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In an epidemiologic study of the Zika virus outbreak in Columbia in 2015 and 2016, brain or eye defects were diagnosed in 93 of 5673 infants or fetuses (2%) carried by women with laboratory-confirmed Zika virus disease.

Project Vigilancia de Embarazadas con Zika (VEZ), an intensified surveillance of pregnant women with symptoms of the Zika virus disease (ZVD) in Colombia, aimed to evaluate the relationship between symptoms of ZVD during pregnancy and adverse pregnancy, birth, and infant outcomes and early childhood neurodevelopmental outcomes. During May–November 2016, pregnant women in three Colombian cities who were reported with symptoms of ZVD to the national surveillance system, or with symptoms of ZVD visiting participating clinics, were enrolled in Project VEZ. Data from maternal and pediatric (up to two years of age) medical records were abstracted. Available maternal specimens were tested for the presence of the Zika virus ribonucleic acid and/or anti-Zika virus immunoglobulin antibodies. Of 1213 enrolled pregnant women with symptoms of ZVD, 1180 had a known pregnancy outcome. Results of the Zika virus laboratory testing were available for 569 (48.2%) pregnancies with a known pregnancy outcome though testing timing varied and was often distal to the timing of symptoms; 254 (21.5% of the whole cohort; 44.6% of those with testing results) were confirmed or presumptive positive for the Zika virus infection. Of pregnancies with a known outcome, 50 (4.2%) fetuses/infants had Zika-associated brain or eye defects, which included microcephaly at birth. Early childhood adverse neurodevelopmental outcomes were more common among those with Zika-associated birth defects than among those without and more common among those with laboratory evidence of a Zika virus infection compared with the full cohort. The proportion of fetuses/infants with any Zika-associated brain or eye defect was consistent with the proportion seen in other studies. Enhancements to Colombia’s existing national surveillance enabled the assessment of adverse outcomes associated with ZVD in pregnancy.

Project Vigilancia de Embarazadas con Zika (VEZ), an intensified surveillance of pregnant women with symptoms of the Zika virus disease (ZVD) in Colombia, aimed to evaluate the relationship between symptoms of ZVD during pregnancy and adverse pregnancy, birth, and infant outcomes and early childhood neurodevelopmental outcomes. During May-November 2016, pregnant women in three Colombian cities who were reported with symptoms of ZVD to the national surveillance system, or with symptoms of ZVD visiting participating clinics, were enrolled in Project VEZ. Data from maternal and pediatric (up to two years of age) medical records were abstracted. Available maternal specimens were tested for the presence of the Zika virus ribonucleic acid and/or anti-Zika virus immunoglobulin antibodies. Of 1213 enrolled pregnant women with symptoms of ZVD, 1180 had a known pregnancy outcome. Results of the Zika virus laboratory testing were available for 569 (48.2%) pregnancies with a known pregnancy outcome though testing timing varied and was often distal to the timing of symptoms; 254 (21.5% of the whole cohort; 44.6% of those with testing results) were confirmed or presumptive positive for the Zika virus infection. Of pregnancies with a known outcome, 50 (4.2%) fetuses/infants had Zika-associated brain or eye defects, which included microcephaly at birth. Early childhood adverse neurodevelopmental outcomes were more common among those with Zika-associated birth defects than among those without and more common among those with laboratory evidence of a Zika virus infection compared with the full cohort. The proportion of fetuses/infants with any Zika-associated brain or eye defect was consistent with the proportion seen in other studies. Enhancements to Colombia's existing national surveillance enabled the assessment of adverse outcomes associated with ZVD in pregnancy.

The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive, non-systematic and clinically-oriented overview of 74 DL-based segmentation studies, published between January 2016 and December 2020, for bladder, prostate, cervical and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations.

