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STUDY DESIGN: This is an in vitro biomechanical study. OBJECTIVE: The current investigation was performed to evaluate adjacent level kinematic change following unilateral and bilateral facet violation and laminectomy following 1-, 2-, and 3-level reconstruction. SUMMARY OF BACKGROUND DATA: The incidence of superior-segment facet violation with lumbar transpedicular fixation has been reported as high as 35%; however, its contribution to biomechanical instability at the supradjacent level is unknown. In addition, superior-segment laminectomy has been implicated as a risk factor for the development of adjacent level disease. The authors assess the acute biomechanical effects of proximal facet violation and subsequent laminectomy in an instrumented posterior fusion model in 10 cadaveric specimens. METHODS: Biomechanical testing was performed on 10 human cadaveric spines under axial rotation (AR), flexion-extension (FE), and lateral bending (LB) loading. After intact analysis, pedicle screws were inserted from L5-S1 and testing repeated with: (1) preserved L4-L5 facets, (2) unilateral facet breach, (3) bilateral breach, and (4) L5 laminectomy. Following biomechanical analysis, instrumentation was extended to L4, then L3 and biomechanical testing repeated. Full range of motion (ROM) at the proximal adjacent levels were recorded and normalized to intact (100%). RESULTS: Supradjacent level ROM was increased for all groups under all loading methods relative to intact (P < 0.05). However, AR testing revealed progressive instability at the adjacent level in groups 3 and 4, relative to group 1, following 1-, 2- and 3-level fixation (P < 0.05). During FE, supradjacent level ROM was significantly increased for group 4 specimens compared with group 1 after L5-S1 fixation (P < 0.05), and was greater than all other groups for L3-S1 constructs (P < 0.05). Interestingly, under lateral bending, facet joint destabilization did not change adjacent segment ROM. CONCLUSION: There were significant changes in proximal level ROM immediately after posterior stabilization. However, an additional increase in supradjacent segment ROM was recorded during AR after bilateral facet breach.Subsequent complete laminectomy at the uppermostfixation level further destabilized the supradjacent segment in FE and AR. Therefore, meticulous preservation of the cephalad-most segment facet joints-is paramount to ensure stability. [ABSTRACT FROM AUTHOR]

Key Message: Climate change can limit yields of naturally resilient crops, like sorghum, challenging global food security. Agriculture under an erratic climate requires tapping into a reservoir of flexible adaptive loci that can lead to lasting yield stability under multiple abiotic stress conditions. Domesticated in the hot and dry regions of Africa, sorghum is considered a harsh crop, which is adapted to important stress factors closely related to climate change. To investigate the genetic basis of drought stress adaptation in sorghum, we used a multi-environment multi-locus genome-wide association study (MEML-GWAS) in a subset of a diverse sorghum association panel (SAP) phenotyped for performance both under well-watered and water stress conditions. We selected environments in Brazil that foreshadow agriculture where both drought and temperature stresses coincide as in many tropical agricultural frontiers. Drought reduced average grain yield (Gy) by up to 50% and also affected flowering time (Ft) and plant height (Ph). We found 15 markers associated with Gy on all sorghum chromosomes except for chromosomes 7 and 9, in addition to loci associated with phenology traits. Loci associated with Gy strongly interacted with the environment in a complex way, while loci associated with phenology traits were less affected by G × E. Studying environmental covariables potentially underpinning G × E, increases in relative humidity and evapotranspiration favored and disfavored grain yield, respectively. High temperatures influenced G × E and reduced sorghum yields, with a ~ 100 kg ha -1 average decrease in grain yield for each unit increase in maximum temperature between 29 and 38 °C. Extreme G × E for sorghum stress resilience poses an additional challenge to breed crops for moving, erratic weather conditions., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: The management of early breast cancer (BC) has witnessed an uprise in the use of neoadjuvant therapy and a remarkable reshaping of the systemic therapy postneoadjuvant treatment in the last few years, with the evolution of many controversial clinical situations that require consensus., Methods: During the 14th Breast-Gynecological and Immuno-Oncology International Cancer Conference held in Egypt in 2022, a panel of 44 BC experts from 13 countries voted on statements concerning debatable challenges in the neo/adjuvant treatment setting. The recommendations were subsequently updated based on the most recent data emerging. A modified Delphi approach was used to develop this consensus. A consensus was achieved when ≥75% of voters selected an answer., Results and Conclusions: The consensus recommendations addressed different escalation and de-escalation strategies in the setting of neoadjuvant therapy for early BC. The recommendations recapitulate the available clinical evidence and expert opinion to individualize patient management and optimize therapy outcomes. Consensus was reached in 63% of the statements (52/83), and the rationale behind each statement was clarified., (© 2024 American Cancer Society.)

