252 results found
Rockall AG, Jalaguier-Coudray A, Thomassin-Naggara I, 2023, MR imaging of the Adnexa: Technique and Imaging Acquisition., Magn Reson Imaging Clin N Am, Vol: 31, Pages: 149-161
MR imaging has a high diagnostic accuracy and reproducibility to classify adnexal masses as benign or malignant, using a risk stratification scoring system, the Ovarian-Adnexal Reporting and Data System (O-RADS) MR imaging score. The first step in achieving high accuracy is to ensure high technical quality of the MR scan. The sequences needed are clearly described in this article, with tips for handling difficult cases. This information will assist in obtaining the best possible images, to allow for accurate use of the O-RADS MR imaging risk score.
Silkens MEWM, Ross J, Hall M, et al., 2023, The time is now: making the case for a UK registry of deployment of radiology artificial intelligence applications., Clin Radiol, Vol: 78, Pages: 107-114
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.
Rockall AG, Shelmerdine SC, Chen M, 2023, AI and ML in radiology: Making progress., Clin Radiol, Vol: 78, Pages: 81-82
Sadowski EA, Stein EB, Thomassin-Naggara I, et al., 2023, O-RADS MRI After Initial Ultrasound for Adnexal Lesions: AJR Expert Panel Narrative Review., AJR Am J Roentgenol, Vol: 220, Pages: 6-15
The Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound (US) and MRI risk stratification systems were developed by an international group of experts in adnexal imaging to aid radiologists in assessing adnexal lesions. The goal of imaging is to appropriately triage patients with adnexal lesions. US is the first-line imaging modality for assessment, whereas MRI can be used as a problem-solving tool. Both US and MRI can accurately characterize benign lesions such as simple cysts, endometriomas, hemorrhagic cysts, and dermoid cysts, avoiding unnecessary or inappropriate surgery. In patients with a lesion that does not meet criteria for one of these benign diagnoses, MRI can further characterize the lesion with an improved specificity for cancer and the ability to provide a probable histologic subtype in the presence of certain MRI features. This allows personalized treatment, including avoiding overly extensive surgery or allowing fertility-sparing procedures for suspected benign, borderline, or low-grade tumors. When MRI findings indicate a risk of an invasive cancer, patients can be expeditiously referred to a gynecologic oncologic surgeon. This narrative review provides expert opinion on the utility of multiparametric MRI when using the O-RADS US and MRI management systems.
Li X, Marcus D, Russell J, et al., 2022, An integrated clinical-MR radiomics model to estimate survival time in patients with endometrial cancer, Journal of Magnetic Resonance Imaging, ISSN: 1053-1807
Background:Determination of survival time in women with endometrial cancer using clinical features remains imprecise. Features from MRI may improve the survival estimation allowing improved treatment planning.Purpose:To identify clinical features and imaging signatures on T2-weighted MRI that can be used in an integrated model to estimate survival time for endometrial cancer subjects.Study Type:Retrospective.Population:Four hundred thirteen patients with endometrial cancer as training (N = 330, 66.41 ± 11.42 years) and validation (N = 83, 67.60 ± 11.89 years) data and an independent set of 82 subjects as testing data (63.26 ± 12.38 years).Field Strength/Sequence:1.5-T and 3-T scanners with sagittal T2-weighted spin echo sequence.Assessment:Tumor regions were manually segmented on T2-weighted images. Features were extracted from segmented masks, and clinical variables including age, cancer histologic grade and risk score were included in a Cox proportional hazards (CPH) model. A group least absolute shrinkage and selection operator method was implemented to determine the model from the training and validation datasets.Statistical Tests:A likelihood-ratio test and decision curve analysis were applied to compare the models. Concordance index (CI) and area under the receiver operating characteristic curves (AUCs) were calculated to assess the model.Results:Three radiomic features (two image intensity and volume features) and two clinical variables (age and cancer grade) were selected as predictors in the integrated model. The CI was 0.797 for the clinical model (includes clinical variables only) and 0.818 for the integrated model using training and validation datasets, the associated mean AUC value was 0.805 and 0.853. Using the testing dataset, the CI was 0.792 and 0.882, significantly different and the mean AUC was 0.624 and 0.727 for the clinical model and integrated model, respective