ObjectivesThe aim of this study was to identify apparent diffusion coefficient (ADC) values for typical haemangiomas in the spine and to compare them with active malignant focal deposits.MethodsThis was a retrospective single-institution study. Whole-body magnetic resonance imaging (MRI) scans of 106 successive patients with active multiple myeloma, metastatic prostate or breast cancer were analysed. ADC values of typical vertebral haemangiomas and malignant focal deposits were recorded.ResultsThe ADC of haemangiomas (72 ROIs, median ADC 1,085×10-6mm2s-1, interquartile range 927–1,295×10-6mm2s-1) was significantly higher than the ADC of malignant focal deposits (97 ROIs, median ADC 682×10-6mm2s-1, interquartile range 583–781×10-6mm2s-1) with a p-value < 10-6. Receiver operating characteristic (ROC) analysis produced an area under the curve of 0.93. An ADC threshold of 872×10-6mm2s-1 separated haemangiomas from malignant focal deposits with a sensitivity of 84.7 % and specificity of 91.8 %.ConclusionsADC values of classical vertebral haemangiomas are significantly higher than malignant focal deposits. The high ADC of vertebral haemangiomas allows them to be distinguished visually and quantitatively from active sites of disease, which show restricted diffusion.Key Points• Whole-body diffusion-weighted MRI is becoming widely used in myeloma and bone metastases.• ADC values of vertebral haemangiomas are significantly higher than malignant focal deposits.• High ADCs of haemangiomas allows them to be distinguished from active disease.

Objective To establish optimised diffusion weightings (‘ b -values’) for acquisition of whole-body diffusion-weighted MRI (WB-DWI) for estimation of the apparent diffusion coefficient (ADC) in patients with metastatic melanoma (MM). Existing recommendations for WB-DWI have not been optimised for the tumour properties in MM; therefore, evaluation of acquisition parameters is essential before embarking on larger studies. Methods Retrospective clinical data and phantom experiments were used. Clinical data comprised 125 lesions from 14 examinations in 11 patients with multifocal MM, imaged before and/or after treatment with immunotherapy at a single institution. ADC estimates from these data were applied to a model to estimate the optimum b -value. A large non-diffusing phantom was used to assess eddy current–induced geometric distortion. Results Considering all tumour sites from pre- and post-treatment examinations together, metastases exhibited a large range of mean ADC values, [0.67–1.49] × 10 −3 mm 2 /s, and the optimum high b -value ( b high ) for ADC estimation was 1100 (10th–90th percentile: 740–1790) s/mm 2 . At higher b -values, geometric distortion increased, and longer echo times were required, leading to reduced signal. Conclusions Theoretical optimisation gave an optimum b high of 1100 (10th–90th percentile: 740–1790) s/mm 2 for ADC estimation in MM, with the large range of optimum b -values reflecting the wide range of ADC values in these tumours. Geometric distortion and minimum echo time increase at higher b -values and are not included in the theoretical optimisation; b high in the range 750–1100 s/mm 2 should be adopted to maintain acceptable image quality but performance should be evaluated for a specific scanner. Key Points • Theoretical optimisation gave an optimum high b-value of 1100 (10th–90th percentile: 740–1790) s/mm 2 for ADC estimation in metastatic melanoma. • Considering geometric distortion and minimum echo time (TE), a b-value in the range 750–1100 s/mm 2 is recommended. • Sites should evaluate the performance of specific scanners to assess the effect of geometric distortion and minimum TE.

BackgroundThe Myeloma Response Assessment and Diagnosis System (MY-RADS) guidelines establish a standardised acquisition and analysis pipeline for whole-body MRI (WB-MRI) in patients with myeloma. This is the first study to assess image quality in a multi-centre prospective trial using MY-RADS.MethodsThe cohort consisted of 121 examinations acquired across ten sites with a range of prior WB-MRI experience, three scanner manufacturers and two field strengths. Image quality was evaluated qualitatively by a radiologist and quantitatively using a semi-automated pipeline to quantify common artefacts and image quality issues. The intra- and inter-rater repeatability of qualitative and quantitative scoring was also assessed.ResultsQualitative radiological scoring found that the image quality was generally good, with 94% of examinations rated as good or excellent and only one examination rated as non-diagnostic. There was a significant correlation between radiological and quantitative scoring for most measures, and intra- and inter-rater repeatability were generally good.When the quality of an overall examination was low, this was often due to low quality diffusion-weighted imaging (DWI), where signal to noise ratio (SNR), anterior thoracic signal loss and brain geometric distortion were found as significant predictors of examination quality.ConclusionsIt is possible to successfully deliver a multi-centre WB-MRI study using the MY-RADS protocol involving scanners with a range of manufacturers, models and field strengths. Quantitative measures of image quality were developed and shown to be significantly correlated with radiological assessment. The SNR of DW images was identified as a significant factor affecting overall examination quality.Trial registrationClinicalTrials.gov, NCT03188172, Registered on 15 June 2017.Critical relevance statementGood overall image quality, assessed both qualitatively and quantitatively, can be achieved in a multi-centre whole-body MRI study using the MY-RADS guidelines.Key points• A prospective multi-centre WB-MRI study using MY-RADS can be successfully delivered.• Quantitative image quality metrics were developed and correlated with radiological assessment.• SNR in DWI was identified as a significant predictor of quality, allowing for rapid quality adjustment.