The last few years have seen a boom in using generative models to augment real datasets, as synthetic data can effectively model real data distributions and provide privacy-preserving, shareable datasets that can be used to train deep learning models. However, most of these methods are 2D and provide synthetic datasets that come, at most, with categorical annotations. The generation of paired images and segmentation samples that can be used in downstream, supervised segmentation tasks remains fairly uncharted territory. This work proposes a two-stage generative model capable of producing 2D and 3D semantic label maps and corresponding multi-modal images. We use a latent diffusion model for label synthesis and a VAE-GAN for semantic image synthesis. Synthetic datasets provided by this model are shown to work in a wide variety of segmentation tasks, supporting small, real datasets or fully replacing them while maintaining good performance. We also demonstrate its ability to improve downstream performance on out-of-distribution data., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Virginia Fernandez reports financial support, administrative support, and travel were provided by EPSRC Centre for Doctoral Training in Smart Medical Imaging. Tom Vercauteren reports a relationship with Hypervision Surgical that includes: board membership and equity or stocks. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Several health institutions developed strategies to improve health content on Wikimedia platforms given their unparalleled reach. The objective of this study was to compare an online volunteer-based Wikimedia outreach campaign with university course Wikipedia assignments (both focused on improving hearing health content in Wikimedia's public digital knowledge archives), in terms of the reach of the contributions and the extent of the participants' input. A secondary objective was to examine the feasibility and the implementation of the different strategies., Methods: The research team partnered for the (1) coordination of improvements in hearing and healthcare content through educational programs using Wikimedia platforms, (2) participation in the global campaign Wiki4WorldHearingDay2023 and (3) evaluation of the proposed strategies. Metrics used in the comparison of the two strategies included the number of articles edited, number of views of the edited articles (as reach) and the extent of edits, captured as the number of words. The feasibility evaluation included assessing recruitment success and the implementation of the proposed plan among faculty, students from various university programs, and volunteers representing different constituencies., Results: The effort increased the availability of quality plain language information on hearing conditions and hearing care. Both strategies demonstrated to be feasible by their success in recruiting participants who contributed to the effort and by measurable outputs as edits. The contribution of content to Wikimedia platforms as part of education activities provided a more robust result. Wiki4WorldHearingDay2023 145 participants (78 from educational programs) contributed 167,000 words, 258 + references and 140 images to 322 Wikipedia articles (283 existing and 39 new ones), which were viewed 16.5 million times. Contributions occurred in six languages. Edits in Portuguese, mainly by those involved in educational programs, led the number of articles (226 or 70.2%) that were expanded or created during the 5-month tracking period., Conclusions: The elements that contributed to the success of the studied strategies include an impact topic, coordination with educational programs, international multidisciplinary collaborations, the dissemination of the initiative in several platforms, connection with a robust local Wikimedia affiliate, and the use of a technical infrastructure that provides metrics and coordination mechanisms., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Interest in measuring immunoglobulin G Subclasses (IgG Subclasses) is increasing as more information is gathered and understanding regarding conditions associated with deficiencies of each IgG Subclass grows. Different methodologies are available for the measurement of IgG Subclasses, but their specificities vary. As a result, laboratories choose the methodology that better suits their routine, but which may not necessarily align with the needs of their population. In addition, the lack of standardization for the quantification of IgG Subclasses causes diagnostic gaps when comparing results provided by different methodologies. Thus, the purpose of our research is to compare the analytical performance of The Binding Site's (TBS) Optilite® human Immunoglobulin G (IgG) and IgG Subclasses Immunoturbidimetry assay, with the Nephelometry method routinely used in our clinical laboratory, Siemens BNII®. Our results show that the Immunoturbidimetry assay appears to be the most reliable to evaluate IgG Subclasses: the sum of IgG Subclasses and Total IgG correlate better than by Nephelometry. Although these methodologies share a similar principle, the comparison of results appears to be compromised. Therefore, prior to switching methodologies, further studies should be conducted to assess which methodology could be better applied to specific populations. It is also essential to standardise IgG Subclasses assays to reduce discrepancies that arise from comparing results., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Predicting diabetic retinopathy (DR) progression could enable individualised screening with prompt referral for high-risk individuals for sight-saving treatment, whilst reducing screening burden for low-risk individuals. We developed and validated deep learning systems (DLS) that predict 1, 2 and 3 year emergent referable DR and maculopathy using risk factor characteristics (tabular DLS), colour fundal photographs (image DLS) or both (multimodal DLS)., Methods: From 162,339 development-set eyes from south-east London (UK) diabetic eye screening programme (DESP), 110,837 had eligible longitudinal data, with the remaining 51,502 used for pretraining. Internal and external (Birmingham DESP, UK) test datasets included 27,996, and 6928 eyes respectively., Results: Internal multimodal DLS emergent referable DR, maculopathy or either area-under-the receiver operating characteristic (AUROC) were 0.95 (95% CI: 0.92-0.98), 0.84 (0.82-0.86), 0.85 (0.83-0.87) for 1 year, 0.92 (0.87-0.96), 0.84 (0.82-0.87), 0.85 (0.82-0.87) for 2 years, and 0.85 (0.80-0.90), 0.79 (0.76-0.82), 0.79 (0.76-0.82) for 3 years. External multimodal DLS emergent referable DR, maculopathy or either AUROC were 0.93 (0.88-0.97), 0.85 (0.80-0.89), 0.85 (0.76-0.85) for 1 year, 0.93 (0.89-0.97), 0.79 (0.74-0.84), 0.80 (0.76-0.85) for 2 years, and 0.91 (0.84-0.98), 0.79 (0.74-0.83), 0.79 (0.74-0.84) for 3 years., Conclusions: Multimodal and image DLS performance is significantly better than tabular DLS at all intervals. DLS accurately predict 1, 2 and 3 year emergent referable DR and referable maculopathy using colour fundal photographs, with additional risk factor characteristics conferring improvements in prognostic performance. Proposed DLS are a step towards individualised risk-based screening, whereby AI-assistance allows high-risk individuals to be closely monitored while reducing screening burden for low-risk individuals., (© 2024. The Author(s).)

