ESUR recommendations for MR imaging of the sonographically indeterminate adnexal mass: an update

An update of the 2010 published ESUR recommendations of MRI of the sonographically indeterminate adnexal mass integrating functional techniques is provided. An algorithmic approach using sagittal T2 and a set of transaxial T1 and T2WI allows categorization of adnexal masses in one of the following three types according to its predominant signal characteristics. T1 'bright' masses due to fat or blood content can be simply and effectively determined using a combination of T1W, T2W and FST1W imaging. When there is concern for a solid component within such a mass, it requires additional assessment as for a complex cystic or cystic-solid mass. For low T2 solid adnexal masses, DWI is now recommended. Such masses with low DWI signal on high b value image (e.g. > b 1000 s/mm2 ) can be regarded as benign. Any other solid adnexal mass, displaying intermediate or high DWI signal, requires further assessment by contrast-enhanced (CE)T1W imaging, ideally with DCE MR, where a type 3 curve is highly predictive of malignancy. For complex cystic or cystic-solid masses, both DWI and CET1W—preferably DCE MRI—is recommended. Characteristic enhancement curves of solid components can discriminate between lesions that are highly likely malignant and highly likely benign

Repeatability of quantitative FDG-PET/CT and contrast-enhanced CT in recurrent ovarian carcinoma: test-retest measurements for tumor FDG uptake, diameter, and volume.

PURPOSE: Repeatability of baseline FDG-PET/CT measurements has not been tested in ovarian cancer. This dual-center, prospective study assessed variation in tumor 2[18F]fluoro-2-deoxy-D-glucose (FDG) uptake, tumor diameter, and tumor volume from sequential FDG-PET/CT and contrast-enhanced computed tomography (CECT) in patients with recurrent platinum-sensitive ovarian cancer. EXPERIMENTAL DESIGN: Patients underwent two pretreatment baseline FDG-PET/CT (n = 21) and CECT (n = 20) at two clinical sites with different PET/CT instruments. Patients were included if they had at least one target lesion in the abdomen with a standardized uptake value (SUV) maximum (SUVmax) of ≥ 2.5 and a long axis diameter of ≥ 15 mm. Two independent reading methods were used to evaluate repeatability of tumor diameter and SUV uptake: on site and at an imaging clinical research organization (CRO). Tumor volume reads were only performed by CRO. In each reading set, target lesions were independently measured on sequential imaging. RESULTS: Median time between FDG-PET/CT was two days (range 1-7). For site reads, concordance correlation coefficients (CCC) for SUVmean, SUVmax, and tumor diameter were 0.95, 0.94, and 0.99, respectively. Repeatability coefficients were 16.3%, 17.3%, and 8.8% for SUVmean, SUVmax, and tumor diameter, respectively. Similar results were observed for CRO reads. Tumor volume CCC was 0.99 with a repeatability coefficient of 28.1%. CONCLUSIONS: There was excellent test-retest repeatability for FDG-PET/CT quantitative measurements across two sites and two independent reading methods. Cutoff values for determining change in SUVmean, SUVmax, and tumor volume establish limits to determine metabolic and/or volumetric response to treatment in platinum-sensitive relapsed ovarian cancer.This study was funded by Merck and Co.This version is the author accepted manuscript. The OnlineFirst version of this article can be found on the publisher's website at: http://clincancerres.aacrjournals.org/content/20/10/2751.full.pdf+htm

Interesting case of ovarian sarcoidosis: The value of multi disciplinary team working

BACKGROUND: Sarcoidosis of the genital tract is a rare condition. Ovarian manifestation of this disease is rarer still. CASE PRESENTATION: The case presented here represents ovarian manifestation of sarcoidosis. At the point of referral to our hospital, based on computerised tomography (CT) ovarian carcinoma was a differential diagnosis. Further magnetic resonance imaging along with CT guided biopsy aided by laboratory study supported a diagnosis of sarcoidosis. Patient responded to medical management by a multidisciplinary team. CONCLUSION: The case shows the importance of FNAC and biopsy in case or ovarian masses and multi disciplinary team approach to management

Whole-body MRI compared with standard pathways for staging metastatic disease in lung and colorectal cancer: the Streamline diagnostic accuracy studies.

