offers comparable test accuracies to mpMRI in detecting prostate cancer. These data are broadly supportive of the bpMRI approach but heterogeneity does not allow definitive recommendations to be made. There is a need for prospective multicentre studies of bpMRI in biopsy naïve men.

An Inter-observer Study to Determine Radiotherapy Planning Target Volumes for Recurrent Gynaecological Cancer Comparing Magnetic Resonance Imaging Only With Computed Tomography-Magnetic Resonance Imaging.

AIMS: Target delineation uncertainty is arguably the largest source of geometric uncertainty in radiotherapy. Several factors can affect it, including the imaging modality used for delineation. It is accounted for by applying safety margins to the target to produce a planning target volume (PTV), to which treatments are designed. To determine the margin, the delineation uncertainty is measured as the delineation error, and then a margin recipe used. However, there is no published evidence of such analysis for recurrent gynaecological cancers (RGC). The aims of this study were first to quantify the delineation uncertainty for RGC gross tumour volumes (GTVs) and to calculate the associated PTV margins and then to quantify the difference in GTV, delineation uncertainty and PTV margin, between a computed tomography-magnetic resonance imaging (CT-MRI) and MRI workflow. MATERIALS AND METHODS: Seven clinicians delineated the GTV for 20 RGC tumours on co-registered CT and MRI datasets (CT-MRI) and on MRI alone. The delineation error, the standard deviation of distances from each clinician's outline to a reference, was measured and the required PTV margin determined. Differences between using CT-MRI and MRI alone were assessed. RESULTS: The overall delineation error and the resulting margin were 3.1 mm and 8.5 mm, respectively, for CT-MRI, reducing to 2.5 mm and 7.1 mm, respectively, for MRI alone. Delineation errors and therefore the theoretical margins, varied widely between patients. MRI tumour volumes were on average 15% smaller than CT-MRI tumour volumes. DISCUSSION: This study is the first to quantify delineation error for RGC tumours and to calculate the corresponding PTV margin. The determined margins were larger than those reported in the literature for similar patients, bringing into question both current margins and margin calculation methods. The wide variation in delineation error between these patients suggests that applying a single population-based margin may result in PTVs that are suboptimal for many. Finally, the reduced tumour volumes and safety margins suggest that patients with RGC may benefit from an MRI-only treatment workflow.