This manuscript describes the Advanced Breast Cancer (ABC) international consensus guidelines updated at the last two ABC international consensus conferences (ABC 6 in 2021, virtual, and ABC 7 in 2023, in Lisbon, Portugal), organized by the ABC Global Alliance. It provides the main recommendations on how to best manage patients with advanced breast cancer (inoperable locally advanced or metastatic), of all breast cancer subtypes, as well as palliative and supportive care. These guidelines are based on available evidence or on expert opinion when a higher level of evidence is lacking. Each guideline is accompanied by the level of evidence (LoE), grade of recommendation (GoR) and percentage of consensus reached at the consensus conferences. Updated diagnostic and treatment algorithms are also provided. The guidelines represent the best management options for patients living with ABC globally, assuming accessibility to all available therapies. Their adaptation (i.e. resource-stratified guidelines) is often needed in settings where access to care is limited., Competing Interests: Declaration of competing interest Matti S. Aapro: Consultant for Accord Pharmaceuticals, Amgen, BMS, Celgene, Clinigen Group, Daiichi Sankyo, Eisai Co.Ltd, Eli Lilly, Genomic Health (Exact Sciences), G1 Therapeutics, Inc., GlaxoSmithKline (GSK), Helsinn Healthcare SA, Hospira (Pfizer), Johnson & Johnson, Merck, Merck Serono (Merck KGaA), Mundipharma International Limited, Novartis, Pfizer, Pierre Fabre, Roche, Sandoz, Tesaro (GSK), Teva Pharmaceuticals Industries Ltd., Vifor Pharma. Received honoraria for lectures at symposia of Accord Pharmaceuticals, Amgen, Astellas, Bayer HealthCare Pharmaceuticals (Schering), Biocon, Boeringer Ingelheim, Cephalon, Chugai Pharmaceutical Co., Ltd., Daiichi Sankyo, Eisai Co., Ltd., Dr Reddy's Laboratories, Genomic Health (Exact Sciences), Glenmark Pharmaceuticals Limited, GSK, Helsinn Healthcare SA, Hospira (Pfizer), Ipsen, Janssen Biotech, Kyowa Kirin Group, Merck, Merck Serono (Merck KGaA), Mundipharma International Limited, Novartis, Pfizer, Pierre Fabre, Roche, Sandoz, Sanofi, Tesaro (GSK), Taiho Pharmaceutical, Teva Pharmaceutical Industries Ltd., Vifor Pharma. Grant/Research supports: Amgen, Eisai, Genomic Health (Exact Sciences), Helsinn, Hospira, Novartis, Merck, Mundipharma, Pfizer, Roche, Sandoz, Tesaro, Teva, Vifor. Jyoti Bajpai: Institutional financial interests for conducting research: Eli Lilly, MSD, Novartis, Roche, Paxman Coolers Ltd, Samsung Bioepis co. Ltd, Sun Pharma. Carlos H. Barrios: Receipt of grants/research supports: (to the institution) Nektar, Pfizer, Polyphor, Amgen, Daiichi Sankyo, Sanofi, Exelixis, Regeneron, Novartis, GSK, Janssen, OBI Pharma, Lilly, Seagen, Roche, BMS, MSD, Merck Serono, AstraZeneca, Novocure, Aveo Oncology, Takeda, TRIO, PharmaMar, Celgene, PPD, Syneos Health, Labcorp, ICON, IQVIA, Parexel, Nuvisan, PSI, Worldwide, Gilead Sciences, Bayer, Servier. Receipt of honoraria or consultation fees: Advisory Boards and Consulting: Boehringer-Ingelheim, GSK, Novartis, Pfizer, Roche/Genentech, Eisai, Bayer, MSD, AstraZeneca, Zodiac, Lilly, Sanofi, Daiichi. Stock shareholder: Ownership or stocks: Tummi, MEDSir. Jonas Bergh: Receipt of grants/research supports: Research grants from Amgen, AstraZeneca, Bayer, Merck, Pfizer, Roche & Sanofi-Aventis to Karolinska Institutet and/or University Hospital. No personal payments. Other support: Payment for a chapter in UpToDate on breast cancer prediction to Asklepios Medicin HB. Laura Biganzoli: Personal financial interests (Honoraria, consultancy or advisory role): Amgen, AstraZeneca, Boehringer-Ingelheim, Daiichi-Sankyo, Eisai, Exact Sciences, Gilead, Lilly, Menarini, Novartis, Pfizer, Pierre Fabre, Roche, Sanofi, SeaGen. Institutional financial interests: Celgene, Genomic Health, Novartis. Travel grants: AstraZeneca, Daiichi-Sankyo. Fatima Cardoso: Receipt of honoraria or consultation fees: Amgen, Astellas/Medivation, AstraZeneca, Celgene, Daiichi-Sankyo, Eisai, GE Oncology, Genentech, Gilead, GlaxoSmithKline, Iqvia, Macrogenics, Medscape, Merck-Sharp, Merus BV, Mylan, Mundipharma, Novartis, Pfizer, Pierre-Fabre, prIME Oncology, Roche, Sanofi, Samsung Bioepis, Seagen, Teva, Touchime. Maria-Joao Cardoso: Receipt of honoraria or consultation fees: AstraZeneca, Merck-Sharp, Novartis and Roche. Lisa A. Carey: Other support: Research funding (institution): NanoString Technologies, Seagen, Veracyte, AstraZeneca. Uncompensated relationships: Lilly, Seagen, Novartis, Genentech/Roche, GlaxoSmithKline. Mariana Chavez-MacGregor: Receipt of grants/research supports: BCRF, Susan G Komen. Receipt of honoraria or consultation fees: Astra Zeneca, Pfizer, Lilly, Roche, Merck. Javier Cortés: Receipt of grants/research supports: Roche, Celgene, Cellestia, AstraZeneca, Seattle Genetics, Daiichi Sankyo, Erytech, Athenex, Polyphor, Lilly, Merck Sharp&Dohme, GSK, Leuko, Bioasis, Clovis Oncology, Boehringer Ingelheim, Ellipses, Hibercell, BioInvent, Gemoab, Gilead, Menarini, Zymeworks, Reveal Genomics, Expres2ion Biotechnologies. Receipt of honoraria or consultation fees: Roche, Novartis, Celgene, Eisai, Pfizer, Samsung Bioepis, Lilly, Merck Sharp&Dohme, Daiichi Sankyo, Astrazeneca. Stock shareholder: MedSIR, Nektar Pharmaceuticals, Leuko (relative). Other support: Research funding to the Institution: Roche, Ariad pharmaceuticals, AstraZeneca, Baxalta GMBH/Servier Affaires, Bayer healthcare, Eisai, F.Hoffman-La Roche, Guardanth health, Merck Sharp&Dohme, Pfizer, Piqur Therapeutics, Puma C, Queen Mary University of London. Travel, accommodation, expenses: Roche, Novartis, Eisai, pfizer, Daiichi Sankyo, Astrazeneca, Gilead. Patents: Pharmaceutical Combinations of a Pi3k Inhibitor and A Microtubule Destabilizing