Maheshwari E, Nougaret S, Stein EB, et al., 2022, Update on MRI in Evaluation and Treatment of Endometrial Cancer, RADIOGRAPHICS, Vol: 42, Pages: 2112-2130, ISSN: 0271-5333
Satchwell L, Wedlake L, Greenlay E, et al., 2022, Development of machine learning support for reading whole body diffusion-weighted MRI (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment (MALIMAR): protocol for a cross-sectional diagnostic test accuracy study, BMJ Open, Vol: 12, Pages: 1-9, ISSN: 2044-6055
Introduction Whole-body MRI (WB-MRI) is recommended by the National Institute of Clinical Excellence as the first-line imaging tool for diagnosis of multiple myeloma. Reporting WB-MRI scans requires expertise to interpret and can be challenging for radiologists who need to meet rapid turn-around requirements. Automated computational tools based on machine learning (ML) could assist the radiologist in terms of sensitivity and reading speed and would facilitate improved accuracy, productivity and cost-effectiveness. The MALIMAR study aims to develop and validate a ML algorithm to increase the diagnostic accuracy and reading speed of radiological interpretation of WB-MRI compared with standard methods.Methods and analysis This phase II/III imaging trial will perform retrospective analysis of previously obtained clinical radiology MRI scans and scans from healthy volunteers obtained prospectively to implement training and validation of an ML algorithm. The study will comprise three project phases using approximately 633 scans to (1) train the ML algorithm to identify active disease, (2) clinically validate the ML algorithm and (3) determine change in disease status following treatment via a quantification of burden of disease in patients with myeloma. Phase 1 will primarily train the ML algorithm to detect active myeloma against an expert assessment (‘reference standard’). Phase 2 will use the ML output in the setting of radiology reader study to assess the difference in sensitivity when using ML-assisted reading or human-alone reading. Phase 3 will assess the agreement between experienced readers (with and without ML) and the reference standard in scoring both overall burden of disease before and after treatment, and response.Ethics and dissemination MALIMAR has ethical approval from South Central—Oxford C Research Ethics Committee (REC Reference: 17/SC/0630). IRAS Project ID: 233501. CPMS Portfolio adoption (CPMS ID: 36766). Participants gave informe
Rockall AG, Justich C, Helbich T, et al., 2022, Patient communication in radiology: Moving up the agenda, EUROPEAN JOURNAL OF RADIOLOGY, Vol: 155, ISSN: 0720-048X
ElGendy K, Barwick T, Auner HW, et al., 2022, Repeatability and test-retest reproducibility of mean apparent diffusion coefficient measurements of focal and diffuse disease in relapsed multiple myeloma at 3t whole body diffusion weighted MRI (WB-DW-MRI), The British Journal of Radiology, Vol: 95, ISSN: 0007-1285
Objectives:To assess the test-retest reproducibility and intra/inter observer agreement of Apparent Diffusion Coefficient (ADC) measurements of myeloma lesions using WB-DW-MRI at 3T MRI.Methods:Following ethical approval, eleven consenting patients with relapsed multiple myeloma were prospectively recruited and underwent baseline WB-DW-MRI. For a single bed position, axial DWI was repeated after a short interval to permit test- retest measurements.Mean ADC measurement was performed by two experienced observers. Intra and inter observer agreement and test-retest reproducibility were assessed, using coefficient of variation (CV) and interclass correlation coefficient (ICC) measures, for diffuse and focal lesions (small ≤10 mm and large >10 mm).Results:Forty seven sites of disease were outlined (23 focal, 24 diffuse) in different bed positions (pelvis = 22, thorax = 20, head and neck = 5). For all lesions, there was excellent intra observer agreement with ICC of 0.99 (0.98–0.99) and COV of 5%. For inter observer agreement, ICC was 0.89 (0.8–0.934) and COV was 17%. There was poor inter observer agreement for diffuse (ICC = 0.46) and small lesions (ICC = 0.54).For test-retest reproducibility, excellent ICC (0.916) and COV (14.5%) values for mean ADC measurements were observed. ICCs of test-retest were similar between focal lesions (0.83) and diffuse infiltration (0.80), while ICCs were higher in pelvic (0.95) compared to thoracic (0.81) region and in small (0.96) compared to large (0.8) lesions.Conclusions:ADC measurements of focal lesions in multiple myeloma are repeatable and reproducible, while there is more variation in ADC measurements of the diffuse disease in patients with multiple myeloma.Advances in knowledge:Mean ADC measurements are repeatable and reproducible in focal lesions in multiple myeloma, while the ADC measurements of diffuse disease in multiple myeloma are more subject to variation. The evidence supports the future pot