BACKGROUND: Whole-body magnetic resonance imaging is advocated as an alternative to standard pathways for staging cancer. OBJECTIVES: The objectives were to compare diagnostic accuracy, efficiency, patient acceptability, observer variability and cost-effectiveness of whole-body magnetic resonance imaging and standard pathways in staging newly diagnosed non-small-cell lung cancer (Streamline L) and colorectal cancer (Streamline C). DESIGN: The design was a prospective multicentre cohort study. SETTING: The setting was 16 NHS hospitals. PARTICIPANTS: Consecutive patients aged ≥ 18 years with histologically proven or suspected colorectal (Streamline C) or non-small-cell lung cancer (Streamline L). INTERVENTIONS: Whole-body magnetic resonance imaging. Standard staging investigations (e.g. computed tomography and positron emission tomography-computed tomography). REFERENCE STANDARD: Consensus panel decision using 12-month follow-up data. MAIN OUTCOME MEASURES: The primary outcome was per-patient sensitivity difference between whole-body magnetic resonance imaging and standard staging pathways for metastasis. Secondary outcomes included differences in specificity, the nature of the first major treatment decision, time and number of tests to complete staging, patient experience and cost-effectiveness. RESULTS: Streamline C - 299 participants were included. Per-patient sensitivity for metastatic disease was 67% (95% confidence interval 56% to 78%) and 63% (95% confidence interval 51% to 74%) for whole-body magnetic resonance imaging and standard pathways, respectively, a difference in sensitivity of 4% (95% confidence interval -5% to 13%; p = 0.51). Specificity was 95% (95% confidence interval 92% to 97%) and 93% (95% confidence interval 90% to 96%) respectively, a difference of 2% (95% confidence interval -2% to 6%). Pathway treatment decisions agreed with the multidisciplinary team treatment decision in 96% and 95% of cases, respectively, a difference of 1% (95% confidence interval -2% to 4%). Time for staging was 8 days (95% confidence interval 6 to 9 days) and 13 days (95% confidence interval 11 to 15 days) for whole-body magnetic resonance imaging and standard pathways, respectively, a difference of 5 days (95% confidence interval 3 to 7 days). The whole-body magnetic resonance imaging pathway was cheaper than the standard staging pathway: £216 (95% confidence interval £211 to £221) versus £285 (95% confidence interval £260 to £310). Streamline L - 187 participants were included. Per-patient sensitivity for metastatic disease was 50% (95% confidence interval 37% to 63%) and 54% (95% confidence interval 41% to 67%) for whole-body magnetic resonance imaging and standard pathways, respectively, a difference in sensitivity of 4% (95% confidence interval -7% to 15%; p = 0.73). Specificity was 93% (95% confidence interval 88% to 96%) and 95% (95% confidence interval 91% to 98%), respectively, a difference of 2% (95% confidence interval -2% to 7%). Pathway treatment decisions agreed with the multidisciplinary team treatment decision in 98% and 99% of cases, respectively, a difference of 1% (95% confidence interval -2% to 4%). Time for staging was 13 days (95% confidence interval 12 to 14 days) and 19 days (95% confidence interval 17 to 21 days) for whole-body magnetic resonance imaging and standard pathways, respectively, a difference of 6 days (95% confidence interval 4 to 8 days). The whole-body magnetic resonance imaging pathway was cheaper than the standard staging pathway: £317 (95% confidence interval £273 to £361) versus £620 (95% confidence interval £574 to £666). Participants generally found whole-body magnetic resonance imaging more burdensome than standard imaging but most participants preferred the whole-body magnetic resonance imaging staging pathway if it reduced time to staging and/or number of tests. LIMITATIONS: Whole-body magnetic resonance imaging was interpreted by practitioners blinded to other clinical data, which may not fully reflect how it is used in clinical practice. CONCLUSIONS: In colorectal and non-small-cell lung cancer, the whole-body magnetic resonance imaging staging pathway has similar accuracy to standard staging pathways, is generally preferred by patients, improves staging efficiency and has lower staging costs. Future work should address the utility of whole-body magnetic resonance imaging for treatment response assessment. TRIAL REGISTRATION: Current Controlled Trials ISRCTN43958015 and ISRCTN50436483. FUNDING: This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 23, No. 66. See the NIHR Journals Library website for further project information

European Society of Coloproctology: guidelines for the management of diverticular disease of the colon

The guideline was developed during several working phases including three voting rounds and one consensus meeting. The two project leads (JKS and EA) appointed by the ESCP guideline committee together with one member of the guideline committee (WB) agreed on the methodology, decided on six themes for working groups (WGs) and drafted a list of research questions. Senior WG members, mostly colorectal surgeons within the ESCP, were invited based on publication records and geographical aspects. Other specialties were included in the WGs where relevant. In addition, one trainee or PhD fellow was invited in each WG. All six WGs revised the research questions if necessary, did a literature search, created evidence tables where feasible, and drafted supporting text to each research question and statement. The text and statement proposals from each WG were arranged as one document by the first and last authors before online voting by all authors in two rounds. For the second voting ESCP national representatives were also invited. More than 90% agreement was considered a consensus. The final phrasing of the statements with < 90% agreement was discussed in a consensus meeting at the ESCP annual meeting in Vienna in September 2019. Thereafter, the first and the last author drafted the final text of the guideline and circulated it for final approval and for a third and final online voting of rephrased statements

Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic

The prevalence of the coronavirus SARS-CoV-2 disease has resulted in the unprecedented collection of health data to support research. Historically, coordinating the collation of such datasets on a national scale has been challenging to execute for several reasons, including issues with data privacy, the lack of data reporting standards, interoperable technologies, and distribution methods. The coronavirus SARS-CoV-2 disease pandemic has highlighted the importance of collaboration between government bodies, healthcare institutions, academic researchers and commercial companies in overcoming these issues during times of urgency. The National COVID-19 Chest Imaging Database, led by NHSX, British Society of Thoracic Imaging, Royal Surrey NHS Foundation Trust and Faculty, is an example of such a national initiative. Here, we summarise the experiences and challenges of setting up the National COVID-19 Chest Imaging Database, and the implications for future ambitions of national data curation in medical imaging to advance the safe adoption of artificial intelligence in healthcare

Pulmonary neuroendocrine (carcinoid) tumors: European Neuroendocrine Tumor Society expert consensus and recommendations for best practice for typical and atypical pulmonary carcinoids

This is an expert consensus from the European Neuroendocrine Tumor Society recommending best practice for the management of pulmonary neuroendocrine tumors including typical and atypical carcinoids. It emphasizes the latest discussion on nomenclature, advances and utility of new diagnostic techniques as well as the limited evidence and difficulties in determining the optimal therapeutic strateg

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