Agent. Javier Cortés Castán, Alejandro Piris Giménez, Violeta Serra Elizalde. WO 2014/199294 A. ISSUED. Her2 as a predictor of response to dual HER2 blockade in the absence of cytotoxic therapy. Aleix Prat, Antonio Llombart, Javier Cortés.US 2019/0338368 A1. LICENSED. Giuseppe Curigliano: Receipt of grants/research supports: Merck. Receipt of honoraria or consultation fees: Merck, Lilly, Pfizer, Daichi Sankyo, Seagen, Novartis, Roche, Astra Zeneca, Ellipsis. Participation in a sponsored speakers' bureau: Seagen, Novartis, Lilly, Pfizer, Daichii Sankyo. Rebecca A. Dent: Receipt of grants/research supports: AstraZeneca, Roche. Receipt of honoraria or consultation fees: AstraZeneca, DKSH, Eisai, Merck, Novartis, Pfizer, Roche. Nagi S. El Saghir: Receipt of honoraria or consultation fees: Roche, Novartis, Lilly. Alexandru Eniu: Receipt of honoraria or consultation fees: Astra Zeneca, Daiichi Sankyo, Gilead, Janssen, MSD, Novartis, SeaGen. Receipt of grants/research supports: AstraZeneca. Full or part-time employment: European School of Oncology (ESO). Lesley Fallowfield: Receipt of grants/research supports: Novartis, Bristol-Myers Squibb, Lilly. Receipt of honoraria or consultation fees: Voluntis, Genomic Health, NanoString Technologies, Novartis, Pfizer, MSD, Novartis, Abbvie, Clovis Oncology, Puma Biotechnology, AstraZeneca, Takeda, Genomic Health/Exact Sciences, Lilly, Seagen, Roche. Sandra X. Franco Millan: Receipt of honoraria or consultation fees: Novartis, Pfizer, Eli Lilly. Participation in a sponsored speakers' bureau: Novartis, Pfizer, Eli Lilly. Karen Gelmon: Receipt of grants/research supports: Pfizer, AstraZeneca. Receipt of honoraria or consultation fees: Pfizer, Lilly, Novartis, AstraZeneca, Merck, Gilead, Seagen. Jenny Gilchrist: Receipt of honoraria or consultation fees: Eli-Lilly, AstraZeneca, Novartis, Pfizer, MSD, Gilead, Juniper Biologics. Joseph Gligorov: Receipt of grants/research supports: Eisai, Exact Science, Guardant, Roche Genentech. Receipt of honoraria or consultation fees: Astra Zeneca, Daiichi, Eisai, Evapharma, Exact Science, Gilead, Guardant Lilly, Merck, Novartis, Onxeo, Pfizer, Roche Genentech, Seattle Genetics. Participation in a sponsored speakers' bureau: Eisai, Eva Pharm, Novartis, Roche Genentech, Seattle Genetics. Other support (please specify): Institutional: Institut Universitaire de Cancérologie AP-HP Sorbonne Université; French breast cancer guidelines St Paul. Nadia Harbeck: Receipt of grants/research supports: all to institution. Receipt of honoraria or consultation fees: AstraZeneca, Daiichi-Sankyo, Gilead, Lilly, MSD, Novartis, PierreFabre, Pfizer, Roche, Sandoz, Seagen, Viatris, Zuelligpharma. Participation on a sponsored speakers' bureau: EPG Communication, MEDSCAPE, Springer. Stock shareholder: WSG minority ownership. Spouse/Partner: Consulting for WSG. Ranjit Kaur: Receipt of grants/research supports: Novartis, Roche, Pfizer, Astrazeneca. Receipt of honoraria or consultation fees: Novartis, Roche, Pfizer, Astrazeneca. Participation in a sponsored speakers' bureau: Novartis, Roche, Pfizer, Astrazeneca. Belinda Kiely: Receipt of honoraria or consultation fees: Speakers fees: Novartis, Eisai. Advisory boards: Roche, Gilead, Novartis. Other support (please specify): meeting registrations fees: Novartis, Pfizer, MSD. Sung-Bae Kim: Receipt of grants/research supports: Novartis, Sanofi-Aventis, and DongKook Pharm Co. Receipt of honoraria or consultation fees: Novartis, AstraZeneca, Lilly, Dae Hwa Pharmaceutical Co. Ltd, ISU Abxis, OBI pharma, Beigene and Daiichi-Sankyo. Stock shareholder: Genopeaks. Smruti Koppikar: Receipt of honoraria or consultation fees: Eli Lilly, Novartis, Cipla, Roche. Participation in a sponsored speakers' bureau: Eli Lilly, Novartis, Cipla, Roche. Marion Kuper-Hommel: Receipt of honoraria or consulting fees: Astra Zeneca. Ginny Mason: Participation in a sponsored speakers' bureau: Novartis. Volkmar Mueller: Receipt of grants/research supports: Institutional research support from Novartis, Roche, Seagen, Genentech, Astra Zeneca. Receipt of honoraria or consultation fees: Speaker honoraria: Astra Zeneca, Daiichi-Sankyo, Eisai, GSK, Pfizer, MSD, Medac, Novartis, Roche, Seagen, Onkowissen, high5 Oncology, Medscape, Gilead, Pierre Fabre. Consultancy honoraria from Roche, Pierre Fabre, Amgen, ClinSol, Novartis, MSD, DaiichiSankyo, Eisai, Lilly, Sanofi, Seagen, Gilead, Stemline, ClinSol. Other support (please specify): Travel grants: Roche, Pfizer, Daiichi Sankyo, Gilead. Claire Myerson: Receipt of honoraria or consultation fees: Novartis Pharma. Silvia Neciosup: Receipt of grants/research supports: Roche, Pfizer. Participation in a sponsored speakers' bureau: Tecnofarma, AZ, Roche, Pfizer. Larry Norton: Receipt of honoraria or consultation fees: Blackrock QLS Advisors, Cold Spring Harbor Laboratory, UNC Breast Spore EAB Meeting, Codagenix Scientific Advisory Board Meeting, Martell Diagnostic Laboratories, Inc., Celgene, Agenus, Immix Biopharma, Inc. Shinji Ohno: Participation in a sponsored speakers' bureau: Chugai, Lilly, MSD, Nippon Kayaku. Shani Paluch-Shimon: Receipt of grants/research supports: Pfizer. Receipt of honoraria or consultation fees: Pfizer, Lily, Novartis, Astra Zeneca, Roche, MSD, Gilead, Rhenium/Exact Sciences, Stemline, Daichi Sankyo. Participation in a sponsored speakers' bureau: Pfizer, Lily, Novartis, Astra Zeneca, Roche, MSD, Gilead. Ann H. Partridge: Uptodate Royalties. Aleix Prat: Receipt of grants/research supports: Boehringer, Novartis, Roche, Nanostring, Sysmex Europa GmbH, Medica Scientia inno. Research, SL, Celgene, Astellas and Pzifer. Receipt of honoraria or consultation fees: Roche, Pfizer, Novartis, Amgen, BMS, Puma, Oncolytics Biotech, MSD, Guardan Health, Peptomyc