, 2022, The role of radiologist in the changing world of healthcare: a White Paper of the European Society of Radiology (ESR), INSIGHTS INTO IMAGING, Vol: 13, ISSN: 1869-4101
Wengert GJ, Dabi Y, Kermarrec E, et al., 2022, O-RADS MRI Classification of Indeterminate Adnexal Lesions: Time-Intensity Curve Analysis Is Better Than Visual Assessment., Radiology, Vol: 303
Sadowski EA, Thomassin-Naggara I, Rockall A, et al., 2022, O-RADS MRI Risk Stratification System: Guide for Assessing Adnexal Lesions from the ACR O-RADS Committee, RADIOLOGY, Vol: 303, Pages: 35-47, ISSN: 0033-8419
- Author Web Link
- Citations: 8
Carrie D, Cruwys C, Brady A, et al., 2022, What radiologists need to know about patients' expectations: PATIENTS CARERS AIMS, INSIGHTS INTO IMAGING, Vol: 13, ISSN: 1869-4101
- Author Web Link
- Citations: 1
Mahoney MC, McGinty G, Figueroa Sanchez GM, et al., 2022, Summary of the proceedings of the International Forum 2021: "A more visible radiologist can never be replaced by AI", Insights into Imaging, Vol: 13, Pages: 1-9, ISSN: 1869-4101
The ESR International Forum at the ECR 2021 discussed effects of artificial intelligence on the future of radiology and the need for increased visibility of radiologists. The participating societies were invited to submit written reports detailing the current situation in their country or region. The European Society of Radiology (ESR) established the ESR International Forum in order to discuss hot topics in the profession of radiology with non-European radiological partner societies. At the ESR International Forum 2021, different strategies, initiatives and ideas were presented with regard to radiology community’s response to the changes caused by the emerging AI technology.
Wengert GJ, Dabi Y, Kermarrec E, et al., 2022, O-RADS MRI classification of indeterminate adnexal lesions: time-intensity curve analysis is better than visual assessment, Radiology, Vol: 303, Pages: 1-10, ISSN: 0033-8419
Background The MRI Ovarian-Adnexal Reporting and Data System (O-RADS) enables risk stratification of sonographically indeterminate adnexal lesions, partly based on time-intensity curve (TIC) analysis, which may not be universally available. Purpose To compare the diagnostic accuracy of visual assessment with that of TIC assessment of dynamic contrast-enhanced MRI scans to categorize adnexal lesions as benign or malignant and to evaluate the influence on the O-RADS MRI score. Materials and Methods The European Adnex MR Study Group, or EURAD, database, a prospective multicenter study of women undergoing MRI for indeterminate adnexal lesions between March 2013 and March 2018, was queried retrospectively. Women undergoing surgery for an adnexal lesion with solid tissue were included. Solid tissue enhancement relative to outer myometrium was assessed visually and with TIC. Contrast material washout was recorded. Lesions were categorized according to the O-RADS MRI score with visual and TIC assessment. Per-lesion diagnostic accuracy was calculated. Results A total of 320 lesions (207 malignant, 113 benign) in 244 women (mean age, 55.3 years ± 15.8 [standard deviation]) were analyzed. Sensitivity for malignancy was 96% (198 of 207) and 76% (157 of 207) for TIC and visual assessment, respectively. TIC was more accurate than visual assessment (86% [95% CI: 81, 90] vs 78% [95% CI: 73, 82]; P < .001) for benign lesions, predominantly because of higher specificity (95% [95% CI: 92, 98] vs 76% [95% CI: 68, 81]). A total of 21% (38 of 177) of invasive lesions were rated as low risk visually. Contrast material washout and high-risk enhancement (defined as earlier enhancement than in the myometrium) were highly specific for malignancy for both TIC (97% [95% CI: 91, 99] and 94% [95% CI: 90, 97], respectively) and visual assessment (97% [95% CI: 92, 99] and 93% [95% CI: 88, 97], respectively). O-RADS MRI score was more accurate with TIC than with visual assessment (area und
Santhirasekaram A, Kori A, Winkler M, et al., 2022, Vector Quantisation for Robust Segmentation, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG
Koh D-M, Papanikolaou N, Bick U, et al., 2022, Artificial intelligence and machine learning in cancer imaging., Commun Med (Lond), Vol: 2
An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging.
