262 results found
As manmade climate change threatens the health of the planet, it is important that we understand and address the contribution of healthcare to global emissions. Medical imaging is a significant contributor to overall emissions. This article aims to highlight key issues and examples of sustainable practices, in order to empower radiologists to make a change within their department.
Aboagye E, Lu H, Lou H, et al., 2023, Tumour and local lymphoid tissue interaction determines prognosis in high grade serous ovarian cancer, Cell Reports Medicine, Vol: 4, Pages: 1-24, ISSN: 2666-3791
Tertiary lymphoid structure (TLS) is associated with prognosis in copy number-driven tumours,including high grade serous ovarian cancer (HGSOC), although the function of TLS and its interactionwith copy-number alterations in HGSOC is not fully understood. In the current study, we confirmthat TLS-high HGSOC patients show significantly better progression free survival. We show thatpresence of TLS in HGSOC tumours is associated with B-cell maturation and cytotoxic tumourspecific T-cells activation and proliferation. Additionally, the copy number loss of IL15 and CXCL10may limit TLS formation in HGSOC; a list of genes that may dysregulate TLS function is also proposed.Manuscript Click here to view linked ReferencesLastly, a radiomics-based signature is developed to predict presence of TLS, which independentlypredicts PFS in both HGSOC patients and ICI-treated NSCLC patients. Overall, we reveal that TLScoordinates intratumoural B-cell and T-cell response against HGSOC tumour, while cancer genomeevolves to counteract TLS formation and function.
Sadowski EA, Thomassin-Naggara I, Rockall A, et al., 2023, Erratum for: O-RADS MRI Risk Stratification System: Guide for Assessing Adnexal Lesions from the ACR O-RADS Committee., Radiology, Vol: 308
Rockall AG, Li X, Johnson N, et al., 2023, 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., Investigative Radiology, ISSN: 0020-9996
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 spec
Li X, Marcus D, Russell J, et al., 2023, An integrated clinical-MR radiomics model to estimate survival time in patients with endometrial cancer, Journal of Magnetic Resonance Imaging, Vol: 57, Pages: 1922-1933, 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
Sadowski EA, Rockall A, Thomassin-Naggara I, et al., 2023, Adnexal Lesion Imaging: Past, Present, and Future., Radiology, Vol: 307
Currently, imaging is part of the standard of care for patients with adnexal lesions prior to definitive management. Imaging can identify a physiologic finding or classic benign lesion that can be followed up conservatively. When one of these entities is not present, imaging is used to determine the probability of ovarian cancer prior to surgical consultation. Since the inclusion of imaging in the evaluation of adnexal lesions in the 1970s, the rate of surgery for benign lesions has decreased. More recently, data-driven Ovarian-Adnexal Reporting and Data System (O-RADS) scoring systems for US and MRI with standardized lexicons have been developed to allow for assignment of a cancer risk score, with the goal of further decreasing unnecessary interventions while expediting the care of patients with ovarian cancer. US is used as the initial modality for the assessment of adnexal lesions, while MRI is used when there is a clinical need for increased specificity and positive predictive value for the diagnosis of cancer. This article will review how the treatment of adnexal lesions has changed due to imaging over the decades; the current data supporting the use of US, CT, and MRI to determine the likelihood of cancer; and future directions of adnexal imaging for the early detection of ovarian cancer.
Thomassin-Naggara I, Razakamanantsoa L, Rockall A, 2023, O-RADS MRI: where are we and where we are going?, Eur Radiol
Li X, Aboagye E, Michele D, et al., 2023, Prediction of deep myometrial infiltration, clinical risk category, histological type, and lymphovascular space invasion in women with endometrial cancer based on clinical and T2-weighted MRI radiomic features, Cancers, Vol: 15, ISSN: 2072-6694
Deep myometrial infiltration, clinical risk score, histological type, and lymphovascular space invasion are important clinical variables that have significant management implications for endometrial cancer patients. Determination of these factors using pure T2-weighted MRI is time-consuming, and the accuracy of this relies on the experience of the clinicians. Combining clinical information and radiomic features from MRI, we developed machine learning classification models to predict these clinical variables. Based on a training dataset, an automatic selection classification model with an optimized hyperparameters method was adopted to find the optimal classifiers. The accuracy of the model predictions was evaluated using an independent external testing dataset. The results suggest that an integrated model (combining clinical and radiomic features) achieved a reasonable accuracy for endometrial cancer clinical variable prediction. The application of these models in clinical practice could potentially lead to cost reductions and personalized treatment.
Santhirasekaram A, Kori A, Winkler M, et al., 2023, Robust Hierarchical Symbolic Explanations in Hyperbolic Space for Image Classification, Computer Vision and Pattern Recognition
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
Santhirasekaram A, Winkler M, Rockall A, et al., 2023, Topology Preserving Compositionality for Robust Medical Image Segmentation, Pages: 543-552, ISSN: 2160-7508
Deep Learning based segmentation models for medical imaging often fail under subtle distribution shifts calling into question the robustness of these models. Medical images however have the unique feature that there is limited structural variability between patients. We propose to exploit this notion and improve the robustness of deep learning based segmentation models by constraining the latent space to a learnt dictionary of base components. We incorporate a topological prior using persistent homology in the sampling of our dictionary to ensure topological accuracy after composition of the components. We further improve robustness by deep topological supervision applied in an hierarchical manner. We demonstrate the effectiveness of our method under various perturbations and in two single domain generalisation tasks.
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.
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
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
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
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
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