and Lilly, Nanostring Technologies and Daiichi Sankyo. Other support: Clinical trials: Boehringer, Lilly, Roche, Novartis, Amgen and Daiichi Sankyo. Hope S. Rugo: Receipt of grants/research supports: Astellas Pharma Inc.; AstraZeneca; Daiichi Sankyo, Inc.; F. Hoffmann-La Roche AG/Genentech, Inc.; Gilead Sciences, Inc.; GlaxoSmithKline; Lilly; Merck & Co., Inc.; Novartis Pharmaceuticals Corporation; Pfizer; Pionyr Immunotherapeutics; Sermonix Pharmaceuticals Inc.; Taiho Oncology, Inc. and Veru Inc. Receipt of honoraria or consultation fees: Puma, NAPO, Blueprint, and Scorpion Therapeutics Daichi Sankyo. Elzbieta Senkus: Receipt of honoraria or consultation fees: AstraZeneca, Curio Science, Egis, Eli Lilly, Gilead, high5md, MSD, Novartis, Pfizer, Roche, Swixx. Other support: travel support: AstraZeneca, Egis, Gilead, Novartis, Roche. Contracted research: Amgen, AstraZeneca, Eli Lilly, Gilead, Novartis, OBI Pharma, Roche, Samsung, Seagen. Medical writing: AstraZeneca, Eli Lilly. Royalties: Springer. Sandra M. Swain: Receipt of grants/research supports: Kailos Genetics, Genentech/Roche. Receipt of honoraria or consultation fees: Athenex, AstraZeneca, Biotheranostics, Natera, Exact Sciences, Lilly, Merck, Genentech/Roche, Sanofi, Daiichi Sankyo, Molecular Templates, Napo Pharmaceuticals. Stock shareholder: SEAGEN. Other support: CO- PI of INAVO 122 Genentech/Roche with in-kind travel to investigator meetings, Travel related expenses in kind Sanofi and Daiichi Sankyo. Peter Vuylsteke: Receipt of grants/research supports: UICC grant. Receipt of honoraria or consultation fees: Roche, Novartis and MSD. Theresa Wiseman: Receipt of grants/research supports: Receiver of SPCC Pfizer grant. Binghe Xu: Receipt of grants/research supports: sponsored research to my institution from Roche, AstraZeneca and Pfizer. Receipt of honoraria or consultation fees: advisory or consultancy fees from Novartis and Roche. Participation in a sponsored speakers' bureau: speakers’ bureau fees from AstraZeneca, Pfizer, Roche, Eisai. Elizabeth Bergsten-Nordström, Alberto Costa, Runcie C. W. Chidebe, Prudence A. Francis, Xichun Hu, Renate Haidinger, Leonor Matos, Shirley A. Mertz, Birgitte V. Offersen, Olivia Pagani, Eva Schumacher-Wulf, Daniel A. Vorobiof, Frédéric E. Lecouvet, and Eric P. Winer reported no significant relationships. William J. Gradishar, George W. Sledge and Christoph Thomssen have not submitted a disclosure of interests’ form., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The lack of annotated datasets is a major bottleneck for training new task-specific supervised machine learning models, considering that manual annotation is extremely expensive and time-consuming. To address this problem, we present MONAI Label, a free and open-source framework that facilitates the development of applications based on artificial intelligence (AI) models that aim at reducing the time required to annotate radiology datasets. Through MONAI Label, researchers can develop AI annotation applications focusing on their domain of expertise. It allows researchers to readily deploy their apps as services, which can be made available to clinicians via their preferred user interface. Currently, MONAI Label readily supports locally installed (3D Slicer) and web-based (OHIF) frontends and offers two active learning strategies to facilitate and speed up the training of segmentation algorithms. MONAI Label allows researchers to make incremental improvements to their AI-based annotation application by making them available to other researchers and clinicians alike. Additionally, MONAI Label provides sample AI-based interactive and non-interactive labeling applications, that can be used directly off the shelf, as plug-and-play to any given dataset. Significant reduced annotation times using the interactive model can be observed on two public datasets., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Background: Optimal secondary prevention antithrombotic therapy for patients with antiphospholipid syndrome (APS)-associated ischemic stroke, transient ischemic attack, or other ischemic brain injury is undefined. The standard of care, warfarin or other vitamin K antagonists at standard or high intensity (international normalized ratio (INR) target range 2.0-3.0/3.0-4.0, respectively), has well-recognized limitations. Direct oral anticoagulants have several advantages over warfarin, and the potential role of high-dose direct oral anticoagulants vs high-intensity warfarin in this setting merits investigation., Objectives: The Rivaroxaban for Stroke patients with APS trial (RISAPS) seeks to determine whether high-dose rivaroxaban could represent a safe and effective alternative to high-intensity warfarin in adult patients with APS and previous ischemic stroke, transient ischemic attack, or other ischemic brain manifestations., Methods: This phase IIb prospective, randomized, controlled, noninferiority, open-label, proof-of-principle trial compares rivaroxaban 15 mg twice daily vs warfarin, target INR range 3.0-4.0. The sample size target is 40 participants. Triple antiphospholipid antibody-positive patients are excluded. The primary efficacy outcome is the rate of change in brain white matter hyperintensity volume on magnetic resonance imaging, a surrogate marker of presumed ischemic damage, between baseline and 24 months follow-up. Secondary outcomes include additional neuroradiological and clinical measures of efficacy and safety. Exploratory outcomes include high-dose rivaroxaban pharmacokinetic modeling., Conclusion: Should RISAPS demonstrate noninferior efficacy and safety of high-dose rivaroxaban in this APS subgroup, it could justify larger prospective randomized controlled trials., (© 2024 The Author(s).)