T2-weighted magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) are essential components of cervical cancer diagnosis. However, combining these channels for the training of deep learning models is challenging due to image misalignment. Here, we propose a novel multi-head framework that uses dilated convolutions and shared residual connections for the separate encoding of multiparametric MRI images. We employ a residual U-Net model as a baseline, and perform a series of architectural experiments to evaluate the tumor segmentation performance based on multiparametric input channels and different feature encoding configurations. All experiments were performed on a cohort of 207 patients with locally advanced cervical cancer. Our proposed multi-head model using separate dilated encoding for T2W MRI and combined b1000 DWI and apparent diffusion coefficient (ADC) maps achieved the best median Dice similarity coefficient (DSC) score, 0.823 (confidence interval (CI), 0.595–0.797), outperforming the conventional multi-channel model, DSC 0.788 (95% CI, 0.568–0.776), although the difference was not statistically significant (p > 0.05). We investigated channel sensitivity using 3D GRAD-CAM and channel dropout, and highlighted the critical importance of T2W and ADC channels for accurate tumor segmentation. However, our results showed that b1000 DWI had a minor impact on the overall segmentation performance. We demonstrated that the use of separate dilated feature extractors and independent contextual learning improved the model’s ability to reduce the boundary effects and distortion of DWI, leading to improved segmentation performance. Our findings could have significant implications for the development of robust and generalizable models that can extend to other multi-modal segmentation applications.

ObjectivesMAchine Learning In MyelomA Response (MALIMAR) is an observational clinical study combining “real-world” and clinical trial data, both retrospective and prospective. Images were acquired on three MRI scanners over a 10-year window at two institutions, leading to a need for extensive curation.MethodsCuration involved image aggregation, pseudonymisation, allocation between project phases, data cleaning, upload to an XNAT repository visible from multiple sites, annotation, incorporation of machine learning research outputs and quality assurance using programmatic methods.ResultsA total of 796 whole-body MR imaging sessions from 462 subjects were curated. A major change in scan protocol part way through the retrospective window meant that approximately 30% of available imaging sessions had properties that differed significantly from the remainder of the data. Issues were found with a vendor-supplied clinical algorithm for “composing” whole-body images from multiple imaging stations. Historic weaknesses in a digital video disk (DVD) research archive (already addressed by the mid-2010s) were highlighted by incomplete datasets, some of which could not be completely recovered. The final dataset contained 736 imaging sessions for 432 subjects. Software was written to clean and harmonise data. Implications for the subsequent machine learning activity are considered.ConclusionsMALIMAR exemplifies the vital role that curation plays in machine learning studies that use real-world data. A research repository such as XNAT facilitates day-to-day management, ensures robustness and consistency and enhances the value of the final dataset. The types of process described here will be vital for future large-scale multi-institutional and multi-national imaging projects.Critical relevance statementThis article showcases innovative data curation methods using a state-of-the-art image repository platform; such tools will be vital for managing the large multi-institutional datasets required to train and validate generalisable ML algorithms and future foundation models in medical imaging.Key points• Heterogeneous data in the MALIMAR study required the development of novel curation strategies.• Correction of multiple problems affecting the real-world data was successful, but implications for machine learning are still being evaluated.• Modern image repositories have rich application programming interfaces enabling data enrichment and programmatic QA, making them much more than simple “image marts”.