Fotopoulou C, Rockall A, Lu H, et al., 2021, Validation analysis of the novel imaging-based prognostic radiomic signature in patients undergoing primary surgery for advanced high-grade serous ovarian cancer (HGSOC), British Journal of Cancer, Vol: 126, Pages: 1047-1054, ISSN: 0007-0920
BackgroundPredictive models based on radiomics features are novel, highly promising approaches for gynaecological oncology. Here, we wish to assess the prognostic value of the newly discovered Radiomic Prognostic Vector (RPV) in an independent cohort of high-grade serous ovarian cancer (HGSOC) patients, treated within a Centre of Excellence, thus avoiding any bias in treatment quality.MethodsRPV was calculated using standardised algorithms following segmentation of routine preoperative imaging of patients (n = 323) who underwent upfront debulking surgery (01/2011-07/2018). RPV was correlated with operability, survival and adjusted for well-established prognostic factors (age, postoperative residual disease, stage), and compared to previous validation models.ResultsThe distribution of low, medium and high RPV scores was 54.2% (n = 175), 33.4% (n = 108) and 12.4% (n = 40) across the cohort, respectively. High RPV scores independently associated with significantly worse progression-free survival (PFS) (HR = 1.69; 95% CI:1.06–2.71; P = 0.038), even after adjusting for stage, age, performance status and residual disease. Moreover, lower RPV was significantly associated with total macroscopic tumour clearance (OR = 2.02; 95% CI:1.56–2.62; P = 0.00647).ConclusionsRPV was validated to independently identify those HGSOC patients who will not be operated tumour-free in an optimal setting, and those who will relapse early despite complete tumour clearance upfront. Further prospective, multicentre trials with a translational aspect are warranted for the incorporation of this radiomics approach into clinical routine.
Reinhold C, Sadowski EA, Rockall A, et al., 2021, Clarifying Postcontrast Enhancement Sequences for Implementation and Interpretation of the ACR OvarianAdnexal Reporting and Data Systems MRI Risk Stratification and Management System Response, JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY, Vol: 18, Pages: 1594-1595, ISSN: 1546-1440
Shinagare AB, Sadowski EA, Park H, et al., 2021, Ovarian cancer reporting lexicon for computed tomography (CT) and magnetic resonance (MR) imaging developed by the SAR Uterine and Ovarian Cancer Disease-Focused Panel and the ESUR Female Pelvic Imaging Working Group, EUROPEAN RADIOLOGY, Vol: 32, Pages: 3220-3235, ISSN: 0938-7994
- Author Web Link
- Citations: 4
Raison N, Servian P, Patel A, et al., 2021, Is tumour volume an independent predictor of outcome after radical prostatectomy for high-risk prostate cancer?, PROSTATE CANCER AND PROSTATIC DISEASES, ISSN: 1365-7852
- Author Web Link
- Citations: 1
Seebacher V, Rockall A, Nobbenhuis M, et al., 2021, The impact of nutritional risk factors and sarcopenia on survival in patients treated with pelvic exenteration for recurrent gynaecological malignancy: a retrospective cohort study, ARCHIVES OF GYNECOLOGY AND OBSTETRICS, Vol: 305, Pages: 1343-1352, ISSN: 0932-0067
- Author Web Link
- Citations: 1
Cushnan D, Berka R, Bertolli O, et al., 2021, Towards nationally curated data archives for clinical radiology image analysis at scale: learnings from national data collection in response to a pandemic, Digital Health, Vol: 7, Pages: 1-13, ISSN: 2055-2076
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.
Santhirasekaram A, Pinto K, Winkler M, et al., 2021, Multi-scale hybrid transformer networks: application to prostate disease classification, 11th Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support (ML-CDS) held at 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 12-21, ISSN: 0302-9743
Automated disease classification could significantly improve the accuracy of prostate cancer diagnosis on MRI, which is a difficult task even for trained experts. Convolutional neural networks (CNNs) have shown some promising results for disease classification on multi-parametric MRI. However, CNNs struggle to extract robust global features about the anatomy which may provide important contextual information for further improving classification accuracy. Here, we propose a novel multi-scale hybrid CNN/transformer architecture with the ability of better contextualising local features at different scales. In our application, we found this to significantly improve performance compared to using CNNs. Classification accuracy is even further improved with a stacked ensemble yielding promising results for binary classification of prostate lesions into clinically significant or non-significant.