Purpose: To determine associations between deprivation using the Index of Multiple Deprivation (IMD and individual IMD subdomains) with incident referable diabetic retinopathy/maculopathy (termed rDR)., Methods: Anonymised demographic and screening data collected by the South-East London Diabetic Eye Screening Programme were extracted from September 2013 to December 2019. Multivariable Cox proportional models were used to explore the association between the IMD, IMD subdomains and rDR., Results: From 118 508 people with diabetes who attended during the study period, 88 910 (75%) were eligible. The mean (± SD) age was 59.6 (±14.7) years; 53.94% were male, 52.58% identified as white, 94.28% had type 2 diabetes and the average duration of diabetes was 5.81 (±6.9) years; rDR occurred in 7113 patients (8.00%). Known risk factors of younger age, Black ethnicity, type 2 diabetes, more severe baseline DR and diabetes duration conferred a higher risk of incident rDR. After adjusting for these known risk factors, the multivariable analysis did not show a significant association between IMD (decile 1 vs decile 10) and rDR (HR: 1.08, 95% CI: 0.87 to 1.34, p=0.511). However, high deprivation (decile 1) in three IMD subdomains was associated with rDR, namely living environment (HR: 1.64, 95% CI: 1.12 to 2.41, p=0.011), education skills (HR: 1.64, 95% CI: 1.12 to 2.41, p=0.011) and income (HR: 1.19, 95% CI: 1.02 to 1.38, p=0.024)., Conclusion: IMD subdomains allow for the detection of associations between aspects of deprivation and rDR, which may be missed when using the aggregate IMD. The generalisation of these findings outside the UK population requires corroboration internationally., Competing Interests: Competing interests: PG, PN, JMNdR, LW, SM, DH, MJC, MM and CB has no conflicts of interest to declare. TLJ’ employer (King’s College Hospital) receives funding for participants enrolled on commercial clinical trials of diabetic retinopathy including THR149-002 (sponsor: OXURION), NEON NPDR (sponsor: BAYER), RHONE-X (sponsor: ROCHE) and ALTIMETER (sponsor: ROCHE). He is a paid advisor to solicitors acting for REGENERON and has received conference support from ROCHE., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

To predict protective immunity to SARS-CoV-2, cellular immunity seems to be more sensitive than humoral immunity. Through an Interferon-Gamma (IFN-γ) Release Assay (IGRA), we show that, despite a marked decrease in total antibodies, 94.3% of 123 healthcare workers have a positive cellular response 6 months after inoculation with the 2nd dose of BNT162b2 vaccine. Despite the qualitative relationship found, we did not observe a quantitative correlation between IFN-γ and IgG levels against SARS-CoV-2. Using stimulated whole blood from a subset of participants, we confirmed the specific T-cell response to SARS-CoV-2 by dosing elevated levels of the IL-6, IL-10 and TNF-α. Through a 20-month follow-up, we found that none of the infected participants had severe COVID-19 and that the first positive cases were only 12 months after the 2nd dose inoculation. Future studies are needed to understand if IGRA-SARS-CoV-2 can be a powerful diagnostic tool to predict future COVID-19 severe disease, guiding vaccination policies., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ramos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Seroepidemiological studies provide estimates of population-level immunity, prevalence/incidence of infections, and evaluation of vaccination programs. We assessed the seroprevalence of protective antibodies against influenza and evaluated the correlation of seroprevalence with the cumulative annual influenza incidence rate., Methods: We conducted an annual repeated cross-sectional seroepidemiological survey, during June-August, from 2014 to 2019, in Portugal. A total of 4326 sera from all age groups, sex, and regions was tested by hemagglutination inhibition assay. Seroprevalence and geometric mean titers (GMT) of protective antibodies against influenza were assessed by age group, sex, and vaccine status (65+ years old). The association between summer annual seroprevalence and the difference of influenza incidence rates between one season and the previous one was measured by Pearson correlation coefficient (r)., Results: Significant differences in seroprevalence of protective antibodies against influenza were observed in the population. Higher seroprevalence and GMT for A(H1N1)pdm09 and A(H3N2) were observed in children (5-14); influenza B seroprevalence in adults 65+ was 1.6-4.4 times than in children (0-4). Vaccinated participants (65+) showed significant higher seroprevalence/GMT for influenza. A strong negative and significant correlation was found between seroprevalence and ILI incidence rate for A(H1N1)pdm09 in children between 5 and 14 (r = -0.84; 95% CI, -0.98 to -0.07); a weak negative correlation was observed for A(H3N2) and B/Yamagata (r ≤ -0.1)., Conclusions: The study provides new insight into the anti-influenza antibodies seroprevalence measured in summer on the ILI incidence rate in the next season and the need for adjusted preventive health care measures to prevent influenza infection and transmission., (© 2024 The Authors. Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd.)

Competing Interests: None of the authors have an conflict of interest related to this work. Maria-Joao Cardoso reported payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events Educational events from Novartis and from Lily, as well as Leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid related to Mama Help President–Support Association for Breast Cancer Patients.

This narrative work highlights a selection of published work from 2023 with potential implications for breast cancer practice. We feature publications that have provided new knowledge immediately relevant to patient care or for future research. We also highlight guidelines that have reported evidence-based or consensus recommendations to support practice and evaluation in breast cancer diagnosis and treatment. The scope of selected highlights represents various domains and disciplines in cancer control, from prevention to treatment of early and advanced breast cancer., Competing Interests: Declaration of competing interest N. Houssami receives funding via a NBCF Chair in Breast Cancer Prevention grant (EC-21-001) and a NHMRC Investigator (Leader) grant (1194410); MJ Cardoso and O Gentilini receive institutional funding as Leading Investigator and Principal Investigator of the Project: 101057389 — CINDERELLA — HORIZON-HLTH-2021-DISEASE-04. Maria-Joao Cardoso honoraria: AstraZeneca, Eli Lilly, MSD, Roche, Novartis. Oreste Gentilini honoraria: MSD, Astra-Zeneca, BD, Bayer, Eli-Lilly. Elżbieta Senkus honoraria: AstraZeneca, Cancérodigest, Curio Science, Egis, Eli Lilly, Exact Sciences, Gilead, high5md, MSD, Novartis, Pfizer, Pierre Fabre, Roche; travel support: AstraZeneca, Egis, Gilead, Novartis, Pfizer, Roche; contracted research: Amgen, AstraZeneca, Daiichi Sankyo, Eli Lilly, Novartis, OBI Pharma, Pfizer, Roche, Samsung; grants: Pfizer (Stowarzyszenie Różowy Motyl); medical writing: Astellas, AstraZeneca, Eli Lilly; royalties: Springer; leadership or fiduciary role: Stowarzyszenie Różowy Motyl; stock: AstraZeneca, Eli Lilly, Pfizer. Philip Poortmans no conflicts no disclose., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Combining multi-site data can strengthen and uncover trends, but is a task that is marred by the influence of site-specific covariates that can bias the data and, therefore, any downstream analyses. Post-hoc multi-site correction methods exist but have strong assumptions that often do not hold in real-world scenarios. Algorithms should be designed in a way that can account for site-specific effects, such as those that arise from sequence parameter choices, and in instances where generalisation fails, should be able to identify such a failure by means of explicit uncertainty modelling. This body of work showcases such an algorithm that can become robust to the physics of acquisition in the context of segmentation tasks while simultaneously modelling uncertainty. We demonstrate that our method not only generalises to complete holdout datasets, preserving segmentation quality but does so while also accounting for site-specific sequence choices, which also allows it to perform as a harmonisation tool., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Introduction: Quality care in breast cancer is higher if patients are treated in a Breast Center with a dedicated and specialized multidisciplinary team. Quality control is an essential activity to ensure quality care, which has to be based on the monitoring of specific quality indicators. Eusoma has proceeded with the up-dating of the 2017 Quality indicators for non-metastatic breast cancer based on the new diagnostic, locoregional and systemic treatment modalities., Methods: To proceed with the updating, EUSOMA setup a multidisciplinary working group of BC experts and patients' representatives. It is a comprehensive set of QIs for early breast cancer care, which are classified as mandatory, recommended, or observational. For the first time patient reported outcomes (PROMs) have been included. As used in the 2017 EUSOMA QIs, evidence levels were based on the short version of the US Agency for Healthcare Research and Quality., Results: This is a set of quality indicators representative for the different steps of the patient pathway in non-metastatic setting, which allow Breast Centres to monitor their performance with referring standards, i.e minimum standard and target., Conclusions: Monitoring these Quality Indicators, within the Eusoma datacentre will allow to have a state of the art picture at European Breast Centres level and the development of challenging research projects., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests., (Copyright © 2024. Published by Elsevier Ltd.)

Competing Interests: R. Wuerstlein: Served as advisor, consultant, speaker and travel grant: Agendia, Amgen, APOGHEVA, Aristo, AstraZeneca, Celgene, Clovis Oncology, Daiichi-Sankyo, Eisai, Esteve, Exact Sciences, Gilead, Glaxo Smith Kline, Hexal, Lilly, Medstrom Medical, MSD, Mundipharma, Mylan, Nanostring, Novartis, Odonate, Paxman, Palleos, Pfizer, Pierre Fabre, PINK, PumaBiotechnolgogy, Riemser, Roche, Sandoz/Hexal, Sanofi Genzyme, Seattle Genetics/Seagen, Sidekick, Stemline, Tesaro Bio, Teva, Veracyte, Viatris, Wiley, FOMF, Aurikamed, Clinsol, Pomme Med, medconcept, MCI, MediSeminar. M.J. Cardoso: No conflict of interest to declare. Joana M. Ribeiro: Consulting fees: AstraZeneca (institutional), Gilead (personal), Novartis (institutional), Lilly (institutional), MSD (institutional), and Daiichi-Sanky (institutional/personal); research funding (to my institution): AstraZeneca and ARC Foundation; travel support: MSD; Gilead, Daiichi-Sankyo, and Novartis. R. Haidinger: No conflict of interest to declare. V. Müller: Speaker honoraria: AstraZeneca, Daiichi-Sankyo, Eisai, Pfizer, MSD, Medac, Novartis, Roche, Seagen, Onkowissen, high5 Oncology, Medscape, Gilead, Pierre Fabre, iMED Institut; Consultancy honoraria: Roche, Pierre Fabre, PINK, ClinSol, Novartis, MSD, Daiichi-Sankyo, Eisai, Lilly, Seagen, Gilead, Stemline; Institutional research support: Novartis, Roche, Seagen, Genentech, Astra Zeneca; Travel grants: AstraZeneca, Roche, Pfizer, Daiichi Sankyo, Gilead. M. Lambertini: Advisory role for Roche, Lilly, Novartis, AstraZeneca, Pfizer, Seagen, Gilead, MSD, and Exact Sciences; Speaker honoraria: Roche, Lilly, Novartis, AstraZeneca, Pfizer, Sandoz, Libbs, Daiichi Sankyo, Knight, Ipsen, and Takeda; Travel grants: Gilead, Daiichi Sankyo, Roche; Research support (to the Institution) from Gilead outside the submitted work. E. Schumacher-Wulf: No conflict of interest to declare.