Thomassin-Naggara I, Sadowski E, Rockall A, et al., 2021, Correspondence on "ESGO/ISUOG/IOTA/ESGE consensus statement on pre-operative diagnosis of ovarian tumors" by Timmerman et al, INTERNATIONAL JOURNAL OF GYNECOLOGICAL CANCER, Vol: 31, Pages: 1394-1395, ISSN: 1048-891X
Qaiser T, Winzeck S, Barfoot T, et al., 2021, Multiple instance learning with auxiliary task weighting for multiple myeloma classification, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: Springer, Pages: 786-796, ISSN: 0302-9743
Whole body magnetic resonance imaging (WB-MRI) is the recommended modality for diagnosis of multiple myeloma (MM). WB-MRI is used to detect sites of disease across the entire skeletal system, but it requires significant expertise and is time-consuming to report due to the great number of images. To aid radiological reading, we propose an auxiliary task-based multiple instance learning approach (ATMIL) for MM classification with the ability to localize sites of disease. This approach is appealing as it only requires patient-level annotations where an attention mechanism is used to identify local regions with active disease. We borrow ideas from multi-task learning and define an auxiliary task with adaptive reweighting to support and improve learning efficiency in the presence of data scarcity. We validate our approach on both synthetic and real multi-center clinical data. We show that the MIL attention module provides a mechanism to localize bone regions while the adaptive reweighting of the auxiliary task considerably improves the performance.
Rockall A, Barwick T, Wilson W, et al., 2021, Diagnostic accuracy of FEC-PET/CT, FDG-PET/CT and diffusion-weighted MRI in detection of nodal metastases in surgically treated endometrial and cervical carcinoma, Clinical Cancer Research, Vol: 27, Pages: 6457-6466, ISSN: 1078-0432
Purpose:Pre-operative nodal staging is important for planning treatment in cervical cancer (CC) and endometrial cancer (EC) but remains challenging. We compare nodal staging accuracy of 18F-ethyl-choline-(FEC)-PET/CT, 18F-Fluoro-deoxy-glucose-(FDG)-PET/CT and diffusion-weighted-MRI (DW-MRI) with conventional morphological MRI.Experimetal Design:A prospective, multicentre observational study of diagnostic accuracy for nodal metastases was undertaken in 5 gyne-oncology centres. FEC-PET/CT, FDG-PET/CT and DW-MRI were compared to nodal size and morphology on MRI. Reference standard was strictly correlated nodal histology. Eligibility included operable CC stage=>1B1 or EC (grade 3 any stage with myometrial invasion or grade 1-2 stage=>II). Results:Among 162 consenting participants, 136 underwent study DW-MRI and FDG-PET/CT, and 60 underwent FEC-PET/CT. 267 nodal regions in 118 women were strictly correlated at histology (nodal positivity rate 25%). Sensitivity per-patient (n=118) for nodal size, morphology, DW-MRI, FDG- and FEC-PET/CT were 40%*, 53%, 53%, 63%* and 67% for all cases (*p=0.016); 10%, 10%, 20%, 30% and 25% in CC (n=40); 65%, 75%, 70%, 80% and 88% in EC (n=78). FDG-PET/CT outperformed nodal size (p=0.006) and size ratio (p=0.04) for per-region sensitivity. False positive rates were all <10%. Conclusions:All imaging techniques had low sensitivity for detection of nodal metastases and cannot replace surgical nodal staging. The performance of FEC-PET/CT was not statistically different to other techniques that are more widely available. FDG-PET/CT had higher sensitivity than size in detecting nodal metastases. False positive rates were low across all methods. The low false positive rate demonstrated by FDG-PET/CT may be helpful in arbitration of challenging surgical planning decisions.
Bass E, Pantovic A, Connor M, et al., 2021, A systematic review and meta-analysis of the diagnostic accuracy of biparametric prostate MRI for prostate cancer in men at risk, Prostate Cancer and Prostatic Diseases, Vol: 24, Pages: 596-611, ISSN: 1365-7852
IntroductionMultiparametric 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.MethodsA 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.ResultsForty-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.ConclusionsThis 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 al
Brady AP, Visser J, Frija G, et al., 2021, Value-based radiology: what is the ESR doing, and what should we do in the future?, INSIGHTS INTO IMAGING, Vol: 12, ISSN: 1869-4101
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- Citations: 3
Manganaro L, Lakhman Y, Bharwani N, et al., 2021, Staging, recurrence and follow-up of uterine cervical cancer using MRI: Updated Guidelines of the European Society of Urogenital Radiology after revised FIGO staging 2018 (Apr, 10.1007/s00330-020-07632-9, 2021), EUROPEAN RADIOLOGY, Vol: 32, Pages: 738-738, ISSN: 0938-7994
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- Citations: 1
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