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases., (© 2024. Springer Nature America, Inc.)

Here, we report the complete genome of Bordetella parapertussis strain 400431-b isolated from a nasopharyngeal swab from a 4-year-old patient presenting with a protracted course of whooping cough, vaccinated with three doses of diphtheria-tetanus-acellular pertussis-hepatitis B-poliomyelitis- Haemophilus influenzae type b vaccine., Competing Interests: The authors declare no conflict of interest.

Medical imaging research is often limited by data scarcity and availability. Governance, privacy concerns and the cost of acquisition all restrict access to medical imaging data, which, compounded by the data-hungry nature of deep learning algorithms, limits progress in the field of healthcare AI. Generative models have recently been used to synthesize photorealistic natural images, presenting a potential solution to the data scarcity problem. But are current generative models synthesizing morphologically correct samples? In this work we present a three-dimensional generative model of the human brain that is trained at the necessary scale to generate diverse, realistic-looking, high-resolution and morphologically preserving samples and conditioned on patient characteristics (for example, age and pathology). We show that the synthetic samples generated by the model preserve biological and disease phenotypes and are realistic enough to permit use downstream in well-established image analysis tools. While the proposed model has broad future applicability, such as anomaly detection and learning under limited data, its generative capabilities can be used to directly mitigate data scarcity, limited data availability and algorithmic fairness., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2024.)

Introduction: The present study was designed to describe tumour features and treatments for patients with breast cancer. It also aimed at assessing the risk of distant metastases in relation to biological profiles, disease stages and treatment., Methods: Data were analysed from 81,882 patients in the EUSOMA database (disease stages at diagnosis 0-IV; median age 61 years; range 20-100 years). All patients were treated between January 2016 and December 2021 in 53 Breast Centres within the EUSOMA certification process in 13 European countries. Cases were classified as HR+ /HER2-, HR+ /HER2 + , HR-/HER2 + or HR-/HER2- and data were analysed accordingly., Results: Univariable and multivariable analyses for distant metastases were conducted on a subset of 38,119 cases with information on whether or not they had developed them. Potential determinants included sub-group type, Ki67 value, disease stage, adjuvant systemic therapies and post-operative radiation therapy. In multivariable analysis, the HR-/HER2 + and HR-/HER2- sub-groups were associated with a higher risk of distant metastases than HR+ /HER2-. Ki67 > 20 % and advanced stage disease also carried a high risk. Radiation therapy emerged as a protective factor against distant metastases., Conclusions: Present results show a large patient database offers an information stream that can be applied to reduce uncertainties in clinical practice. Database parameters need to be updated dynamically for outcome monitoring. Molecular prognostic factors, gene-expression signatures, tumour-infiltrating lymphocytes and circulating tumoral DNA should be added., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: LG: Advisory Board: Astra Zeneca/Daiichi Sankyo, Seagen. Support for attending meetings and/or travel: Ipsen, Novartis, Pfizer. Other non-financial interests: Member of Olympia Steering committee. FPD: Grants or contracts from any entity: Fondation belge contre le cancer (post-doctoral research grant). Consulting fees: Roche, Pfizer, AstraZeneca, Lilly, Novartis, Amgen, Daiichi Sankyo, Pierre Fabre, Gilead Sciences, Seagen, MSD (payment made to my institution). Support for attending meetings and/or travel: Amgen, Roche, Teva, Pfizer, Daiichi Sankyo/AstraZeneca, Gilead Sciences., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Anomaly detection and segmentation pose an important task across sectors ranging from medical imaging analysis to industry quality control. However, current unsupervised approaches require training data to not contain any anomalies, a requirement that can be especially challenging in many medical imaging scenarios. In this paper, we propose Iterative Latent Token Masking, a self-supervised framework derived from a robust statistics point of view, translating an iterative model fitting with M-estimators to the task of anomaly detection. In doing so, this allows the training of unsupervised methods on datasets heavily contaminated with anomalous images. Our method stems from prior work on using Transformers, combined with a Vector Quantized-Variational Autoencoder, for anomaly detection, a method with state-of-the-art performance when trained on normal (non-anomalous) data. More importantly, we utilise the token masking capabilities of Transformers to filter out suspected anomalous tokens from each sample's sequence in the training set in an iterative self-supervised process, thus overcoming the difficulties of highly anomalous training data. Our work also highlights shortfalls in current state-of-the-art self-supervised, self-trained and unsupervised models when faced with small proportions of anomalous training data. We evaluate our method on whole-body PET data in addition to showing its wider application in more common computer vision tasks such as the industrial MVTec Dataset. Using varying levels of anomalous training data, our method showcases a superior performance over several state-of-the-art models, drawing attention to the potential of this approach.

Any clinically-deployed image-processing pipeline must be robust to the full range of inputs it may be presented with. One popular approach to this challenge is to develop predictive models that can provide a measure of their uncertainty. Another approach is to use generative modelling to quantify the likelihood of inputs. Inputs with a low enough likelihood are deemed to be out-of-distribution and are not presented to the downstream predictive model. In this work, we evaluate several approaches to segmentation with uncertainty for the task of segmenting bleeds in 3D CT of the head. We show that these models can fail catastrophically when operating in the far out-of-distribution domain, often providing predictions that are both highly confident and wrong. We propose to instead perform out-of-distribution detection using the Latent Transformer Model: a VQ-GAN is used to provide a highly compressed latent representation of the input volume, and a transformer is then used to estimate the likelihood of this compressed representation of the input. We demonstrate this approach can identify images that are both far- and near- out-of-distribution, as well as provide spatial maps that highlight the regions considered to be out-of-distribution. Furthermore, we find a strong relationship between an image's likelihood and the quality of a model's segmentation on it, demonstrating that this approach is viable for filtering out unsuitable images., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mark Graham, Paul Wright, Walter Diaz Sanz, Parashkev Nachev, Sebastian Ourselin, Geraint Rees reports financial support was provided by Wellcome Trust. Yee Mah reports financial support was provided by UKRI Medical Research Council., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)